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Esteban-Medina M, de la Oliva Roque VM, Herráiz-Gil S, Peña-Chilet M, Dopazo J, Loucera C. drexml: A command line tool and Python package for drug repurposing. Comput Struct Biotechnol J 2024; 23:1129-1143. [PMID: 38510973 PMCID: PMC10950807 DOI: 10.1016/j.csbj.2024.02.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 02/27/2024] [Accepted: 02/27/2024] [Indexed: 03/22/2024] Open
Abstract
We introduce drexml, a command line tool and Python package for rational data-driven drug repurposing. The package employs machine learning and mechanistic signal transduction modeling to identify drug targets capable of regulating a particular disease. In addition, it employs explainability tools to contextualize potential drug targets within the functional landscape of the disease. The methodology is validated in Fanconi Anemia and Familial Melanoma, two distinct rare diseases where there is a pressing need for solutions. In the Fanconi Anemia case, the model successfully predicts previously validated repurposed drugs, while in the Familial Melanoma case, it identifies a promising set of drugs for further investigation.
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Affiliation(s)
- Marina Esteban-Medina
- Platform for Computational Medicine, Andalusian Public Foundation Progress and Health-FPS, Seville, Spain
- Computational Systems Medicine, Institute of Biomedicine of Seville (IBIS), Hospital Virgen del Rocío, Seville, Spain
| | - Víctor Manuel de la Oliva Roque
- Platform for Computational Medicine, Andalusian Public Foundation Progress and Health-FPS, Seville, Spain
- Computational Systems Medicine, Institute of Biomedicine of Seville (IBIS), Hospital Virgen del Rocío, Seville, Spain
| | - Sara Herráiz-Gil
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER-ISCIII), U714, Madrid, Spain
- Departamento de Bioingeniería, Universidad Carlos III de Madrid (UC3M), Madrid, Spain
- Regenerative Medicine and Tissue Engineering Group, Instituto de Investigación Sanitaria-Fundación Jiménez Díaz University Hospital (IIS-FJD), Madrid, Spain
- Epithelial Biomedicine Division, Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas (CIEMAT), Madrid, Spain
| | - María Peña-Chilet
- Platform for Computational Medicine, Andalusian Public Foundation Progress and Health-FPS, Seville, Spain
- Platform of Big Data, AI and Biostatistics, Health Research Institute La Fe (IISLAFE), Valencia, Spain
| | - Joaquín Dopazo
- Platform for Computational Medicine, Andalusian Public Foundation Progress and Health-FPS, Seville, Spain
- Computational Systems Medicine, Institute of Biomedicine of Seville (IBIS), Hospital Virgen del Rocío, Seville, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER-ISCIII), U715, Seville, Spain
- FPS/ELIXIR-es, Hospital Virgen del Rocío, Seville, Spain
| | - Carlos Loucera
- Platform for Computational Medicine, Andalusian Public Foundation Progress and Health-FPS, Seville, Spain
- Computational Systems Medicine, Institute of Biomedicine of Seville (IBIS), Hospital Virgen del Rocío, Seville, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER-ISCIII), U715, Seville, Spain
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2
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Lim HJ, Yoo JE, Rho M, Ryu JJ. Exploration of Variables Predicting Sense of School Belonging Using the Machine Learning Method-Group Mnet. Psychol Rep 2024; 127:1502-1526. [PMID: 36219194 DOI: 10.1177/00332941221133005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The purpose of this study was to explore variables related to school belonging from a holistic perspective, including a large number of variables in one model, different to the traditional analytical method. Using 2015 data from the Program for International Student Assessment (PISA), we sought to identify variables related to school belonging by searching for hundreds of predictors in one model using the group Mnet machine learning technique. The study repeated 100 rounds of model building after random data splitting. After exploring 504 variables (384 student and 99 parent), 32 variables were finally selected after selection counts. Variables predicting a sense of school belonging were categorized as individual/parent variables (e.g. motivation to achieve, tendency to cooperative learning, parental support) and school-related variables (e.g. school satisfaction, peer/teacher relationship, learning/physical activities). The significance and implications of the study as well as future research topics were discussed.
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Affiliation(s)
- Hyo Jin Lim
- Seoul National University of Education, Seoul, Korea
| | - Jin Eun Yoo
- Korea National University of Education, Cheongju, Korea
| | - Minjeong Rho
- Korea National University of Education, Cheongju, Korea
| | - Jae Jun Ryu
- Seoul National University of Education, Seoul, Korea
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3
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Lancia G, Varkila MRJ, Cremer OL, Spitoni C. Two-step interpretable modeling of ICU-AIs. Artif Intell Med 2024; 151:102862. [PMID: 38579437 DOI: 10.1016/j.artmed.2024.102862] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Revised: 03/25/2024] [Accepted: 03/25/2024] [Indexed: 04/07/2024]
Abstract
We present a novel methodology for integrating high resolution longitudinal data with the dynamic prediction capabilities of survival models. The aim is two-fold: to improve the predictive power while maintaining the interpretability of the models. To go beyond the black box paradigm of artificial neural networks, we propose a parsimonious and robust semi-parametric approach (i.e., a landmarking competing risks model) that combines routinely collected low-resolution data with predictive features extracted from a convolutional neural network, that was trained on high resolution time-dependent information. We then use saliency maps to analyze and explain the extra predictive power of this model. To illustrate our methodology, we focus on healthcare-associated infections in patients admitted to an intensive care unit.
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Affiliation(s)
- G Lancia
- Mathematics Department, Utrecht University, Budapestlaan, 6, Utrecht, 3584CD, The Netherlands.
| | - M R J Varkila
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Universiteitsweg 100, Utrecht, 3584 CG, The Netherlands
| | - O L Cremer
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Universiteitsweg 100, Utrecht, 3584 CG, The Netherlands
| | - C Spitoni
- Mathematics Department, Utrecht University, Budapestlaan, 6, Utrecht, 3584CD, The Netherlands
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4
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Abstract
Metabolomics as a research field and a set of techniques is to study the entire small molecules in biological samples. Metabolomics is emerging as a powerful tool generally for precision medicine. Particularly, integration of microbiome and metabolome has revealed the mechanism and functionality of microbiome in human health and disease. However, metabolomics data are very complicated. Preprocessing/pretreating and normalizing procedures on metabolomics data are usually required before statistical analysis. In this review article, we comprehensively review various methods that are used to preprocess and pretreat metabolomics data, including MS-based data and NMR -based data preprocessing, dealing with zero and/or missing values and detecting outliers, data normalization, data centering and scaling, data transformation. We discuss the advantages and limitations of each method. The choice for a suitable preprocessing method is determined by the biological hypothesis, the characteristics of the data set, and the selected statistical data analysis method. We then provide the perspective of their applications in the microbiome and metabolome research.
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Affiliation(s)
- Jun Sun
- Division of Gastroenterology and Hepatology, Department of Medicine, Department of Microbiology/Immunology, UIC Cancer Center, University of Illinois Chicago, Jesse Brown VA Medical Center Chicago (537), Chicago, IL 60612, USA
| | - Yinglin Xia
- Division of Gastroenterology and Hepatology, Department of Medicine, University of Illinois Chicago, Chicago, IL 60612, USA
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5
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Hurtado Silva M, van Waardenberg AJ, Mostafa A, Schoch S, Dietrich D, Graham ME. Multiomics of early epileptogenesis in mice reveals phosphorylation and dephosphorylation-directed growth and synaptic weakening. iScience 2024; 27:109534. [PMID: 38600976 PMCID: PMC11005001 DOI: 10.1016/j.isci.2024.109534] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Revised: 01/26/2024] [Accepted: 03/16/2024] [Indexed: 04/12/2024] Open
Abstract
To investigate the phosphorylation-based signaling and protein changes occurring early in epileptogenesis, the hippocampi of mice treated with pilocarpine were examined by quantitative mass spectrometry at 4 and 24 h post-status epilepticus at vast depth. Hundreds of posttranscriptional regulatory proteins were the major early targets of increased phosphorylation. At 24 h, many protein level changes were detected and the phosphoproteome continued to be perturbed. The major targets of decreased phosphorylation at 4 and 24 h were a subset of postsynaptic density scaffold proteins, ion channels, and neurotransmitter receptors. Many proteins targeted by dephosphorylation at 4 h also had decreased protein abundance at 24 h, indicating a phosphatase-mediated weakening of synapses. Increased translation was indicated by protein changes at 24 h. These observations, and many additional indicators within this multiomic resource, suggest that early epileptogenesis is characterized by signaling that stimulates both growth and a homeostatic response that weakens excitability.
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Affiliation(s)
- Mariella Hurtado Silva
- Synapse Proteomics, Children’s Medical Research Institute, The University of Sydney, Westmead, NSW 2145, Australia
| | | | - Aya Mostafa
- Department of Neuropathology, University Hospital Bonn, Synaptic Neuroscience Unit, 53127 Bonn, North Rhine-Westphalia, Germany
| | - Susanne Schoch
- Department of Neuropathology, University Hospital Bonn, Synaptic Neuroscience Unit, 53127 Bonn, North Rhine-Westphalia, Germany
| | - Dirk Dietrich
- Department of Neurosurgery, University Hospital Bonn, Synaptic Neuroscience Unit, 53127 Bonn, North Rhine-Westphalia, Germany
| | - Mark E. Graham
- Synapse Proteomics, Children’s Medical Research Institute, The University of Sydney, Westmead, NSW 2145, Australia
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6
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Qiu L, Zhao L, Zhao W, Zhao J. Dual-space disentangled-multimodal network (DDM-net) for glioma diagnosis and prognosis with incomplete pathology and genomic data. Phys Med Biol 2024; 69:085028. [PMID: 38595094 DOI: 10.1088/1361-6560/ad37ec] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Accepted: 03/26/2024] [Indexed: 04/11/2024]
Abstract
Objective. Effective fusion of histology slides and molecular profiles from genomic data has shown great potential in the diagnosis and prognosis of gliomas. However, it remains challenging to explicitly utilize the consistent-complementary information among different modalities and create comprehensive representations of patients. Additionally, existing researches mainly focus on complete multi-modality data and usually fail to construct robust models for incomplete samples.Approach. In this paper, we propose adual-space disentangled-multimodal network (DDM-net)for glioma diagnosis and prognosis. DDM-net disentangles the latent features generated by two separate variational autoencoders (VAEs) into common and specific components through a dual-space disentangled approach, facilitating the construction of comprehensive representations of patients. More importantly, DDM-net imputes the unavailable modality in the latent feature space, making it robust to incomplete samples.Main results. We evaluated our approach on the TCGA-GBMLGG dataset for glioma grading and survival analysis tasks. Experimental results demonstrate that the proposed method achieves superior performance compared to state-of-the-art methods, with a competitive AUC of 0.952 and a C-index of 0.768.Significance. The proposed model may help the clinical understanding of gliomas and can serve as an effective fusion model with multimodal data. Additionally, it is capable of handling incomplete samples, making it less constrained by clinical limitations.
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Affiliation(s)
- Lu Qiu
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, People's Republic of China
| | - Lu Zhao
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, People's Republic of China
| | - Wangyuan Zhao
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, People's Republic of China
| | - Jun Zhao
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, People's Republic of China
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7
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Rentzsch P, Kollotzek A, Mohammadi P, Lappalainen T. Recalibrating differential gene expression by genetic dosage variance prioritizes functionally relevant genes. bioRxiv 2024:2024.04.10.588830. [PMID: 38645217 PMCID: PMC11030425 DOI: 10.1101/2024.04.10.588830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
Differential expression (DE) analysis is a widely used method for identifying genes that are functionally relevant for an observed phenotype or biological response. However, typical DE analysis includes selection of genes based on a threshold of fold change in expression under the implicit assumption that all genes are equally sensitive to dosage changes of their transcripts. This tends to favor highly variable genes over more constrained genes where even small changes in expression may be biologically relevant. To address this limitation, we have developed a method to recalibrate each gene's differential expression fold change based on genetic expression variance observed in the human population. The newly established metric ranks statistically differentially expressed genes not by nominal change of expression, but by relative change in comparison to natural dosage variation for each gene. We apply our method to RNA sequencing datasets from rare disease and in-vitro stimulus response experiments. Compared to the standard approach, our method adjusts the bias in discovery towards highly variable genes, and enriches for pathways and biological processes related to metabolic and regulatory activity, indicating a prioritization of functionally relevant driver genes. With that, our method provides a novel view on DE and contributes towards bridging the existing gap between statistical and biological significance. We believe that this approach will simplify the identification of disease causing genes and enhance the discovery of therapeutic targets.
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Affiliation(s)
- Philipp Rentzsch
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Solna, Sweden
| | - Aaron Kollotzek
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Solna, Sweden
| | - Pejman Mohammadi
- Center for Immunity and Immunotherapies, Seattle Children's Research Institute, Seattle, WA, USA; Department of Pediatrics, University of Washington School of Medicine, Seattle, WA, USA; Department of Genome Science, University of Washington, Seattle, WA, USA
| | - Tuuli Lappalainen
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Solna, Sweden
- New York Genome Center, New York, NY, USA
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8
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Decorte T, Mortier S, Lembrechts JJ, Meysman FJR, Latré S, Mannens E, Verdonck T. Missing Value Imputation of Wireless Sensor Data for Environmental Monitoring. Sensors (Basel) 2024; 24:2416. [PMID: 38676032 PMCID: PMC11053546 DOI: 10.3390/s24082416] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Revised: 04/04/2024] [Accepted: 04/08/2024] [Indexed: 04/28/2024]
Abstract
Over the past few years, the scale of sensor networks has greatly expanded. This generates extended spatiotemporal datasets, which form a crucial information resource in numerous fields, ranging from sports and healthcare to environmental science and surveillance. Unfortunately, these datasets often contain missing values due to systematic or inadvertent sensor misoperation. This incompleteness hampers the subsequent data analysis, yet addressing these missing observations forms a challenging problem. This is especially the case when both the temporal correlation of timestamps within a single sensor and the spatial correlation between sensors are important. Here, we apply and evaluate 12 imputation methods to complete the missing values in a dataset originating from large-scale environmental monitoring. As part of a large citizen science project, IoT-based microclimate sensors were deployed for six months in 4400 gardens across the region of Flanders, generating 15-min recordings of temperature and soil moisture. Methods based on spatial recovery as well as time-based imputation were evaluated, including Spline Interpolation, MissForest, MICE, MCMC, M-RNN, BRITS, and others. The performance of these imputation methods was evaluated for different proportions of missing data (ranging from 10% to 50%), as well as a realistic missing value scenario. Techniques leveraging the spatial features of the data tend to outperform the time-based methods, with matrix completion techniques providing the best performance. Our results therefore provide a tool to maximize the benefit from costly, large-scale environmental monitoring efforts.
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Affiliation(s)
- Thomas Decorte
- Department of Mathematics, University of Antwerp-imec, Middelheimlaan 1, 2000 Antwerp, Belgium;
| | - Steven Mortier
- IDLab, Department of Computer Science, University of Antwerp-imec, Sint-Pietersvliet 7, 2000 Antwerp, Belgium; (S.M.); (S.L.); (E.M.)
| | - Jonas J. Lembrechts
- Plants and Ecosystems, Department of Biology, University of Antwerp, Universiteitsplein 1, 2610 Antwerp, Belgium;
| | - Filip J. R. Meysman
- Geobiology, Department of Biology, University of Antwerp, Universiteitsplein 1, 2610 Antwerp, Belgium;
| | - Steven Latré
- IDLab, Department of Computer Science, University of Antwerp-imec, Sint-Pietersvliet 7, 2000 Antwerp, Belgium; (S.M.); (S.L.); (E.M.)
| | - Erik Mannens
- IDLab, Department of Computer Science, University of Antwerp-imec, Sint-Pietersvliet 7, 2000 Antwerp, Belgium; (S.M.); (S.L.); (E.M.)
| | - Tim Verdonck
- Department of Mathematics, University of Antwerp-imec, Middelheimlaan 1, 2000 Antwerp, Belgium;
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9
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Sinitsky M, Repkin E, Sinitskaya A, Markova V, Shishkova D, Barbarash O. Proteomic Profiling of Endothelial Cells Exposed to Mitomycin C: Key Proteins and Pathways Underlying Genotoxic Stress-Induced Endothelial Dysfunction. Int J Mol Sci 2024; 25:4044. [PMID: 38612854 PMCID: PMC11011977 DOI: 10.3390/ijms25074044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Revised: 04/03/2024] [Accepted: 04/03/2024] [Indexed: 04/14/2024] Open
Abstract
Mitomycin C (MMC)-induced genotoxic stress can be considered to be a novel trigger of endothelial dysfunction and atherosclerosis-a leading cause of cardiovascular morbidity and mortality worldwide. Given the increasing genotoxic load on the human organism, the decryption of the molecular pathways underlying genotoxic stress-induced endothelial dysfunction could improve our understanding of the role of genotoxic stress in atherogenesis. Here, we performed a proteomic profiling of human coronary artery endothelial cells (HCAECs) and human internal thoracic endothelial cells (HITAECs) in vitro that were exposed to MMC to identify the biochemical pathways and proteins underlying genotoxic stress-induced endothelial dysfunction. We denoted 198 and 71 unique, differentially expressed proteins (DEPs) in the MMC-treated HCAECs and HITAECs, respectively; only 4 DEPs were identified in both the HCAECs and HITAECs. In the MMC-treated HCAECs, 44.5% of the DEPs were upregulated and 55.5% of the DEPs were downregulated, while in HITAECs, these percentages were 72% and 28%, respectively. The denoted DEPs are involved in the processes of nucleotides and RNA metabolism, vesicle-mediated transport, post-translation protein modification, cell cycle control, the transport of small molecules, transcription and signal transduction. The obtained results could improve our understanding of the fundamental basis of atherogenesis and help in the justification of genotoxic stress as a risk factor for atherosclerosis.
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Affiliation(s)
- Maxim Sinitsky
- Laboratory of Genome Medicine, Research Institute for Complex Issues of Cardiovascular Diseases, 6 Academician Barbarash Boulevard, 650002 Kemerovo, Russia
| | - Egor Repkin
- Centre for Molecular and Cell Technologies, St. Petersburg State University, 7/9 Universitetskaya Embankment, 199034 St. Petersburg, Russia
| | - Anna Sinitskaya
- Laboratory of Genome Medicine, Research Institute for Complex Issues of Cardiovascular Diseases, 6 Academician Barbarash Boulevard, 650002 Kemerovo, Russia
| | - Victoria Markova
- Laboratory for Molecular, Translation and Digital Medicine, Research Institute for Complex Issues of Cardiovascular Diseases, 6 Academician Barbarash Boulevard, 650002 Kemerovo, Russia
| | - Daria Shishkova
- Laboratory for Molecular, Translation and Digital Medicine, Research Institute for Complex Issues of Cardiovascular Diseases, 6 Academician Barbarash Boulevard, 650002 Kemerovo, Russia
| | - Olga Barbarash
- Research Institute for Complex Issues of Cardiovascular Diseases, 6 Academician Barbarash Boulevard, 650002 Kemerovo, Russia
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10
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Cao H, Jia C, Li Z, Yang H, Fang R, Zhang Y, Cui Y. wMKL: multi-omics data integration enables novel cancer subtype identification via weight-boosted multi-kernel learning. Br J Cancer 2024; 130:1001-1012. [PMID: 38278975 PMCID: PMC10951206 DOI: 10.1038/s41416-024-02587-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 01/09/2024] [Accepted: 01/15/2024] [Indexed: 01/28/2024] Open
Abstract
BACKGROUND Cancer is a heterogeneous disease driven by complex molecular alterations. Cancer subtypes determined from multi-omics data can provide novel insight into personalised precision treatment. It is recognised that incorporating prior weight knowledge into multi-omics data integration can improve disease subtyping. METHODS We develop a weighted method, termed weight-boosted Multi-Kernel Learning (wMKL) which incorporates heterogeneous data types as well as flexible weight functions, to boost subtype identification. Given a series of weight functions, we propose an omnibus combination strategy to integrate different weight-related P-values to improve subtyping precision. RESULTS wMKL models each data type with multiple kernel choices, thus alleviating the sensitivity and robustness issue due to selecting kernel parameters. Furthermore, wMKL integrates different data types by learning weights of different kernels derived from each data type, recognising the heterogeneous contribution of different data types to the final subtyping performance. The proposed wMKL outperforms existing weighted and non-weighted methods. The utility and advantage of wMKL are illustrated through extensive simulations and applications to two TCGA datasets. Novel subtypes are identified followed by extensive downstream bioinformatics analysis to understand the molecular mechanisms differentiating different subtypes. CONCLUSIONS The proposed wMKL method provides a novel strategy for disease subtyping. The wMKL is freely available at https://github.com/biostatcao/wMKL .
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Affiliation(s)
- Hongyan Cao
- Division of Health Statistics, Shanxi Provincial Key Laboratory of Major Diseases Risk Assessment, Shanxi Medical University, 030001, Taiyuan, Shanxi, China
- MOE Key Laboratory of Coal Environmental Pathogenicity and Prevention, Shanxi Medical University, 030001, Taiyuan, Shanxi, China
- Division of Mathematics, School of Basic Medical Science, Shanxi Medical University, 030001, Taiyuan, Shanxi, China
| | - Congcong Jia
- Division of Health Statistics, Shanxi Provincial Key Laboratory of Major Diseases Risk Assessment, Shanxi Medical University, 030001, Taiyuan, Shanxi, China
| | - Zhi Li
- Department of Hematology, Taiyuan Central Hospital of Shanxi Medical University, 030001, Taiyuan, Shanxi, China
| | - Haitao Yang
- Division of Health Statistics, School of Public Health, Hebei Medical University, 050017, Shijiazhuang, China
| | - Ruiling Fang
- Division of Health Statistics, Shanxi Provincial Key Laboratory of Major Diseases Risk Assessment, Shanxi Medical University, 030001, Taiyuan, Shanxi, China
| | - Yanbo Zhang
- Division of Health Statistics, Shanxi Provincial Key Laboratory of Major Diseases Risk Assessment, Shanxi Medical University, 030001, Taiyuan, Shanxi, China
| | - Yuehua Cui
- Department of Statistics and Probability, Michigan State University, East Lansing, MI, 48824, USA.
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11
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Fioredda F, Beccaria A, Casartelli P, Turrini E, Contratto C, Giarratana MC, Bagnasco F, Saettini F, Pillon M, Marzollo A, Zanardi S, Civino A, Onofrillo D, Lanciotti M, Ceccherini I, Grossi A, Coviello D, Terranova P, Lupia M, Del Borrello G, Uva P, Cangelosi D, Cavalca G, Miano M, Dufour C. Late-onset and long-lasting neutropenias in the young: A new entity anticipating immune-dysregulation disorders. Am J Hematol 2024; 99:534-542. [PMID: 38282561 DOI: 10.1002/ajh.27221] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 12/09/2023] [Accepted: 01/01/2024] [Indexed: 01/30/2024]
Abstract
This study identifies a new chronic form of immune neutropenia in the young with or without detectable indirect anti-neutrophil antibodies, characterized by mild/moderate neutropenia low risk of severe infection (14%), tendency to develop autoimmune phenomena over the course of the disease (cumulative incidence of 58.6% after 20 years of disease duration), leukopenia, progressive reduction of absolute lymphocyte count and a T- and B-cell profile similar to autoimmune disorders like Sjogren syndrome, rheumatoid arthritis, and systemic lupus erythematosus (increased HLADR+ and CD3 + TCRγδ cells, reduced T regulatory cells, increased double-negative B and a tendency to reduced B memory cells). In a minority of patients, P/LP variants related to primary immuno-regulatory disorders were found. This new form may fit the group of "Likely acquired neutropenia," a provisional category included in the recent International Guidelines on Diagnosis and Management of Neutropenia of EHA and EUNET INNOCHRON ACTION 18233. The early recognition of this form of neutropenia would help clinicians to delineate better specific monitoring plans, genetic counseling, and potentially targeted therapies.
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Affiliation(s)
- F Fioredda
- Haematology Unit-IRCCS Istituto Giannina Gaslini, Genova, Italy
| | - A Beccaria
- Epidemiology and Biostatistics Unit and DOPO Clinic-IRCCS Istituto Giannina Gaslini, Genova, Italy
| | - P Casartelli
- Haematology Unit-IRCCS Istituto Giannina Gaslini, Genova, Italy
| | - E Turrini
- Unit of Pediatric and OncoHematology, Department of Mother and Child, Azienda Ospedaliera Universitaria, Parma, Italy
| | - C Contratto
- Haematology Unit-IRCCS Istituto Giannina Gaslini, Genova, Italy
| | - M C Giarratana
- Haematology Unit-IRCCS Istituto Giannina Gaslini, Genova, Italy
| | - F Bagnasco
- Biostatistics Unit, Scientific Directorate, IRCCS Istituto Giannina Gaslini, Genova, Italy
| | - F Saettini
- Department of Pediatric Onco-Hematology, San Gerardo Hospital, Fondazione MBBM, Università degli Studi di Milano-Bicocca, Monza, Italy
| | - M Pillon
- Pediatric Hematology-Oncology Unit, Department of Women's and Children's Health, AziendaOspedaliera-University of Padova, Padua, Italy
| | - A Marzollo
- Pediatric Hematology-Oncology Unit, Department of Women's and Children's Health, AziendaOspedaliera-University of Padova, Padua, Italy
| | - S Zanardi
- Haematology Unit-IRCCS Istituto Giannina Gaslini, Genova, Italy
| | - A Civino
- Unit of Rheumathology and Immunology-ospedale Vito Fazzi, Lecce, Italy
| | - D Onofrillo
- Pediatric Hematology and Oncology Unit, Department of Hematology, Spirito Santo Hospital, Pescara, Italy
| | - M Lanciotti
- Haematology Unit-IRCCS Istituto Giannina Gaslini, Genova, Italy
| | - I Ceccherini
- Laboratory of Genetics and Genomics of Rare Diseases, IRCCS Istituto Giannina Gaslini, Genova, Italy
| | - A Grossi
- Laboratory of Genetics and Genomics of Rare Diseases, IRCCS Istituto Giannina Gaslini, Genova, Italy
| | - D Coviello
- Laboratory of Human Genetics, IRCCS Istituto Giannina Gaslini, Genova, Italy
| | - P Terranova
- Haematology Unit-IRCCS Istituto Giannina Gaslini, Genova, Italy
| | - M Lupia
- Haematology Unit-IRCCS Istituto Giannina Gaslini, Genova, Italy
| | - G Del Borrello
- Haematology Unit-IRCCS Istituto Giannina Gaslini, Genova, Italy
- Pediatric OncoHematology, Pediatrics Department, Hospital Città Della Salute e Della Scienza, University of Turin, Turin, Italy
| | - P Uva
- Clinical Bioinformatics Unit, IRCCS Istituto Giannina Gaslini, Genova, Italy
| | - D Cangelosi
- Clinical Bioinformatics Unit, IRCCS Istituto Giannina Gaslini, Genova, Italy
| | - G Cavalca
- Clinical Bioinformatics Unit, IRCCS Istituto Giannina Gaslini, Genova, Italy
- University of Bologna, Bologna, Italy
| | - M Miano
- Haematology Unit-IRCCS Istituto Giannina Gaslini, Genova, Italy
| | - C Dufour
- Haematology Unit-IRCCS Istituto Giannina Gaslini, Genova, Italy
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Vermeulen R, Bodinier B, Dagnino S, Wada R, Wang X, Silverman D, Albanes D, Freedman N, Rahman M, Bell D, Chadeau-Hyam M, Rothman N. A prospective study of smoking-related white blood cell DNA methylation markers and risk of bladder cancer. Eur J Epidemiol 2024:10.1007/s10654-024-01110-y. [PMID: 38554236 DOI: 10.1007/s10654-024-01110-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 02/20/2024] [Indexed: 04/01/2024]
Abstract
Bladder cancer, a common neoplasm, is primarily caused by tobacco smoking. Epigenetic alterations including DNA methylation have the potential to be used as prospective markers of increased risk, particularly in at-risk populations such as smokers. We aimed to investigate the potential of smoking-related white blood cell (WBC) methylation markers to contribute to an increase in bladder cancer risk prediction over classical questionnaire-based smoking metrics (i.e., duration, intensity, packyears) in a nested case-control study within the prospective prostate, lung, colorectal, and ovarian (PLCO) Cancer Screening Trial and the alpha-tocopherol, beta-carotene cancer (ATBC) Prevention Study (789 cases; 849 controls). We identified 200 differentially methylated sites associated with smoking status and 28 significantly associated (after correction for multiple testing) with bladder cancer risk among 2670 previously reported smoking-related cytosine-phosphate-guanines sites (CpGs). Similar patterns were observed across cohorts. Receiver operating characteristic (ROC) analyses indicated that cg05575921 (AHHR), the strongest smoking-related association we identified for bladder cancer risk, alone yielded similar predictive performance (AUC: 0.60) than classical smoking metrics (AUC: 0.59-0.62). Best prediction was achieved by including the first principal component (PC1) from the 200 smoking-related CpGs alongside smoking metrics (AUC: 0.63-0.65). Further, PC1 remained significantly associated with elevated bladder cancer risk after adjusting for smoking metrics. These findings suggest DNA methylation profiles reflect aspects of tobacco smoke exposure in addition to those captured by smoking duration, intensity and packyears, and/or individual susceptibility relevant to bladder cancer etiology, warranting further investigation.
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Affiliation(s)
- Roel Vermeulen
- Institute for Risk Assessment Sciences, Division of Environmental Epidemiology, Utrecht University, PO Box 80178, 3508 TD, Utrecht, The Netherlands.
| | - Barbara Bodinier
- Faculty of Medicine, School of Public Health, Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- MRC Centre for Environment and Health, Imperial College London, London, UK
| | - Sonia Dagnino
- Faculty of Medicine, School of Public Health, Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- MRC Centre for Environment and Health, Imperial College London, London, UK
- Commissariat À L'Energie Atomique Et Aux Énergies Alternatives (CEA), Institut Des Sciences du Vivant Fréderic Joliot, Université Côte d'Azur, Nice, France
| | - Rin Wada
- Faculty of Medicine, School of Public Health, Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- MRC Centre for Environment and Health, Imperial College London, London, UK
| | - Xuting Wang
- Immunity Inflammation and Disease Laboratory, National Institute of Environmental Health Sciences, National Institutes of Health, RTP, Durham, NC, USA
| | - Debra Silverman
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD, USA
| | - Demetrius Albanes
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD, USA
| | - Neal Freedman
- Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, Rockville, MD, USA
| | - Mohammad Rahman
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD, USA
| | - Douglas Bell
- Immunity Inflammation and Disease Laboratory, National Institute of Environmental Health Sciences, National Institutes of Health, RTP, Durham, NC, USA
| | - Marc Chadeau-Hyam
- Faculty of Medicine, School of Public Health, Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- Commissariat À L'Energie Atomique Et Aux Énergies Alternatives (CEA), Institut Des Sciences du Vivant Fréderic Joliot, Université Côte d'Azur, Nice, France
| | - Nathaniel Rothman
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD, USA
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13
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Muludi K, Setianingsih R, Sholehurrohman R, Junaidi A. Exploiting nearest neighbor data and fuzzy membership function to address missing values in classification. PeerJ Comput Sci 2024; 10:e1968. [PMID: 38660203 PMCID: PMC11042039 DOI: 10.7717/peerj-cs.1968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Accepted: 03/07/2024] [Indexed: 04/26/2024]
Abstract
The accuracy of most classification methods is significantly affected by missing values. Therefore, this study aimed to propose a data imputation method to handle missing values through the application of nearest neighbor data and fuzzy membership function as well as to compare the results with standard methods. A total of five datasets related to classification problems obtained from the UCI Machine Learning Repository were used. The results showed that the proposed method had higher accuracy than standard imputation methods. Moreover, triangular method performed better than Gaussian fuzzy membership function. This showed that the combination of nearest neighbor data and fuzzy membership function was more effective in handling missing values and improving classification accuracy.
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Affiliation(s)
- Kurnia Muludi
- Informatics and Business Institute Darmajaya, Bandar Lampung, Lampung Province, Indonesia
| | - Revita Setianingsih
- Computer Science Department, Faculty of Science, Lampung University, Bandar Lampung, Lampung Province, Indonesia
| | - Ridho Sholehurrohman
- Computer Science Department, Faculty of Science, Lampung University, Bandar Lampung, Lampung Province, Indonesia
| | - Akmal Junaidi
- Computer Science Department, Faculty of Science, Lampung University, Bandar Lampung, Lampung Province, Indonesia
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14
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Knuchel R, Erlic Z, Gruber S, Amar L, Larsen CK, Gimenez-Roqueplo AP, Mulatero P, Tetti M, Pecori A, Pamporaki C, Langton K, Peitzsch M, Ceccato F, Prejbisz A, Januszewicz A, Adolf C, Remde H, Lenzini L, Dennedy M, Deinum J, Jefferson E, Blanchard A, Zennaro MC, Eisenhofer G, Beuschlein F. Association of adrenal steroids with metabolomic profiles in patients with primary and endocrine hypertension. Front Endocrinol (Lausanne) 2024; 15:1370525. [PMID: 38596218 PMCID: PMC11002274 DOI: 10.3389/fendo.2024.1370525] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/14/2024] [Accepted: 03/05/2024] [Indexed: 04/11/2024] Open
Abstract
Introduction Endocrine hypertension (EHT) due to pheochromocytoma/paraganglioma (PPGL), Cushing's syndrome (CS), or primary aldosteronism (PA) is linked to a variety of metabolic alterations and comorbidities. Accordingly, patients with EHT and primary hypertension (PHT) are characterized by distinct metabolic profiles. However, it remains unclear whether the metabolomic differences relate solely to the disease-defining hormonal parameters. Therefore, our objective was to study the association of disease defining hormonal excess and concomitant adrenal steroids with metabolomic alterations in patients with EHT. Methods Retrospective European multicenter study of 263 patients (mean age 49 years, 50% females; 58 PHT, 69 PPGL, 37 CS, 99 PA) in whom targeted metabolomic and adrenal steroid profiling was available. The association of 13 adrenal steroids with differences in 79 metabolites between PPGL, CS, PA and PHT was examined after correction for age, sex, BMI, and presence of diabetes mellitus. Results After adjustment for BMI and diabetes mellitus significant association between adrenal steroids and metabolites - 18 in PPGL, 15 in CS, and 23 in PA - were revealed. In PPGL, the majority of metabolite associations were linked to catecholamine excess, whereas in PA, only one metabolite was associated with aldosterone. In contrast, cortisone (16 metabolites), cortisol (6 metabolites), and DHEA (8 metabolites) had the highest number of associated metabolites in PA. In CS, 18-hydroxycortisol significantly influenced 5 metabolites, cortisol affected 4, and cortisone, 11-deoxycortisol, and DHEA each were linked to 3 metabolites. Discussions Our study indicates cortisol, cortisone, and catecholamine excess are significantly associated with metabolomic variances in EHT versus PHT patients. Notably, catecholamine excess is key to PPGL's metabolomic changes, whereas in PA, other non-defining adrenal steroids mainly account for metabolomic differences. In CS, cortisol, alongside other non-defining adrenal hormones, contributes to these differences, suggesting that metabolic disorders and cardiovascular morbidity in these conditions could also be affected by various adrenal steroids.
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Affiliation(s)
- Robin Knuchel
- Klinik für Endokrinologie, Diabetologie und Klinische Ernährung, UniversitätsSpital Zürich (USZ) und Universität Zürich (UZH), Zurich, Switzerland
| | - Zoran Erlic
- Klinik für Endokrinologie, Diabetologie und Klinische Ernährung, UniversitätsSpital Zürich (USZ) und Universität Zürich (UZH), Zurich, Switzerland
| | - Sven Gruber
- Klinik für Endokrinologie, Diabetologie und Klinische Ernährung, UniversitätsSpital Zürich (USZ) und Universität Zürich (UZH), Zurich, Switzerland
| | - Laurence Amar
- Université Paris Cité, Paris Cardiovascular Research Center (PARCC), L'Institut National de la Santé et de la Recherche Médicale (INSERM), Paris, France
- Département de Médecine Génomique des Tumeurs et des Cancers, Assistance Publique-Hôpitaux de Paris, Hôpital Européen Georges Pompidou, Paris, France
- Centre de référence en maladies rares de la surrénale, Hôpital Européen Georges Pompidou, Paris, France
| | - Casper K. Larsen
- Université Paris Cité, Paris Cardiovascular Research Center (PARCC), L'Institut National de la Santé et de la Recherche Médicale (INSERM), Paris, France
| | - Anne-Paule Gimenez-Roqueplo
- Université Paris Cité, Paris Cardiovascular Research Center (PARCC), L'Institut National de la Santé et de la Recherche Médicale (INSERM), Paris, France
- Département de Médecine Génomique des Tumeurs et des Cancers, Assistance Publique-Hôpitaux de Paris, Hôpital Européen Georges Pompidou, Paris, France
| | - Paolo Mulatero
- Division of Internal Medicine and Hypertension Unit, Department of Medical Sciences, University of Torino, Torino, Italy
| | - Martina Tetti
- Division of Internal Medicine and Hypertension Unit, Department of Medical Sciences, University of Torino, Torino, Italy
| | - Alessio Pecori
- Division of Internal Medicine and Hypertension Unit, Department of Medical Sciences, University of Torino, Torino, Italy
| | - Christina Pamporaki
- Medical Clinic III, University Hospital Carl Gustav Carus, Technische Universität (TU) Dresden, Dresden, Germany
| | - Katharina Langton
- Medical Clinic III, University Hospital Carl Gustav Carus, Technische Universität (TU) Dresden, Dresden, Germany
| | - Mirko Peitzsch
- Institute for Clinical Chemistry and Laboratory Medicine, University Hospital and Medical Faculty Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Filippo Ceccato
- Unita' Operativa Complessa (UOC) Endocrinologia, Dipartimento di Medicina DIMED, Azienda Ospedaliera-Università di Padova, Padua, Italy
| | - Aleksander Prejbisz
- Department of Hypertension, National Institute of Cardiology, Warsaw, Poland
| | - Andrzej Januszewicz
- Department of Hypertension, National Institute of Cardiology, Warsaw, Poland
| | - Christian Adolf
- Medizinische Klinik und Poliklinik IV, Klinikum der Universität München, Ludwig-Maximilians-Universität (LMU) München, Munich, Germany
| | - Hanna Remde
- Department of Internal Medicine I, Division of Endocrinology and Diabetes, University Hospital, University of Würzburg, Würzburg, Germany
| | - Livia Lenzini
- Internal & Emergency Medicine Unit, Department of Medicine - DIMED, University of Padua, Padua, Italy
| | - Michael Dennedy
- The Discipline of Pharmacology and Therapeutics, School of Medicine, National University of Ireland, Galway, Ireland
| | - Jaap Deinum
- Department of Medicine, Section of Vascular Medicine, Radboud University Medical Center, Nijmegen, Netherlands
| | - Emily Jefferson
- Division of Population Health and Genomics, School of Medicine, University of Dundee, Dundee, United Kingdom
| | - Anne Blanchard
- Assistance Publique-Hôpitaux de Paris, Hôpital Européen Georges Pompidou, Centre d’Investigations Cliniques, Paris, France
| | - Maria-Christina Zennaro
- Université Paris Cité, Paris Cardiovascular Research Center (PARCC), L'Institut National de la Santé et de la Recherche Médicale (INSERM), Paris, France
- Assistance Publique-Hôpitaux de Paris, Hôpital Européen Georges Pompidou, Unité Hypertension artérielle, Paris, France
| | - Graeme Eisenhofer
- Medical Clinic III, University Hospital Carl Gustav Carus, Technische Universität (TU) Dresden, Dresden, Germany
| | - Felix Beuschlein
- Klinik für Endokrinologie, Diabetologie und Klinische Ernährung, UniversitätsSpital Zürich (USZ) und Universität Zürich (UZH), Zurich, Switzerland
- Medizinische Klinik und Poliklinik IV, Klinikum der Universität München, Ludwig-Maximilians-Universität (LMU) München, Munich, Germany
- The LOOP Zurich - Medical Research Center, Zurich, Switzerland
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15
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Xiang X, He Y, Zhang Z, Yang X. Interrogations of single-cell RNA splicing landscapes with SCASL define new cell identities with physiological relevance. Nat Commun 2024; 15:2164. [PMID: 38461306 PMCID: PMC10925056 DOI: 10.1038/s41467-024-46480-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Accepted: 02/28/2024] [Indexed: 03/11/2024] Open
Abstract
RNA splicing shapes the gene regulatory programs that underlie various physiological and disease processes. Here, we present the SCASL (single-cell clustering based on alternative splicing landscapes) method for interrogating the heterogeneity of RNA splicing with single-cell RNA-seq data. SCASL resolves the issue of biased and sparse data coverage on single-cell RNA splicing and provides a new scheme for classifications of cell identities. With previously published datasets as examples, SCASL identifies new cell clusters indicating potentially precancerous and early-tumor stages in triple-negative breast cancer, illustrates cell lineages of embryonic liver development, and provides fine clusters of highly heterogeneous tumor-associated CD4 and CD8 T cells with functional and physiological relevance. Most of these findings are not readily available via conventional cell clustering based on single-cell gene expression data. Our study shows the potential of SCASL in revealing the intrinsic RNA splicing heterogeneity and generating biological insights into the dynamic and functional cell landscapes in complex tissues.
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Affiliation(s)
- Xianke Xiang
- MOE Key Laboratory of Bioinformatics, School of Life Sciences, Tsinghua University, Beijing, 100084, China
- Center for Synthetic & Systems Biology, Tsinghua University, Beijing, 100084, China
| | - Yao He
- Biomedical Pioneering Innovation Center and School of Life Sciences, Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871, China
| | - Zemin Zhang
- Biomedical Pioneering Innovation Center and School of Life Sciences, Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871, China
- Cancer Research Institute, Shenzhen Bay Lab, Shenzhen, 518132, China
| | - Xuerui Yang
- MOE Key Laboratory of Bioinformatics, School of Life Sciences, Tsinghua University, Beijing, 100084, China.
- Center for Synthetic & Systems Biology, Tsinghua University, Beijing, 100084, China.
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16
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Benson M, Smelik M, Li X, Loscalzo J, Sysoev O, Mahmud F, Aly DM, Zhao Y. An interactive atlas of genomic, proteomic, and metabolomic biomarkers promotes the potential of proteins to predict complex diseases. Res Sq 2024:rs.3.rs-3921099. [PMID: 38496611 PMCID: PMC10942575 DOI: 10.21203/rs.3.rs-3921099/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
Multiomics analyses have identified multiple potential biomarkers of the incidence and prevalence of complex diseases. However, it is not known which type of biomarker is optimal for clinical purposes. Here, we make a systematic comparison of 90 million genetic variants, 1,453 proteins, and 325 metabolites from 500,000 individuals with complex diseases from the UK Biobank. A machine learning pipeline consisting of data cleaning, data imputation, feature selection, and model training using cross-validation and comparison of the results on holdout test sets showed that proteins were most predictive, followed by metabolites, and genetic variants. Only five proteins per disease resulted in median (min-max) areas under the receiver operating characteristic curves for incidence of 0.79 (0.65-0.86) and 0.84 (0.70-0.91) for prevalence. In summary, our work suggests the potential of predicting complex diseases based on a limited number of proteins. We provide an interactive atlas (macd.shinyapps.io/ShinyApp/) to find genomic, proteomic, or metabolomic biomarkers for different complex diseases.
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Nyholm J, Ghazi AN, Ghazi SN, Sanmartin Berglund J. Prediction of dementia based on older adults' sleep disturbances using machine learning. Comput Biol Med 2024; 171:108126. [PMID: 38342045 DOI: 10.1016/j.compbiomed.2024.108126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 12/14/2023] [Accepted: 02/06/2024] [Indexed: 02/13/2024]
Abstract
BACKGROUND The most common degenerative condition in older adults is dementia, which can be predicted using a number of indicators and whose progression can be slowed down. One of the indicators of an increased risk of dementia is sleep disturbances. This study aims to examine if machine learning can predict dementia and which sleep disturbance factors impact dementia. METHODS This study uses five machine learning algorithms (gradient boosting, logistic regression, gaussian naive Bayes, random forest and support vector machine) and data on the older population (60+) in Sweden from the Swedish National Study on Ageing and Care - Blekinge (n=4175). Each algorithm uses 10-fold stratified cross-validation to obtain the results, which consist of the Brier score for checking accuracy and the feature importance for examining the factors which impact dementia. The algorithms use 16 features which are on personal and sleep disturbance factors. RESULTS Logistic regression found an association between dementia and sleep disturbances. However, it is slight for the features in the study. Gradient boosting was the most accurate algorithm with 92.9% accuracy, 0.926 f1-score, 0.974 ROC AUC and 0.056 Brier score. The significant factors were different in each machine learning algorithm. If the person sleeps more than two hours during the day, their sex, education level, age, waking up during the night and if the person snores are the variables that most consistently have the highest feature importance in all algorithms. CONCLUSION There is an association between sleep disturbances and dementia, which machine learning algorithms can predict. Furthermore, the risk factors for dementia are different across the algorithms, but sleep disturbances can predict dementia.
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Affiliation(s)
- Joel Nyholm
- Department of Computer Science, Blekinge Institute of Technology, Karlskrona, 37179, Blekinge, Sweden
| | - Ahmad Nauman Ghazi
- Department of Software Engineering, Blekinge Institute of Technology, Karlskrona, 37179, Blekinge, Sweden.
| | - Sarah Nauman Ghazi
- Department of Health, Blekinge Institute of Technology, Karlskrona, 37179, Blekinge, Sweden
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18
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Rios Fuck JV, Cechinel MAP, Neves J, Campos de Andrade R, Tristão R, Spogis N, Riella HG, Soares C, Padoin N. Predicting effluent quality parameters for wastewater treatment plant: A machine learning-based methodology. Chemosphere 2024; 352:141472. [PMID: 38382719 DOI: 10.1016/j.chemosphere.2024.141472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/02/2023] [Revised: 02/05/2024] [Accepted: 02/14/2024] [Indexed: 02/23/2024]
Abstract
Wastewater Treatment Plants (WWTPs) present complex biochemical processes of high variability and difficult prediction. This study presents an innovative approach using Machine Learning (ML) models to predict wastewater quality parameters. In particular, the models are applied to datasets from both a simulated wastewater treatment plant (WWTP), using DHI WEST software (WEST WWTP), and a real-world WWTP database from Santa Catarina Brewery AMBEV, located in Lages/SC - Brazil (AMBEV WWTP). A distinctive aspect is the evaluation of predictive performance in continuous data scenarios and the impact of changes in WWTP operations on predictive model performance, including changes in plant layout. For both plants, three different scenarios were addressed, and the quality of predictions by random forest (RF), support vector machine (SVM), and multilayer perceptron (MLP) models were evaluated. The prediction quality by the MLP model reached an R2 of 0.72 for TN prediction in the WEST WWTP output, and the RF model better adapted to the real data of the AMBEV WWTP, despite the significant discrepancy observed between the real and the predicted data. Techniques such as Partial Dependence Plots (PDP) and Permutation Importance (PI) were used to assess the importance of features, particularly in the simulated WEST tool scenario, showing a strong correlation of prediction results with influent parameters related to nitrogen content. The results of this study highlight the importance of collecting and storing high-quality data and the need for information on changes in WWTP operation for predictive model performance. These contributions advance the understanding of predictive modeling for wastewater quality and provide valuable insights for future practice in wastewater treatment.
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Affiliation(s)
- João Vitor Rios Fuck
- Hydroinfo - Hydroinformatics Solutions Ltda, Florianópolis, SC, Brazil; Laboratory of Materials and Scientific Computing (LabMAC), Department of Chemical and Food Engineering, Federal University of Santa Catarina (UFSC), Florianópolis, SC, Brazil
| | - Maria Alice Prado Cechinel
- Hydroinfo - Hydroinformatics Solutions Ltda, Florianópolis, SC, Brazil; Laboratory of Materials and Scientific Computing (LabMAC), Department of Chemical and Food Engineering, Federal University of Santa Catarina (UFSC), Florianópolis, SC, Brazil
| | - Juliana Neves
- Hydroinfo - Hydroinformatics Solutions Ltda, Florianópolis, SC, Brazil; Laboratory of Materials and Scientific Computing (LabMAC), Department of Chemical and Food Engineering, Federal University of Santa Catarina (UFSC), Florianópolis, SC, Brazil
| | | | | | - Nicolas Spogis
- Faculty of Chemical Engineering, State University of Campinas, Campinas, SP, Brazil
| | - Humberto Gracher Riella
- Laboratory of Materials and Scientific Computing (LabMAC), Department of Chemical and Food Engineering, Federal University of Santa Catarina (UFSC), Florianópolis, SC, Brazil
| | - Cíntia Soares
- Laboratory of Materials and Scientific Computing (LabMAC), Department of Chemical and Food Engineering, Federal University of Santa Catarina (UFSC), Florianópolis, SC, Brazil.
| | - Natan Padoin
- Laboratory of Materials and Scientific Computing (LabMAC), Department of Chemical and Food Engineering, Federal University of Santa Catarina (UFSC), Florianópolis, SC, Brazil.
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Lumour-Mensah T, Lemos B. Evidence of reduced gestational age in response to in utero arsenic exposure and implications for aging trajectories of the newborn. Environ Int 2024; 185:108566. [PMID: 38461780 DOI: 10.1016/j.envint.2024.108566] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 01/09/2024] [Accepted: 03/04/2024] [Indexed: 03/12/2024]
Abstract
Arsenic exposure is associated with a plethora of age-related health outcomes of disparate etiology. However, evidence of the impact of arsenic on aging remains limited. Here, we investigated the utility of epigenetic clocks in two different populations and the impact of maternal arsenic exposure during pregnancy on epigenetic gestational age at birth. To do this, we examined publicly available DNA methylation data and estimated gestational age across five gestational clocks in two unrelated human populations. These populations also differ in the extent of arsenic exposure and the targeted tissue of analysis (cord blood and placental tissue). Our results indicate that same-tissue clocks produce gestational age estimates that are more highly correlated with clinical gestational age. Interestingly, our results also indicate that arsenic exposure is associated with gestational age, with higher arsenic exposures associated with decreased gestational age. We also applied two pediatric clocks to evaluate infant biological age in the same samples. The data is suggestive of higher pediatric age in infants exposed to higher arsenic levels during gestation. Taken altogether, our findings are consistent with past work indicating that that in utero arsenic exposure is associated with decreased gestational maturity as characterized by infant outcomes such as low birthweight and lung underdevelopment and dysfunction in arsenic exposed infants. The findings are also consistent with arsenic exposure setting infants on a trajectory of accelerated epigenetic aging that starts at birth.
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Affiliation(s)
- Tabitha Lumour-Mensah
- Department of Environmental Health, Harvard School of Public Health, Boston, MA, United States
| | - Bernardo Lemos
- Department of Environmental Health, Harvard School of Public Health, Boston, MA, United States; R. Ken Coit College of Pharmacy, Department of Pharmacology and Toxicology, The University of Arizona, Tucson, AZ, United States; Coit Center for Longevity and Neurotherapeutics, The University of Arizona, Tucson, AZ, United States.
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Salamattalab MM, Hasani Zonoozi M, Molavi-Arabshahi M. Innovative approach for predicting biogas production from large-scale anaerobic digester using long-short term memory (LSTM) coupled with genetic algorithm (GA). Waste Manag 2024; 175:30-41. [PMID: 38154165 DOI: 10.1016/j.wasman.2023.12.046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 12/21/2023] [Accepted: 12/23/2023] [Indexed: 12/30/2023]
Abstract
An artificial neural network (ANN) model called long-short term memory (LSTM), coupled with a genetic algorithm (GA) for feature selection, was used to predict biogas production of large-scale anaerobic digesters (ADs) of Tehran South Wastewater Treatment Plant (Iran), with a biogas production of approximately 30,000 Nm3/d. In order to employ the real conditions, the hydraulic retention time (HRT) of the ADs (21 days) was considered as the LSTM look-back window. To evaluate the model predictions, three different scenarios were defined. In the first scenario, the model predicted the produced biogas by using raw wastewater characteristics and reached the coefficient of determination of R2 = 0.84. The GA selected four out of eleven parameters of raw wastewater, including loads of BOD5, COD, TSS, and TN (kg/d), as the most informative data for the model. In the second scenario, the model predicted the produced biogas by employing the data of the thickened sludge streams entering the ADs and yielded a higher accuracy (R2 = 0.89). In this scenario, GA selected two out of six parameters of the sludge streams, including total flow rate (m3/d) and average solids content (w/w%). Finally, in the third scenario, by putting the parameters of the two previous scenarios together, the model's prediction accuracy increased slightly (R2 = 0.90). The results demonstrated that the GA-LSTM modeling technique could achieve reliable performance in predicting biogas production of large-scale ADs by including HRT in modeling procedure. It was also found that the raw wastewater characteristics severely affect AD behavior and can be successfully used as the input data of the AD models.
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Affiliation(s)
- Mohammad Milad Salamattalab
- Department of Civil Engineering, Iran University of Science and Technology (IUST), Narmak, Tehran 16846-13114, Iran.
| | - Maryam Hasani Zonoozi
- Department of Civil Engineering, Iran University of Science and Technology (IUST), Narmak, Tehran 16846-13114, Iran.
| | - Mahboubeh Molavi-Arabshahi
- Department of Mathematics, Iran University of Science and Technology (IUST), Narmak, Tehran 16846-13114, Iran.
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21
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Bravo MC, Jiménez R, Parrado-Hernández E, Fernández JJ, Pellicer A. Predicting the effectiveness of drugs used for treating cardiovascular conditions in newborn infants. Pediatr Res 2024; 95:1124-1131. [PMID: 38092963 DOI: 10.1038/s41390-023-02964-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 09/08/2023] [Accepted: 11/27/2023] [Indexed: 03/09/2024]
Abstract
BACKGROUND Cardiovascular support (CVS) treatment failure (TF) is associated with a poor prognosis in preterm infants. METHODS Medical charts of infants with a birth weight <1500 g who received either dopamine (Dp) or dobutamine (Db), were reviewed. Treatment response (TR) occurred if blood pressure increased >3rd centile for gestational age or superior vena cava flow was maintained >55 ml/kg/min, with decreased lactate or less negative base excess, without additional CVS. A predictive model of Dp and Db on TR was designed and the impact of TR on survival was analyzed. RESULTS Sixty-six infants (median gestational age 27.3 weeks, median birth weight 864 g) received Dp (n = 44) or Db (n = 22). TR occurred in 59% of the cases treated with Dp and 31% with Db, p = 0.04. Machine learning identified a model that correctly labeled Db response in 90% of the cases and Dp response in 61.4%. Sixteen infants died (9% of the TR group, 39% of the TF group; p = 0.004). Brain or gut morbidity-free survival was observed in 52% vs 30% in the TR and TF groups, respectively (p = 0.08). CONCLUSIONS New predictive models can anticipate Db but not Dp effectiveness in preterm infants. These algorithms may help the clinicians in the decision-making process. IMPACT Failure of cardiovascular support treatment increases the risk of mortality in very low birth weight infants. A predictive model built with machine learning techniques can help anticipate treatment response to dobutamine with high accuracy. Predictive models based on artificial intelligence may guide the clinicians in the decision-making process.
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Affiliation(s)
- María Carmen Bravo
- Department of Neonatology, La Paz University Hospital and IdiPaz (La Paz Hospital Institute for Health Research), Madrid, Spain.
| | - Raquel Jiménez
- Department of Neonatology, La Paz University Hospital and IdiPaz (La Paz Hospital Institute for Health Research), Madrid, Spain
- Department of Signal Theory and Communications, Carlos III University, Madrid, Spain
| | | | | | - Adelina Pellicer
- Department of Neonatology, La Paz University Hospital and IdiPaz (La Paz Hospital Institute for Health Research), Madrid, Spain
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22
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Charon C, Wuillemin PH, Havreng-Théry C, Belmin J. One Month Prediction of Pressure Ulcers in Nursing Home Residents with Bayesian Networks. J Am Med Dir Assoc 2024:104945. [PMID: 38431264 DOI: 10.1016/j.jamda.2024.01.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 01/12/2024] [Accepted: 01/17/2024] [Indexed: 03/05/2024]
Abstract
OBJECTIVES Pressure ulcers (PUs) are a common and avoidable condition among residents of nursing homes, and their consequences are severe. Reliable and simple identification of high-risk residents is a major challenge for prevention. Available tools like the Braden and Norton scale have imperfect predictive performance. The objective is to predict the occurrence of PUs in nursing home residents from electronic health record (EHR) data. DESIGN Longitudinal retrospective nested case-control study. SETTING AND PARTICIPANTS EHR database of French nursing homes from 2013 to 2022. METHODS Residents who suffered from PUs were cases and those who did not were controls. For cases, we analyzed the data available in their EHR 1 month before the occurrence of the first PU. For controls, we used available data 1 month before an index date adjusted on the delays of PU onset. We conducted a Bayesian network (BN) analysis, an explainable machine learning method, using 136 input variables of potential medical interest determined with experts. To validate the model, we used scores, features selection, and explainability tools such as Shapley values. RESULTS Among 58,368 residents analyzed, 29% suffered from PUs during their stay. The obtained BN model predicts the occurrence of a PU at a 1-month horizon with a sensitivity of 0.94 (±0.01), a precision of 0.32 (±0.01) and an area under the curve of 0.69 (±0.02). It selects 3 variables: length of stay, delay since last hospitalization, and dependence for transfer. This BN model is suitable and simpler than models provided by other machine learning methods. CONCLUSIONS AND IMPLICATIONS One-month prediction for incident PU is possible in nursing home residents from their EHR data. The study paves the way for the development of a predictive tool fueled by routinely collected data that do not require additional work from health care professionals, thereby opening a new preventive strategy for PUs.
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Affiliation(s)
- Clara Charon
- LIP6 (UMR 7606), Sorbonne Université, Paris, France; Teranga Software, Paris, France
| | | | | | - Joël Belmin
- LIMICS (UMR 1142), Sorbonne Université, Paris, France; AP-HP, Hôpital Charles-Foix, Ivry-sur-Seine, France.
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23
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Yu W, Li Y, Zhong F, Deng Z, Wu J, Yu W, Lü Y. Disease-Associated Neurotoxic Astrocyte Markers in Alzheimer Disease Based on Integrative Single-Nucleus RNA Sequencing. Cell Mol Neurobiol 2024; 44:20. [PMID: 38345650 PMCID: PMC10861702 DOI: 10.1007/s10571-024-01453-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 01/11/2024] [Indexed: 02/15/2024]
Abstract
Alzheimer disease (AD) is an irreversible neurodegenerative disease, and astrocytes play a key role in its onset and progression. The aim of this study is to analyze the characteristics of neurotoxic astrocytes and identify novel molecular targets for slowing down the progression of AD. Single-nucleus RNA sequencing (snRNA-seq) data were analyzed from various AD cohorts comprising about 210,654 cells from 53 brain tissue. By integrating snRNA-seq data with bulk RNA-seq data, crucial astrocyte types and genes associated with the prognosis of patients with AD were identified. The expression of neurotoxic astrocyte markers was validated using 5 × FAD and wild-type (WT) mouse models, combined with experiments such as western blot, quantitative real-time PCR (qRT-PCR), and immunofluorescence. A group of neurotoxic astrocytes closely related to AD pathology was identified, which were involved in inflammatory responses and pathways related to neuron survival. Combining snRNA and bulk tissue data, ZEP36L, AEBP1, WWTR1, PHYHD1, DST and RASL12 were identified as toxic astrocyte markers closely related to disease severity, significantly elevated in brain tissues of 5 × FAD mice and primary astrocytes treated with Aβ. Among them, WWTR1 was significantly increased in astrocytes of 5 × FAD mice, driving astrocyte inflammatory responses, and has been identified as an important marker of neurotoxic astrocytes. snRNA-seq analysis reveals the biological functions of neurotoxic astrocytes. Six genes related to AD pathology were identified and validated, among which WWTR1 may be a novel marker of neurotoxic astrocytes.
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Affiliation(s)
- Wuhan Yu
- Department of Geriatrics, The First Affiliated Hospital of Chongqing Medical University, No.1 Youyi Road, Yuzhong, Chongqing, 400016, China
| | - Yin Li
- Department of Thoracic Surgery, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Fuxin Zhong
- Department of Geriatrics, The First Affiliated Hospital of Chongqing Medical University, No.1 Youyi Road, Yuzhong, Chongqing, 400016, China
| | - Zhangjing Deng
- Department of Geriatrics, The First Affiliated Hospital of Chongqing Medical University, No.1 Youyi Road, Yuzhong, Chongqing, 400016, China
| | - Jiani Wu
- Department of Geriatrics, The First Affiliated Hospital of Chongqing Medical University, No.1 Youyi Road, Yuzhong, Chongqing, 400016, China
| | - Weihua Yu
- Institutes of Neuroscience, Chongqing Medical University, Chongqing, 400016, China
| | - Yang Lü
- Department of Geriatrics, The First Affiliated Hospital of Chongqing Medical University, No.1 Youyi Road, Yuzhong, Chongqing, 400016, China.
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24
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Piccolo B, Chen A, Louey S, Thornburg K, Jonker S. Physiological response to fetal intravenous lipid emulsion. Clin Sci (Lond) 2024; 138:117-134. [PMID: 38261523 PMCID: PMC10876438 DOI: 10.1042/cs20231419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 01/08/2024] [Accepted: 01/23/2024] [Indexed: 01/25/2024]
Abstract
In preterm neonates unable to obtain sufficient oral nutrition, intravenous lipid emulsion is life-saving. The contribution of post-conceptional level of maturation to pathology that some neonates experience is difficult to untangle from the global pathophysiology of premature birth. In the present study, we determined fetal physiological responses to intravenous lipid emulsion. Fetal sheep were given intravenous Intralipid 20® (n = 4 females, 7 males) or Lactated Ringer's Solution (n = 7 females, 4 males) between 125 ± 1 and 133 ± 1 d of gestation (term = 147 d). Manufacturer's recommendation for premature human infants was followed: 0.5-1 g/kg/d initial rate, increased by 0.5-1 to 3 g/kg/d. Hemodynamic parameters and arterial blood chemistry were measured, and organs were studied postmortem. Red blood cell lipidomics were analyzed by LC-MS. Intravenous Intralipid did not alter hemodynamic or most blood parameters. Compared with controls, Intralipid infusion increased final day plasma protein (P=0.004; 3.5 ± 0.3 vs. 3.9 ± 0.2 g/dL), albumin (P = 0.031; 2.2 ± 0.1 vs. 2.4 ± 0.2 g/dL), and bilirubin (P<0.001; conjugated: 0.2 ± 0.1 vs. 0.6 ± 0.2 mg/dL; unconjugated: 0.2 ± 0.1 vs. 1.1 ± 0.4 mg/dL). Circulating IGF-1 decreased following Intralipid infusion (P<0.001; 66 ± 24 vs. 46 ± 24 ng/mL). Compared with control Oil Red O liver stains (median score 0), Intralipid-infused fetuses scored 108 (P=0.0009). Lipidomic analysis revealed uptake and processing of infused lipids into red blood cells, increasing abundance of saturated fatty acids. The near-term fetal sheep tolerates intravenous lipid emulsion well, although lipid accumulates in the liver. Increased levels of unconjugated bilirubin may reflect increased red blood cell turnover or impaired placental clearance. Whether Intralipid is less well tolerated earlier in gestation remains to be determined.
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Affiliation(s)
- Brian D. Piccolo
- USDA/ARS-Arkansas Children’s Nutrition Center, Little Rock, AR, U.S.A
- Department of Pediatrics, University of Arkansas for Medical Sciences, Little Rock, AR, U.S.A
| | - Athena Chen
- Department of Pathology, Oregon Health and Science University, Portland, OR, U.S.A
- Center for Developmental Health, Knight Cardiovascular Institute, Oregon Health and Science University, Portland, OR, U.S.A
| | - Samantha Louey
- Center for Developmental Health, Knight Cardiovascular Institute, Oregon Health and Science University, Portland, OR, U.S.A
| | - Kent L.R. Thornburg
- Center for Developmental Health, Knight Cardiovascular Institute, Oregon Health and Science University, Portland, OR, U.S.A
| | - Sonnet S. Jonker
- Center for Developmental Health, Knight Cardiovascular Institute, Oregon Health and Science University, Portland, OR, U.S.A
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25
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Shajari E, Gagné D, Malick M, Roy P, Noël JF, Gagnon H, Brunet MA, Delisle M, Boisvert FM, Beaulieu JF. Application of SWATH Mass Spectrometry and Machine Learning in the Diagnosis of Inflammatory Bowel Disease Based on the Stool Proteome. Biomedicines 2024; 12:333. [PMID: 38397935 PMCID: PMC10886680 DOI: 10.3390/biomedicines12020333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 01/17/2024] [Accepted: 01/25/2024] [Indexed: 02/25/2024] Open
Abstract
Inflammatory bowel disease (IBD) flare-ups exhibit symptoms that are similar to other diseases and conditions, making diagnosis and treatment complicated. Currently, the gold standard for diagnosing and monitoring IBD is colonoscopy and biopsy, which are invasive and uncomfortable procedures, and the fecal calprotectin test, which is not sufficiently accurate. Therefore, it is necessary to develop an alternative method. In this study, our aim was to provide proof of concept for the application of Sequential Window Acquisition of All Theoretical Mass Spectra-Mass spectrometry (SWATH-MS) and machine learning to develop a non-invasive and accurate predictive model using the stool proteome to distinguish between active IBD patients and symptomatic non-IBD patients. Proteome profiles of 123 samples were obtained and data processing procedures were optimized to select an appropriate pipeline. The differentially abundant analysis identified 48 proteins. Utilizing correlation-based feature selection (Cfs), 7 proteins were selected for proceeding steps. To identify the most appropriate predictive machine learning model, five of the most popular methods, including support vector machines (SVMs), random forests, logistic regression, naive Bayes, and k-nearest neighbors (KNN), were assessed. The generated model was validated by implementing the algorithm on 45 prospective unseen datasets; the results showed a sensitivity of 96% and a specificity of 76%, indicating its performance. In conclusion, this study illustrates the effectiveness of utilizing the stool proteome obtained through SWATH-MS in accurately diagnosing active IBD via a machine learning model.
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Affiliation(s)
- Elmira Shajari
- Laboratory of Intestinal Physiopathology, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada
- Centre de Recherche du Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada
- Department of Immunology and Cell Biology, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada
| | - David Gagné
- Laboratory of Intestinal Physiopathology, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada
- Centre de Recherche du Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada
- Department of Immunology and Cell Biology, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada
- Allumiqs, 975 Rue Léon-Trépanier, Sherbrooke, QC J1G 5J6, Canada
| | - Mandy Malick
- Laboratory of Intestinal Physiopathology, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada
- Centre de Recherche du Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada
- Department of Immunology and Cell Biology, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada
| | - Patricia Roy
- Laboratory of Intestinal Physiopathology, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada
- Centre de Recherche du Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada
- Department of Immunology and Cell Biology, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada
| | | | - Hugo Gagnon
- Allumiqs, 975 Rue Léon-Trépanier, Sherbrooke, QC J1G 5J6, Canada
| | - Marie A. Brunet
- Centre de Recherche du Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada
- Department of Pediatrics, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada
| | - Maxime Delisle
- Centre de Recherche du Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada
- Department of Medicine, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada
| | - François-Michel Boisvert
- Centre de Recherche du Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada
- Department of Immunology and Cell Biology, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada
| | - Jean-François Beaulieu
- Laboratory of Intestinal Physiopathology, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada
- Centre de Recherche du Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada
- Department of Immunology and Cell Biology, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada
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26
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Geng H, Qian R, Zhong Y, Tang X, Zhang X, Zhang L, Yang C, Li T, Dong Z, Wang C, Zhang Z, Zhu C. Leveraging synthetic lethality to uncover potential therapeutic target in gastric cancer. Cancer Gene Ther 2024; 31:334-348. [PMID: 38040871 DOI: 10.1038/s41417-023-00706-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Revised: 11/10/2023] [Accepted: 11/16/2023] [Indexed: 12/03/2023]
Abstract
Since trastuzumab was approved in 2012 for the first-line treatment of gastric cancer (GC), no significant advancement in GC targeted therapies has occurred. Synthetic lethality refers to the concept that simultaneous dysfunction of a pair of genes results in a lethal effect on cells, while the loss of an individual gene does not cause this effect. Through exploiting synthetic lethality, novel targeted therapies can be developed for the individualized treatment of GC. In this study, we proposed a computational strategy named Gastric cancer Specific Synthetic Lethality inference (GSSL) to identify synthetic lethal interactions in GC. GSSL analysis was used to infer probable synthetic lethality in GC using four accessible clinical datasets. In addition, prediction results were confirmed by experiments. GSSL analysis identified a total of 34 candidate synthetic lethal pairs, which included 33 unique targets. Among the synthetic lethal gene pairs, TP53-CHEK1 was selected for further experimental validation. Both computational and experimental results indicated that inhibiting CHEK1 could be a potential therapeutic strategy for GC patients with TP53 mutation. Meanwhile, in vitro experimental validation of two novel synthetic lethal pairs TP53-AURKB and ARID1A-EP300 further proved the universality and reliability of GSSL. Collectively, GSSL has been shown to be a reliable and feasible method for comprehensive analysis of inferring synthetic lethal interactions of GC, which may offer novel insight into the precision medicine and individualized treatment of GC.
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Affiliation(s)
- Haigang Geng
- Department of Gastrointestinal Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Ruolan Qian
- State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yiqing Zhong
- Department of Gastrointestinal Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Xiangyu Tang
- State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiaojun Zhang
- State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Linmeng Zhang
- State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Chen Yang
- State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Tingting Li
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Institute of Biostatistics, School of Life Sciences, Fudan University, Shanghai, China
| | - Zhongyi Dong
- Department of Gastrointestinal Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Cun Wang
- State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zizhen Zhang
- Department of Gastrointestinal Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
| | - Chunchao Zhu
- Department of Gastrointestinal Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
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27
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Wang J, Lock EF. Multiple augmented reduced rank regression for pan-cancer analysis. Biometrics 2024; 80:ujad002. [PMID: 38281771 PMCID: PMC10826883 DOI: 10.1093/biomtc/ujad002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Revised: 08/23/2023] [Accepted: 10/25/2023] [Indexed: 01/30/2024]
Abstract
Statistical approaches that successfully combine multiple datasets are more powerful, efficient, and scientifically informative than separate analyses. To address variation architectures correctly and comprehensively for high-dimensional data across multiple sample sets (ie, cohorts), we propose multiple augmented reduced rank regression (maRRR), a flexible matrix regression and factorization method to concurrently learn both covariate-driven and auxiliary structured variations. We consider a structured nuclear norm objective that is motivated by random matrix theory, in which the regression or factorization terms may be shared or specific to any number of cohorts. Our framework subsumes several existing methods, such as reduced rank regression and unsupervised multimatrix factorization approaches, and includes a promising novel approach to regression and factorization of a single dataset (aRRR) as a special case. Simulations demonstrate substantial gains in power from combining multiple datasets, and from parsimoniously accounting for all structured variations. We apply maRRR to gene expression data from multiple cancer types (ie, pan-cancer) from The Cancer Genome Atlas, with somatic mutations as covariates. The method performs well with respect to prediction and imputation of held-out data, and provides new insights into mutation-driven and auxiliary variations that are shared or specific to certain cancer types.
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Affiliation(s)
- Jiuzhou Wang
- Division of Biostatistics and Health Data Science, University of Minnesota, Minneapolis, MN 55414, United States
| | - Eric F Lock
- Division of Biostatistics and Health Data Science, University of Minnesota, Minneapolis, MN 55414, United States
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28
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Braisted J, Henderson T, Newman JW, Moore SC, Sampson J, McClain K, Ross S, Baer DJ, Mathé EA, Zanetti KA. Effects of Preanalytical Sample Collection and Handling on Comprehensive Metabolite Measurements in Human Urine Biospecimens. medRxiv 2024:2024.01.24.24301735. [PMID: 38410429 PMCID: PMC10896411 DOI: 10.1101/2024.01.24.24301735] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/28/2024]
Abstract
Epidemiology studies evaluate associations between the metabolome and disease risk. Urine is a common biospecimen used for such studies due to its wide availability and non-invasive collection. Evaluating the robustness of urinary metabolomic profiles under varying preanalytical conditions is thus of interest. Here we evaluate the impact of sample handling conditions on urine metabolome profiles relative to the gold standard condition (no preservative, no refrigeration storage, single freeze thaw). Conditions tested included the use of borate or chlorhexidine preservatives, various storage and freeze/thaw cycles. We demonstrate that sample handling conditions impact metabolite levels, with borate showing the largest impact with 125 of 1,048 altered metabolites (adjusted P < 0.05). When simulating a case-control study with expected inconsistencies in sample handling, we predicted the occurrence of false positive altered metabolites to be low (< 11). Predicted false positives increased substantially (³63) when cases were simulated to undergo alternate handling. Finally, we demonstrate that sample handling impacts on the urinary metabolome were markedly smaller than those in serum. While changes in urine metabolites incurred by sample handling are generally small, we recommend implementing consistent handling conditions and evaluating robustness of metabolite measurements for those showing significant associations with disease outcomes.
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Affiliation(s)
- John Braisted
- Division of Preclinical Innovation, National Center for Advancing Translational Sciences, Rockville, MD
| | - Theresa Henderson
- Food Components and Health Laboratory, Agriculture Research Service, United States Department of Agriculture, Beltsville, MD
| | - John W Newman
- Obesity and Metabolism Research, Agriculture Research Service, United States Department of Agriculture, Davis, CA
- Department of Nutrition, University of California, Davis, CA
| | - Steven C Moore
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD
| | - Joshua Sampson
- Division of Cancer Prevention, National Cancer Institute, Rockville, MD
| | - Kathleen McClain
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD
| | - Sharon Ross
- Division of Cancer Prevention, National Cancer Institute, Rockville, MD
| | - David J Baer
- Food Components and Health Laboratory, Agriculture Research Service, United States Department of Agriculture, Beltsville, MD
| | - Ewy A Mathé
- Division of Preclinical Innovation, National Center for Advancing Translational Sciences, Rockville, MD
| | - Krista A Zanetti
- Office of Nutrition Research, Division of Program Coordination, Planning, and Strategic Initiatives, Office of the Director, National Institutes of Health, Bethesda, MD
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29
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Zhong L, Yang F, Sun S, Wang L, Yu H, Nie X, Liu A, Xu N, Zhang L, Zhang M, Qi Y, Ji H, Liu G, Zhao H, Jiang Y, Li J, Song C, Yu X, Yang L, Yu J, Feng H, Guo X, Yang F, Xue F. Predicting lung cancer survival prognosis based on the conditional survival bayesian network. BMC Med Res Methodol 2024; 24:16. [PMID: 38254038 PMCID: PMC10801949 DOI: 10.1186/s12874-023-02043-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Accepted: 09/25/2023] [Indexed: 01/24/2024] Open
Abstract
Lung cancer is a leading cause of cancer deaths and imposes an enormous economic burden on patients. It is important to develop an accurate risk assessment model to determine the appropriate treatment for patients after an initial lung cancer diagnosis. The Cox proportional hazards model is mainly employed in survival analysis. However, real-world medical data are usually incomplete, posing a great challenge to the application of this model. Commonly used imputation methods cannot achieve sufficient accuracy when data are missing, so we investigated novel methods for the development of clinical prediction models. In this article, we present a novel model for survival prediction in missing scenarios. We collected data from 5,240 patients diagnosed with lung cancer at the Weihai Municipal Hospital, China. Then, we applied a joint model that combined a BN and a Cox model to predict mortality risk in individual patients with lung cancer. The established prognostic model achieved good predictive performance in discrimination and calibration. We showed that combining the BN with the Cox proportional hazards model is highly beneficial and provides a more efficient tool for risk prediction.
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Affiliation(s)
- Lu Zhong
- Department of Epidemiology and Health Statistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China.
- Hainan Center for Disease Control and Prevention, Institute for Prevention and Control of Tropical Diseases and Chronic Noninfectious Diseases, Haikou, Hainan, China.
| | - Fan Yang
- Department of Epidemiology and Health Statistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China.
- Institute for Medical Dataology, Shandong University, Jinan, China.
| | - Shanshan Sun
- Department of Oncology, Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, China
| | - Lijie Wang
- Department of Epidemiology and Health Statistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
- Institute for Medical Dataology, Shandong University, Jinan, China
| | - Hong Yu
- Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Xiushan Nie
- School of Computer Science and Technology, Shandong Jianzhu University, Jinan, China
| | - Ailing Liu
- Department of Pulmonary and Critical Care Medicine, Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, China
| | - Ning Xu
- Department of Pulmonary and Critical Care Medicine, Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, China
| | - Lanfang Zhang
- Department of Oncology, Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, China
| | - Mingjuan Zhang
- Department of Oncology, Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, China
| | - Yue Qi
- Department of Oncology, Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, China
| | - Huaijun Ji
- Department of Thoracic Surgery, Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, China
| | - Guiyuan Liu
- Department of Radiology, Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, China
| | - Huan Zhao
- Department of Oncology, Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, China
- The Second School of Clinical Medicine of Binzhou Medical University, Yantai, China
| | - Yinan Jiang
- Department of Oncology, Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, China
| | - Jingyi Li
- Department of Oncology, Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, China
| | - Chengcun Song
- Department of Oncology, Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, China
| | - Xin Yu
- Department of Oncology, Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, China
| | - Liu Yang
- Department of Oncology, Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, China
| | - Jinchao Yu
- Department of Radiology, Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, China
| | - Hu Feng
- Department of Oncology, Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, China
| | - Xiaolei Guo
- The Department for Chronic and Non-communicable Disease Control and Prevention, Shandong Center for Disease Control and Prevention, Jinan, China
| | - Fujun Yang
- Department of Oncology, Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, China.
| | - Fuzhong Xue
- Department of Epidemiology and Health Statistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China.
- Institute for Medical Dataology, Shandong University, Jinan, China.
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Walter N, Wenzel J, Haas SS, Squarcina L, Bonivento C, Ruef A, Dwyer D, Lichtenstein T, Bastrük Ö, Stainton A, Antonucci LA, Brambilla P, Wood SJ, Upthegrove R, Borgwardt S, Lencer R, Meisenzahl E, Salokangas RKR, Pantelis C, Bertolino A, Koutsouleris N, Kambeitz J, Kambeitz-Ilankovic L. A multivariate cognitive approach to predict social functioning in recent onset psychosis in response to computerized cognitive training. Prog Neuropsychopharmacol Biol Psychiatry 2024; 128:110864. [PMID: 37717645 DOI: 10.1016/j.pnpbp.2023.110864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 08/01/2023] [Accepted: 09/13/2023] [Indexed: 09/19/2023]
Abstract
Clinical and neuroimaging data has been increasingly used in recent years to disentangle heterogeneity of treatment response to cognitive training (CT) and predict which individuals may achieve the highest benefits. CT has small to medium effects on improving cognitive and social functioning in recent onset psychosis (ROP) patients, who show the most profound cognitive and social functioning deficits among psychiatric patients. We employed multivariate pattern analysis (MVPA) to investigate the potential of cognitive data to predict social functioning improvement in response to 10 h of CT in patients with ROP. A support vector machine (SVM) classifier was trained on the naturalistic data of the Personalized Prognostic Tools for Early Psychosis Management (PRONIA) study sample to predict functioning in an independent sample of 70 ROP patients using baseline cognitive data. PRONIA is a part of a FP7 EU grant program that involved 7 sites across 5 European countries, designed and conducted with the main aim of identifying (bio)markers associated with an enhanced risk of developing psychosis in order to improve early detection and prognosis. Social functioning was predicted with a balanced accuracy (BAC) of 66.4% (Sensitivity 78.8%; Specificity 54.1%; PPV 60.5%; NPV 74.1%; AUC 0.64; P = 0.01). The most frequently selected cognitive features (mean feature weights > ± 0.2) included the (1) correct number of symbol matchings within the Digit Symbol Substitution Test, (2) the number of distracting stimuli leading to an error within 300 and 200 trials in the Continuous Performance Test and (3) the dynamics of verbal fluency between 15 and 30 s within the Verbal Fluency Test, phonetic part. Next, the SVM classifier generated on the PRONIA sample was applied to the intervention sample, that obtained 54 ROP patients who were randomly assigned to a social cognitive training (SCT) or treatment as usual (TAU) group and dichotomized into good (GF-S ≥ 7) and poor (GF-S < 7) functioning patients based on their level of Global Functioning-Social (GF-S) score at follow-up (FU). By applying the initial PRONIA classifier, using out-of-sample cross-validation (OOCV) to the sample of ROP patients who have undergone the CT intervention, a BAC of 59.3% (Sensitivity 70.4%; Specificity 48.1%; PPV 57.6%; NPV 61.9%; AUC 0.63) was achieved at T0 and a BAC of 64.8% (Sensitivity 66.7%; Specificity 63.0%; PPV 64.3%; NPV 65.4%; AUC 0.66) at FU. After SCT intervention, a significant improvement in predicted social functioning values was observed in the SCT compared to TAU group (P ≤0.05; ES[Cohens' d] = 0.18). Due to a small sample size and modest variance of social functioning of the intervention sample it was not feasible to predict individual response to SCT in the current study. Our findings suggest that the use of baseline cognitive data could provide a robust individual estimate of future social functioning, while prediction of individual response to SCT using cognitive data that can be generated in the routine patient care remains to be addressed in large-scale cognitive training trials.
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Affiliation(s)
- Nina Walter
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Kerpenerstr.62, 50931, Cologne, Germany
| | - Julian Wenzel
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Kerpenerstr.62, 50931, Cologne, Germany
| | - Shalaila S Haas
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, NY, New York, United States of America
| | | | | | - Anne Ruef
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany
| | - Dominic Dwyer
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany; Orygen Youth Health, Melbourne, Australia; Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia
| | - Theresa Lichtenstein
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Kerpenerstr.62, 50931, Cologne, Germany
| | - Öznur Bastrük
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Kerpenerstr.62, 50931, Cologne, Germany
| | - Alexandra Stainton
- Orygen Youth Health, Melbourne, Australia; Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia
| | - Linda A Antonucci
- Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy
| | - Paolo Brambilla
- Department of Neuosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy; Department of Pathophysiology and Mental Health, University of Milan, Milan, Italy
| | - Stephen J Wood
- Orygen Youth Health, Melbourne, Australia; School of Psychology, University of Birmingham, United Kingdom; Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia
| | - Rachel Upthegrove
- School of Psychology, University of Birmingham, United Kingdom; Institute of Mental Health, University of Birmingham, Birmingham, United Kingdom
| | - Stefan Borgwardt
- Translational Psychiatry Unit (TPU), Department of Psychiatry and Psychotherapy, University of Luebeck, Germany
| | - Rebekka Lencer
- Translational Psychiatry Unit (TPU), Department of Psychiatry and Psychotherapy, University of Luebeck, Germany
| | - Eva Meisenzahl
- Department of Psychiatry and Psychotherapy, Medical Faculty, Heinrich-Heine University, Düsseldorf, Germany
| | | | - Christos Pantelis
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne & NorthWestern Mental Health, Melbourne, Australia
| | - Alessandro Bertolino
- Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy
| | - Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany; Max Planck Institute for Psychiatry, Munich, Germany; Institute of Psychiatry, Psychology & Neuroscience, Department of Psychosis Studies, King's College London, London, United Kingdom
| | - Joseph Kambeitz
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Kerpenerstr.62, 50931, Cologne, Germany
| | - Lana Kambeitz-Ilankovic
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Kerpenerstr.62, 50931, Cologne, Germany; Faculty of Psychology and Educational Sciences, Department of Psychology, Ludwig-Maximilian University, Munich, Germany.
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Beyene BB, Li J, Yuan J, Liu D, Chen Z, Kim J, Kang H, Freeman C, Ding W. Climatic zone effects of non-native plant invasion on CH 4 and N 2O emissions from natural wetland ecosystems. Sci Total Environ 2024; 906:167855. [PMID: 37844632 DOI: 10.1016/j.scitotenv.2023.167855] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Revised: 09/24/2023] [Accepted: 10/13/2023] [Indexed: 10/18/2023]
Abstract
Plant invasion can significantly alter the carbon and nitrogen cycles of wetlands, which potentially affects the emission of greenhouse gases (GHGs). The extent of these effects can vary depending on several factors, including the species of invasive plants, their growth patterns, and the climatic conditions prevailing in the wetland. Understanding the global effects of plant invasion on the emission of methane (CH4) and nitrous oxide (N2O) is crucial for the climate-smart management of wetlands. Here, we performed a global meta-analysis of 207 paired case studies that quantified the effect of non-native plant invasion on CH4 and N2O emissions in tropical/sub-tropical (TS) and temperate (TE) wetlands. The average emission rate of CH4 from the TS wetlands increased significantly from 337 to 577 kg CH4 ha-1 yr-1 in areas where native plants had been displaced by invasive plants. Similarly, in TE wetlands, the emission rates increased from 211 to 299 kg CH4 ha-1 yr-1 following the invasion of alien plant species. The increase in CH4 emissions at invaded sites was attributed to the increase in plant biomass, soil organic carbon (SOC), and soil moisture (SM). The effects of plant invasion on N2O emissions differed between TS and TE wetlands in that there was no significant effect in TS wetlands, whereas the N2O emissions reduced in TE wetlands. This difference in N2O emissions between climate zones was attributed to the depletion of NH4+ and NO3- in soils and the lower soil temperature in temperate regions. Overall, plant invasion increased the global net CH4 emissions from natural wetlands by 10.54 Tg CH4 yr-1. However, there were variations in CH4 emissions across different climatic zones, indicated by a net increase in CH4 emissions, of 9.97 and 0.57 Tg CH4 yr-1 in TS and TE wetlands, respectively. These findings highlight that plant invasion not only strongly stimulates the emission of CH4 from TS wetlands, but also suppresses N2O emissions from TE wetlands. These novel insights immensely improve our current understanding of the effects of climatic zones on biogeochemical controlling factors that influence the production of greenhouse gases (GHGs) from wetlands following plant invasion. By analyzing the specific mechanisms by which invasive plants affect GHG emissions in different climatic zones, effective strategies can be devised to reduce GHG emissions and preserve wetland ecosystems.
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Affiliation(s)
- Bahilu Bezabih Beyene
- State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China; University of Chinese Academy of Sciences, Beijing 10049, China; Department of Natural Resources Management, Jimma University College of Agriculture and Veterinary Medicine, Jimma 307, Ethiopia
| | - Junjie Li
- State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China; University of Chinese Academy of Sciences, Beijing 10049, China
| | - Junji Yuan
- State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China.
| | - Deyan Liu
- State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
| | - Zengming Chen
- State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
| | - Jinhyun Kim
- School of Civil and Environmental Engineering, Yonsei University, Seoul 120-749, Republic of Korea
| | - Hojeong Kang
- School of Civil and Environmental Engineering, Yonsei University, Seoul 120-749, Republic of Korea
| | - Chris Freeman
- School of Natural Sciences, Bangor University, Gwynedd LL57 2UW, UK
| | - Weixin Ding
- State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China.
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Chiu CH, Ke SW, Tsai CF, Lin WC, Huang MW, Ko YH. Deep learning based decision tree ensembles for incomplete medical datasets. Technol Health Care 2024; 32:75-87. [PMID: 37248924 DOI: 10.3233/thc-220514] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
BACKGROUND In practice, the collected datasets for data analysis are usually incomplete as some data contain missing attribute values. Many related works focus on constructing specific models to produce estimations to replace the missing values, to make the original incomplete datasets become complete. Another type of solution is to directly handle the incomplete datasets without missing value imputation, with decision trees being the major technique for this purpose. OBJECTIVE To introduce a novel approach, namely Deep Learning-based Decision Tree Ensembles (DLDTE), which borrows the bounding box and sliding window strategies used in deep learning techniques to divide an incomplete dataset into a number of subsets and learning from each subset by a decision tree, resulting in decision tree ensembles. METHOD Two medical domain problem datasets contain several hundred feature dimensions with the missing rates of 10% to 50% are used for performance comparison. RESULTS The proposed DLDTE provides the highest rate of classification accuracy when compared with the baseline decision tree method, as well as two missing value imputation methods (mean and k-nearest neighbor), and the case deletion method. CONCLUSION The results demonstrate the effectiveness of DLDTE for handling incomplete medical datasets with different missing rates.
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Affiliation(s)
- Chien-Hung Chiu
- Division of Thoracic Surgery, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan
| | - Shih-Wen Ke
- Department of Information Management, National Central University, Taoyuan, Taiwan
| | - Chih-Fong Tsai
- Department of Information Management, National Central University, Taoyuan, Taiwan
| | - Wei-Chao Lin
- Division of Thoracic Surgery, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan
- Department of Information Management, Chang Gung University, Taoyuan, Taiwan
| | - Min-Wei Huang
- Kaohsiung Municipal Kai-Syuan Psychiatric Hospital, Kaohsiung, Taiwan
- Graduate Institute of Rehabilitation Science, Department of Physical Therapy, China Medical University, Taichung, Taiwan
| | - Yi-Hsiu Ko
- Department of Information Management, National Central University, Taoyuan, Taiwan
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Abid MSR, Qiu H, Checco JW. Label-Free Quantitation of Endogenous Peptides. Methods Mol Biol 2024; 2758:125-150. [PMID: 38549012 PMCID: PMC11027169 DOI: 10.1007/978-1-0716-3646-6_7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/02/2024]
Abstract
Liquid chromatography-mass spectrometry (LC-MS)-based peptidomics methods allow for the detection and identification of many peptides in a complex biological mixture in an untargeted manner. Quantitative peptidomics approaches allow for comparisons of peptide abundance between different samples, allowing one to draw conclusions about peptide differences as a function of experimental treatment or physiology. While stable isotope labeling is a powerful approach for quantitative proteomics and peptidomics, advances in mass spectrometry instrumentation and analysis tools have allowed label-free methods to gain popularity in recent years. In a general label-free quantitative peptidomics experiment, peak intensity information for each peptide is compared across multiple LC-MS runs. Here, we outline a general approach for label-free quantitative peptidomics experiments, including steps for sample preparation, LC-MS data acquisition, data processing, and statistical analysis. Special attention is paid to address run-to-run variability, which can lead to several major problems in label-free experiments. Overall, our method provides researchers with a framework for the development of their own quantitative peptidomics workflows applicable to quantitation of peptides from a wide variety of different biological sources.
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Affiliation(s)
| | - Haowen Qiu
- Center for Biotechnology, University of Nebraska-Lincoln, Lincoln, NE, USA
- The Nebraska Center for Integrated Biomolecular Communication (NCIBC), University of Nebraska-Lincoln, Lincoln, NE, USA
| | - James W Checco
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE, USA.
- The Nebraska Center for Integrated Biomolecular Communication (NCIBC), University of Nebraska-Lincoln, Lincoln, NE, USA.
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Chatterjee S, Mishra J, Sundram F, Roop P. Towards Personalised Mood Prediction and Explanation for Depression from Biophysical Data. Sensors (Basel) 2023; 24:164. [PMID: 38203024 PMCID: PMC10781272 DOI: 10.3390/s24010164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 11/30/2023] [Accepted: 12/19/2023] [Indexed: 01/12/2024]
Abstract
Digital health applications using Artificial Intelligence (AI) are a promising opportunity to address the widening gap between available resources and mental health needs globally. Increasingly, passively acquired data from wearables are augmented with carefully selected active data from depressed individuals to develop Machine Learning (ML) models of depression based on mood scores. However, most ML models are black box in nature, and hence the outputs are not explainable. Depression is also multimodal, and the reasons for depression may vary significantly between individuals. Explainable and personalised models will thus be beneficial to clinicians to determine the main features that lead to a decline in the mood state of a depressed individual, thus enabling suitable personalised therapy. This is currently lacking. Therefore, this study presents a methodology for developing personalised and accurate Deep Learning (DL)-based predictive mood models for depression, along with novel methods for identifying the key facets that lead to the exacerbation of depressive symptoms. We illustrate our approach by using an existing multimodal dataset containing longitudinal Ecological Momentary Assessments of depression, lifestyle data from wearables and neurocognitive assessments for 14 mild to moderately depressed participants over one month. We develop classification- and regression-based DL models to predict participants' current mood scores-a discrete score given to a participant based on the severity of their depressive symptoms. The models are trained inside eight different evolutionary-algorithm-based optimisation schemes that optimise the model parameters for a maximum predictive performance. A five-fold cross-validation scheme is used to verify the DL model's predictive performance against 10 classical ML-based models, with a model error as low as 6% for some participants. We use the best model from the optimisation process to extract indicators, using SHAP, ALE and Anchors from explainable AI literature to explain why certain predictions are made and how they affect mood. These feature insights can assist health professionals in incorporating personalised interventions into a depressed individual's treatment regimen.
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Affiliation(s)
- Sobhan Chatterjee
- Department of Electrical, Computer and Software Engineering, Faculty of Engineering, University of Auckland, Auckland 1010, New Zealand
| | - Jyoti Mishra
- Neural Engineering and Translation Labs, Department of Psychiatry, University of California, San Diego, CA 92093, USA;
| | - Frederick Sundram
- Department of Psychological Medicine, Faculty of Medical and Health Sciences, University of Auckland, Auckland 1023, New Zealand;
| | - Partha Roop
- Department of Electrical, Computer and Software Engineering, Faculty of Engineering, University of Auckland, Auckland 1010, New Zealand
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Gong Y, Ding W, Wang P, Wu Q, Yao X, Yang Q. Evaluating Machine Learning Methods of Analyzing Multiclass Metabolomics. J Chem Inf Model 2023; 63:7628-7641. [PMID: 38079572 DOI: 10.1021/acs.jcim.3c01525] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2023]
Abstract
Multiclass metabolomic studies have become popular for revealing the differences in multiple stages of complex diseases, various lifestyles, or the effects of specific treatments. In multiclass metabolomics, there are multiple data manipulation steps for analyzing raw data, which consist of data filtering, the imputation of missing values, data normalization, marker identification, sample separation, classification, and so on. In each step, several to dozens of machine learning methods can be chosen for the given data set, with potentially hundreds or thousands of method combinations in the whole data processing chain. Therefore, a clear understanding of these machine learning methods is helpful for selecting an appropriate method combination for obtaining stable and reliable analytical results of specific data. However, there has rarely been an overall introduction or evaluation of these methods based on multiclass metabolomic data. Herein, detailed descriptions of these machine learning methods in multiple data manipulation steps are reviewed. Moreover, an assessment of these methods was performed using a benchmark data set for multiclass metabolomics. First, 12 imputation methods for imputing missing values were evaluated based on the PSS (Procrustes statistical shape analysis) and NRMSE (normalized root-mean-square error) values. Second, 17 normalization methods for processing multiclass metabolomic data were evaluated by applying the PMAD (pooled median absolute deviation) value. Third, different methods of identifying markers of multiclass metabolomics were evaluated based on the CWrel (relative weighted consistency) value. Fourth, nine classification methods for constructing multiclass models were assessed using the AUC (area under the curve) value. Performance evaluations of machine learning methods are highly recommended to select the most appropriate method combination before performing the final analysis of the given data. Overall, detailed descriptions and evaluation of various machine learning methods are expected to improve analyses of multiclass metabolomic data.
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Affiliation(s)
- Yaguo Gong
- State Key Laboratory of Quality Research in Chinese Medicine, School of Pharmacy, Macau University of Science and Technology, Macau 999078, China
| | - Wei Ding
- State Key Laboratory of Quality Research in Chinese Medicine, School of Pharmacy, Macau University of Science and Technology, Macau 999078, China
| | - Panpan Wang
- College of Chemistry and Pharmaceutical Engineering, Huanghuai University, Zhumadian 463000, China
| | - Qibiao Wu
- State Key Laboratory of Quality Research in Chinese Medicine, School of Pharmacy, Macau University of Science and Technology, Macau 999078, China
| | - Xiaojun Yao
- Centre for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macao 999078, China
| | - Qingxia Yang
- Zhejiang Provincial Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Women's Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China
- Department of Bioinformatics, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
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Schaefer N, Lindner HA, Hahn B, Schefzik R, Velásquez SY, Schulte J, Fuderer T, Centner FS, Schoettler JJ, Himmelhan BS, Sturm T, Thiel M, Schneider-Lindner V, Coulibaly A. Pneumonia in the first week after polytrauma is associated with reduced blood levels of soluble herpes virus entry mediator. Front Immunol 2023; 14:1259423. [PMID: 38187375 PMCID: PMC10770833 DOI: 10.3389/fimmu.2023.1259423] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2023] [Accepted: 12/04/2023] [Indexed: 01/09/2024] Open
Abstract
Background Pneumonia develops frequently after major surgery and polytrauma and thus in the presence of systemic inflammatory response syndrome (SIRS) and organ dysfunction. Immune checkpoints balance self-tolerance and immune activation. Altered checkpoint blood levels were reported for sepsis. We analyzed associations of pneumonia incidence in the presence of SIRS during the first week of critical illness and trends in checkpoint blood levels. Materials and methods Patients were studied from day two to six after admission to a surgical intensive care unit (ICU). Blood was sampled and physician experts retrospectively adjudicated upon the presence of SIRS and Sepsis-1/2 every eight hours. We measured the daily levels of immune checkpoints and inflammatory markers by bead arrays for polytrauma patients developing pneumonia. Immune checkpoint time series were additionally determined for clinically highly similar polytrauma controls remaining infection-free during follow-up. We performed cluster analyses. Immune checkpoint time trends in cases and controls were compared with hierarchical linear models. For patients with surgical trauma and with and without sepsis, selected immune checkpoints were determined in study baseline samples. Results In polytrauma patients with post-injury pneumonia, eleven immune checkpoints dominated subcluster 3 that separated subclusters 1 and 2 of myeloid markers from subcluster 4 of endothelial activation, tissue inflammation, and adaptive immunity markers. Immune checkpoint blood levels were more stable in polytrauma cases than controls, where they trended towards an increase in subcluster A and a decrease in subcluster B. Herpes virus entry mediator (HVEM) levels (subcluster A) were lower in cases throughout. In unselected surgical patients, sepsis was not associated with altered HVEM levels at the study baseline. Conclusion Pneumonia development after polytrauma until ICU-day six was associated with decreased blood levels of HVEM. HVEM signaling may reduce pneumonia risk by strengthening myeloid antimicrobial defense and dampening lymphoid-mediated tissue damage. Future investigations into the role of HVEM in pneumonia and sepsis development and as a predictive biomarker should consider the etiology of critical illness and the site of infection.
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Sardari A, Usefi H. Machine learning-based meta-analysis of colorectal cancer and inflammatory bowel disease. PLoS One 2023; 18:e0290192. [PMID: 38134011 PMCID: PMC10745176 DOI: 10.1371/journal.pone.0290192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Accepted: 11/30/2023] [Indexed: 12/24/2023] Open
Abstract
Colorectal cancer (CRC) is a major global health concern, resulting in numerous cancer-related deaths. CRC detection, treatment, and prevention can be improved by identifying genes and biomarkers. Despite extensive research, the underlying mechanisms of CRC remain elusive, and previously identified biomarkers have not yielded satisfactory insights. This shortfall may be attributed to the predominance of univariate analysis methods, which overlook potential combinations of variants and genes contributing to disease development. Here, we address this knowledge gap by presenting a novel multivariate machine-learning strategy to pinpoint genes associated with CRC. Additionally, we applied our analysis pipeline to Inflammatory Bowel Disease (IBD), as IBD patients face substantial CRC risk. The importance of the identified genes was substantiated by rigorous validation across numerous independent datasets. Several of the discovered genes have been previously linked to CRC, while others represent novel findings warranting further investigation. A Python implementation of our pipeline can be accessed publicly at https://github.com/AriaSar/CRCIBD-ML.
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Affiliation(s)
- Aria Sardari
- Department of Computer Science, Memorial University of Newfoundland, St. John’s, NL, Canada
| | - Hamid Usefi
- Department of Computer Science, Memorial University of Newfoundland, St. John’s, NL, Canada
- Department of Mathematics & Statistics, Memorial University of Newfoundland, St. John’s, NL, Canada
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Arya N, Saha S. Deviation-support based fuzzy ensemble of multi-modal deep learning classifiers for breast cancer prognosis prediction. Sci Rep 2023; 13:21326. [PMID: 38044381 PMCID: PMC10694142 DOI: 10.1038/s41598-023-47543-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Accepted: 11/15/2023] [Indexed: 12/05/2023] Open
Abstract
Breast cancer is the fifth leading cause of death in females worldwide. Early detection and treatment are crucial for improving health outcomes and preventing more serious conditions. Analyzing diverse information from multiple sources without errors, particularly with the growing burden of cancer cases, is a daunting task for humans. In this study, our main objective is to improve the accuracy of breast cancer survival prediction using a novel ensemble approach. It is novel due to the consideration of deviation (closeness between predicted classes and actual classes) and support (sparsity between predicted classes and actual classes) of the predicted class with respect to the actual class, a feature lacking in traditional ensembles. The ensemble uses fuzzy integrals on support and deviation scores from base classifiers to calculate aggregated scores while considering how confident or uncertain each classifier is. The proposed ensemble mechanism has been evaluated on a multi-modal breast cancer dataset of breast tumors collected from participants in the METABRIC trial. The proposed architecture proves its efficiency by achieving the accuracy, sensitivity, F1-score, and balanced accuracy of 82.88%, 58.64%, 62.94%, and 74.75% respectively. The obtained results are superior to the performance of individual classifiers and existing ensemble approaches.
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Affiliation(s)
- Nikhilanand Arya
- Department of Computer Science & Engineering, Indian Institute of Technology Patna, Bihar, 801106, India.
| | - Sriparna Saha
- Department of Computer Science & Engineering, Indian Institute of Technology Patna, Bihar, 801106, India
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Wang H, Lim KP, Kong W, Gao H, Wong BJH, Phua SX, Guo T, Goh WWB. MultiPro: DDA-PASEF and diaPASEF acquired cell line proteomic datasets with deliberate batch effects. Sci Data 2023; 10:858. [PMID: 38042886 PMCID: PMC10693559 DOI: 10.1038/s41597-023-02779-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 11/23/2023] [Indexed: 12/04/2023] Open
Abstract
Mass spectrometry-based proteomics plays a critical role in current biological and clinical research. Technical issues like data integration, missing value imputation, batch effect correction and the exploration of inter-connections amongst these technical issues, can produce errors but are not well studied. Although proteomic technologies have improved significantly in recent years, this alone cannot resolve these issues. What is needed are better algorithms and data processing knowledge. But to obtain these, we need appropriate proteomics datasets for exploration, investigation, and benchmarking. To meet this need, we developed MultiPro (Multi-purpose Proteome Resource), a resource comprising four comprehensive large-scale proteomics datasets with deliberate batch effects using the latest parallel accumulation-serial fragmentation in both Data-Dependent Acquisition (DDA) and Data Independent Acquisition (DIA) modes. Each dataset contains a balanced two-class design based on well-characterized and widely studied cell lines (A549 vs K562 or HCC1806 vs HS578T) with 48 or 36 biological and technical replicates altogether, allowing for investigation of a multitude of technical issues. These datasets allow for investigation of inter-connections between class and batch factors, or to develop approaches to compare and integrate data from DDA and DIA platforms.
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Affiliation(s)
- He Wang
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, 308232, Singapore
- School of Biological Sciences, Nanyang Technological University, Singapore, 637551, Singapore
| | - Kai Peng Lim
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, 308232, Singapore
- School of Biological Sciences, Nanyang Technological University, Singapore, 637551, Singapore
| | - Weijia Kong
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, 308232, Singapore
- School of Biological Sciences, Nanyang Technological University, Singapore, 637551, Singapore
| | - Huanhuan Gao
- Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang Province, 310030, China
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang Province, 310030, China
- Research Center for Industries of the Future, Westlake University, 600 Dunyu Road, Hangzhou, Zhejiang, 310030, China
| | - Bertrand Jern Han Wong
- School of Biological Sciences, Nanyang Technological University, Singapore, 637551, Singapore
| | - Ser Xian Phua
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, 308232, Singapore
- School of Biological Sciences, Nanyang Technological University, Singapore, 637551, Singapore
| | - Tiannan Guo
- Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang Province, 310030, China
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang Province, 310030, China
- Research Center for Industries of the Future, Westlake University, 600 Dunyu Road, Hangzhou, Zhejiang, 310030, China
| | - Wilson Wen Bin Goh
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, 308232, Singapore.
- School of Biological Sciences, Nanyang Technological University, Singapore, 637551, Singapore.
- Center for Biomedical Informatics, Nanyang Technological University, Singapore, 636921, Singapore.
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Sabat NK, Pati UC, Das SK. ABTCN: an efficient hybrid deep learning approach for atmospheric temperature prediction. Environ Sci Pollut Res Int 2023; 30:125295-125312. [PMID: 37418192 DOI: 10.1007/s11356-023-27985-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Accepted: 05/25/2023] [Indexed: 07/08/2023]
Abstract
Temperature prediction is an important and significant step for monitoring global warming and the environment to save and protect human lives. The climatology parameters such as temperature, pressure, and wind speed are time-series data and are well predicted with data driven models. However, data-driven models have certain constraints, due to which these models are unable to predict the missing values and erroneous data caused by factors like sensor failure and natural disasters. In order to solve this issue, an efficient hybrid model, i.e., attention-based bidirectional long short term memory temporal convolution network (ABTCN) architecture is proposed. ABTCN uses k-nearest neighbor (KNN) imputation method for handling the missing data. A bidirectional long short term memory (Bi-LSTM) network with self-attention mechanism and temporal convolutional network (TCN) model that aids in the extraction of features from complex data and prediction of long data sequence. The performance of the proposed model is evaluated in comparison to various state-of-the-art deep learning models using error metrics such as MAE, MSE, RMSE, and R2 score. It is observed that our proposed model is superior over other models with high accuracy.
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Affiliation(s)
- Naba Krushna Sabat
- Department of Electronics and Communication Engineering, National Institute of Technology, Rourkela, Sector-1, Rourkela, 769008, Odisha, India
| | - Umesh Chandra Pati
- Department of Electronics and Communication Engineering, National Institute of Technology, Rourkela, Sector-1, Rourkela, 769008, Odisha, India
| | - Santos Kumar Das
- Department of Electronics and Communication Engineering, National Institute of Technology, Rourkela, Sector-1, Rourkela, 769008, Odisha, India.
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Assefi M, Leurent A, Blanchard F, Quemeneur C, Deransy R, Monsel A, Constantin JM. Impact of increasing post-filter ionized calcium target on filter lifespan in renal replacement therapy with regional citrate anticoagulation: A before-and-after study. J Crit Care 2023; 78:154364. [PMID: 37379797 DOI: 10.1016/j.jcrc.2023.154364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 06/16/2023] [Accepted: 06/20/2023] [Indexed: 06/30/2023]
Abstract
INTRODUCTION Regional citrate anticoagulation (RCA) is the recommended method for anticoagulation in continuous renal replacement therapy (CRRT). However, the optimal post-filter ionized calcium (iCa) target level remains unclear. This study aims to assess the effect of increasing the post-filter iCa target level from 0.25-0.35 mmol/L to 0.30-0.40 mmol/L on filter lifespan until clotting during RCA-CRRT. METHODS This before-and-after single-center study included patients who underwent RCA-CRRT sessions without systemic anticoagulation during two periods. The first period included patients with a post-filter iCa target between 0.25 and 0.35 mmol/L, while the second period included those with a target between 0.30 and 0.40 mmol/L. The primary outcome was filter lifespan until clotting. RESULTS A total of 1037 CRRT sessions were analyzed, with 610 sessions in the first period and 427 sessions in the second period. After adjusting for confounding factors, there was no significant difference in filter lifespan until clotting between the two groups (hazard ratio, 1.020 [0.703; 1.481]; p = 0.92). CONCLUSION Increasing the post-filter iCa target level from 0.25-0.35 mmol/L to 0.30-0.40 mmol/L during RCA-CRRT does not reduce filter lifespan until clotting and may decrease unnecessary citrate exposure. However, the optimal post-filter iCa target should be individualized according to the patient's clinical and biological status.
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Affiliation(s)
- Mona Assefi
- Sorbonne University, GRC 29, AP-HP, DMU DREAM and Department of Anaesthesiology and Critical Care, Pitié-Salpêtrière Hospital, Paris, France.
| | - Alix Leurent
- Sorbonne University, GRC 29, AP-HP, DMU DREAM and Department of Anaesthesiology and Critical Care, Pitié-Salpêtrière Hospital, Paris, France
| | - Florian Blanchard
- Sorbonne University, GRC 29, AP-HP, DMU DREAM and Department of Anaesthesiology and Critical Care, Pitié-Salpêtrière Hospital, Paris, France
| | - Cyril Quemeneur
- Sorbonne University, GRC 29, AP-HP, DMU DREAM and Department of Anaesthesiology and Critical Care, Pitié-Salpêtrière Hospital, Paris, France
| | - Romain Deransy
- Université de Nantes, CHU Nantes, Pôle Anesthésie Réanimations, Service d'Anesthésie Réanimation Chirurgicale, Hôtel Dieu, Nantes, France
| | - Antoine Monsel
- Sorbonne University, GRC 29, AP-HP, DMU DREAM and Department of Anaesthesiology and Critical Care, Pitié-Salpêtrière Hospital, Paris, France; Sorbonne University-INSERM UMRS_959, Immunology-Immunopathology-Immunotherapy (I3), 75013 Paris, France
| | - Jean-Michel Constantin
- Sorbonne University, GRC 29, AP-HP, DMU DREAM and Department of Anaesthesiology and Critical Care, Pitié-Salpêtrière Hospital, Paris, France
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Sibert NT, Kurth T, Breidenbach C, Wesselmann S, Feick G, Carl EG, Dieng S, Albarghouth MH, Aziz A, Baltes S, Bartolf E, Bedke J, Blana A, Brock M, Conrad S, Darr C, Distler F, Drosos K, Duwe G, Gaber A, Giessing M, Harke NN, Heidenreich A, Hijazi S, Hinkel A, Kaftan BT, Kheiderov S, Knoll T, Lümmen G, Peters I, Polat B, Schrodi V, Stolzenburg JU, Varga Z, von Süßkind-Schwendi J, Zugor V, Kowalski C. Prediction models of incontinence and sexual function one year after radical prostatectomy based on data from 20 164 prostate cancer patients. PLoS One 2023; 18:e0295179. [PMID: 38039308 PMCID: PMC10691723 DOI: 10.1371/journal.pone.0295179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 11/16/2023] [Indexed: 12/03/2023] Open
Abstract
BACKGROUND Incontinence and sexual dysfunction are long-lasting side effects after surgical treatment (radical prostatectomy, RP) of prostate cancer (PC). For an informed treatment decision, physicians and patients should discuss expected impairments. Therefore, this paper firstly aims to develop and validate prognostic models that predict incontinence and sexual function of PC patients one year after RP and secondly to provide an online decision making tool. METHODS Observational cohorts of PC patients treated between July 2016 and March 2021 in Germany were used. Models to predict functional outcomes one year after RP measured by the EPIC-26 questionnaire were developed using lasso regression, 80-20 splitting of the data set and 10-fold cross validation. To assess performance, R2, RMSE, analysis of residuals and calibration-in-the-large were applied. Final models were externally temporally validated. Additionally, percentages of functional impairment (pad use for incontinence and firmness of erection for sexual score) per score decile were calculated to be used together with the prediction models. RESULTS For model development and internal as well as external validation, samples of 11 355 and 8 809 patients were analysed. Results from the internal validation (incontinence: R2 = 0.12, RMSE = 25.40, sexual function: R2 = 0.23, RMSE = 21.44) were comparable with those of the external validation. Residual analysis and calibration-in-the-large showed good results. The prediction tool is freely accessible: https://nora-tabea.shinyapps.io/EPIC-26-Prediction/. CONCLUSION The final models showed appropriate predictive properties and can be used together with the calculated risks for specific functional impairments. Main strengths are the large study sample (> 20 000) and the inclusion of an external validation. The models incorporate meaningful and clinically available predictors ensuring an easy implementation. All predictions are displayed together with risks of frequent impairments such as pad use or erectile dysfunction such that the developed online tool provides a detailed and informative overview for clinicians as well as patients.
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Affiliation(s)
| | - Tobias Kurth
- Institute of Public Health, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | | | | | - Günther Feick
- Bundesverband Prostatakrebs Selbsthilfe, Bonn, Germany
| | | | | | | | | | - Stefan Baltes
- KRH Klinikum Region Hannover, Klinikum Siloah—Oststadt—Heidehaus, Hannover, Germany
| | | | - Jens Bedke
- University Hospital Tübingen, Tübingen, Germany
| | | | - Marko Brock
- Ruhr-University Bochum, Marien Hospital, Herne, Germany
| | | | | | | | | | | | - Amr Gaber
- Carl-Thiem-Klinikum, Cottbus, Germany
| | | | | | | | | | | | | | | | - Thomas Knoll
- Klinikum Sindelfingen-Böblingen, Sindelfingen, Germany
| | | | - Inga Peters
- Krankenhaus Nordwest, Frankfurt am Main, Germany
| | | | | | | | - Zoltan Varga
- SRH Kliniken Landkreis Sigmaringen, Sigmaringen, Germany
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An U, Pazokitoroudi A, Alvarez M, Huang L, Bacanu S, Schork AJ, Kendler K, Pajukanta P, Flint J, Zaitlen N, Cai N, Dahl A, Sankararaman S. Deep learning-based phenotype imputation on population-scale biobank data increases genetic discoveries. Nat Genet 2023; 55:2269-2276. [PMID: 37985819 PMCID: PMC10703681 DOI: 10.1038/s41588-023-01558-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Accepted: 10/04/2023] [Indexed: 11/22/2023]
Abstract
Biobanks that collect deep phenotypic and genomic data across many individuals have emerged as a key resource in human genetics. However, phenotypes in biobanks are often missing across many individuals, limiting their utility. We propose AutoComplete, a deep learning-based imputation method to impute or 'fill-in' missing phenotypes in population-scale biobank datasets. When applied to collections of phenotypes measured across ~300,000 individuals from the UK Biobank, AutoComplete substantially improved imputation accuracy over existing methods. On three traits with notable amounts of missingness, we show that AutoComplete yields imputed phenotypes that are genetically similar to the originally observed phenotypes while increasing the effective sample size by about twofold on average. Further, genome-wide association analyses on the resulting imputed phenotypes led to a substantial increase in the number of associated loci. Our results demonstrate the utility of deep learning-based phenotype imputation to increase power for genetic discoveries in existing biobank datasets.
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Affiliation(s)
- Ulzee An
- Computer Science Department, UCLA, Los Angeles, CA, USA.
| | | | - Marcus Alvarez
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Lianyun Huang
- Helmholtz Pioneer Campus, Helmholtz Zentrum München, Neuherberg, Germany
- Computational Health Centre, Helmholtz Zentrum München, Neuherberg, Germany
- School of Medicine, Technical University of Munich, Munich, Germany
| | - Silviu Bacanu
- Virginia Institute for Psychiatric and Behavioral Genetics and Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA
| | - Andrew J Schork
- Institute of Biological Psychiatry, Mental Health Center - Sct Hans, Copenhagen University Hospital, Copenhagen, Denmark
- Neurogenomics Division, The Translational Genomics Research Institute (TGEN), Phoenix, AZ, USA
- Section for Geogenetics, GLOBE Institute, Faculty of Health and Medical Sciences, Copenhagen University, Copenhagen, Denmark
| | - Kenneth Kendler
- Virginia Institute for Psychiatric and Behavioral Genetics and Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA
| | - Päivi Pajukanta
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
- Institute for Precision Health, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Jonathan Flint
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Noah Zaitlen
- Neurology Department, UCLA, Los Angeles, CA, USA
| | - Na Cai
- Helmholtz Pioneer Campus, Helmholtz Zentrum München, Neuherberg, Germany
- Computational Health Centre, Helmholtz Zentrum München, Neuherberg, Germany
- School of Medicine, Technical University of Munich, Munich, Germany
| | - Andy Dahl
- Section of Genetic Medicine, University of Chicago, Chicago, IL, USA
| | - Sriram Sankararaman
- Computer Science Department, UCLA, Los Angeles, CA, USA.
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
- Department of Computational Medicine, UCLA, Los Angeles, CA, USA.
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You J, Guo Y, Zhang Y, Kang JJ, Wang LB, Feng JF, Cheng W, Yu JT. Plasma proteomic profiles predict individual future health risk. Nat Commun 2023; 14:7817. [PMID: 38016990 PMCID: PMC10684756 DOI: 10.1038/s41467-023-43575-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Accepted: 11/13/2023] [Indexed: 11/30/2023] Open
Abstract
Developing a single-domain assay to identify individuals at high risk of future events is a priority for multi-disease and mortality prevention. By training a neural network, we developed a disease/mortality-specific proteomic risk score (ProRS) based on 1461 Olink plasma proteins measured in 52,006 UK Biobank participants. This integrative score markedly stratified the risk for 45 common conditions, including infectious, hematological, endocrine, psychiatric, neurological, sensory, circulatory, respiratory, digestive, cutaneous, musculoskeletal, and genitourinary diseases, cancers, and mortality. The discriminations witnessed high accuracies achieved by ProRS for 10 endpoints (e.g., cancer, dementia, and death), with C-indexes exceeding 0.80. Notably, ProRS produced much better or equivalent predictive performance than established clinical indicators for almost all endpoints. Incorporating clinical predictors with ProRS enhanced predictive power for most endpoints, but this combination only exhibited limited improvement when compared to ProRS alone. Some proteins, e.g., GDF15, exhibited important discriminative values for various diseases. We also showed that the good discriminative performance observed could be largely translated into practical clinical utility. Taken together, proteomic profiles may serve as a replacement for complex laboratory tests or clinical measures to refine the comprehensive risk assessments of multiple diseases and mortalities simultaneously. Our models were internally validated in the UK Biobank; thus, further independent external validations are necessary to confirm our findings before application in clinical settings.
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Affiliation(s)
- Jia You
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, Institute of Science and Technology for Brain-Inspired Intelligence, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Yu Guo
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, Institute of Science and Technology for Brain-Inspired Intelligence, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Yi Zhang
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, Institute of Science and Technology for Brain-Inspired Intelligence, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Ju-Jiao Kang
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, Institute of Science and Technology for Brain-Inspired Intelligence, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Lin-Bo Wang
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, Institute of Science and Technology for Brain-Inspired Intelligence, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Jian-Feng Feng
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, Institute of Science and Technology for Brain-Inspired Intelligence, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China.
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China.
- Zhangjiang Fudan International Innovation Center, Shanghai, China.
- School of Data Science, Fudan University, Shanghai, China.
- Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Zhejiang, China.
| | - Wei Cheng
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, Institute of Science and Technology for Brain-Inspired Intelligence, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China.
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China.
- Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Zhejiang, China.
- Shanghai Medical College and Zhongshan Hospital Immunotherapy Technology Transfer Center, Shanghai, China.
| | - Jin-Tai Yu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, Institute of Science and Technology for Brain-Inspired Intelligence, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China.
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Walsh CR, Rissman J. Behavioral representational similarity analysis reveals how episodic learning is influenced by and reshapes semantic memory. Nat Commun 2023; 14:7548. [PMID: 37985774 PMCID: PMC10662157 DOI: 10.1038/s41467-023-42770-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 10/20/2023] [Indexed: 11/22/2023] Open
Abstract
While semantic and episodic memory have been shown to influence each other, uncertainty remains as to how this interplay occurs. We introduce a behavioral representational similarity analysis approach to assess whether semantic space can be subtly re-sculpted by episodic learning. Eighty participants learned word pairs that varied in semantic relatedness, and learning was bolstered via either testing or restudying. Next-day recall is superior for semantically related pairs, but there is a larger benefit of testing for unrelated pairs. Analyses of representational change reveal that successful recall is accompanied by a pulling together of paired associates, with cue words in semantically related (but not unrelated) pairs changing more across learning than target words. Our findings show that episodic learning is associated with systematic and asymmetrical distortions of semantic space which improve later recall by making cues more predictive of targets, reducing interference from potential lures, and establishing novel connections within pairs.
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Affiliation(s)
- Catherine R Walsh
- Department of Psychology, University of California, Los Angeles, CA, USA.
| | - Jesse Rissman
- Department of Psychology, University of California, Los Angeles, CA, USA
- Department of Psychiatry & Biobehavioral Sciences, University of California, Los Angeles, CA, USA
- Brain Research Institute, University of California, Los Angeles, CA, USA
- Integrative Center for Learning and Memory, University of California, Los Angeles, CA, USA
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Abraham JR, Allen FM, Barnard J, Schlatzer D, Natowicz MR. Proteomic investigations of adult polyglucosan body disease: insights into the pathobiology of a neurodegenerative disorder. Front Neurol 2023; 14:1261125. [PMID: 38033781 PMCID: PMC10683643 DOI: 10.3389/fneur.2023.1261125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 09/26/2023] [Indexed: 12/02/2023] Open
Abstract
Inadequate glycogen branching enzyme 1 (GBE1) activity results in different forms of glycogen storage disease type IV, including adult polyglucosan body disorder (APBD). APBD is clinically characterized by adult-onset development of progressive spasticity, neuropathy, and neurogenic bladder and is histologically characterized by the accumulation of structurally abnormal glycogen (polyglucosan bodies) in multiple cell types. How insufficient GBE1 activity causes the disease phenotype of APBD is poorly understood. We hypothesized that proteomic analysis of tissue from GBE1-deficient individuals would provide insights into GBE1-mediated pathobiology. In this discovery study, we utilized label-free LC-MS/MS to quantify the proteomes of lymphoblasts from 3 persons with APBD and 15 age- and gender-matched controls, with validation of the findings by targeted MS. There were 531 differentially expressed proteins out of 3,427 detected between APBD subjects vs. controls, including pronounced deficiency of GBE1. Bioinformatic analyses indicated multiple canonical pathways and protein-protein interaction networks to be statistically markedly enriched in APBD subjects, including: RNA processing/transport/translation, cell cycle control/replication, mTOR signaling, protein ubiquitination, unfolded protein and endoplasmic reticulum stress responses, glycolysis and cell death/apoptosis. Dysregulation of these processes, therefore, are primary or secondary factors in APBD pathobiology in this model system. Our findings further suggest that proteomic analysis of GBE1 mutant lymphoblasts can be leveraged as part of the screening for pharmaceutical agents for the treatment of APBD.
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Affiliation(s)
- Joseph R. Abraham
- Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, OH, United States
| | - Frederick M. Allen
- Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, OH, United States
| | - John Barnard
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, United States
| | - Daniela Schlatzer
- Center for Proteomics, Case Western Reserve University School of Medicine, Cleveland, OH, United States
| | - Marvin R. Natowicz
- Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, OH, United States
- Pathology and Laboratory Medicine, Genomic Medicine, Neurological and Pediatrics Institutes, Cleveland Clinic, Cleveland, OH, United States
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Böttcher L, Breedvelt JJF, Warren FC, Segal Z, Kuyken W, Bockting CLH. Identifying relapse predictors in individual participant data with decision trees. BMC Psychiatry 2023; 23:835. [PMID: 37957596 PMCID: PMC10644580 DOI: 10.1186/s12888-023-05214-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Accepted: 09/22/2023] [Indexed: 11/15/2023] Open
Abstract
BACKGROUND Depression is a highly common and recurrent condition. Predicting who is at most risk of relapse or recurrence can inform clinical practice. Applying machine-learning methods to Individual Participant Data (IPD) can be promising to improve the accuracy of risk predictions. METHODS Individual data of four Randomized Controlled Trials (RCTs) evaluating antidepressant treatment compared to psychological interventions with tapering ([Formula: see text]) were used to identify predictors of relapse and/or recurrence. Ten baseline predictors were assessed. Decision trees with and without gradient boosting were applied. To study the robustness of decision-tree classifications, we also performed a complementary logistic regression analysis. RESULTS The combination of age, age of onset of depression, and depression severity significantly enhances the prediction of relapse risk when compared to classifiers solely based on depression severity. The studied decision trees can (i) identify relapse patients at intake with an accuracy, specificity, and sensitivity of about 55% (without gradient boosting) and 58% (with gradient boosting), and (ii) slightly outperform classifiers that are based on logistic regression. CONCLUSIONS Decision tree classifiers based on multiple-rather than single-risk indicators may be useful for developing treatment stratification strategies. These classification models have the potential to contribute to the development of methods aimed at effectively prioritizing treatment for those individuals who require it the most. Our results also underline the existing gaps in understanding how to accurately predict depressive relapse.
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Affiliation(s)
- Lucas Böttcher
- Frankfurt School of Finance and Management, Frankfurt am Main, Germany.
- Department of Medicine, University of Florida, Gainesville, FL, USA.
| | - Josefien J F Breedvelt
- Department of Psychiatry, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, the Netherlands
- NatCen Social Research, London, UK
| | - Fiona C Warren
- Institute of Health Research, College of Medicine and Health, University of Exeter, Exeter, UK
| | - Zindel Segal
- Department of Clinical Psychological Science, University of Toronto Scarborough, Toronto, Ontario, Canada
| | - Willem Kuyken
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Claudi L H Bockting
- Department of Psychiatry, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, the Netherlands
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Fan M, Peng X, Niu X, Cui T, He Q. Missing data imputation, prediction, and feature selection in diagnosis of vaginal prolapse. BMC Med Res Methodol 2023; 23:259. [PMID: 37932660 PMCID: PMC10629145 DOI: 10.1186/s12874-023-02079-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2023] [Accepted: 10/24/2023] [Indexed: 11/08/2023] Open
Abstract
BACKGROUND Data loss often occurs in the collection of clinical data. Directly discarding the incomplete sample may lead to low accuracy of medical diagnosis. A suitable data imputation method can help researchers make better use of valuable medical data. METHODS In this paper, five popular imputation methods including mean imputation, expectation-maximization (EM) imputation, K-nearest neighbors (KNN) imputation, denoising autoencoders (DAE) and generative adversarial imputation nets (GAIN) are employed on an incomplete clinical data with 28,274 cases for vaginal prolapse prediction. A comprehensive comparison study for the performance of these methods has been conducted through certain classification criteria. It is shown that the prediction accuracy can be greatly improved by using the imputed data, especially by GAIN. To find out the important risk factors to this disease among a large number of candidate features, three variable selection methods: the least absolute shrinkage and selection operator (LASSO), the smoothly clipped absolute deviation (SCAD) and the broken adaptive ridge (BAR) are implemented in logistic regression for feature selection on the imputed datasets. In pursuit of our primary objective, which is accurate diagnosis, we employed diagnostic accuracy (classification accuracy) as a pivotal metric to assess both imputation and feature selection techniques. This assessment encompassed seven classifiers (logistic regression (LR) classifier, random forest (RF) classifier, support machine classifier (SVC), extreme gradient boosting (XGBoost) , LASSO classifier, SCAD classifier and Elastic Net classifier)enhancing the comprehensiveness of our evaluation. RESULTS The proposed framework imputation-variable selection-prediction is quite suitable to the collected vaginal prolapse datasets. It is observed that the original dataset is well imputed by GAIN first, and then 9 most significant features were selected using BAR from the original 67 features in GAIN imputed dataset, with only negligible loss in model prediction. BAR is superior to the other two variable selection methods in our tests. CONCLUDES Overall, combining the imputation, classification and variable selection, we achieve good interpretability while maintaining high accuracy in computer-aided medical diagnosis.
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Affiliation(s)
- Mingxuan Fan
- Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science, BNU-HKBU United International College, Zhuhai, 519087, China
| | - Xiaoling Peng
- Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science, BNU-HKBU United International College, Zhuhai, 519087, China
| | - Xiaoyu Niu
- Department of Gynecology and Obstetrics, West China Second University Hospital, Sichuan University, Chengdu, 610064, China.
- Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, 610064, China.
| | - Tao Cui
- Department of Gynecology and Obstetrics, West China Second University Hospital, Sichuan University, Chengdu, 610064, China.
- Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, 610064, China.
| | - Qiaolin He
- School of Mathematics, Sichuan University, Chengdu, 610064, China
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Harris L, Fondrie WE, Oh S, Noble WS. Evaluating Proteomics Imputation Methods with Improved Criteria. J Proteome Res 2023; 22:3427-3438. [PMID: 37861703 PMCID: PMC10949645 DOI: 10.1021/acs.jproteome.3c00205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2023]
Abstract
Quantitative measurements produced by tandem mass spectrometry proteomics experiments typically contain a large proportion of missing values. Missing values hinder reproducibility, reduce statistical power, and make it difficult to compare across samples or experiments. Although many methods exist for imputing missing values, in practice, the most commonly used methods are among the worst performing. Furthermore, previous benchmarking studies have focused on relatively simple measurements of error such as the mean-squared error between imputed and held-out values. Here we evaluate the performance of commonly used imputation methods using three practical, "downstream-centric" criteria. These criteria measure the ability to identify differentially expressed peptides, generate new quantitative peptides, and improve the peptide lower limit of quantification. Our evaluation comprises several experiment types and acquisition strategies, including data-dependent and data-independent acquisition. We find that imputation does not necessarily improve the ability to identify differentially expressed peptides but that it can identify new quantitative peptides and improve the peptide lower limit of quantification. We find that MissForest is generally the best performing method per our downstream-centric criteria. We also argue that existing imputation methods do not properly account for the variance of peptide quantifications and highlight the need for methods that do.
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Affiliation(s)
- Lincoln Harris
- Department of Genome Sciences, University of Washington, Seattle, Washington 98195, United States
| | | | - Sewoong Oh
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, Washington 98195, United States
| | - William S Noble
- Department of Genome Sciences, University of Washington, Seattle, Washington 98195, United States
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, Washington 98195, United States
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Revelo X, Fredrickson G, Florczak K, Barrow F, Dietsche K, Wang H, Parthiban P, Almutlaq R, Adeyi O, Herman A, Bartolomucci A, Staley C, Jahansouz C, Williams J, Mashek D, Ikramuddin S. Hepatic lipid-associated macrophages mediate the beneficial effects of bariatric surgery against MASH. Res Sq 2023:rs.3.rs-3446960. [PMID: 37961666 PMCID: PMC10635378 DOI: 10.21203/rs.3.rs-3446960/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
For patients with obesity and metabolic syndrome, bariatric procedures such as vertical sleeve gastrectomy (VSG) have a clear benefit in ameliorating metabolic dysfunction-associated steatohepatitis (MASH). While the effects of bariatric surgeries have been mainly attributed to nutrient restriction and malabsorption, whether immuno-modulatory mechanisms are involved remains unclear. Here we report that VSG ameliorates MASH progression in a weight loss-independent manner. Single-cell RNA sequencing revealed that hepatic lipid-associated macrophages (LAMs) expressing the triggering receptor expressed on myeloid cells 2 (TREM2) increase their lysosomal activity and repress inflammation in response to VSG. Remarkably, TREM2 deficiency in mice ablates the reparative effects of VSG, suggesting that TREM2 is required for MASH resolution. Mechanistically, TREM2 prevents the inflammatory activation of macrophages and is required for their efferocytotic function. Overall, our findings indicate that bariatric surgery improves MASH through a reparative process driven by hepatic LAMs, providing insights into the mechanisms of disease reversal that may result in new therapies and improved surgical interventions.
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