Kinetics of plasma cell-free DNA as a prospective biomarker to predict the prognosis and radiotherapy effect of esophageal cancer

PURPOSE

The lack of reliable biomarkers for the prognosis and radiotherapy efficacy in esophageal cancer (EC) necessitates further research. The aim of our study was to investigate the predictive utility of plasma cell-free DNA (cfDNA) kinetics in patients with EC.

MATERIALS AND METHODS

We retrospectively analyzed the clinical data and cfDNA levels (pre-radiotherapy [pre-RT] and post-radiotherapy [post-RT]) and the cfDNA kinetics (cfDNA ratio: post-RT cfDNA/pre-RT cfDNA) of 88 patients. We employed Kaplan-Meier curves to examine the relationship between cfDNA and overall survival (OS) as well as progression-free survival (PFS). Univariate and multivariate Cox regression analyses were executed to ascertain the independent risk factors in EC.

RESULTS

The pre-RT cfDNA levels were positively correlated with clinical stage (P=0.001). The pre-RT cfDNA levels (cutoff value=16.915ng/mL), but not the post-RT cfDNA levels, were linked to a diminished OS (P<0.001) and PFS (P=0.0137). CfDNA kinetics (cutoff value=0.883) were positively associated with OS (P=0.0326) and PFS (P=0.0020). Notably, we identified independent risk factors for OS in EC treated with RT, including cfDNA ratio (high/low) (HR=0.447 [0.221-0.914] P=0.025), ECOG (0/1/2) (HR=0.501 [0.285-0.880] p=0.016), and histological type (esophagal squamous cell carcinoma [ESCC]/non-ESCC) (HR=3.973 [1.074-14.692] P=0.039).

CONCLUSION

Plasma cfDNA kinetics is associated with prognosis and radiotherapy effect in EC undergoing RT, suggesting potential clinical application of a cheap and simple blood-based test.

The Genetic Architecture of Biological Age in Nine Human Organ Systems

Understanding the genetic basis of biological aging in multi-organ systems is vital for elucidating age-related disease mechanisms and identifying therapeutic interventions. This study characterized the genetic architecture of the biological age gap (BAG) across nine human organ systems in 377,028 individuals of European ancestry from the UK Biobank. We discovered 393 genomic loci-BAG pairs (P-value<5×10 -8 ) linked to the brain, eye, cardiovascular, hepatic, immune, metabolic, musculoskeletal, pulmonary, and renal systems. We observed BAG-organ specificity and inter-organ connections. Genetic variants associated with the nine BAGs are predominantly specific to the respective organ system while exerting pleiotropic effects on traits linked to multiple organ systems. A gene-drug-disease network confirmed the involvement of the metabolic BAG-associated genes in drugs targeting various metabolic disorders. Genetic correlation analyses supported Cheverud's Conjecture 1 - the genetic correlation between BAGs mirrors their phenotypic correlation. A causal network revealed potential causal effects linking chronic diseases (e.g., Alzheimer's disease), body weight, and sleep duration to the BAG of multiple organ systems. Our findings shed light on promising therapeutic interventions to enhance human organ health within a complex multi-organ network, including lifestyle modifications and potential drug repositioning strategies for treating chronic diseases. All results are publicly available at https://labs-laboratory.com/medicine .

Employing Informatics Strategies in Alzheimer's Disease Research: A Review from Genetics, Multiomics, and Biomarkers to Clinical Outcomes

Alzheimer's disease (AD) is a critical national concern, affecting 5.8 million people and costing more than 250 billion annually. However, there is no available cure. Thus, effective strategies are in urgent need to discover AD biomarkers for disease early detection and drug development. In this review, we study AD from a biomedical data scientist perspective to discuss the four fundamental components in AD research: genetics (G), molecular multiomics (M), multimodal imaging biomarkers (B), and clinical outcomes (O) (collectively referred to as the GMBO framework). We provide a comprehensive review of common statistical and informatics methodologies for each component within the GMBO framework, accompanied by the major findings from landmark AD studies. Our review highlights the potential of multimodal biobank data in addressing key challenges in AD, such as early diagnosis, disease heterogeneity, and therapeutic development. We identify major hurdles in AD research, including data scarcity and complexity, and advocate for enhanced collaboration, data harmonization, and advanced modeling techniques. This review aims to be an essential guide for understanding current biomedical data science strategies in AD research, emphasizing the need for integrated, multidisciplinary approaches to advance our understanding and management of AD.

A novel strategy for predicting the efficacy of temozolomide treatment for metastatic pheochromocytomas/paragangliomas

BACKGROUND

There are few studies on the efficacy of temozolomide (TMZ) in the treatment of Metastatic pheochromocytoma / paraganglioma (MPP) patients. And it remains unclear which MPP patients may benefit from TMZ treatment.

METHODS

This was a prospective study. MPP patients were enrolled. Patients were treated with TMZ until disease progression or intolerable toxicities. The primary endpoints were disease control rate (DCR) and objective response rate (ORR). Secondary endpoints included biochemical response rate progression-free survival (PFS) and safety. We compared the difference between effective and ineffective groups, to explore which patients are more suitable for TMZ treatment.

RESULTS

62 patients with MPP were enrolled and tumor response were evaluated in 54 patients. The DCR was 83% (35/42), and the ORR was 24% (10/41) among the progressive patients. PFS was 25.2 ± 3.1 months. The most common adverse event was nausea (41/55). We found that 92.9% (13/14) of patients with MGMT methylation greater than 7% respond to treatment. For the patients with MGMT methylation less than 7%, Ki-67 index could be used to guide the use of TMZ in these patients. Among the patients with Ki-67 index less than 5%, 66% (8/12) patients showed respond to treatment, and only 33% (4/12) patients with Ki-67 index more than 5% showed respond to TMZ.

CONCLUSIONS

This study indicated that TMZ is a potential choice for the treatment of MPP with the high ability on disease control and well tolerability. We recommended to MGMT methylation analysis test and Ki-67 index to guide TMZ application.

[Analysis of the progress in the field of oral microbiology and regenerative medicine from 2014 to 2023]

Objective: To analyze the trends in literature related to oral microbiology and regenerative medicine from 2014 to 2023. By identifying key research countries, institutions, and their collaboration networks, as well as exploring research hotspots and development directions, the study seeks to provide references for researchers and decision-makers in the field of oral microbiology and regenerative medicine, thereby guiding the direction of future research. Methods: Relevant literature was retrieved using the Web of Science Core Collection database, with data processing and analysis conducted using CiteSpace 6.2.R6 software. Time slicing, node type selection, and the application of the g-index (g-index) were used for filtering, analyzing countries, institutions, authors, journals, and keywords. Results: The volume of literature in the field of oral microbiology and regenerative medicine has steadily increased from 2014 to 2023, with the number of publications first exceeding one hundred in 2020 and reaching 134 in 2022, accompanied by a citation frequency of 3 363 times. China and the United States have been at the forefront in terms of the volume of publications, while the United States and Germany lead in terms of intermediary centrality. The research primarily spans disciplines such as oral medicine, interdisciplinary studies, materials science, and immunology. High-frequency keywords include stem cells, scaffold materials, and gut microbiota, while cluster analysis indicates that inflammation, drug delivery, and antimicrobial activity remain consistent research themes. In recent years, the research heat in "tissue regeneration""gut microbiota " and "maxillofacial surgery" has risen, suggesting these may become focal points of future research. Conclusions: Over the past decade, the volume of literature published in the fields of oral microbiology and regenerative medicine, along with their citation frequencies, has increased annually. The research focus has shifted over time. Understanding the interactions between oral and gut microbiomes is crucial for developing innovative regenerative treatment strategies.

Automated Cytometric Gating with Human-Level Performance Using Bivariate Segmentation

Recent advances in cytometry technology have enabled high-throughput data collection with multiple single-cell protein expression measurements. The significant biological and technical variance between samples in cytometry has long posed a formidable challenge during the gating process, especially for the initial gates which deal with unpredictable events, such as debris and technical artifacts. Even with the same experimental machine and protocol, the target population, as well as the cell population that needs to be excluded, may vary across different measurements. To address this challenge and mitigate the labor-intensive manual gating process, we propose a deep learning framework UNITO to rigorously identify the hierarchical cytometric subpopulations. The UNITO framework transformed a cell-level classification task into an image-based semantic segmentation problem. For reproducibility purposes, the framework was applied to three independent cohorts and successfully detected initial gates that were required to identify single cellular events as well as subsequent cell gates. We validated the UNITO framework by comparing its results with previous automated methods and the consensus of at least four experienced immunologists. UNITO outperformed existing automated methods and differed from human consensus by no more than each individual human. Most critically, UNITO framework functions as a fully automated pipeline after training and does not require human hints or prior knowledge. Unlike existing multi-channel classification or clustering pipelines, UNITO can reproduce a similar contour compared to manual gating for each intermediate gating to achieve better interpretability and provide post hoc visual inspection. Beyond acting as a pioneering framework that uses image segmentation to do auto-gating, UNITO gives a fast and interpretable way to assign the cell subtype membership, and the speed of UNITO will not be impacted by the number of cells from each sample. The pre-gating and gating inference takes approximately 2 minutes for each sample using our pre-defined 9 gates system, and it can also adapt to any sequential prediction with different configurations.

Dimensional Neuroimaging Endophenotypes: Neurobiological Representations of Disease Heterogeneity Through Machine Learning

Machine learning has been increasingly used to obtain individualized neuroimaging signatures for disease diagnosis, prognosis, and response to treatment in neuropsychiatric and neurodegenerative disorders. Therefore, it has contributed to a better understanding of disease heterogeneity by identifying disease subtypes with different brain phenotypic measures. In this review, we first present a systematic literature overview of studies using machine learning and multimodal magnetic resonance imaging to unravel disease heterogeneity in various neuropsychiatric and neurodegenerative disorders, including Alzheimer's disease, schizophrenia, major depressive disorder, autism spectrum disorder, and multiple sclerosis, as well as their potential in a transdiagnostic framework, where neuroanatomical and neurobiological commonalities were assessed across diagnostic boundaries. Subsequently, we summarize relevant machine learning methodologies and their clinical interpretability. We discuss the potential clinical implications of the current findings and envision future research avenues. Finally, we discuss an emerging paradigm called dimensional neuroimaging endophenotypes. Dimensional neuroimaging endophenotypes dissects the neurobiological heterogeneity of neuropsychiatric and neurodegenerative disorders into low-dimensional yet informative, quantitative brain phenotypic representations, serving as robust intermediate phenotypes (i.e., endophenotypes), presumably reflecting the interplay of underlying genetic, lifestyle, and environmental processes associated with disease etiology.

Genetic and Clinical Correlates of AI-Based Brain Aging Patterns in Cognitively Unimpaired Individuals

Importance

Brain aging elicits complex neuroanatomical changes influenced by multiple age-related pathologies. Understanding the heterogeneity of structural brain changes in aging may provide insights into preclinical stages of neurodegenerative diseases.

Objective

To derive subgroups with common patterns of variation in participants without diagnosed cognitive impairment (WODCI) in a data-driven manner and relate them to genetics, biomedical measures, and cognitive decline trajectories.

Design, Setting, and Participants

Data acquisition for this cohort study was performed from 1999 to 2020. Data consolidation and harmonization were conducted from July 2017 to July 2021. Age-specific subgroups of structural brain measures were modeled in 4 decade-long intervals spanning ages 45 to 85 years using a deep learning, semisupervised clustering method leveraging generative adversarial networks. Data were analyzed from July 2021 to February 2023 and were drawn from the Imaging-Based Coordinate System for Aging and Neurodegenerative Diseases (iSTAGING) international consortium. Individuals WODCI at baseline spanning ages 45 to 85 years were included, with greater than 50 000 data time points.

Exposures

Individuals WODCI at baseline scan.

Main Outcomes and Measures

Three subgroups, consistent across decades, were identified within the WODCI population. Associations with genetics, cardiovascular risk factors (CVRFs), amyloid β (Aβ), and future cognitive decline were assessed.

Results

In a sample of 27 402 individuals (mean [SD] age, 63.0 [8.3] years; 15 146 female [55%]) WODCI, 3 subgroups were identified in contrast with the reference group: a typical aging subgroup, A1, with a specific pattern of modest atrophy and white matter hyperintensity (WMH) load, and 2 accelerated aging subgroups, A2 and A3, with characteristics that were more distinct at age 65 years and older. A2 was associated with hypertension, WMH, and vascular disease-related genetic variants and was enriched for Aβ positivity (ages ≥65 years) and apolipoprotein E (APOE) ε4 carriers. A3 showed severe, widespread atrophy, moderate presence of CVRFs, and greater cognitive decline. Genetic variants associated with A1 were protective for WMH (rs7209235: mean [SD] B = -0.07 [0.01]; P value = 2.31 × 10-9) and Alzheimer disease (rs72932727: mean [SD] B = 0.1 [0.02]; P value = 6.49 × 10-9), whereas the converse was observed for A2 (rs7209235: mean [SD] B = 0.1 [0.01]; P value = 1.73 × 10-15 and rs72932727: mean [SD] B = -0.09 [0.02]; P value = 4.05 × 10-7, respectively); variants in A3 were associated with regional atrophy (rs167684: mean [SD] B = 0.08 [0.01]; P value = 7.22 × 10-12) and white matter integrity measures (rs1636250: mean [SD] B = 0.06 [0.01]; P value = 4.90 × 10-7).

Conclusions and Relevance

The 3 subgroups showed distinct associations with CVRFs, genetics, and subsequent cognitive decline. These subgroups likely reflect multiple underlying neuropathologic processes and affect susceptibility to Alzheimer disease, paving pathways toward patient stratification at early asymptomatic stages and promoting precision medicine in clinical trials and health care.

Quasi-optical design for the cross-polarization scattering diagnostic on the HL-3 tokamak

As the plasma beta (β) increases in high-performance tokamaks, electromagnetic turbulence becomes more significant, potentially constraining their operational range. To investigate this turbulence, a cross-polarization scattering (CPS) diagnostic system is being developed on the HL-3 tokamak for simultaneous measurements of density and magnetic fluctuations. In this work, a quasi-optical system has been designed and analyzed for the Q-band CPS diagnostic. The system includes a lens group for beam waist size optimization, a rotatable wire-grid polarizer for polarization adjustment, and a reflector group for measurement range regulation and system response enhancement. Laboratory tests demonstrated a beam radius of order 4 cm at the target measurement location (near the plasma pedestal), cross-polarization isolation exceeding 30 dB, and poloidal and toroidal angle adjustment ranges of ±40° and ±15°, respectively. These results verify the system's feasibility through laboratory evaluations. The quasi-optical system has been installed on the HL-3 tokamak during the 2023 experimental campaign to support the development of CPS diagnostics.

Adaptive robust structure exploration for complex systems based on model configuration and fusion

Analyzing and obtaining useful information is challenging when facing a new complex system. Traditional methods often focus on specific structural aspects, such as communities, which may overlook the important features and result in biased conclusions. To address this, this article suggests an adaptive algorithm for exploring complex system structures using a generative model. This method calculates and optimizes node parameters, which can reflect the latent structural characteristics of the complex system. The effectiveness and stability of this method have been demonstrated in comparative experiments on 10 sets of benchmark networks using our model parameter configuration scheme. To enhance adaptability, algorithm fusion strategies were also proposed and tested on two real-world networks. The results indicate that the algorithm can uncover multiple structural features, including clustering, overlapping, and local chaining. This adaptive algorithm provides a promising approach for exploring complex system structures.

The genetic architecture of multimodal human brain age

The complex biological mechanisms underlying human brain aging remain incompletely understood. This study investigated the genetic architecture of three brain age gaps (BAG) derived from gray matter volume (GM-BAG), white matter microstructure (WM-BAG), and functional connectivity (FC-BAG). We identified sixteen genomic loci that reached genome-wide significance (P-value < 5×10-8). A gene-drug-disease network highlighted genes linked to GM-BAG for treating neurodegenerative and neuropsychiatric disorders and WM-BAG genes for cancer therapy. GM-BAG displayed the most pronounced heritability enrichment in genetic variants within conserved regions. Oligodendrocytes and astrocytes, but not neurons, exhibited notable heritability enrichment in WM and FC-BAG, respectively. Mendelian randomization identified potential causal effects of several chronic diseases on brain aging, such as type 2 diabetes on GM-BAG and AD on WM-BAG. Our results provide insights into the genetics of human brain aging, with clinical implications for potential lifestyle and therapeutic interventions. All results are publicly available at https://labs.loni.usc.edu/medicine .

Inflammatory factors mediated the effect of air pollution on ischemic stroke: a two-step, mediation Mendelian randomization study

OBJECTIVE

Numerous investigations have indicated a correlation between air pollution (AP) and an elevated ischemic stroke (IS) likelihood. The existing literature does not provide a consensus about the possible link between AP and IS. A two-sample Mendelian randomization (MR) analysis was utilized to systematically measure the causal link between AP and ischemic stroke. Furthermore, the mediating impact of inflammatory factors was also performed by a two-step MR.

MATERIALS AND METHODS

A two-sample MR analysis was utilized to examine the AP impact on the incidence of IS. Additionally, a two-step MR approach was carried out to account for possible mediating variables. The indirect impact was determined by employing the product approach, which included multiplying the AP impact on inflammatory factors by the inflammatory factors' impacts on IS. The MR effect was identified through inverse variance-weighted (IVW) meta-analysis of each Wald Ratio. Additionally, complementary studies were conducted using the weighted median and MR-egger approaches.

RESULTS

The IVW method with random effects showed that the per unit increase in genetically predicted PM2.5 was linked to the 0.362-fold elevated ischemic stroke risk (OR: 1.362, 95% CI: 1.032-1.796, p=0.029). Furthermore, the IVM technique, incorporating random effects, demonstrated that the per unit increase in genetically predicted PM2.5 was related to an elevated Interleukin (IL)-1β risk (OR: 1.529, 95% CI: 1.191-1.963, p=0.001), IL-6 (OR: 1.498, 95% CI: 1.094-2.052, p=0.012) and IL-17 (OR: 1.478, 95% CI: 1.021-2.139, p=0.038). IL-1β, IL-6, and IL-17 modulated the PM2.5 impact on ischemic stroke, while the proportion mediated by them was 59.5%.

CONCLUSIONS

A positive correlation between genetically predicted PM2.5 levels and elevated ischemic stroke risk is mediated by IL-1β, IL-6, and IL-17.

[The association between body mass index and in-hospital major adverse cardiovascular and cerebral events in patients with acute coronary syndrome]

Objective: To assess the association between body mass index (BMI) and major adverse cardiovascular and cerebrovascular events (MACCE) among patients with acute coronary syndrome (ACS). Methods: This was a multicenter prospective cohort study, which was based on the Improving Care for Cardiovascular Disease in China (CCC) project. The hospitalized patients with ACS aged between 18 and 80 years, registered in CCC project from November 1, 2014 to December 31, 2019 were included. The included patients were categorized into four groups based on their BMI at the time of admission: underweight (BMI<18.5 kg/m2), normal weight (BMI between 18.5 and 24.9 kg/m2), overweight (BMI between 25.0 and 29.9 kg/m2), and obese (BMI≥30.0 kg/m2). Multivariate logistic regression models was used to analyze the relationship between BMI and the risk of in-hospital MACCE. Results: A total of 71 681 ACS inpatients were included in the study. The age was (63.4±14.7) years, and 26.5% (18 979/71 681) were female. And the incidence of MACCE for the underweight, normal weight, overweight, and obese groups were 14.9% (322/2 154), 9.5% (3 997/41 960), 7.9% (1 908/24 140) and 7.0% (240/3 427), respectively (P<0.001). Multivariate logistic regression analysis showed a higher incidence of MACCE in the underweight group compared to the normal weight group (OR=1.30, 95%CI 1.13-1.49, P<0.001), while the overweight and obese groups exhibited no statistically significant difference in the incidence of MACCE compared to the normal weight group (both P>0.05). Conclusion: ACS patients with BMI below normal have a higher risk of in-hospital MACCE, suggesting that BMI may be an indicator for evaluating short-term prognosis in ACS patients.

Dimensional Neuroimaging Endophenotypes: Neurobiological Representations of Disease Heterogeneity Through Machine Learning

Machine learning has been increasingly used to obtain individualized neuroimaging signatures for disease diagnosis, prognosis, and response to treatment in neuropsychiatric and neurodegenerative disorders. Therefore, it has contributed to a better understanding of disease heterogeneity by identifying disease subtypes that present significant differences in various brain phenotypic measures. In this review, we first present a systematic literature overview of studies using machine learning and multimodal MRI to unravel disease heterogeneity in various neuropsychiatric and neurodegenerative disorders, including Alzheimer's disease, schizophrenia, major depressive disorder, autism spectrum disorder, multiple sclerosis, as well as their potential in transdiagnostic settings. Subsequently, we summarize relevant machine learning methodologies and discuss an emerging paradigm which we call dimensional neuroimaging endophenotype (DNE). DNE dissects the neurobiological heterogeneity of neuropsychiatric and neurodegenerative disorders into a low-dimensional yet informative, quantitative brain phenotypic representation, serving as a robust intermediate phenotype (i.e., endophenotype) largely reflecting underlying genetics and etiology. Finally, we discuss the potential clinical implications of the current findings and envision future research avenues.

Gene-SGAN: discovering disease subtypes with imaging and genetic signatures via multi-view weakly-supervised deep clustering

Disease heterogeneity has been a critical challenge for precision diagnosis and treatment, especially in neurologic and neuropsychiatric diseases. Many diseases can display multiple distinct brain phenotypes across individuals, potentially reflecting disease subtypes that can be captured using MRI and machine learning methods. However, biological interpretability and treatment relevance are limited if the derived subtypes are not associated with genetic drivers or susceptibility factors. Herein, we describe Gene-SGAN - a multi-view, weakly-supervised deep clustering method - which dissects disease heterogeneity by jointly considering phenotypic and genetic data, thereby conferring genetic correlations to the disease subtypes and associated endophenotypic signatures. We first validate the generalizability, interpretability, and robustness of Gene-SGAN in semi-synthetic experiments. We then demonstrate its application to real multi-site datasets from 28,858 individuals, deriving subtypes of Alzheimer's disease and brain endophenotypes associated with hypertension, from MRI and single nucleotide polymorphism data. Derived brain phenotypes displayed significant differences in neuroanatomical patterns, genetic determinants, biological and clinical biomarkers, indicating potentially distinct underlying neuropathologic processes, genetic drivers, and susceptibility factors. Overall, Gene-SGAN is broadly applicable to disease subtyping and endophenotype discovery, and is herein tested on disease-related, genetically-associated neuroimaging phenotypes.

Five dominant dimensions of brain aging are identified via deep learning: associations with clinical, lifestyle, and genetic measures

Brain aging is a complex process influenced by various lifestyle, environmental, and genetic factors, as well as by age-related and often co-existing pathologies. MRI and, more recently, AI methods have been instrumental in understanding the neuroanatomical changes that occur during aging in large and diverse populations. However, the multiplicity and mutual overlap of both pathologic processes and affected brain regions make it difficult to precisely characterize the underlying neurodegenerative profile of an individual from an MRI scan. Herein, we leverage a state-of-the art deep representation learning method, Surreal-GAN, and present both methodological advances and extensive experimental results that allow us to elucidate the heterogeneity of brain aging in a large and diverse cohort of 49,482 individuals from 11 studies. Five dominant patterns of neurodegeneration were identified and quantified for each individual by their respective (herein referred to as) R-indices. Significant associations between R-indices and distinct biomedical, lifestyle, and genetic factors provide insights into the etiology of observed variances. Furthermore, baseline R-indices showed predictive value for disease progression and mortality. These five R-indices contribute to MRI-based precision diagnostics, prognostication, and may inform stratification into clinical trials.

Genomic loci influence patterns of structural covariance in the human brain

Normal and pathologic neurobiological processes influence brain morphology in coordinated ways that give rise to patterns of structural covariance (PSC) across brain regions and individuals during brain aging and diseases. The genetic underpinnings of these patterns remain largely unknown. We apply a stochastic multivariate factorization method to a diverse population of 50,699 individuals (12 studies and 130 sites) and derive data-driven, multi-scale PSCs of regional brain size. PSCs were significantly correlated with 915 genomic loci in the discovery set, 617 of which are newly identified, and 72% were independently replicated. Key pathways influencing PSCs involve reelin signaling, apoptosis, neurogenesis, and appendage development, while pathways of breast cancer indicate potential interplays between brain metastasis and PSCs associated with neurodegeneration and dementia. Using support vector machines, multi-scale PSCs effectively derive imaging signatures of several brain diseases. Our results elucidate genetic and biological underpinnings that influence structural covariance patterns in the human brain.

Chromothripsis orchestrates leukemic transformation in blast phase MPN through targetable amplification of DYRK1A

Chromothripsis, the process of catastrophic shattering and haphazard repair of chromosomes, is a common event in cancer. Whether chromothripsis might constitute an actionable molecular event amenable to therapeutic targeting remains an open question. We describe recurrent chromothripsis of chromosome 21 in a subset of patients in blast phase of a myeloproliferative neoplasm (BP-MPN), which alongside other structural variants leads to amplification of a region of chromosome 21 in ∼25% of patients ('chr21amp'). We report that chr21amp BP-MPN has a particularly aggressive and treatment-resistant phenotype. The chr21amp event is highly clonal and present throughout the hematopoietic hierarchy. DYRK1A , a serine threonine kinase and transcription factor, is the only gene in the 2.7Mb minimally amplified region which showed both increased expression and chromatin accessibility compared to non-chr21amp BP-MPN controls. We demonstrate that DYRK1A is a central node at the nexus of multiple cellular functions critical for BP-MPN development, including DNA repair, STAT signalling and BCL2 overexpression. DYRK1A is essential for BP-MPN cell proliferation in vitro and in vivo , and DYRK1A inhibition synergises with BCL2 targeting to induce BP-MPN cell apoptosis. Collectively, these findings define the chr21amp event as a prognostic biomarker in BP-MPN and link chromothripsis to a druggable target.

Genome-wide association study identifies variants associated with semen volume in white-feathered broilers

Semen is a measure of the reproductive efficiency of roosters, which affects the economic benefits of white-feathered broilers. Over the years, research in this field has mainly focused on hens, while there have been fewer studies on the reproductive traits of roosters. To identify the genes related to the semen traits of roosters, we used a chicken 55 K SNP chip to genetically type the white-feathered population (220) and performed imputation with resequencing data from 97 roosters. In total, 1 048 576 SNPs were obtained and used for genome-wide association analysis of semen volume, from which 197 genome-wide significant markers were identified, all within the interval of 13.82-16.12 Mb on chromosome 7. By combining our results with the biological functions of genes in the interval, four candidate genes were identified that potentially relate to semen volume: FAPP1, OSBPL6, SESTD1 and SSFA2. Our findings may provide a basis for further research on the genetic mechanism and marker-assisted selection of semen volume in white-feathered broilers.

LAGAM: A Length-Adaptive Genetic Algorithm With Markov Blanket for High-Dimensional Feature Selection in Classification

Feature selection (FS) is an essential technique widely applied in data mining. Recent studies have shown that evolutionary computing (EC) is very promising for FS due to its powerful search capability. However, most existing EC-based FS methods use a length-fixed encoding to represent feature subsets. This inflexible encoding turns ineffective when high-dimension data are handled, because it results in a huge search space, as well as a large amount of training time and memory overhead. In this article, we propose a length-adaptive genetic algorithm with Markov blanket (LAGAM), which adopts a length-variable individual encoding and enables individuals to evolve in their own search space. In LAGAM, features are rearranged decreasingly based on their relevance, and an adaptive length changing operator is introduced, which extends or shortens an individual to guide it to explore in a better search space. Local search based on Markov blanket (MB) is embedded to further improve individuals. Experiments are conducted on 12 high-dimensional datasets and results reveal that LAGAM performs better than existing methods. Specifically, it achieves a higher classification accuracy by using fewer features.

[Application effects of 3D visualization reconstruction technique in pheochromocytoma/ paraganglioma surgery]

To investigate the value of 3D visualization reconstruction technology in pheochromocytoma/paraganglioma surgery.The clinical data of 87 patients with pheochromocytoma/paraganglioma admitted to the Department of Urology of Peking Union Medical College Hospital between January 2019 and December 2022 were retrospectively analyzed, and 3D visualization model reconstruction was performed preoperatively in 47 patients [Group A:males was 24 cases,the age M(Q1, Q3)42.00(30.00, 54.00)]. while the remaining 40 patients [Group B: males was 23 cases,the age M(Q1, Q3) 44.00(30.25, 53.75)] was not. The maximum tumor diameter, operation time, intraoperative bleeding, drain retention time and postoperative hospital stay were compared between the two groups. Surgery was successfully completed in both groups. 37 (78.7%) patients in group A underwent laparoscopic surgery, 7 (14.9%) patients underwent open surgery, and 3 (6.4%) patients underwent laparoscopic-to-open surgery. Thirty-one (77.5%) patients in group B underwent laparoscopic surgery, 5 (12.5%) patients underwent open surgery, and 4 (10.0%) patients underwent laparoscopic to open surgery. There was a difference in the maximum diameter of the tumor between the two groups [(6.09±3.02) cm vs (5.32±1.76) cm, P<0.05], the retention time of the drainage tube was significantly shorter in group A compared with group B [(3.20±1.38) d vs (4.02±1.98) d, P<0.05], and the length of the hospital stay after surgery was significantly shorter [(5.75±2.12) d vs (6.49±3.37) d, P<0.05]. Comparison of operation time and intraoperative bleeding between the two groups showed no statistically significant difference (P>0.05).Two cases of postoperative anemia and one case of pulmonary atelectasis in group B patients improved before discharge. Conclusion when the tumor diameter is>6 cm or has a close relationship with the surrounding organs and blood vessels, the use of 3D visual reconstruction technology can formulate and implement a more accurate and safe surgical plan, shorten the retention time of the drainage tube and postoperative hospitalization time, which is conducive to the patient's postoperative recovery and reduce postoperative complications.

Brain-wide genome-wide colocalization study for integrating genetics, transcriptomics and brain morphometry in Alzheimer's disease

Alzheimer's disease (AD) is one of the most common neurodegenerative diseases. However, the AD mechanism has not yet been fully elucidated to date, hindering the development of effective therapies. In our work, we perform a brain imaging genomics study to link genetics, single-cell gene expression data, tissue-specific gene expression data, brain imaging-derived volumetric endophenotypes, and disease diagnosis to discover potential underlying neurobiological pathways for AD. To do so, we perform brain-wide genome-wide colocalization analyses to integrate multidimensional imaging genomic biobank data. Specifically, we use (1) the individual-level imputed genotyping data and magnetic resonance imaging (MRI) data from the UK Biobank, (2) the summary statistics of the genome-wide association study (GWAS) from multiple European ancestry cohorts, and (3) the tissue-specific cis-expression quantitative trait loci (cis-eQTL) summary statistics from the GTEx project. We apply a Bayes factor colocalization framework and mediation analysis to these multi-modal imaging genomic data. As a result, we derive the brain regional level GWAS summary statistics for 145 brain regions with 482,831 single nucleotide polymorphisms (SNPs) followed by posthoc functional annotations. Our analysis yields the discovery of a potential AD causal pathway from a systems biology perspective: the SNP chr10:124165615:G>A (rs6585827) mutation upregulates the expression of BTBD16 gene in oligodendrocytes, a specialized glial cells, in the brain cortex, leading to a reduced risk of volumetric loss in the entorhinal cortex, resulting in the protective effect on AD. We substantiate our findings with multiple evidence from existing imaging, genetic and genomic studies in AD literature. Our study connects genetics, molecular and cellular signatures, regional brain morphologic endophenotypes, and AD diagnosis, providing new insights into the mechanistic understanding of the disease. Our findings can provide valuable guidance for subsequent therapeutic target identification and drug discovery in AD.

Preference matrix guided sparse canonical correlation analysis for mining brain imaging genetic associations in Alzheimer's disease

Investigating the relationship between genetic variation and phenotypic traits is a key issue in quantitative genetics. Specifically for Alzheimer's disease, the association between genetic markers and quantitative traits remains vague while, once identified, will provide valuable guidance for the study and development of genetics-based treatment approaches. Currently, to analyze the association of two modalities, sparse canonical correlation analysis (SCCA) is commonly used to compute one sparse linear combination of the variable features for each modality, giving a pair of linear combination vectors in total that maximizes the cross-correlation between the analyzed modalities. One drawback of the plain SCCA model is that the existing findings and knowledge cannot be integrated into the model as priors to help extract interesting correlations as well as identify biologically meaningful genetic and phenotypic markers. To bridge this gap, we introduce preference matrix guided SCCA (PM-SCCA) that not only takes priors encoded as a preference matrix but also maintains computational simplicity. A simulation study and a real-data experiment are conducted to investigate the effectiveness of the model. Both experiments demonstrate that the proposed PM-SCCA model can capture not only genotype-phenotype correlation but also relevant features effectively.

The Genetic Architecture of Multimodal Human Brain Age

The complex biological mechanisms underlying human brain aging remain incompletely understood, involving multiple body organs and chronic diseases. In this study, we used multimodal magnetic resonance imaging and artificial intelligence to examine the genetic architecture of the brain age gap (BAG) derived from gray matter volume (GM-BAG, N=31,557 European ancestry), white matter microstructure (WM-BAG, N=31,674), and functional connectivity (FC-BAG, N=32,017). We identified sixteen genomic loci that reached genome-wide significance (P-value<5×10-8). A gene-drug-disease network highlighted genes linked to GM-BAG for treating neurodegenerative and neuropsychiatric disorders and WM-BAG genes for cancer therapy. GM-BAG showed the highest heritability enrichment for genetic variants in conserved regions, whereas WM-BAG exhibited the highest heritability enrichment in the 5' untranslated regions; oligodendrocytes and astrocytes, but not neurons, showed significant heritability enrichment in WM and FC-BAG, respectively. Mendelian randomization identified potential causal effects of several exposure variables on brain aging, such as type 2 diabetes on GM-BAG (odds ratio=1.05 [1.01, 1.09], P-value=1.96×10-2) and AD on WM-BAG (odds ratio=1.04 [1.02, 1.05], P-value=7.18×10-5). Overall, our results provide valuable insights into the genetics of human brain aging, with clinical implications for potential lifestyle and therapeutic interventions. All results are publicly available at the MEDICINE knowledge portal: https://labs.loni.usc.edu/medicine.