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Wu Y, Ma M, Choi W, Xu W, Dong J. Identification of immune-related gene signatures for chronic obstructive pulmonary disease with metabolic syndrome: evidence from integrated bulk and single-cell RNA sequencing data. Int Immunol 2024; 36:17-32. [PMID: 37878760 DOI: 10.1093/intimm/dxad043] [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: 05/04/2023] [Accepted: 10/24/2023] [Indexed: 10/27/2023] Open
Abstract
Chronic obstructive pulmonary disease (COPD) is closely related to innate and adaptive inflammatory immune responses. It is increasingly becoming evident that metabolic syndrome (MetS) affects a significant portion of COPD patients. Through this investigation, we identify shared immune-related candidate biological markers. The Weighted Gene Co-Expression Network Analysis (WGCNA) was utilized to reveal the co-expression modules linked to COPD and MetS. The commonly expressed genes in the COPD and MetS were utilized to conduct an enrichment analysis. We adopted machine-learning to screen and validate hub genes. We also assessed the relationship between hub genes and immune cell infiltration in COPD and MetS, respectively. Moreover, associations across hub genes and metabolic pathways were also explored. Finally, we chose a single-cell RNA sequencing (scRNA-seq) dataset to investigate the hub genes and shared mechanisms at the level of the cells. We also applied cell trajectory analysis and cell-cell communication analysis to focus on the vital immune cell we were interested in. As a result, we selected and validated 13 shared hub genes for COPD and MetS. The enrichment analysis and immune infiltration analysis illustrated strong associations between hub genes and immunology. Additionally, we applied metabolic pathway enrichment analysis, indicating the significant role of reactive oxygen species (ROS) in COPD with MetS. Through scRNA-seq analysis, we found that ROS might accumulate the most in the alveolar macrophages. In conclusion, the 13 hub genes related to the immune response and metabolism may serve as diagnostic biomarkers and treatment targets of COPD with MetS.
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Affiliation(s)
- Yueren Wu
- Department of Integrative Medicine, Huashan Hospital, Fudan University, Shanghai, People's Republic of China
- Institutes of Integrative Medicine, Fudan University, Shanghai, People's Republic of China
| | - Mengyu Ma
- Department of Integrative Medicine, Huashan Hospital, Fudan University, Shanghai, People's Republic of China
- Institutes of Integrative Medicine, Fudan University, Shanghai, People's Republic of China
| | - Wenglam Choi
- Department of Integrative Medicine, Huashan Hospital, Fudan University, Shanghai, People's Republic of China
- Institutes of Integrative Medicine, Fudan University, Shanghai, People's Republic of China
| | - Weifang Xu
- Shenzhen Hospital of Guangzhou University of Chinese Medicine (Futian), Shenzhen, People's Republic of China
| | - Jingcheng Dong
- Department of Integrative Medicine, Huashan Hospital, Fudan University, Shanghai, People's Republic of China
- Institutes of Integrative Medicine, Fudan University, Shanghai, People's Republic of China
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2
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Cambon MC, Trillat M, Lesur-Kupin I, Burlett R, Chancerel E, Guichoux E, Piouceau L, Castagneyrol B, Le Provost G, Robin S, Ritter Y, Van Halder I, Delzon S, Bohan DA, Vacher C. Microbial biomarkers of tree water status for next-generation biomonitoring of forest ecosystems. Mol Ecol 2023; 32:5944-5958. [PMID: 37815414 DOI: 10.1111/mec.17149] [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: 03/08/2023] [Revised: 09/14/2023] [Accepted: 09/19/2023] [Indexed: 10/11/2023]
Abstract
Next-generation biomonitoring proposes to combine machine-learning algorithms with environmental DNA data to automate the monitoring of the Earth's major ecosystems. In the present study, we searched for molecular biomarkers of tree water status to develop next-generation biomonitoring of forest ecosystems. Because phyllosphere microbial communities respond to both tree physiology and climate change, we investigated whether environmental DNA data from tree phyllosphere could be used as molecular biomarkers of tree water status in forest ecosystems. Using an amplicon sequencing approach, we analysed phyllosphere microbial communities of four tree species (Quercus ilex, Quercus robur, Pinus pinaster and Betula pendula) in a forest experiment composed of irrigated and non-irrigated plots. We used these microbial community data to train a machine-learning algorithm (Random Forest) to classify irrigated and non-irrigated trees. The Random Forest algorithm detected tree water status from phyllosphere microbial community composition with more than 90% accuracy for oak species, and more than 75% for pine and birch. Phyllosphere fungal communities were more informative than phyllosphere bacterial communities in all tree species. Seven fungal amplicon sequence variants were identified as candidates for the development of molecular biomarkers of water status in oak trees. Altogether, our results show that microbial community data from tree phyllosphere provides information on tree water status in forest ecosystems and could be included in next-generation biomonitoring programmes that would use in situ, real-time sequencing of environmental DNA to help monitor the health of European temperate forest ecosystems.
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Affiliation(s)
- Marine C Cambon
- INRAE, University of Bordeaux, BIOGECO, Pessac, France
- School of Natural Sciences, Bangor University, Bangor, UK
| | | | - Isabelle Lesur-Kupin
- INRAE, University of Bordeaux, BIOGECO, Pessac, France
- HelixVenture, Mérignac, France
| | - Régis Burlett
- INRAE, University of Bordeaux, BIOGECO, Pessac, France
| | | | | | | | | | | | | | - Yves Ritter
- INRAE, University of Bordeaux, BIOGECO, Pessac, France
| | | | | | - David A Bohan
- Agroécologie, INRAE, Université Bourgogne Franche-Comté, Dijon, France
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Gao Q, Jin H, Xu W, Wang Y. Predicting diagnostic gene biomarkers in patients with diabetic kidney disease based on weighted gene co expression network analysis and machine learning algorithms. Medicine (Baltimore) 2023; 102:e35618. [PMID: 37904449 PMCID: PMC10615450 DOI: 10.1097/md.0000000000035618] [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] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 09/21/2023] [Indexed: 11/01/2023] Open
Abstract
The present study was designed to identify potential diagnostic markers for diabetic kidney disease (DKD). Two publicly available gene expression profiles (GSE142153 and GSE30528 datasets) from human DKD and control samples were downloaded from the GEO database. Differentially expressed genes (DEGs) were screened between 23 DKD and 10 control samples using the gene data from GSE142153. Weighted gene co expression network analysis was used to find the modules related to DKD. The overlapping genes of DEGs and Turquoise modules were narrowed down and using the least absolute shrinkage and selection operator regression model and support vector machine-recursive feature elimination analysis to identify candidate biomarkers. The area under the receiver operating characteristic curve value was obtained and used to evaluate discriminatory ability using the gene data from GSE30528. A total of 110 DEGs were obtained: 64 genes were significantly upregulated and 46 genes were significantly downregulated. Weighted gene co expression network analysis found that the turquoise module had the strongest correlation with DKD (R = -0.58, P = 4 × 10-4). Thirty-eight overlapping genes of DEGs and turquoise modules were extracted. The identified DEGs were mainly involved in p53 signaling pathway, HIF-1 signaling pathway, JAK - STAT signaling pathway and FoxO signaling pathway between and the control. C-X-C motif chemokine ligand 3 was identified as diagnostic markers of DKD with an area under the receiver operating characteristic curve of 0.735 (95% CI 0.487-0.932). C-X-C motif chemokine ligand 3 was identified as diagnostic biomarkers of DKD and can provide new insights for future studies on the occurrence and the molecular mechanisms of DKD.
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Affiliation(s)
- Qian Gao
- Affiliated Hospital of Shaoxing University of Edocrine and Metabolism Department, Zhejiang, China
| | - Huawei Jin
- Affiliated Hospital of Shaoxing University of Edocrine and Metabolism Department, Zhejiang, China
| | - Wenfang Xu
- Affiliated Hospital of Shaoxing University of Clinical Laboratory, Zhejiang, China
| | - Yanan Wang
- Affiliated Hospital of Shaoxing University of Clinical Laboratory, Zhejiang, China
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Huai H, Li J, Zhang X, Xu Q, Lan H. Creation of a Rat Takotsubo Syndrome Model and Utilization of Machine Learning Algorithms for Screening Diagnostic Biomarkers. J Inflamm Res 2023; 16:4833-4843. [PMID: 37901384 PMCID: PMC10612482 DOI: 10.2147/jir.s423544] [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] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Accepted: 09/27/2023] [Indexed: 10/31/2023] Open
Abstract
Introduction Ferroptosis, a crucial type of programmed cell death, is directly linked to various cardiac disorders. However, the contribution of ferroptosis-related genes (FRGs) to Takotsubo syndrome (TTS) has not been completely understood. Purpose The objective of this study was to investigate the relationship between the FRGs and TTS. Methods TTS rat models were established by isoprenaline injection. Heart tissues were subsequently harvested for total RNA extraction and library construction. Transcriptome data wereobtained transcriptome data for TTS and FRGs from our laboratory, and sources such as the Ferroptosis Database (FerrDb) and the Gene Expression Omnibus Database (GEO). 57 differentially expressed FRGs (DE-FRGs) were discovered. The LASSO and SVM-RFE algorithms were employed to identify Enpp2, Pla2g6, Etv4, and Il1b as marker genes, and logistic regression was applied to construct a diagnostic model. The important genes were validated by real time PCR and the external dataset. Finally, the extent of immune infiltration was explored. Results Among the 57 genes, there were 36 up-regulated and 21 down-regulated genes that exhibited distinct expression patterns in the TTS and healthy control samples. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis indicated that the enriched pathways were primarily associated with pathways of neurodegeneration-multiple disease, while Gene Ontology (GO) analysis revealed that these genes were primarily linked to cellular response to external stimuli, outer membrane functions, and ubiquitin protein ligase binding. After the identification of four marker genes as potentially effective biomarkers for TTS diagnosis, subsequent logistic regression modeling revealed a receiver operating characteristic curve (ROC) with an AUC of 1.0. The examination of immune cell infiltration showed significantly higher prevalence of activated CD4+ T cells, mast cells, etc., in TTS. Conclusion Our findings support the theoretical importance of ferroptosis in TTS, highlighting Enpp2, Pla2g6, Etv4, and Il1b as potential diagnostic and therapeutic biomarkers for TTS.
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Affiliation(s)
- Hongyu Huai
- Key Laboratory of Medical Electrophysiology, Ministry of Education & Medical Electrophysiological Key Laboratory of Sichuan Province, Institute of Cardiovascular Research, Southwest Medical University, Luzhou, People’s Republic of China
| | - Junliang Li
- Department of Otolaryngology Head and Neck Surgery, the Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, People’s Republic of China
| | - Xiangjie Zhang
- Key Laboratory of Medical Electrophysiology, Ministry of Education & Medical Electrophysiological Key Laboratory of Sichuan Province, Institute of Cardiovascular Research, Southwest Medical University, Luzhou, People’s Republic of China
| | - Qiang Xu
- School of Basic Medical Science, Southwest Medical University, Luzhou, People’s Republic of China
| | - Huan Lan
- Key Laboratory of Medical Electrophysiology, Ministry of Education & Medical Electrophysiological Key Laboratory of Sichuan Province, Institute of Cardiovascular Research, Southwest Medical University, Luzhou, People’s Republic of China
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La Monica L, Cenerini C, Vollero L, Pennazza G, Santonico M, Keller F. Development of a Universal Validation Protocol and an Open-Source Database for Multi-Contextual Facial Expression Recognition. Sensors (Basel) 2023; 23:8376. [PMID: 37896470 PMCID: PMC10611000 DOI: 10.3390/s23208376] [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: 09/05/2023] [Revised: 10/06/2023] [Accepted: 10/09/2023] [Indexed: 10/29/2023]
Abstract
Facial expression recognition (FER) poses a complex challenge due to diverse factors such as facial morphology variations, lighting conditions, and cultural nuances in emotion representation. To address these hurdles, specific FER algorithms leverage advanced data analysis for inferring emotional states from facial expressions. In this study, we introduce a universal validation methodology assessing any FER algorithm's performance through a web application where subjects respond to emotive images. We present the labelled data database, FeelPix, generated from facial landmark coordinates during FER algorithm validation. FeelPix is available to train and test generic FER algorithms, accurately identifying users' facial expressions. A testing algorithm classifies emotions based on FeelPix data, ensuring its reliability. Designed as a computationally lightweight solution, it finds applications in online systems. Our contribution improves facial expression recognition, enabling the identification and interpretation of emotions associated with facial expressions, offering profound insights into individuals' emotional reactions. This contribution has implications for healthcare, security, human-computer interaction, and entertainment.
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Affiliation(s)
- Ludovica La Monica
- Department of Engineering, Unit of Computational Systems and Bioinformatics, Università Campus Bio-Medico di Roma, 00128 Rome, Italy; (L.L.M.); (L.V.)
| | - Costanza Cenerini
- Department of Engineering, Unit of Electronics for Sensor Systems, Università Campus Bio-Medico di Roma, 00128 Rome, Italy;
| | - Luca Vollero
- Department of Engineering, Unit of Computational Systems and Bioinformatics, Università Campus Bio-Medico di Roma, 00128 Rome, Italy; (L.L.M.); (L.V.)
| | - Giorgio Pennazza
- Department of Engineering, Unit of Electronics for Sensor Systems, Università Campus Bio-Medico di Roma, 00128 Rome, Italy;
| | - Marco Santonico
- Department of Science and Technology for Sustainable Development and One Health, Unit of Electronics for Sensor Systems, Università Campus Bio-Medico di Roma, 00128 Rome, Italy;
| | - Flavio Keller
- Department of Medicine, Unit of Developmental Neuroscience, Università Campus Bio-Medico di Roma, 00128 Rome, Italy;
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Ishizawa M, Tanaka S, Takagi H, Kadoya N, Sato K, Umezawa R, Jingu K, Takeda K. Development of a prediction model for head and neck volume reduction by clinical factors, dose-volume histogram parameters and radiomics in head and neck cancer†. J Radiat Res 2023; 64:783-794. [PMID: 37466450 PMCID: PMC10516738 DOI: 10.1093/jrr/rrad052] [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] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 05/05/2023] [Indexed: 07/20/2023]
Abstract
In external radiotherapy of head and neck (HN) cancers, the reduction of irradiation accuracy due to HN volume reduction often causes a problem. Adaptive radiotherapy (ART) can effectively solve this problem; however, its application to all cases is impractical because of cost and time. Therefore, finding priority cases is essential. This study aimed to predict patients with HN cancers are more likely to need ART based on a quantitative measure of large HN volume reduction and evaluate model accuracy. The study included 172 cases of patients with HN cancer who received external irradiation. The HN volume was calculated using cone-beam computed tomography (CT) for irradiation-guided radiotherapy for all treatment fractions and classified into two groups: cases with a large reduction in the HN volume and cases without a large reduction. Radiomic features were extracted from the primary gross tumor volume (GTV) and nodal GTV of the planning CT. To develop the prediction model, four feature selection methods and two machine-learning algorithms were tested. Predictive performance was evaluated by the area under the curve (AUC), accuracy, sensitivity and specificity. Predictive performance was the highest for the random forest, with an AUC of 0.662. Furthermore, its accuracy, sensitivity and specificity were 0.692, 0.700 and 0.813, respectively. Selected features included radiomic features of the primary GTV, human papillomavirus in oropharyngeal cancer and the implementation of chemotherapy; thus, these features might be related to HN volume change. Our model suggested the potential to predict ART requirements based on HN volume reduction .
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Affiliation(s)
- Miyu Ishizawa
- Department of Radiological Technology, Faculty of Medicine, School of Health Sciences, Tohoku University, 21 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8575, Japan
| | - Shohei Tanaka
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8574, Japan
| | - Hisamichi Takagi
- Department of Radiological Technology, Faculty of Medicine, School of Health Sciences, Tohoku University, 21 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8575, Japan
| | - Noriyuki Kadoya
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8574, Japan
| | - Kiyokazu Sato
- Department of Radiation Technology, Tohoku University Hospital, 1-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8574, Japan
| | - Rei Umezawa
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8574, Japan
| | - Keiichi Jingu
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8574, Japan
| | - Ken Takeda
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8574, Japan
- Department of Radiological Technology, Faculty of Medicine, School of Health Sciences, Tohoku University, 21 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8575, Japan
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Babbar H, Rani S, Sah DK, AlQahtani SA, Kashif Bashir A. Detection of Android Malware in the Internet of Things through the K-Nearest Neighbor Algorithm. Sensors (Basel) 2023; 23:7256. [PMID: 37631793 PMCID: PMC10460029 DOI: 10.3390/s23167256] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Revised: 08/14/2023] [Accepted: 08/16/2023] [Indexed: 08/27/2023]
Abstract
Predicting attacks in Android malware devices using machine learning for recommender systems-based IoT can be a challenging task. However, it is possible to use various machine-learning techniques to achieve this goal. An internet-based framework is used to predict and recommend Android malware on IoT devices. As the prevalence of Android devices grows, the malware creates new viruses on a regular basis, posing a threat to the central system's security and the privacy of the users. The suggested system uses static analysis to predict the malware in Android apps used by consumer devices. The training of the presented system is used to predict and recommend malicious devices to block them from transmitting the data to the cloud server. By taking into account various machine-learning methods, feature selection is performed and the K-Nearest Neighbor (KNN) machine-learning model is proposed. Testing was carried out on more than 10,000 Android applications to check malicious nodes and recommend that the cloud server block them. The developed model contemplated all four machine-learning algorithms in parallel, i.e., naive Bayes, decision tree, support vector machine, and the K-Nearest Neighbor approach and static analysis as a feature subset selection algorithm, and it achieved the highest prediction rate of 93% to predict the malware in real-world applications of consumer devices to minimize the utilization of energy. The experimental results show that KNN achieves 93%, 95%, 90%, and 92% accuracy, precision, recall and f1 measures, respectively.
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Affiliation(s)
- Himanshi Babbar
- Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, Punjab, India
| | - Shalli Rani
- Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, Punjab, India
| | - Dipak Kumar Sah
- Department of Computer Engineering and Application, GLA University, Mathura 281406, Uttar Pradesh, India;
| | - Salman A. AlQahtani
- Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh 11543, Saudi Arabia;
| | - Ali Kashif Bashir
- Department of Computing and Mathematics, Manchaster Metropolitian University, Manchaster M15 6BH, UK;
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Gu L, Ding D, Wei C, Zhou D. Cancer-associated fibroblasts refine the classifications of gastric cancer with distinct prognosis and tumor microenvironment characteristics. Front Oncol 2023; 13:1158863. [PMID: 37404754 PMCID: PMC10316023 DOI: 10.3389/fonc.2023.1158863] [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] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2023] [Accepted: 04/21/2023] [Indexed: 07/06/2023] Open
Abstract
Background Cancer-associated fibroblasts (CAFs) are essential tumoral components of gastric cancer (GC), contributing to the development, therapeutic resistance and immune-suppressive tumor microenvironment (TME) of GC. This study aimed to explore the factors related to matrix CAFs and establish a CAF model to evaluate the prognosis and therapeutic effect of GC. Methods Sample information from the multiply public databases were retrieved. Weighted gene co-expression network analysis was used to identify CAF-related genes. EPIC algorithm was used to construct and verify the model. Machine-learning methods characterized CAF risk. Gene set enrichment analysis was employed to elucidate the underlying mechanism of CAF in the development of GC. Results A three-gene (GLT8D2, SPARC and VCAN) prognostic CAF model was established, and patients were markedly divided according to the riskscore of CAF model. The high-risk CAF clusters had significantly worse prognoses and less significant responses to immunotherapy than the low-risk group. Additionally, the CAF risk score was positively associated with CAF infiltration in GC. Moreover, the expression of the three model biomarkers were significantly associated with the CAF infiltration. GSEA revealed significant enrichment of cell adhesion molecules, extracellular matrix receptors and focal adhesions in patients at a high risk of CAF. Conclusion The CAF signature refines the classifications of GC with distinct prognosis and clinicopathological indicators. The three-gene model could effectively aid in determining the prognosis, drug resistance and immunotherapy efficacy of GC. Thus, this model has promising clinical significance for guiding precise GC anti-CAF therapy combined with immunotherapy.
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Affiliation(s)
- Lei Gu
- Department of General Surgery, Shanghai Tenth People’s Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Dan Ding
- Department of Gastroenterology, Changhai Hospital, Navy/Second Military Medical University, Shanghai, China
| | - Cuicui Wei
- Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, China
| | - Donglei Zhou
- Department of Gastric Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
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Hinkle JF, Bove LA, Roberto A, Arms T, Rowan NL, Sigmon L. An Innovative Nurse Practitioner Curricular Design Using an Adaptive Learning Platform, Adaptive Case Studies, and Telehealth Simulations: A Feasibility Study. SAGE Open Nurs 2023; 9:23779608231187273. [PMID: 37448971 PMCID: PMC10336759 DOI: 10.1177/23779608231187273] [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] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 06/13/2023] [Accepted: 06/24/2023] [Indexed: 07/18/2023] Open
Abstract
Introduction This project explored the feasibility of implementing an innovative cross-curricular framework using an adaptive learning (AL) platform and telehealth simulations. Objective To determine the feasibility of implementing an innovative cross-curricular framework using an AL platform and telehealth simulations. Methods A mixed-method pilot study was conducted using novel AL modules, adaptive case studies, and telehealth simulation. Results Quantitative data analysis demonstrated significant correlations within and across demographics using the Technology Acceptance Model (TAM) and Simulation Effectiveness Tool-Modified (SET-M). Specifically, significant correlations are evident between TAM ease of use items 1-6, 8, and 10 and TAM usefulness 1, 3, and 9, with SET-M items 3 and 5-15. Thematic analysis revealed that participants felt that the overall project was worthwhile and increased confidence in telehealth. Conclusion Participants found the technology used in this study was easy and useful, and they indicated a positive experience with telehealth simulation. Overall, this study demonstrated that implementation of AL using our paradigm is feasible and supports further investigation into implementing a cross-curricular framework using an AL platform and telehealth simulations.
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Affiliation(s)
- Julie F. Hinkle
- College of Health and Human Services, School of Nursing, University of North Carolina Wilmington, Wilmington, NC, USA
| | - Lisa Anne Bove
- College of Health and Human Services, School of Nursing, University of North Carolina Wilmington, Wilmington, NC, USA
| | - Anka Roberto
- College of Health and Human Services, School of Nursing, University of North Carolina Wilmington, Wilmington, NC, USA
| | - Tamatha Arms
- College of Health and Human Services, School of Nursing, University of North Carolina Wilmington, Wilmington, NC, USA
| | - Noell L. Rowan
- College of Health and Human Services, School of Nursing, University of North Carolina Wilmington, Wilmington, NC, USA
| | - Lorie Sigmon
- College of Health and Human Services, School of Nursing, University of North Carolina Wilmington, Wilmington, NC, USA
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Hani SHB, Ahmad MM. Machine-learning Algorithms for Ischemic Heart Disease Prediction: A Systematic Review. Curr Cardiol Rev 2023; 19:e090622205797. [PMID: 35692135 PMCID: PMC10201879 DOI: 10.2174/1573403x18666220609123053] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 04/03/2022] [Accepted: 04/07/2022] [Indexed: 02/08/2023] Open
Abstract
PURPOSE This review aims to summarize and evaluate the most accurate machinelearning algorithm used to predict ischemic heart disease. METHODS This systematic review was performed following PRISMA guidelines. A comprehensive search was carried out using multiple databases such as Science Direct, PubMed\ MEDLINE, CINAHL, and IEEE explore. RESULTS Thirteen articles published between 2017 to 2021 were eligible for inclusion. Three themes were extracted: the commonly used algorithm to predict ischemic heart disease, the accuracy of algorithms to predict ischemic heart disease, and the clinical outcomes to improve the quality of care. All methods have utilized supervised and unsupervised machine-learning. CONCLUSION Applying machine-learning is expected to assist clinicians in interpreting patients' data and implementing optimal algorithms for their datasets. Furthermore, machine-learning can build evidence-based that supports health care providers to manage individual situations who need invasive procedures such as catheterizations.
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Affiliation(s)
- Salam H. Bani Hani
- Faculty of Nursing, The University of Jordan, Amman, Jordan
- Faculty of Nursing, Al Al-Bayt University, Mafraq, Jordan
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Azrag AG, Mohamed SA, Ndlela S, Ekesi S. Predicting the habitat suitability of the invasive white mango scale, Aulacaspis tubercularis; Newstead, 1906 (Hemiptera: Diaspididae) using bioclimatic variables. Pest Manag Sci 2022; 78:4114-4126. [PMID: 35657692 DOI: 10.1002/ps.7030] [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: 01/19/2022] [Revised: 05/16/2022] [Accepted: 06/03/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND The white mango scale, Aulacaspis tubercularis (Hemiptera: Diaspididae), is an invasive pest that threatens the production of several crops of commercial value including mango. Though it is an important pest, little is known about its biology and ecology. Specifically, information on habitat suitability of A. tubercularis occurrence and potential distribution under climate change is largely unknown. In this study, we used four ecological niche models, namely maximum entropy, random forest, generalized additive models, and classification and regression trees to predict the habitat suitability of A. tubercularis under current and future [representative concentration pathways (RCPs): RCP4.5 and RCP8.5 of the year 2070] climatic scenarios, using bioclimatic variables. Models' performance was evaluated using the true skill statistic (TSS), the area under the curve (AUC), correlation (COR), and the deviance. RESULTS All models sufficiently predicted the occurrence of A. tubercularis with high accuracy (AUC ≥ 0.93, TSS ≥ 0.81 and COR ≥ 0.77). The random forest algorithm had the highest accuracy among the four models (AUC = 0.99, TSS = 0.93, COR = 0.90, deviance = 0.26). Temperature seasonality (Bio4), mean temperature of the driest quarter (Bio9), and precipitation seasonality (Bio15) were the most important variables influencing A. tubercularis occurrence. Models' predictions showed that countries in east, south, and west Africa are highly suitable for A. tubercularis establishment under current conditions. Similarly, Mexico, Brazil, India, Myanmar, Bangladesh, Thailand, Laos, Vietnam, and Cambodia are also highly suitable for the pest to thrive. Under future conditions, the suitable areas might slightly decrease in many countries of sub-Saharan Africa under both RCPs. However, the range of expansion of A. tubercularis is projected to be higher in Australia, Brazil, Spain, Italy, and Portugal under the future climatic scenarios. CONCLUSION The results reported here will be useful for guiding decision-making, developing an effective management strategy, and serving as an early warning tool to prevent further spread toward new areas. © 2022 Society of Chemical Industry.
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Affiliation(s)
- Abdelmutalab Ga Azrag
- International Centre of Insect Physiology and Ecology (ICIPE), Nairobi, Kenya
- Department of Crop Protection, Faculty of Agricultural Sciences, University of Gezira, Wad Medani, Sudan
| | - Samira A Mohamed
- International Centre of Insect Physiology and Ecology (ICIPE), Nairobi, Kenya
| | - Shepard Ndlela
- International Centre of Insect Physiology and Ecology (ICIPE), Nairobi, Kenya
| | - Sunday Ekesi
- International Centre of Insect Physiology and Ecology (ICIPE), Nairobi, Kenya
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Oh SV, Hwang W, Kim K, Lee J, Soon A. Using Feature-Assisted Machine Learning Algorithms to Boost Polarity in Lead-Free Multicomponent Niobate Alloys for High-Performance Ferroelectrics. Adv Sci (Weinh) 2022; 9:e2104569. [PMID: 35253401 PMCID: PMC9434731 DOI: 10.1002/advs.202104569] [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] [Figures] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Revised: 02/15/2022] [Indexed: 06/14/2023]
Abstract
To expand the unchartered materials space of lead-free ferroelectrics for smart digital technologies, tuning their compositional complexity via multicomponent alloying allows access to enhanced polar properties. The role of isovalent A-site in binary potassium niobate alloys, (K,A)NbO3 using first-principles calculations is investigated. Specifically, various alloy compositions of (K,A)NbO3 are considered and their mixing thermodynamics and associated polar properties are examined. To establish structure-property design rules for high-performance ferroelectrics, the sure independence screening sparsifying operator (SISSO) method is employed to extract key features to explain the A-site driven polarization in (K,A)NbO3 . Using a new metric of agreement via feature-assisted regression and classification, the SISSO model is further extended to predict A-site driven polarization in multicomponent systems as a function of alloy composition, reducing the prediction errors to less than 1%. With the machine learning model outlined in this work, a polarity-composition map is established to aid the development of new multicomponent lead-free polar oxides which can offer up to 25% boosting in A-site driven polarization and achieving more than 150% of the total polarization in pristine KNbO3 . This study offers a design-based rational route to develop lead-free multicomponent ferroelectric oxides for niche information technologies.
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Affiliation(s)
- Seung‐Hyun Victor Oh
- Department of Materials Science and Engineering and Center for Artificial Synesthesia MaterialsYonsei UniversitySeoul03722Republic of Korea
| | - Woohyun Hwang
- Department of Materials Science and Engineering and Center for Artificial Synesthesia MaterialsYonsei UniversitySeoul03722Republic of Korea
| | - Kwangrae Kim
- Department of Materials Science and Engineering and Center for Artificial Synesthesia MaterialsYonsei UniversitySeoul03722Republic of Korea
| | - Ji‐Hwan Lee
- Department of Materials Science and Engineering and Center for Artificial Synesthesia MaterialsYonsei UniversitySeoul03722Republic of Korea
| | - Aloysius Soon
- Department of Materials Science and Engineering and Center for Artificial Synesthesia MaterialsYonsei UniversitySeoul03722Republic of Korea
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Xu W, Anwaier A, Ma C, Liu W, Tian X, Su J, Zhu W, Shi G, Wei S, Xu H, Qu Y, Ye D, Zhang H. Prognostic Immunophenotyping Clusters of Clear Cell Renal Cell Carcinoma Defined by the Unique Tumor Immune Microenvironment. Front Cell Dev Biol 2021; 9:785410. [PMID: 34938737 PMCID: PMC8685518 DOI: 10.3389/fcell.2021.785410] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Accepted: 10/29/2021] [Indexed: 12/26/2022] Open
Abstract
Background: The tumor microenvironment affects the occurrence and development of cancers, including clear cell renal cell carcinoma (ccRCC). However, how the immune contexture interacts with the cancer phenotype remains unclear. Methods: We identified and evaluated immunophenotyping clusters in ccRCC using machine-learning algorithms. Analyses for functional enrichment, DNA variation, immune cell distribution, association with independent clinicopathological features, and predictive responses for immune checkpoint therapies were performed and validated. Results: Three immunophenotyping clusters with gradual levels of immune infiltration were identified. The intermediate and high immune infiltration clusters (Clusters B and C) were associated with a worse prognosis for ccRCC patients. Tumors in the immune-hot Clusters B and C showed pro-tumorigenic immune infiltration, and these patients showed significantly worse survival compared with patients in the immune-cold Cluster A in the training and testing cohorts (n = 422). In addition to distinct immune cell infiltrations of immunophenotyping, we detected significant differences in DNA variation among clusters, suggesting a high degree of genetic heterogeneity. Furthermore, expressions of multiple immune checkpoint molecules were significantly increased. Clusters B and C predicted favorable outcomes in 64 ccRCC patients receiving immune checkpoint therapies from the FUSCC cohort. In 360 ccRCC patients from the FUSCC validation cohort, Clusters B and C significantly predicted worse prognosis compared with Cluster A. After immunophenotyping of ccRCC was confirmed, significantly increased tertiary lymphatic structures, aggressive phenotype, elevated glycolysis and PD-L1 expression, higher abundance of CD8+ T cells, and TCRn cell infiltration were found in the immune-hot Clusters B and C. Conclusion: This study described immunophenotyping clusters that improved the prognostic accuracy of the immune contexture in the ccRCC microenvironment. Our discovery of the novel independent prognostic indicators in ccRCC highlights the relationship between tumor phenotype and immune microenvironment.
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Affiliation(s)
- Wenhao Xu
- Department of Urology, Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University, Shanghai, China
| | - Aihetaimujiang Anwaier
- Department of Urology, Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University, Shanghai, China
| | - Chunguang Ma
- Department of Urology, Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University, Shanghai, China
| | - Wangrui Liu
- Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, China
| | - Xi Tian
- Department of Urology, Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University, Shanghai, China
| | - Jiaqi Su
- Department of Urology, Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University, Shanghai, China
| | - Wenkai Zhu
- Department of Urology, Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University, Shanghai, China
| | - Guohai Shi
- Department of Urology, Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University, Shanghai, China
| | - Shiyin Wei
- Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, China
| | - Hong Xu
- Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, China
| | - Yuanyuan Qu
- Department of Urology, Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University, Shanghai, China
| | - Dingwei Ye
- Department of Urology, Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University, Shanghai, China
| | - Hailiang Zhang
- Department of Urology, Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University, Shanghai, China
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Belashov AV, Zhikhoreva AA, Belyaeva TN, Salova AV, Kornilova ES, Semenova IV, Vasyutinskii OS. Machine Learning Assisted Classification of Cell Lines and Cell States on Quantitative Phase Images. Cells 2021; 10:2587. [PMID: 34685568 PMCID: PMC8533984 DOI: 10.3390/cells10102587] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Revised: 09/20/2021] [Accepted: 09/24/2021] [Indexed: 12/20/2022] Open
Abstract
In this report, we present implementation and validation of machine-learning classifiers for distinguishing between cell types (HeLa, A549, 3T3 cell lines) and states (live, necrosis, apoptosis) based on the analysis of optical parameters derived from cell phase images. Validation of the developed classifier shows the accuracy for distinguishing between the three cell types of about 93% and between different cell states of the same cell line of about 89%. In the field test of the developed algorithm, we demonstrate successful evaluation of the temporal dynamics of relative amounts of live, apoptotic and necrotic cells after photodynamic treatment at different doses.
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Affiliation(s)
- Andrey V. Belashov
- Ioffe Institute, 26, Polytekhnicheskaya, 194021 St. Petersburg, Russia; (A.A.Z.); (I.V.S.); (O.S.V.)
| | - Anna A. Zhikhoreva
- Ioffe Institute, 26, Polytekhnicheskaya, 194021 St. Petersburg, Russia; (A.A.Z.); (I.V.S.); (O.S.V.)
| | - Tatiana N. Belyaeva
- Institute of Cytology of RAS, 4, Tikhoretsky pr., 194064 St. Petersburg, Russia; (T.N.B.); (A.V.S.); (E.S.K.)
| | - Anna V. Salova
- Institute of Cytology of RAS, 4, Tikhoretsky pr., 194064 St. Petersburg, Russia; (T.N.B.); (A.V.S.); (E.S.K.)
| | - Elena S. Kornilova
- Institute of Cytology of RAS, 4, Tikhoretsky pr., 194064 St. Petersburg, Russia; (T.N.B.); (A.V.S.); (E.S.K.)
- Institute for Biomedical Systems and Biotechnology, Peter the Great St. Petersburg Polytechnic University, 29, Polytekhnicheskaya, 195251 St. Petersburg, Russia
| | - Irina V. Semenova
- Ioffe Institute, 26, Polytekhnicheskaya, 194021 St. Petersburg, Russia; (A.A.Z.); (I.V.S.); (O.S.V.)
| | - Oleg S. Vasyutinskii
- Ioffe Institute, 26, Polytekhnicheskaya, 194021 St. Petersburg, Russia; (A.A.Z.); (I.V.S.); (O.S.V.)
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Zhang Y, Zhang Q, Li L, Thomas R, Li SZ, He MG, Wang NL. Establishment and Comparison of Algorithms for Detection of Primary Angle Closure Suspect Based on Static and Dynamic Anterior Segment Parameters. Transl Vis Sci Technol 2020; 9:16. [PMID: 32821488 PMCID: PMC7401939 DOI: 10.1167/tvst.9.5.16] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2019] [Accepted: 02/12/2020] [Indexed: 12/14/2022] Open
Abstract
Purpose To establish and evaluate algorithms for detection of primary angle closure suspects (PACS), the risk factor for primary angle closure disease by combining multiple static and dynamic anterior segment optical coherence tomography (ASOCT) parameters. Methods Observational, cross-sectional study. The right eyes of subjects aged ≥40 years who participated in the 5-year follow-up of the Handan Eye Study, and underwent gonioscopy and ASOCT examinations under light and dark conditions were included. All ASOCT images were analyzed by Zhongshan Angle Assessment Program. Backward logistic regression (BLR) was used for inclusion of variables in the prediction models. BLR, naïve Bayes’ classification (NBC), and neural network (NN) were evaluated and compared using the area under the receiver operating characteristic curve (AUC). Results Data from 744 subjects (405 eyes with PACS and 339 normal eyes) were analyzed. Angle recess area at 750 µm, anterior chamber volume, lens vault in light and iris cross-sectional area change/pupil diameter change were included in the prediction models. The AUCs of BLR, NBC, and NN were 0.827 (95% confidence interval [CI], 0.798-0.856), 0.826 (95% CI, 0.797-0.854), and 0.844 (95% CI, 0.817-0.871), respectively. No significant statistical differences were found between the three algorithms (P = 0.622). Conclusions The three algorithms did not meet the requirements for population-based screening of PACS. One possible reason could be the different angle closure mechanisms in enrolled eyes. Translational Relevance This study provides a promise for basis for future research directed toward the development of an image-based, noncontact method to screen for angle closure.
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Affiliation(s)
- Ye Zhang
- Beijing Tongren Eye Center, Beijing Key Laboratory of Ophthalmology and Visual Science, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Qing Zhang
- Beijing Institute of Ophthalmology, Beijing, China
| | - Lei Li
- Beijing Tongren Eye Center, Beijing Key Laboratory of Ophthalmology and Visual Science, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Ravi Thomas
- Queensland Eye Institute, Brisbane, Australia.,University of Queensland, Brisbane, Australia
| | - Si Zhen Li
- Nanjing Tongren Hospital, Jiangsu, China
| | - Ming Guang He
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China.,Department of Surgery, University of Melbourne, Melbourne, Australia.,Ophthalmology, Centre for Eye Research Australia, Melbourne, Australia
| | - Ning Li Wang
- Beijing Tongren Eye Center, Beijing Key Laboratory of Ophthalmology and Visual Science, Beijing Tongren Hospital, Capital Medical University, Beijing, China.,Beijing Institute of Ophthalmology, Beijing, China
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Conforti I, Mileti I, Del Prete Z, Palermo E. Measuring Biomechanical Risk in Lifting Load Tasks Through Wearable System and Machine-Learning Approach. Sensors (Basel) 2020; 20:E1557. [PMID: 32168844 DOI: 10.3390/s20061557] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/30/2020] [Revised: 03/04/2020] [Accepted: 03/06/2020] [Indexed: 11/25/2022]
Abstract
Ergonomics evaluation through measurements of biomechanical parameters in real time has a great potential in reducing non-fatal occupational injuries, such as work-related musculoskeletal disorders. Assuming a correct posture guarantees the avoidance of high stress on the back and on the lower extremities, while an incorrect posture increases spinal stress. Here, we propose a solution for the recognition of postural patterns through wearable sensors and machine-learning algorithms fed with kinematic data. Twenty-six healthy subjects equipped with eight wireless inertial measurement units (IMUs) performed manual material handling tasks, such as lifting and releasing small loads, with two postural patterns: correctly and incorrectly. Measurements of kinematic parameters, such as the range of motion of lower limb and lumbosacral joints, along with the displacement of the trunk with respect to the pelvis, were estimated from IMU measurements through a biomechanical model. Statistical differences were found for all kinematic parameters between the correct and the incorrect postures (p < 0.01). Moreover, with the weight increase of load in the lifting task, changes in hip and trunk kinematics were observed (p < 0.01). To automatically identify the two postures, a supervised machine-learning algorithm, a support vector machine, was trained, and an accuracy of 99.4% (specificity of 100%) was reached by using the measurements of all kinematic parameters as features. Meanwhile, an accuracy of 76.9% (specificity of 76.9%) was reached by using the measurements of kinematic parameters related to the trunk body segment.
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Taborri J, Palermo E, Rossi S. Automatic Detection of Faults in Race Walking: A Comparative Analysis of Machine-Learning Algorithms Fed with Inertial Sensor Data. Sensors (Basel) 2019; 19:E1461. [PMID: 30934643 DOI: 10.3390/s19061461] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/27/2018] [Revised: 03/21/2019] [Accepted: 03/22/2019] [Indexed: 12/02/2022]
Abstract
The validity of results in race walking is often questioned due to subjective decisions in the detection of faults. This study aims to compare machine-learning algorithms fed with data gathered from inertial sensors placed on lower-limb segments to define the best-performing classifiers for the automatic detection of illegal steps. Eight race walkers were enrolled and linear accelerations and angular velocities related to pelvis, thighs, shanks, and feet were acquired by seven inertial sensors. The experimental protocol consisted of two repetitions of three laps of 250 m, one performed with regular race walking, one with loss-of-contact faults, and one with knee-bent faults. The performance of 108 classifiers was evaluated in terms of accuracy, recall, precision, F1-score, and goodness index. Generally, linear accelerations revealed themselves as more characteristic with respect to the angular velocities. Among classifiers, those based on the support vector machine (SVM) were the most accurate. In particular, the quadratic SVM fed with shank linear accelerations was the best-performing classifier, with an F1-score and a goodness index equal to 0.89 and 0.11, respectively. The results open the possibility of using a wearable device for automatic detection of faults in race walking competition.
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Abstract
In all eukaryotic species examined, meiotic recombination, and crossovers in particular, occur non‐randomly along chromosomes. The cause for this non-random distribution remains poorly understood but some specific DNA sequence motifs have been shown to be enriched near crossover hotspots in a number of species. We present analyses using machine learning algorithms to investigate whether DNA motif distribution across the genome can be used to predict crossover variation in Drosophila melanogaster, a species without hotspots. Our study exposes a combinatorial non-linear influence of motif presence able to account for a significant fraction of the genome-wide variation in crossover rates at all genomic scales investigated, from 20% at 5-kb to almost 70% at 2,500-kb scale. The models are particularly predictive for regions with the highest and lowest crossover rates and remain highly informative after removing sub-telomeric and -centromeric regions known to have strongly reduced crossover rates. Transcriptional activity during early meiosis and differences in motif use between autosomes and the X chromosome add to the predictive power of the models. Moreover, we show that population-specific differences in crossover rates can be partly explained by differences in motif presence. Our results suggest that crossover distribution in Drosophila is influenced by both meiosis-specific chromatin dynamics and very local constitutive open chromatin associated with DNA motifs that prevent nucleosome stabilization. These findings provide new information on the genetic factors influencing variation in recombination rates and a baseline to study epigenetic mechanisms responsible for plastic recombination as response to different biotic and abiotic conditions and stresses.
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Affiliation(s)
| | | | - Josep M Comeron
- Department of Biology, University of Iowa Interdisciplinary Graduate Program in Genetics, University of Iowa
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Li C, Shi C, Zhang H, Hui C, Lam KM, Zhang S. Computer-aided diagnosis for preoperative invasion depth of gastric cancer with dual-energy spectral CT imaging. Acad Radiol 2015; 22:149-57. [PMID: 25249448 DOI: 10.1016/j.acra.2014.08.006] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [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: 05/22/2014] [Revised: 08/12/2014] [Accepted: 08/12/2014] [Indexed: 12/11/2022]
Abstract
RATIONALE AND OBJECTIVES This study evaluates the accuracy of dual-energy spectral computed tomography (DEsCT) imaging with the aid of computer-aided diagnosis (CAD) system in assessing serosal invasion in patients with gastric cancer. MATERIALS AND METHODS Thirty patients with gastric cancer were enrolled in this study. Two types of features (information) were collected with the use of DEsCT imaging: conventional features including patient's clinical information (eg, age, gender) and descriptive characteristics on the CT images (eg, location of the lesion, wall thickness at the gastric cardia) and additional spectral CT features extracted from monochromatic images (eg, 60 keV) and material-decomposition images (eg, iodine- and water-density images). The classification results of the CAD system were compared to pathologic findings. Important features can be found out using support vector machine classification method in combination with feature-selection technique thereby helping the radiologists diagnose better. RESULTS Statistical analysis showed that for the collected cases, the feature "long axis" was significantly different between group A (serosa negative) and group B (serosa positive) (P < .05). By adding quantitative spectral features from several regions of interest (ROIs), the total classification accuracy was improved from 83.33% to 90.00%. Two feature ranking algorithms were used in the CAD scheme to derive the top-ranked features. The results demonstrated that low single-energy (approximately 60 keV) CT values, tumor size (long axis and short axis), iodine (water) density, and Effective-Z values of ROIs were important for classification. These findings concurred with the experience of the radiologist. CONCLUSIONS The CAD system designed using machine-learning algorithms may be used to improve the identification accuracy in the assessment of serosal invasion in patients of gastric cancer with DEsCT imaging and provide some indicators which may be useful in predicting prognosis.
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Affiliation(s)
- Chao Li
- Department of Biomedical Engineering, Med-X Research Institute, Shanghai Jiao Tong University, Room 123, No. 1954 Huashan Rd, Xuhui District, Shanghai, China
| | - Cen Shi
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Huan Zhang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Chun Hui
- Department of Biomedical Engineering, Med-X Research Institute, Shanghai Jiao Tong University, Room 123, No. 1954 Huashan Rd, Xuhui District, Shanghai, China
| | - Kin Man Lam
- Centre for Signal Processing, Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Hong Kong, China
| | - Su Zhang
- Department of Biomedical Engineering, Med-X Research Institute, Shanghai Jiao Tong University, Room 123, No. 1954 Huashan Rd, Xuhui District, Shanghai, China.
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Gubskaya AV, Bonates TO, Kholodovych V, Hammer P, Welsh WJ, Langer R, Kohn J. Logical Analysis of Data in Structure-Activity Investigation of Polymeric Gene Delivery. MACROMOL THEOR SIMUL 2011; 20:275-285. [PMID: 25663794 DOI: 10.1002/mats.201000087] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
To date semi-empirical or surrogate modeling has demonstrated great success in the prediction of the biologically relevant properties of polymeric materials. For the first time, a correlation between the chemical structures of poly(β-amino esters) and their efficiency in transfecting DNA was established using the novel technique of logical analysis of data (LAD). Linear combination and explicit representation models were introduced and compared in the framework of the present study. The most successful regression model yielded satisfactory agreement between the predicted and experimentally measured values of transfection efficiency (Pearson correlation coefficient, 0.77; mean absolute error, 3.83). It was shown that detailed analysis of the rules provided by the LAD algorithm offered practical utility to a polymer chemist in the design of new biomaterials.
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Affiliation(s)
- Anna V Gubskaya
- Department of Chemistry and Physics, Mount Saint Vincent University, Halifax, Nova Scotia B3M 2J6 Canada, ;
| | - Tiberius O Bonates
- Rutgers University Center for Operations Research (RUTCOR), Rutgers, The State University of New Jersey, Piscataway, New Jersey 08854, USA
| | - Vladyslav Kholodovych
- Department of Pharmacology, University of Medicine and Dentistry of New Jersey (UMDNJ)-Robert Wood Johnson Medical School (RWJMS), Piscataway, New Jersey 08854, USA
| | - Peter Hammer
- Rutgers University Center for Operations Research (RUTCOR), Rutgers, The State University of New Jersey, Piscataway, New Jersey 08854, USA
| | - William J Welsh
- Department of Pharmacology, University of Medicine and Dentistry of New Jersey (UMDNJ)-Robert Wood Johnson Medical School (RWJMS), Piscataway, New Jersey 08854, USA
| | - Robert Langer
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
| | - Joachim Kohn
- New Jersey Center for Biomaterials, Rutgers, The State University of New Jersey, Piscataway, New Jersey 08854-8087, USA, ;
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