The blood-brain barrier (BBB) plays a central role in brain function and is increasingly implicated in neurodegenerative disease. Major neurodegenerative disorders, including Alzheimer's disease (AD), frontotemporal dementia (FTD), and Huntington's disease (HD), share overlapping pathological features. Yet, the extent to which these diseases converge or diverge at the level of BBB-associated cell types remains poorly understood. Here, we performed a comparative analysis of vessel-enriched human brain transcriptomic datasets across AD, FTD, and HD to define shared and disease-specific neurovascular alterations. We identify a partially conserved transcriptional signature of vascular dysfunction across all three diseases, alongside disease-specific changes in endothelial, pericyte, and perivascular cell populations. Endothelial remodeling was most prominent in capillary and venous segments, highlighting segment-specific vulnerability along the arteriovenous axis. Notably, we identified two molecularly distinct human pericyte subtypes across all three datasets and found a consistent reduction in the matrix-type pericytes (M-peri) fraction, suggesting a selective decline. Cell-cell communication analysis further revealed reorganized endothelial-pericyte signaling networks, with prominent alterations in extracellular matrix-associated pathways, including LAMININ, COLLAGEN, FN1, and NCAM, together with changes in contact-dependent and vascular signaling pathways such as NOTCH and VEGF. Together, our findings define shared and disease-specific neurovascular mechanisms across major neurodegenerative disorders and highlight BBB-associated pathways as central features of neurodegeneration, providing a framework for future diagnostic and therapeutic strategies.
Multiple Sclerosis (MS) pathogenesis is contingent upon the hyper-proliferative infiltration of peripheral macrophages across the blood-brain barrier. While front-line therapeutics, such as Dimethyl Fumarate, achieve clinical efficacy by agonizing the HCAR2 immune cooling switch, the tandemly duplicated HCAR1 lactate sensor has remained entirely unexplored. Here, by cross-referencing MS and Schizophrenia (SCZ) genomic architectures, we identify a massive shared structural fracture strictly localized to the HCAR tandem regulatory domain. We demonstrate that this locus acts as a highly specific neuroimmune ignition switch: it drives disease susceptibility but is unequivocally unassociated with MS severity or classical systemic autoimmune phenotypes (Crohn's Disease, Lupus, Rheumatoid Arthritis, and Psoriasis). Crucially, utilizing high-resolution eQTL mapping in purified human immune lineages, we reveal that the shared MS/SCZ risk allele drives a profound, state-independent transcriptomic collapse of HCAR1 exclusively in peripheral macrophages. This enhancer failure renders activated macrophages physically "lactate blind"-unable to sense their own glycolytic exhaust to engage the cAMP-suppressing negative feedback loop required to halt immune proliferation. By bridging psychiatric genetics and neuroimmunology, this study reframes the HCAR tandem array as a master neuroimmune bifurcation point and introduces the un-drugged HCAR1 lactate brake as a critical therapeutic checkpoint for arresting demyelinating disease.
The human leukocyte antigen (HLA) region is the strongest genetic contributor to many immune-mediated diseases, yet whether HLA architecture is shared across ancestries remains unclear. We analyzed high-resolution HLA variation in 390,823 participants from the All of Us Research Program spanning six genetic ancestry groups, including 262,915 with linked electronic health records. Using whole-genome sequencing and graph-based inference, we genotyped 20 HLA genes at G-group resolution and identified 4,780 distinct alleles. Analyses accounting for disparate sample sizes demonstrated that ancestry-private allelic variation reflected unequal discovery depth rather than ancestry-population specificity. A meta-analysis of ancestry-stratified phenome-wide association analyses with 363 HLA alleles with frequency > 0.001 and 3,430 clinical phenotypes identified 1,461 significant HLA-phenotype associations (FDR < 0.05). Although many associations reached significance in only one ancestry group, effect directions were largely concordant, highlighting differences in allele frequency, linkage disequilibrium, and statistical power among ancestry groups. Stepwise conditional modeling demonstrated that common complex trait variation could be concurrently explained by five to seven independent HLA allele signals. These findings demonstrate that a multi-ancestry, phenome-wide study can distinguish true biological heterogeneity from sampling-driven detectability differences in HLA.
Neural systems exhibit multiple firing states that reflect an organism's internal state and modulate the relationship between external environmental stimuli and behavior. Several studies have inferred these latent states by supplementing the traditional hidden Markov Model (HMM) with generalized linear models (GLMs) with non-Poisson behavioral observations. However, understanding the relationship between internal brain states and behavior also requires modeling the neural activity. Nonetheless, fitting multi-neuron GLM-HMMs is non-trivial due to high sparsity, collinearity, and low trial counts in neuronal datasets. Therefore, we built a robust multi-neuron GLM-HMM framework that uncovers latent states from population activity while incorporating the influence of time-stamped task variables and spike histories. To obtain reliable model parameters, we employ a modified expectation-maximization procedure. Specifically, we show that incorporating neuron-adaptive penalization in the maximization step overcomes the covariate co-linearity issues typical of time-stamped events and sparse spiking, yielding stable estimates of Poisson GLM coefficients. Furthermore, we incorporate a trust-region algorithm to ensure stable M-step convergence in the presence of ill-conditioned Hessians that can lead to unstable Newton-Raphson updates. We further demonstrate the utility of leave-one-out cross-validation analysis for evaluating model performance on datasets with low trial counts and without breaking their temporal structure. We evaluate our framework on three electrophysiological datasets from primates and rodents as they perform a decision-making task, demonstrate stable model convergence, and discuss the behavioral relevance of the inferred states. Neural systems evolve over time: not only do the individual neurons influence each other across the network, but the network and interconnections themselves change as an animal enters different behaviors (e.g., attentive vs. disengaged) or states (e.g., hungry or tired). Analyzing the neural activity that guides behavior thus must incorporate the time-varying nature of the brain. Recent modeling work has extended the popular Generalized Linear Model, a model that can connect task and behavior to recorded neural action potentials, to incorporate a latent Hidden Markov Model. This extension allows the resulting GLM-HMM to exhibit several different relationships (different GLMs) that are switched between over time to account for the animal's changing patterns. While GLM-HMMs have been applied extensively on behavioral data (e.g., task choice in a decision making paradigm), neural data is much more difficult due to the smaller sample sizes, sparser activity, and larger parameter space. Our work presents a new fitting approach and best practices to robustly fit GLM-HMMs to neural data. We demonstrate through numerous applications to a variety of neural datasets that by robustly fitting GLM-HMMs to data, we can identify important features of neural activity that let us better understand its relationship to behavior.
The pace of aging can be delayed by mutations, dietary manipulations, and drugs, yet the metabolic mechanisms underlying longevity interventions remain poorly understood. Here we present a multi-tissue metabolomic analysis of male UM-HET3 mice treated from 4 to 12 months of age with five validated longevity interventions: rapamycin, acarbose, 17α-estradiol, canagliflozin, or caloric restriction. Using a feature-stabilized XGBoost pipeline applied to seven tissues, we show that metabolomic profiles can identify treated mice as likely recipients of a lifespan-extending intervention well before survival differences emerge. A leave-one-intervention-out procedure confirmed that models trained on any four interventions successfully classified mice from a fifth, unseen intervention, implying shared metabolic alterations across mechanistically distinct treatments. The most influential metabolites - defined as the minimum set explaining 50% of cumulative model gain - differed substantially across tissues. Only ergothioneine, a dietary antioxidant, ranked highly in more than two tissues: it was elevated by all five interventions in plasma and brain, and by four of five in muscle. Enrichment analyses further identified coordinated remodeling of lipid classes in plasma, perigonadal fat, and kidney. These findings reveal tissue-specific metabolic reprogramming shared across mechanistically distinct longevity interventions and, pending validation against interventions that do not extend lifespan, suggest a path toward metabolomic screening of candidate anti-aging drugs.
T cell receptor recognition of peptide-MHC depends on sequence, interface chemistry, and three-dimensional geometry, but docking geometry is often summarized at the whole-receptor level, leaving CDR3-local pose difficult to compare across structures. We introduce TCR-FramePose, a local-frame descriptor set that represents each TCR-pMHC complex as three bodies - whole TCR, CDR3α, and CDR3β - measured relative to a pMHC groove frame. For each body, FramePose decomposes the native pose into reach, offset direction on S 2 , and orientation on SO (3); for tangent-space analyses, these components are mapped to six coordinates per body and 18 coordinates per complex. Applied to 378 curated αβTCR-pMHC crystal structures, FramePose recovers known class-associated receptor-placement differences and additionally resolved whole-TCR and CDR3β orientation shifts that were not captured by crossing angle. The same orientation coordinates identified reverse-polarity and off-axis outliers as distinct modes. In cross-validated association analyses, FramePose added nonredundant BSA- and affinity-associated information beyond conventional descriptors, and the modest affinity gain was concentrated in CDR3 orientation blocks which were least recoverable from conventional descriptors. Biological grouping analyses showed that shared receptor pose over peptide-MHC was organized primarily by germline V-region framework. TCRs recognizing the same peptide-MHC target favors shared FramePose geometries rather than strong receptor-specific divergence, whereas CDR3 sequence did not detectably reposition the rigid-body pose after antigen context and germline framework were fixed. MHC allele and peptide length contributed smaller adjustments, localized mainly to CDR3β and groove-normal orientation axes. Finally, interface analyses showed that affinity tracked interface burial, with CDR3β reach linking FramePose geometry to binding through buried surface area. Within engineered panels, mutation-level effects were panel-specific, with CDR3β remodeling localizing to a recurrent interface region but varying in direction across receptors. These properties enable FramePose to serve as a geometric filter for in silico TCR-pMHC models and as a feature layer for structure-guided TCR engineering. Together, TCR-FramePose provides a nonredundant geometric layer for structure-guided TCR-pMHC analysis, linking germline-scaffolded recognition, CDR3-local pose, and interface organization without replacing sequence, contact, or energetic descriptors.
Herpes simplex virus 1 (HSV-1) is a highly prevalent DNA virus with a major impact on human health. The HSV-1 genome is assembled into silenced stable chromatin and minimally transcribed during latency or assembled into permissive highly dynamic chromatin and highly transcribed during lytic infections. It is unclear how HSV-1 genomes transition between chromatin states, but epigenetics, including chromatin dynamics, have been proposed to play a major role. Chromatin remodeling complexes regulate cellular chromatin dynamics and contribute to DNA transcription, replication, and repair. The BAF family of chromatin remodeling complexes includes three ubiquitously expressed complexes (cBAF, PBAF, and GBAF) and several cell type-specific ones. Some common BAF subunits interact with two HSV-1 proteins, VP16 or ICP8. Three subunits shared by all BAF complexes and a unique subunit from each cBAF, PBAF, and GBAF were enriched in herpes nuclear domains (HND), the novel nuclear domains formed during lytic infection in which HSV-1 genomes are transcribed, replicated, and packaged. The shared ATPase SMARCA4 bound, directly or indirectly, to HSV-1 genomes. Bromodomains bind to acetylated histones and may thus be involved in this binding. However, none of four structurally unrelated inhibitors of BAF bromodomains drastically affected the recruitment of BAF subunits to HND, and neither of four commonly acetylated histone residues recognized by BAF bromodomains was enriched in the HND. BAF complexes are thus recruited to the HND by their interactions with VP16, which activates viral transcription, and ICP8. Surprisingly, the BAF complexes recruited by VP16 and ICP8 participate in inhibition of immediate-early, early, and early-late HSV-1 transcription, but not DNA replication or late transcription. We propose that BAF complexes are recruited to the HND by VP16 and ICP8, independently of their bromodomains, to inhibit viral transcription early in infection, thus contributing to the regulated cascade of gene expression. These findings also have implications to epigenetic anticancer drugs, in that it should be considered whether their use may reactivate latent herpes simplex viruses. Herpes simplex virus 1 (HSV-1) infects over two-thirds of the world population. HSV-1 establishes latency in neurons, resulting in life-long infection. Although most infections are asymptomatic, reactivation can produce a wide range of clinical manifestations, including cold sores, stromal keratitis, and encephalitis. Available treatments do not prevent reactivation or eliminate latent viral reservoirs, as no viral proteins are expressed during latency. Epigenetic regulation plays a role during the lytic and latent cycles. Lytic HSV-1 chromatin is highly dynamic whereas latent chromatin is stable. Chromatin dynamics are regulated by multiple factors, including the chromatin remodeling complexes. Here we show that the BAF chromatin remodeling complexes regulate HSV-1 transcription during lytic infection in primary fibroblast and transformed epithelial human cells. Although these complexes are recruited to the viral genomes by viral proteins, they counterintuitively downregulate viral transcription before the onset of DNA replication. We propose that BAF complexes play a major role in the regulation of the orchestrated cascade of viral gene expression and propose to consider the potential for reactivation of herpes simplex viruses when using epigenetic inhibitors in the treatment of cancer.
This perspective article addresses systemic governance gaps in the integration of wearable health technologies into public health infrastructures. While high-frequency physiological data offer potential for chronic disease management and epidemiological surveillance, they remain largely siloed due to fragmented regulatory frameworks and limited data trustworthiness. Recent empirical evidence underscores the magnitude of this gap: a global meta-analysis of 141,193 participants reports a 77.2% willingness to share health data, while only 26.5% of U.S. wearable users actually share their data with clinicians despite a 78.4% stated willingness.1,2 We propose the Good Data Service Practice (GDSP) as a conceptual co-regulatory governance framework designed to bridge the limitations of existing regimes such as the European Union (EU) General Data Protection Regulation (GDPR) and the United States (U.S.) Health Insurance Portability and Accountability Act (HIPAA) in handling continuous, dynamic data streams. Using a transparent, replicable comparative public-health policy analysis with pre-specified document inclusion/exclusion criteria and a structured coding protocol, we examined three purposively selected jurisdictions-the EU, the U.S., and Taiwan-across five operationally defined governance dimensions (consent architecture, accountability allocation, interoperability requirements, oversight capacity, and public-health usability). Drawing on design-science research principles,3 the GDSP framework was iteratively derived from the identified gaps through an explicit gap-to-principle mapping. We theoretically propose-rather than empirically demonstrate-that GDSP creates the institutional preconditions under which wearable data could, in principle, support clinical decision-making and public-health surveillance. No empirical validation, stakeholder consultation, or pilot implementation was conducted; subsequent Delphi consensus, expert panel review, and pilot evaluation are required before policy adoption. This framework offers a policy-relevant governance roadmap for strengthening digital health infrastructures and supporting equitable public health data use.
The spatial organization of the tumor microenvironment shapes immune function and disease progression, yet existing methods for cell-type interaction networks from multiplexed tissue images operate in two dimensions and ignore spatial autocorrelation. We introduce ISPat-3D (Informed Spatially Aware Patterns in 3D), a hierarchical Bayesian framework that recovers spatially varying, zone-specific interaction networks from 3D multiplexed cancer imaging data. The method partitions the tissue volume into tumor intensity zones, fits an anisotropic Gaussian process per cell type and zone with separate lengthscales for the tissue plane and axial direction, decomposes the residuals via multi-study factor analysis, and extracts partial correlation networks from the resulting precision matrices. Simulations demonstrate accurate recovery of shared and zone-specific structure with high power and controlled FDR. We apply ISPat-3D to two 3D serial section multiplexed datasets: the colorectal cancer atlas (CRC1) 3D CyCIF specimen and a HER2-positive ductal breast carcinoma (BC) specimen from a 3D IMC. In CRC1, zone-specific networks reveal a T cell module intensifying with tumor burden, with the dominant regulatory association shifting from CD4$^+$$\leftrightarrow$Treg at intermediate density to CD8$^+$$\leftrightarrow$Treg at maximal density, consistent with cytotoxic suppression at the tumor core. In BC, the shared network shows near-perfect conditional coupling between cancer-associated fibroblasts and the myoepithelial layer, while zone-specific networks reveal CAF$\leftrightarrow$endothelial co-localisation at intermediate and high burden, consistent with angiogenic remodeling, and a B cell$\leftrightarrow$CAF association confined to high-density zones, consistent with tertiary lymphoid structure formation. Across both tumors, ISPat-3D identifies volumetric spatial conditional interactions not recoverable from 2D sections. The code for ISPAT-3D is available at \url{https://github.com/sagnikbhadury/ISPAT-3D}.
Membrane contact sites are organized by protein assemblies that physically couple organelles and coordinate lipid metabolism, yet the structural principles that enable lipid exchange across these junctions remain poorly defined. At the nuclear-vacuolar junction (NVJ) in budding yeast, the tethering protein Mdm1 and its binding partner Nvj3 form a complex that regulates lipid metabolic pathways, but the structural features underlying their interaction have not been resolved. Here, we use AlphaFold-based complex prediction and comparative structural analysis to define the organization of Nvj3-Mdm1 complex assembly. We identify a high-confidence heterodimer in which conserved PXA and PXC domains generate an extended tunnel spanning both proteins. Tunnel analysis predicts a core hydrophobic conduit traversing the Nvj3-Mdm1 interface, consistent with a lipid-compatible architecture. Evolutionary conservation is enriched at the Nvj3-Mdm1 interface. The predicted conduit shares geometric and physicochemical properties with bridge-like lipid transfer proteins, including Atg2, Fmp27, and Hob2, suggesting that heteromeric tether assemblies may contribute directly to inter-organelle lipid transfer. Notably, this conduit is predicted to arise from a heteromeric α-helical assembly rather than the β-sheet-rich architecture characteristic of canonical bridge-like lipid transfer proteins. Comparative phylogenetic analyses showed that Nvj3 and Mdm1 share broadly congruent evolutionary patterns across Saccharomycetes, consistent with their conserved functional association. Together, these findings define Nvj3 as a structural partner of Mdm1 and support a conduit-based model of lipid transfer at the NVJ.
Cellular morphological transitions are observed across many diseases, yet their functional role remains unclear because few technologies profile form and function in the same cell. Linking single-cell morphology to transcriptomics is difficult: the two modalities share no feature correspondence and are typically measured in different cells. We present GeoAdvAE, a geometry-aware adversarial autoencoder for diagonal (unpaired) integration of single-cell morphology and single-cell RNA sequencing. GeoAdvAE couples modality-specific variational autoencoders with a Gromov-Wasserstein regularizer and an adversarial discriminator to embed unpaired morphologies and transcriptomes into a shared latent space that preserves both reconstruction fidelity and cross-modal geometry. Using patch-seq neurons with joint morphology-RNA measurements as ground truth, GeoAdvAE attains the best cross-modal cell-type matching accuracy among diagonal integration methods, outperforming optimal-transport, latent-alignment, and adversarial baselines. Applied to 98 CAJAL-quantified microglial morphologies and 31,948 single-cell transcriptomes from the 5xFAD Alzheimer's disease model, GeoAdvAE recovers a one-dimensional axis that aligns the two modalities. Integrated-gradient attribution highlights transcriptomic shifts (DNA repair in ramified microglia; cell killing in amoeboid microglia), nominates gene markers ( Ms4a6b ; Ftl1 / Fth1 ), and reveals disease-associated microglia signatures that are decoupled from morphology. GeoAd-vAE provides a scalable and interpretable approach to connecting cellular "form" and "function" when joint profiling of morphology and transcriptomics is impractical. Our method is publicly available at https://github.com/turbodu222/GeoAdVAE .
Alzheimer's disease (AD) is the leading cause of dementia worldwide. The retina shares molecular pathways with the brain, yet no study has systematically linked retinal gene expression to AD risk. We performed transcriptome-wide association studies (TWAS) using two independent retinal eQTL panels (Strunz et al., n = 311; EyeGEx, n = 406) and a large meta-analyzed AD genome-wide association study (GWAS) (Bellenguez et al., 111,326 cases, 677,663 controls). Genes were further validated with GWAS in the independent Alzheimer's Disease Sequencing Project (ADSP) using a matched eQTL-panel strategy. We identified 62 AD-associated genes across the two eQTL panels using Bellenguez et al. as the discovery cohort. Of these, 31 were replicated in the ADSP cohort. The findings highlight shared complement-mediated immune dysregulation ( CD55 , CD46, TREM2 ) and provide functional transcriptomic evidence to prioritize novel causal drivers of AD pathogenesis, including STYX and the LRRC37 gene family. Retinal data capture core AD genetic architecture and reveal novel risk genes, highlighting the retina as a molecularly informative tissue for dementia research.
Type 2 diabetes mellitus (T2DM) and coronary artery disease (CAD) are closely linked cardiometabolic disorders that share common inflammatory, metabolic, and vascular mechanisms. However, the molecular mediators underlying their interconnected pathophysiology remain incompletely understood. Orosomucoid 2 (ORM2), a hepatocyte-derived acute-phase glycoprotein, has emerged as a potential mediator at the interface of metabolic and vascular dysfunction. Experimental and clinical evidence suggests that ORM2 plays important roles in hepatic lipid metabolism, insulin sensitivity, and immune regulation. Reduced ORM2 expression has been reported in obesity and insulin-resistant states, while circulating orosomucoid levels have been associated with diabetic nephropathy, microalbuminuria, and long-term risk of myocardial infarction. Mechanistically, ORM2 suppresses hepatic de novo lipogenesis through AMP-activated protein kinase signaling, improves glucose homeostasis by modulating interferon-γ/STAT1 signaling in adipose tissue, and regulates liver macrophage polarization via an inositol 1,4,5-trisphosphate receptor type 2-dependent calcium pathway. Preclinical studies further demonstrate that recombinant ORM2 attenuates atherosclerosis, hepatic steatosis, and steatohepatitis without detrimental metabolic effects, supporting its potential therapeutic relevance. Notably, ORM2 is regulated by pro-inflammatory cytokines, including interleukin-1β, interleukin-6, and tumor necrosis factor-α, which are central to the pathogenesis of both T2DM and CAD. Collectively, these findings position ORM2 as a promising integrative biomarker and therapeutic target within the adipose-liver-vascular axis. Further prospective clinical studies, Mendelian randomization analyses, and tissue-specific experimental models are needed to clarify its causal role in the shared pathophysiology of T2DM and CAD.
Protein kinase activation is driven by conformational changes across multiple structural components, including the conserved Asp-Phe-Gly (DFG) motif, but whether these transitions follow a universal mechanism remains unclear. Here we combine over 8.3 milliseconds of distributed unbiased molecular dynamics simulations with Markov state models (MSMs) to compare the conformational landscapes of the ABL1, EGFR and MET kinase domains. To maximize unbiased sampling of functionally relevant conformational space, we use a transfer seeding strategy that steers AlphaFold2 models derived from homologous templates to sample MET conformational states absent from available experimental databases. We find that related DFG-motif geometries separate into distinct kinetic networks. These shared structural states are connected by kinase-specific activation pathways with different regulatory elements controlling the slowest step of activation. Our findings reveal that the shared nomenclature masks distinct transition mechanisms between kinase domains, revealing new regions critical for activity and targetable conformations for inhibitor design.
While observational studies have indicated a potential link between cancer and coronary atherosclerosis, the relationship remains obscured by confounding factors. Elucidating a shared genetic basis may uncover common pathophysiological pathways. We employed a two-sample Mendelian randomization (MR) approach to evaluate causal relationships between 23 cancer types and coronary atherosclerosis. Reverse MR analysis was also conducted to explore potential bidirectional effects. Given the exploratory nature of this pan-cancer analysis, we report nominal significance (p < 0.05) without multiple-testing correction. Forward MR analysis identified nominally significant associations between coronary atherosclerosis and five cancer types; however, after rigorous sensitivity analyses, only the inverse associations with pancreatic cancer (OR = 0.90, 95%CI = 0.83-0.98 and hepatic cancer (OR = 0.87, 95%CI = 0.81-0.94 remained robust. Reverse MR analysis suggested that genetic predisposition to ovarian cancer was associated with lower odds of coronary atherosclerosis(OR = 0.97, 95%CI = 0.94-0.99 ), though the effect size was modest. This comprehensive large-scale MR study provides hypothesis-generating evidence for a bidirectional genetic interplay between cardiovascular and oncological diseases. These findings point to a complex interplay between these conditions, paving the way for future investigations into shared mechanisms and integrated therapeutic strategies. However, all associations should be interpreted as suggestive rather than definitive causal evidence, and validation in independent cohorts is warranted.
Despite rising investment in health and social services, loneliness, mental distress, and social disconnection continue to worsen across high-income countries. This pattern suggests the challenge may not lie solely in insufficient provision, but in how systems conceptualize people and value. Within increasingly individualized and consumption-oriented contexts, people are often positioned as recipients of services rather than contributors to social and civic life, with value reduced to economic or service-based metrics rather than relational and civic contributions. These assumptions shape system design, giving rise to two recurring patterns of failure. In ethical abandonment, systems retreat into markets and transactions, positioning people as consumers without preserving conditions for meaningful agency or contribution. In ethical overreach, systems use prescriptive, behavioral, or medicalised approaches that substitute institutional control for personal authorship, positioning people as objects to be optimized. Although these approaches appear opposed, both deny that people can matter to others beyond their consumption of support, undermining reciprocity, eroding shared life, and driving system expansion. Contributory reciprocity is proposed as an alternative ethical orientation, recognizing people as potential contributors to shared life, not merely recipients of support. It partitions responsibility: systems secure real opportunity and preserve civic space; individuals retain authorship over engagement. The framework maintains expectation, rather than obligation, that people can create value for others, rejecting the drift toward lives organized around consumption alone. Social prescribing is examined as a practice through which contributory reciprocity can be enacted. When practiced as invitation rather than prescription, it renders opportunities visible, lowers barriers to entry, and returns authorship to individuals. When reduced to behavioral direction or commodified experience, it risks reproducing the consumption-oriented logic it seeks to address. Actionable recommendations are provided across system design, commissioning, evaluation, and policy development, emphasizing opportunity over activity delivery, relational work, and distinguishing invitation from uptake.
Metabolic psychiatry has recently achieved unprecedented clinical rescue in treatment-resistant Schizophrenia (SCZ) utilizing targeted ketogenic interventions. However, the field has operated without a defined genomic anchor, leaving the biophysical mechanism of these therapies largely unexplained. Here, we report the discovery of the definitive metabolic sensor array driving this pathology. By integrating high-resolution topological mapping of SCZ GWAS summary statistics, 3D chromatin conformation (Hi-C), and multi-tissue transcriptomics, we identify massive, non-coding structural variances flanking the HCAR2/HCAR1 tandem locus-the brain's master thermodynamic governor. We demonstrate that while the protein-coding hardware of these receptors remains intact, their shared 3D Topologically Associating Domain (TAD) is fundamentally fractured. This structural collapse drives a perfect transcriptomic double dissociation in the human cortex: the 3' mutational "skyscraper" severely downregulates the HCAR1 lactate emergency brake, while the 5' mutational cluster selectively paralyzes the HCAR2 β-hydroxybutyrate (BHB) and niacin cooling switch. This dual-flank enhancer failure elegantly provides a definitive genomic etiology for historical SCZ biomarkers, physically explaining both chronic cerebrospinal fluid lactate pooling and the infamous "absent niacin flush." Furthermore, peripheral eQTL mapping reveals profound antagonistic pleiotropy, characterized by a hyper-activation of the HCAR1 lactate shuttle in the testis, explaining the evolutionary conservation of this metabolically catastrophic architecture. Ultimately, we reframe Schizophrenia not as an intrinsic neurological defect, but as an evolutionary "fuel mismatch." The high-performance cognitive architecture of the hominid brain, evolved for ancestral ketogenic environments, experiences a catastrophic thermodynamic crash when deprived of its requisite BHB coolant by modern, high-glycemic diets.
Dual proteome-metabolome measurements from limited samples typically require sample splitting or sequential analyses using electrospray ionization mass spectrometry (ESI-MS). Here we show that capillary electrophoresis (CE) can avoid that tradeoff by organizing predominantly singly charged small molecules and multiply charged peptides into partially resolved, analyte-class-dependent regions of migration time-m/z space. Leveraging this intrinsic electrophoretic organization together with charge- and m/z-resolved precursor selection, we developed a single-run CE-ESI-MS workflow that combines single-vial sample processing with class-resolved tandem MS acquisition. In a HeLa digest spiked with 17 amino acids, the integrated analysis detected all amino acids while preserving proteomic depth relative to a dedicated proteomics run, yielding 1,221 versus 1,227 cumulative protein groups. Applied to identified single Xenopus laevis blastomeres, the method provided matched readouts of 86 metabolite features together with 1,097 and 1,083 protein groups from D1.1 and V1.1 cells, respectively. The paired measurements resolved cell-type-dependent molecular differences and mapped protein and metabolite changes into shared pathway context. These results establish analyte-class-dependent electrophoretic organization coupled to class-resolved MS acquisition as an analytical basis for single-run proteome-metabolome analysis by CE-ESI-MS in material-limited samples.
Midwives play a critical role in improving maternal and neonatal outcomes, with midwifery-led care proven to reduce unnecessary interventions and empower women. Yet, across Europe, systemic and cultural barriers continue to hinder midwives' ability to fully adhere to global standards such as the International Confederation of Midwives (ICM) essential competencies. A qualitative study captured the perspectives of ten midwives from five European countries: UK, Germany, Belgium, Bulgaria, and Italy. Purposive sampling captured diverse representation from midwives working within national regulatory frameworks and practicing independently. Semi-structured interviews provided rich insights into essential competencies adherence, with thematic analysis revealing key trends. Participants identified shared challenges, including societal undervaluation of the midwifery profession, workforce strain, and restrictive legislations, alongside regional differences including governance barriers and lack of regulation of out-of-hospital births. Benefits to adherence to ICM competencies included enhanced autonomy and opportunities for international collaboration. Overarching themes were structural and cultural as external dynamics, and professional as internal dynamics. The former included medicalization, subordinate role of midwives, and regulatory barriers, whereas at a professional level emerged relationship-centered care and theory-practice gap, as key subthemes. Proposed strategies included strengthening adherence, fostering academic and institutional support, and enhancing professional standing. This study highlights that essential competencies are not fully implemented across Europe. Urgent collaboration among academic and health institutions, as well as midwifery associations, is essential to create enabling environments and strengthen essential competencies' adherence in Europe. Ultimately to fostering professional identity and improving maternal and neonatal health outcomes.
The reliable prediction of endocrine disorders remains a significant challenge in clinical decision support, particularly regarding the simultaneous achievement of predictive accuracy, interpretability, and reliability. In this study, we propose a Gated Multi-Task Attention Network (GMTAN) for endocrine disorder prediction and clinically oriented decision support. The framework jointly models thyroid disorder and polycystic ovary syndrome (PCOS) prediction using heterogeneous endocrine datasets while integrating uncertainty estimation and explainability within a unified architecture. The proposed model combines shared feature representation learning with task-specific gating and attention mechanisms to capture both common and disease-specific endocrine patterns. To improve reliability, Monte Carlo Dropout is incorporated during inference to estimate predictive uncertainty and confidence. In addition, Shapley Additive exPlanations (SHAP)-based feature attribution and attention visualization are used to provide clinically interpretable explanations for individual predictions. Experiments were conducted using the UCI Thyroid dataset and the Kaggle PCOS clinical dataset. To improve reproducibility, all experiments were repeated across multiple runs using fixed random seeds, and model performance was evaluated using classification, calibration, and reliability metrics. The proposed GMTAN achieved area under the receiver operating characteristic curve (AUROC) scores of 0.98 and 0.97 for thyroid and PCOS prediction tasks, respectively, demonstrating improved calibration performance compared with baseline machine learning and deep learning models. The results suggest that integrating multi-task learning, uncertainty-aware inference, and explainability within a single framework can improve both predictive performance and interpretability for endocrine disorder prediction. While additional clinical validation is still necessary, the proposed framework demonstrates potential as a clinically oriented assistive decision support system.