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PubMed2025

Quantitative Outcomes for Shared Assessment and Management in Forensic Mental Health: A Meta-Analysis and Systematic Review.

Despite leading models of mental health care encouraging user involvement, users in forensic mental health (FMH) report poor involvement given the difficulty in reconciling shared approaches with risk-averse and legally mandated settings. While previous research has demonstrated qualitative benefits to shared approaches in FMH and has led to a proliferation of self-rated assessment tools, there remains to quantify agreement on self-rated tools and to clarify the impact of shared approaches on care. This meta-analysis examines (1) the correlation between clinician and user ratings, (2) the predictive validity of self-ratings for violence, and (3) the effects of shared risk management on violence and restriction in FMH. Five databases were searched from inception to April 2024, selecting for adult FMH inpatients, shared risk assessment, needs assessment or violence management as interventions, and quantitative outcomes (correlation, agreement, predictive validity, and effect on violence or restriction rates). Fifteen quantitative evaluations were retained. One of three planned meta-analyses could be conducted, with seven records providing paired clinician-user t-tests. Eleven more records provided clinical recommendations on operationalizing shared approaches. Random-effects meta-analysis showed a significant and large paired standard difference of .95 (95% CI = [.49,1.42]) across tools, with significant differences in DUNDRUM-3, DUNDRUM-4, and CANFOR sub-models. While acknowledging between-study heterogeneity, results substantiate quantitative differences where clinicians generally rate more needs and lesser progress than users across tools, showing that self-ratings can and should be used to broach collaborative discussions on needs and progress during FMH treatment. There remains an evidence gap for quantitative benefits in care outcomes and a need to standardize agreement measures for future comparisons and clinical sub-group analyses.

Humans↗

PubMed2025

Relative quantification of proteins and post-translational modifications in proteomic experiments with shared peptides: a weight-based approach.

MOTIVATION: Bottom-up mass spectrometry-based proteomics studies changes in protein abundance and structure across conditions. Since the currency of these experiments are peptides, i.e. subsets of protein sequences that carry the quantitative information, conclusions at a different level must be computationally inferred. The inference is particularly challenging in situations where the peptides are shared by multiple proteins or post-translational modifications. While many approaches infer the underlying abundances from unique peptides, there is a need to distinguish the quantitative patterns when peptides are shared. RESULTS: We propose a statistical approach for estimating protein abundances, as well as site occupancies of post-translational modifications, based on quantitative information from shared peptides. The approach treats the quantitative patterns of shared peptides as convex combinations of abundances of individual proteins or modification sites, and estimates the abundance of each source in a sample together with the weights of the combination. In simulation-based evaluations, the proposed approach improved the precision of estimated fold changes between conditions. We further demonstrated the practical utility of the approach in experiments with diverse biological objectives, ranging from protein degradation and thermal proteome stability, to changes in protein post-translational modifications. AVAILABILITY AND IMPLEMENTATION: The approach is implemented in an open-source R package MSstatsWeightedSummary. The package is currently available at https://github.com/Vitek-Lab/MSstatsWeightedSummary (doi: 10.5281/zenodo.14662989). Code required to reproduce the results presented in this article can be found in a repository https://github.com/mstaniak/MWS_reproduction (doi: 10.5281/zenodo.14656053).

Protein Processing, Post-Translational↗

PubMed2026

Common genetic mechanisms between obesity and COVID-19 severity: unravelling pleiotropic loci and biological pathways.

COVID-19 and obesity are complex conditions marked by immune and metabolic dysfunction, with the former still ranking among the leading causes of death from infectious diseases worldwide and the latter reaching pandemic proportions. Clinical evidence consistently shows that obesity increases the risk of severe COVID-19, yet the biological mechanisms underlying this association remain unclear. Given their physiological and clinical overlap, they may share genetic pathways. We investigated genetic variants jointly associated with body mass index (BMI) and COVID-19 using publicly available genome-wide data. A conjunctional false discovery rate (conjFDR) approach identified shared variants between BMI and three COVID-19 phenotypes: infection, hospitalization and very severe respiratory illness. Functional annotation and pathway enrichment analyses were performed to explore the biological context of these variants, followed by a phenome-wide association study (PheWAS) to characterize pleiotropy. Shared variants were enriched in immune, metabolic and hormonal signaling pathways, including metal ion transport and glycosylation. The overlap with BMI was strongest for hospitalized and severe cases, suggesting common mechanisms underlying disease progression rather than infection. These findings suggest a biologically meaningful genetic overlap between obesity and COVID-19 severity, highlighting pleiotropy as a key feature in complex disease interactions and potential shared therapeutic targets.

BMI↗

PubMed2026

Opposing kinase signaling may underlie the inverse relationship between cancer and Alzheimer's disease.

Cancer and Alzheimer's disease (AD) are leading causes of mortality and exhibit an inverse relationship, where AD patients have reduced cancer risk and vice versa. However, the molecular basis of this relationship remains poorly understood. We reanalyzed published proteomic and phosphoproteomic datasets to investigate this relationship. Differentially abundant proteins were identified in lung adenocarcinoma and glioblastoma samples relative to controls and compared with proteins altered in AD brains, revealing 37 proteins with opposing abundance patterns. Protein-protein interaction and pathway analyses revealed enrichment in kinase signaling and phosphorylation pathways. Phosphoproteomic analysis identified 52 differentially phosphorylated sites with opposing patterns, while kinase-substrate enrichment analysis identified 44 kinases with opposing inferred activity profiles. Integration of kinase activity and phosphosite data identified 29 kinase-phosphosite pairs, including 4 prioritized pairs with opposing patterns relevant to both diseases. Across seven independent cancer cohorts, 17 of 20 statistically significant phosphosite-cohort comparisons (85%) were concordant with the discovery findings, supporting reproducibility of the prioritized phosphosites. Together, these findings highlight opposing kinase signaling as a prominent feature of the inverse relationship and suggest potential biomarkers and therapeutic targets. This study provides a novel systems-level framework for investigating inverse relationships, supported by an R Shiny application for data exploration (https://advscancer.shinyapps.io/advscancer/). SIGNIFICANCE: This study presents an integrated proteomic and phosphoproteomic framework for investigating the inverse relationship between cancer and Alzheimer's disease (AD). By integrating differential protein abundance, phosphosite phosphorylation, inferred kinase activity, and curated kinase-substrate relationships, we identified opposing signaling patterns and prioritized four kinase-phosphosite pairs. Independent evaluation across seven CPTAC cancer cohorts supported the reproducibility of the prioritized phosphosite patterns. These findings provide insight into molecular processes potentially associated with the inverse relationship between cancer and AD, identify candidate biomarkers and therapeutic targets, and demonstrate the value of systems-level, data-driven approaches for investigating shared and opposing disease processes.

Humans↗

PubMed2023

Strain-specific alterations in gut microbiome and host immune responses elicited by tolerogenic Bifidobacterium pseudolongum.

The beneficial effects attributed to Bifidobacterium are largely attributed to their immunomodulatory capabilities, which are likely to be species- and even strain-specific. However, their strain-specificity in direct and indirect immune modulation remain largely uncharacterized. We have shown that B. pseudolongum UMB-MBP-01, a murine isolate strain, is capable of suppressing inflammation and reducing fibrosis in vivo. To ascertain the mechanism driving this activity and to determine if it is specific to UMB-MBP-01, we compared it to a porcine tropic strain B. pseudolongum ATCC25526 using a combination of cell culture and in vivo experimentation and comparative genomics approaches. Despite many shared features, we demonstrate that these two strains possess distinct genetic repertoires in carbohydrate assimilation, differential activation signatures and cytokine responses signatures in innate immune cells, and differential effects on lymph node morphology with unique local and systemic leukocyte distribution. Importantly, the administration of each B. pseudolongum strain resulted in major divergence in the structure, composition, and function of gut microbiota. This was accompanied by markedly different changes in intestinal transcriptional activities, suggesting strain-specific modulation of the endogenous gut microbiota as a key to immune modulatory host responses. Our study demonstrated a single probiotic strain can influence local, regional, and systemic immunity through both innate and adaptive pathways in a strain-specific manner. It highlights the importance to investigate both the endogenous gut microbiome and the intestinal responses in response to probiotic supplementation, which underpins the mechanisms through which the probiotic strains drive the strain-specific effect to impact health outcomes.

Mice↗

PubMed2026

Exploring shared biomarkers and their mechanisms in thyroid cancer and systemic lupus erythematosus via bioinformatics analysis.

BACKGROUND: Systemic lupus erythematosus (SLE), an autoimmune disorder, is linked to a heightened risk of multiple malignancies, including thyroid cancer. Thyroid cancer is the most prevalent malignancy of the endocrine system, and its autoimmune-related pathological features render it an optimal subject for investigating the mechanisms of their comorbidity. The molecular mechanisms underlying this comorbidity are still ambiguous. The accurate diagnosis and treatment of thyroid cancer urgently necessitate innovative molecular targets that extend beyond conventional pathological characteristics. This study seeks to employ integrated bioinformatics approaches to elucidate potential shared molecular mechanisms and immunological features between thyroid cancer and systemic lupus erythematosus (SLE), aiming to enhance understanding of their comorbidity and identify novel intervention targets. METHODS: This study initially acquired gene expression data for TC and SLE from the GEO database and subsequently screened and identified differentially expressed genes (DEGs) shared by both diseases. Subsequently, we conducted Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Reactome functional enrichment analyses on these 46 shared differentially expressed genes (DEGs) and further assessed the activation status of pertinent pathways using Gene Set Enrichment Analysis (GSEA). Subsequently, we employed CIBERSORTx to examine immune infiltration patterns and developed protein-protein interaction networks utilising the STRING database. We identified hub genes utilising the MCODE and cytoHubba plugins and visualised the findings with Cytoscape software. We additionally assessed the diagnostic efficacy of these core hub genes in an independent dataset utilising ROC curves and investigated their prognostic relevance in thyroid cancer through Kaplan-Meier survival analysis and multivariate Cox proportional hazards regression. Ultimately, we employed the Network Analyst platform to forecast transcription factor-gene and miRNA-gene regulatory networks and identified potential targeted therapeutic compounds utilising the DSigDB database. RESULTS: This study identified 46 differentially expressed genes (DEGs) commonly linked to thyroid cancer and systemic lupus erythematosus (SLE), which were significantly enriched in signalling pathways associated with immune-inflammatory activation, type I interferon responses, and complement pathway activation. Moreover, GSEA findings validated that immune-inflammatory and autoimmune-related pathways are markedly activated in both conditions. Twelve hub genes were discerned through protein-protein interaction networks. Analysis of immune infiltration indicated that thyroid cancer and systemic lupus erythematosus exhibit a shared characteristic of innate immune dysregulation, marked by the infiltration of myeloid cells (neutrophils, M0/M2 macrophages). Receiver operating characteristic (ROC) curve analysis identified six significant core hub genes with substantial diagnostic value: C1QB, LCN2, C1QC, LTF, VSIG4, and C3AR1. Univariate survival analysis indicated that elevated expression of C1QC and C3AR1 significantly enhances overall survival in thyroid cancer patients; however, multivariate COX regression analysis revealed that their independent prognostic significance necessitates further validation. This study predicted the interaction networks of transcription factors and miRNAs regulating key genes, with LCN2 demonstrating the highest connectivity to miRNAs, and identified candidate therapeutic compounds linked to it. CONCLUSION: This study employed bioinformatics analysis to identify critical shared hub genes and molecular pathways connecting thyroid cancer and systemic lupus erythematosus, offering novel insights into their shared pathogenesis and the advancement of targeted biomarkers and therapeutic strategies.

Bioinformatics analysis↗

PubMed2026

Editorial: Transdiagnostic approaches to child and adolescent mental health-Integrating development, dimensions, and mechanisms.

Co-occurrence of child and adolescent neurodevelopmental and mental health conditions is the rule rather than the exception, yet translating this insight into shared frameworks and clinical practice remains challenging. In this Editorial, we are pleased to introduce the 26 papers included in the 2026 Special Issue of JCPP Advances. This Special Issue aimed to advance our understanding of transdiagnostic mechanisms, dimensions, and practices in child and adolescent mental health, and includes original articles, reviews, commentaries, and an editorial perspective. Collectively, these articles illustrate three main 'transdiagnostic' conceptualisations; (i) mechanistic approaches identifying shared biological, cognitive, or affective processes across diagnostic boundaries; (ii) dimensional approaches mapping symptom covariance onto hierarchical structures such as the general 'p' factor and internalising, externalising, and neurodevelopmental spectra; and (iii) developmental, clinical-staging approaches treating early, non-specific features as precursors to a broad range of later outcomes. Methodologically, the current Special Issue highlights both the opportunities and limitations of large existing datasets, multi-informant assessment, and neuroimaging and genomic approaches, while underscoring the persistent difficulty of modelling transdiagnostic dimensions developmentally. Importantly, while translation into routine clinical care remains limited, the field is now well positioned to test the clinical utility and effectiveness of transdiagnostic dimensional assessments and interventions in routine care.

editorial↗

PubMed2026

A novel transformer model of protein domains for viral taxonomy classification.

MOTIVATION: Viruses with carefully curated taxonomic assignments (such as those in the ICTV taxonomy) still represent only a small fraction of viruses identified through sequencing data from virome or microbiome projects. It is therefore critical to develop methods that can assign viruses at multiple taxonomic ranks, so that a virus deemed novel at a given rank may still be placed into a higher-level taxon. Sequence-similarity-based approaches can classify viruses that share substantial genomic similarity with known viruses (e.g. those belonging to the same species or genus); however, their performance drops significantly when applied to more divergent viruses. Recent deep learning models, such as ViTax, which utilize DNA language models, aim to address these limitations, but their performance also degrades when applied to novel viruses lacking genus-level similarity to known references. Proteins are more conserved than genomic sequences, and the multiple proteins encoded by a virus can be leveraged to reveal evolutionary relationships among viruses. RESULTS: We propose a new tool, D2T (Domain-to-Taxonomy), that leverages recent advances in protein language models to improve viral taxonomic assignment. D2T represents a virus as a sequence of protein domain tokens and learns a transformer-based model for taxonomic classification. Experiments on multiple closed-set and open-set datasets show that D2T excels at assigning higher-level taxonomic labels (family and above). Furthermore, by combining D2T with Kraken2, which performs well at the genus level, the hybrid method (K+D2T) achieves accurate viral taxonomic classification across multiple taxonomic ranks. AVAILABILITY AND IMPLEMENTATION: D2T is available as a GitHub repository at https://github.com/mgtools/D2T.

Viruses↗

PubMed2026

Development of a Blockchain-Based Platform to Enable Indigenous Data Sovereignty and Shared Research Participation With Indigenous Communities: Technology Prototyping and Community Engagement Study.

BACKGROUND: Historic and ongoing problematic practices regarding the collection, storage, and use of Indigenous health data have led to the need to ensure principles of Indigenous Data Sovereignty (IDS) are followed in research practices and technology development. OBJECTIVE: This project, a partnership between UC San Diego and the Native BioData Consortium (NativeBio), sought to explore the practical application of blockchain technology and its potential to facilitate Indigenous-led research collaboration. METHODS: This project first undertook purposeful relationship building with NativeBio to form a Community Advisory Board (CAB) for identifying community and technology needs for a blockchain research collaboration platform with an initial focus on genomic data. Over a 2-year project period, a series of public meetings and presentations at Indigenous-led conferences introduced the concept of exploring compatibility between blockchain and IDS principles, followed by iterative prototyping and co-design of a blockchain platform with NativeBio, using Ethereum as the underlying protocol. RESULTS: Direct engagement with NativeBio and the CAB informed the initial design and development of a "b-IDS" proof-of-concept (POC) blockchain platform. The POC consists of three main components: (1) the web front-end layer, (2) the Ethereum network that executes the smart contract and blockchain storage aspects of the framework, and (3) the back-end database that stores off-chain interactions and data for future use with external genomic data repositories. After refinement of the POC, a community-based participatory research (CBPR) use case aligned with IDS principles was identified as a practical workflow and incorporated into the design of the POC for implementation. CONCLUSIONS: The findings from this project demonstrated the potential use of operationalizing IDS through blockchain technology with proactive and sustained engagement with Indigenous partners. Blockchain technology may have certain advantages over other data governance approaches and systems, facilitating timely oversight, shared decision-making and consent structures, and direct involvement of Indigenous communities in technology design, respecting the core principles of IDS and CBPR. Future development of the blockchain-IDS POC will need to incorporate other research practices and ethics frameworks to expand its use to other public health and biomedical research use cases.

Blockchain↗

PubMed2025

The Interplay Between Sleep and Mental Health: A Genetic Perspective.

Although many facets of sleep, including subjective, behavioral, and neurophysiological features, are closely linked with psychiatric disorders, the natures of these relationships are generally unclear. A given alteration in sleep could reflect a cause (that may mediate genetic risk), consequence, symptom, trigger, epiphenomenon due to shared determinants, or some combination of these. In principle, genetic approaches can be informative: 1) by identifying specific genetic influences on disease mediated by or shared with sleep, which could help the search for biological mechanisms and therapeutic targets, and 2) by providing evidence for causality, which could suggest interventions for modifiable sleep traits. Here, we summarize recent human quantitative and molecular genetic studies on sleep and psychiatric disease, including twin and genome-wide association studies. Despite evidence for shared heritability across many domains, notably depression and insomnia, the field is in its early stages and faces significant challenges including the following: 1) putative causal effects are small, phenotypically nonspecific, not resolved to specific gene pathways, and often bidirectional; 2) most current discovery cohorts are demographically biased and do not capture profound age-related changes in sleep and its genetic architecture; 3) group-level analyses ignore patient-to-patient heterogeneity, including the presence or absence of specific sleep alterations; and 4) a paucity of objective, brain-based data in genetically informative samples hampers making connections with sleep neurophysiology. Nonetheless, as ever-growing genetic tools and resources still hold great potential for translational bridges between basic model systems, human epidemiology, and personalized clinical care, genetic approaches will still likely be needed to reveal sleep's roles in maintaining mental health.

Humans↗

PubMed2025

Rehabilomics Strategies Enabled by Cloud-Based Rehabilitation: Scoping Review.

BACKGROUND: Rehabilomics, or the integration of rehabilitation with genomics, proteomics, metabolomics, and other "-omics" fields, aims to promote personalized approaches to rehabilitation care. Cloud-based rehabilitation offers streamlined patient data management and sharing and could potentially play a significant role in advancing rehabilomics research. This study explored the current status and potential benefits of implementing rehabilomics strategies through cloud-based rehabilitation. OBJECTIVE: This scoping review aimed to investigate the implementation of rehabilomics strategies through cloud-based rehabilitation and summarize the current state of knowledge within the research domain. This analysis aims to understand the impact of cloud platforms on the field of rehabilomics and provide insights into future research directions. METHODS: In this scoping review, we systematically searched major academic databases, including CINAHL, Embase, Google Scholar, PubMed, MEDLINE, ScienceDirect, Scopus, and Web of Science to identify relevant studies and apply predefined inclusion criteria to select appropriate studies. Subsequently, we analyzed 28 selected papers to identify trends and insights regarding cloud-based rehabilitation and rehabilomics within this study's landscape. RESULTS: This study reports the various applications and outcomes of implementing rehabilomics strategies through cloud-based rehabilitation. In particular, a comprehensive analysis was conducted on 28 studies, including 16 (57%) focused on personalized rehabilitation and 12 (43%) on data security and privacy. The distribution of articles among the 28 studies based on specific keywords included 3 (11%) on the cloud, 4 (14%) on platforms, 4 (14%) on hospitals and rehabilitation centers, 5 (18%) on telehealth, 5 (18%) on home and community, and 7 (25%) on disease and disability. Cloud platforms offer new possibilities for data sharing and collaboration in rehabilomics research, underpinning a patient-centered approach and enhancing the development of personalized therapeutic strategies. CONCLUSIONS: This scoping review highlights the potential significance of cloud-based rehabilomics strategies in the field of rehabilitation. The use of cloud platforms is expected to strengthen patient-centered data management and collaboration, contributing to the advancement of innovative strategies and therapeutic developments in rehabilomics.

Cloud Computing↗

PubMed2025

A Sociotechnical Approach to Genomic Data Privacy: A Comparative Analysis.

The sharing of genomic data across international borders presents significant privacy law challenges.Secured computed environments on smartphones allow the storing and processing of sensitive data without the underlying data being shared with processors.A novel technology, described here, to process genomic data within a secured computing environment seems to comport with EU and US privacy laws, despite their differing aims and rules.This technology suggests there may be technological solutions to privacy law fragmentation across jurisdictions, so long as data subjects socially trust the technology and have control over their data.

genome↗

PubMed2026

Shared genetic basis and spatial cellular atlas of psoriasis and metabolic syndrome.

BACKGROUND: Psoriasis (PS) and metabolic syndrome (MetS) frequently co-occur. Characterizing their shared genetic architecture and spatially enriched cellular populations may clarify the context of their co-occurrence and generate hypotheses for functional validation. METHODS: We integrated genome-wide association study (GWAS) summary statistics for PS, MetS, and five related components with spatially resolved single-cell transcriptomic data. Global and local genetic correlations were assessed using linkage disequilibrium score regression, genetic covariance analysis, high-definition likelihood, and local analysis of variant association. A bivariate causal mixture model quantified polygenic overlap. Conditional/conjunctional false discovery rate and composite-null pleiotropy analyses identified shared susceptibility loci. Finally, gsMap evaluated trait-associated enrichment across annotated embryonic tissues at single-cell resolution. RESULTS: Genetic approaches identified significant genome-wide correlations and polygenic sharing between PS, MetS, and its components. Local and cross-trait analyses identified region-specific signals and cross-validated shared loci. gsMap revealed trait-specific tissue enrichment. PS showed the strongest enrichment in the epidermis (pCauchy = 1.0573 × 10  - ⁴), adipose tissue (pCauchy = 1.5366 × 10 - ⁴), and liver (pCauchy = 1.0167 × 10 - ³). Across MetS, FBG, HDL-C, hypertension, and TG, enriched regions mainly involved the liver, adipose tissue, and epidermis. WC enrichment was predominantly observed in adipose tissue (pCauchy = 1.7823 × 10 - ⁴), with no significant liver or epidermal enrichment. CONCLUSION: Integrating GWAS with single-cell transcriptomic and spatial information characterized shared genetic architecture between PS and MetS-related phenotypes and their spatial enrichment patterns. These findings provide a framework for generating testable hypotheses about comorbidity biology and guiding future functional and clinical validation.

Psoriasis↗

PubMed2026

Sex-specific biological aging clocks across organs and omics.

Sex differentially shapes aging, neurodevelopment and neurodegenerative diseases such as Alzheimer's disease (AD). However, most biological aging clocks (artificial intelligence-predicted age minus chronological age) were trained on sex-pooled samples and implicitly assume sex invariance.Here we developed 38 sex-specific biological aging clocks across 15 organ systems. We first demonstrate the importance of sex-stratified training for constructing sex-specific healthy normative references and then reveal marked divergence between female and male clocks. Key genetic parameters and Mendelian randomization results indicate that organ-specific aging liability and its relationships to cardiometabolic, endocrine and mental traits are configured differently in females and males. Proteomic analyses identify distinct, organ-resolved synaptic, immune, vascular and metabolic networks that differentially track female and male biological aging. In longitudinal survival analyses, sex-specific clocks predict whole-body systemic diseases and all-cause mortality in a sex-dependent and organ-dependent manner. Further analyses reveal sex-dependent associations between the brain aging clock and cognitive decline trajectory during a preclinical AD clinical trial. Sex-stratified clocks may offer distinct value by defining biological age against sex-appropriate normative references and revealing sex-dependent genetic, molecular and clinical signatures that pooled models may obscure. Meanwhile, sex-pooled and sex-interaction approaches remain valuable, as human aging and disease also share fundamental biological similarities between females and males. Together, these findings reveal sex-specific biological aging signatures in aging, AD and systemic health, highlighting the need for explicitly sex-stratified modeling approaches.

Journal Article↗

PubMed2024

Multivariate genetic architecture reveals testosterone-driven sexual antagonism in contemporary humans.

Sex difference (SD) is ubiquitous in humans despite shared genetic architecture (SGA) between the sexes. A univariate approach, i.e., studying SD in single traits by estimating genetic correlation, does not provide a complete biological overview, because traits are not independent and are genetically correlated. The multivariate genetic architecture between the sexes can be summarized by estimating the additive genetic (co)variance across shared traits, which, apart from the cross-trait and cross-sex covariances, also includes the cross-sex-cross-trait covariances, e.g., between height in males and weight in females. Using such a multivariate approach, we investigated SD in the genetic architecture of 12 anthropometric, fat depositional, and sex-hormonal phenotypes. We uncovered sexual antagonism (SA) in the cross-sex-cross-trait covariances in humans, most prominently between testosterone and the anthropometric traits - a trend similar to phenotypic correlations. 27% of such cross-sex-cross-trait covariances were of opposite sign, contributing to asymmetry in the SGA. Intriguingly, using multivariate evolutionary simulations, we observed that the SGA acts as a genetic constraint to the evolution of SD in humans only when selection is sexually antagonistic and not concordant. Remarkably, we found that the lifetime reproductive success in both the sexes shows a positive genetic correlation with anthropometric traits, but not with testosterone. Moreover, we demonstrated that genetic variance is depleted along multivariate trait combinations in both the sexes but in different directions, suggesting absolute genetic constraint to evolution. Our results indicate that testosterone drives SA in contemporary humans and emphasize the necessity and significance of using a multivariate framework in studying SD.

Humans↗

PubMed2026

Comparative in silico analysis of Apis mellifera immune responses to Varroa destructor and Tropilaelaps mercedesae: Common and mite-specific molecular signatures.

Parasitic mites Varroa destructor and Tropilaelaps mercedesae represent major threats to global honey bee (Apis mellifera) health and productivity, yet comparative molecular insights into host responses remain limited. To address this, we systematically compiled published studies (2015-2025) reporting genes associated with honey bee interactions with V. destructor (11 studies, 87 genes), T. mercedesae (4 studies, 35 genes), and hygienic behavior (6 studies, 44 genes). Gene identifiers were harmonized to the Amel_HAv3.1 genome assembly, yielding three non-redundant sets: 64 Varroa-associated, 34 Tropilaelaps-associated, and 44 hygienic behavior-associated genes. Venn analysis identified 10 overlapping genes (including A0A088A8D5, A0A088ADL8, ABAE_APIME, Def1, Def2, Gapdh, HYTA_APIME, Imd, LOC726783, and Vg), suggesting conserved defense mechanisms, while 41 and 24 genes were uniquely associated with Varroa and Tropilaelaps, respectively. Enrichment analyses revealed Varroa-responsive genes were enriched in immune processes, chitin catabolism, and signaling pathways (Toll/Imd, MAPK, Wnt). Tropilaelaps-associated genes were enriched for antibacterial defense and stress response, with Toll/Imd signaling as the sole significantly enriched pathway. Overlapping genes reinforced core innate immunity activation. Protein-protein interaction network centrality analysis identified key hub genes: Def1, HYTA_APIME, ABAE_APIME, PPO, Imd, PGRP-LC, Vg for Varroa; and ACPH1_APIME, MRJP1, Vg, LOC726783 for Tropilaelaps. Results demonstrate that, despite differences in mite biology, honey bees show a conserved immune response against both parasites, centered on antibacterial defense, humoral immunity, and activation of the Toll/Imd pathway. Although limited by the in-silico nature and research asymmetries reflecting Tropilaelaps' emergence, this curated resource establishes a comprehensive framework for elucidating shared and distinct molecular defense mechanisms. Ultimately, this approach prioritizes diagnostic markers and candidate genes for functional validation and breeding strategies to enhance colony resilience against mite‑driven disease globally.

Animals↗

PubMed2026

Exploring the emerging concept of precision rehabilitation: a qualitative study.

PURPOSE: This descriptive qualitative study explored knowledge users' perspectives on precision rehabilitation concepts, barriers, facilitators, and future directions as part of a convergent mixed methods scoping review. MATERIALS AND METHODS: Sixteen clinicians, administrators, and researchers from three North American tertiary care rehabilitation centers were recruited using convenience and snowball sampling to participate in individual semi-structured interviews. Conventional qualitative content analysis followed a deductive thematic approach based on predetermined categories. RESULTS: Analyses revealed three main themes: (1) Although precision rehabilitation shares foundational concepts with precision medicine, there are certain elements, such as personalization, that are uniquely expressed; (2) Rehabilitation-specific facilitators to precision approaches include the use of unobtrusive technology to collect large amounts of data in real-world contexts, while barriers include rehabilitation's typically small, heterogeneous sample sizes; and (3) The future of precision rehabilitation will require collaborative data-sharing to focus on determining care trajectories that enhance functional outcomes. CONCLUSION: Findings provide the first qualitative synthesis of knowledge users perspectives to complement quantitative evidence and inform the emerging field of precision rehabilitation.

Humans↗

PubMed2026

Mechanosensitive channels dominate the minimal ion channel repertoire in prokaryotes.

The eukaryotic genomes encode hundreds of proteins that function as ion channels and transporters. Essential for sustaining life, these proteins mediate the movement of inorganic ions (e.g., K+, Na+, Cl-, and Ca2+) across the plasma membrane according to their electrochemical gradients. In multicellular organisms, a diverse array of ion channels contributes to the maintenance of the resting membrane potential, the regulation of pH, osmolarity, and cell volume, and the control of secretion, electrical excitability, and synaptic activity, among many other fundamental physiological processes. Although independent evolutionary origins have been proposed for several ion channel families, their relative hierarchical importance for cellular viability remains poorly understood. To advance our knowledge of ion channel evolutionary history, we focused on determining the minimal combination of permeabilities that allows cellular viability. To this end, we conducted a survey of representative prokaryotes with small genomes across bacterial and archaeal phyla. By focusing on the smallest genomes, our approach enabled the identification of five ion channel architectures shared among prokaryotes. Among these, non-selective mechanosensitive channels (MscS and MscL) are the most abundant, followed by potassium channels, CLC-type channels and proton channels of the MotA/TolQ/ExbB family. The conservation of the mechanosensitive protein architecture across archaeal and bacterial membranes suggests that the capacity to monitor physical membrane integrity predates the requirements for electrical communication.

Journal Article↗