The Irish Wolfhound (IW) is a dog breed characterised by a complex demographic history and reduced population size. In this study, we combined multiple population genomic approaches to characterise the genetic structure of 96 dogs collected from 23 countries worldwide, genotyped using the Illumina CanineHD BeadChip. Analyses of effective population size (Ne), linkage disequilibrium (LD) decay, heterozygosity and principal component analysis (PCA) consistently revealed limited genetic diversity. Complementary analyses of runs of homozygosity (ROH) and integrated haplotype score (iHS) identified extended homozygous segments and signatures of selection across the genome. ROH were predominantly short in length, and 40 samples showed ROH longer than 8 Mb. No ROH exceeding 16 Mb were detected, suggesting that these patterns reflect long-term demographic processes and historical selection rather than exclusively recent inbreeding. Particularly, 26 ROH islands were shared by at least 85% of the analysed individuals, with 3 ROHs shared by 100% of the population. Several ROH islands overlapped with regions previously reported in other hunting dog breeds and harboured genes associated with morphology, behaviour and diseases of major relevance to IWs, including osteosarcoma. Genomic regions identified by iHS also include genes involved in cancer and immune response. Compared with a previous IW population with publicly available genotypes, the dogs analysed here represent a more homogeneous subgroup. Overall, all approaches converged on a coherent genomic scenario, which highlights the combined effects of demographic history and selection in shaping the current genetic architecture of the IWs.
Real-time cyber-attack intrusion detection faces serious challenges in the smart grid communications infrastructure, as intrusion tactics become more advanced. Traditional rule-driven detection methods are unable to adapt to diverse attack patterns in modern power networks. In this work, a supervised deep learning framework is developed, using a CNN for spatial feature extraction, a BiLSTM for temporal dependency modeling, and an Extra Trees ensemble classifier to produce robust decisions, to achieve real-time intrusion detection on high-frequency smart meter data. The CNN layer extracts hierarchical spatial features from 128-dimensional multi-modal meter measurements (e.g., voltage, current, frequency harmonics), and the BiLSTM component captures temporal dynamics by processing whole sequences of meter data in both forward and backward directions to capture attack-evolution patterns that unidirectional models miss. Attention mechanisms dynamically weight the relevance of temporal features and enhance both prediction accuracy and interpretability. The Extra Trees ensemble provides a robust, low-variance decision output as an alternative to a standard Softmax layer. The architecture addresses several challenges: class imbalance (2.49:1 ratio), high dimensionality and noisy sensor data from heterogeneous sources, the requirement for real-time (millisecond-level) inference, and the need for explainability. The model was evaluated on 72,073 labeled power-grid logs from the Mississippi State University Power Grid Testbed, including False Data Injection Attacks, denial-of-service, replay, and man-in-the-middle attacks versus normal operation. With stratified five-fold cross-validation, the model achieves accuracy of 92.17% (± 0.27%), precision of 90.58% (± 0.49%), recall of 81.24% (± 0.86%), F1-score of 85.66% (± 0.20%), and ROC-AUC of 95.60% (± 0.14%), with an inference latency of only 12 ms, which is suitable for utility-scale deployment. A comprehensive comparative study against nine imbalance-handling strategies (no handling, class weighting, SMOTE, ADASYN, Borderline-SMOTE, SMOTETomek, SMOTEENN, random over/under-sampling) confirms the chosen weighted-learning strategy as a Pareto-optimal choice for this dataset. Paired McNemar's tests with Holm correction demonstrate that the proposed model's improvements over the tested baselines are statistically significant ([Formula: see text]). Ablation studies validate that bidirectional processing increases accuracy by 20.12 pp over a unidirectional CNN-LSTM, and that the Extra Trees head boosts precision by 10.09 pp over the standalone Extra Trees baseline. This work contributes to hybrid deep learning for cyber-physical systems and provides deployment guidance in terms of computational cost and a human-in-the-loop framework. In future work, we will investigate cross-dataset validation, multi-simultaneous attack detection, graph neural networks for fault location, and federated learning toward privacy-preserving collaborative training.
Understanding how the spliceosome integrates regulatory cues to generate RNA diversity remains a central question in gene expression control. Emerging evidence reveals a multilayered framework in which splicing is governed by nuclear architecture and the physical state of nuclear speckles. These condensates function as phosphorylation-sensitive hubs that concentrate splicing machinery and couple signaling pathways to RNA processing. Chromatin organization, transcript architecture, and condensate properties are tightly coordinated, adding spatial constraints to spliceosome function. Recent findings further uncover temporal regulation through cell cycle and ultradian dynamics of speckle assembly. In this review, we synthesize these advances and propose a unified model in which charge-dependent phosphorylation of splicing factors drives condensate remodeling, linking nuclear organization to regulated splicing outcomes across space and time.
The ultra-dense vehicle scenarios envisioned in 6G put high requirements on ultra-low latency, secure cooperation, and efficient task offloading decisions. Existing systems usually optimize latency or energy independently but ignore joint privacy problems and long-term trust sustainability. In this work, a distributed intelligence architecture based on the combination of federated learning (FL) and blockchain based trust management for vehicle-to-vehicle (V2V) edge computing is proposed. The proposed architecture enables collaborative prediction and decentralized incentive enforcement in a privacy-preserving manner without revealing raw vehicle data. In this paper, task allocation is defined as a multi-objective optimization problem, which jointly considers latency, energy consumption, communication stability and privacy exposure. The resultant problem is addressed by a learning-coupled primal-dual optimization, where the federated prediction is used to drive the offloading decisions and the dual update is used to impose the limitations of the system. A light-weight distributed ledger layer ensures secure coordination, automatic incentive allocation and reliable detection of fraudulent nodes. The extensive simulations in the integrated traffic-network-blockchain environments show that the proposed method outperforms the state-of-the-art baselines, achieving up to 30-40% reduction in the service latency, approximately 25% improvement in task completion rate, enhanced privacy preservation by the gradient-based learning, and up to 95% accuracy in detecting the malicious nodes. These results validate the efficacy of the suggested framework for attaining scalable, privacy-aware, and trustworthy distributed intelligence for next-generation 6G vehicular edge networks.
Fatal traffic crashes are a rare yet catastrophically consequential event in real-world crash data, typically constituting less than 1% of total records. This extreme class imbalance poses a fundamental challenge for machine learning-based severity prediction, as standard algorithms tend to ignore the minority class in favor of maximizing overall accuracy. This study investigates whether modern deep tabular learning architectures (TabNet, FT-Transformer) offer consistent advantages over the traditional gradient boosting method XGBoost in predicting fatal crashes under conditions of extreme class imbalance. The analysis is conducted on 5,676 traffic crashes recorded in Batman province of Türkiye between 2013 and 2022, with a fatal crash rate of only 0.8%. Methodologically, a leakage-controlled design was implemented through ex-ante variable selection, structured missing value handling, and SMOTE-based balancing applied exclusively to the training set. Model performance was evaluated not only with decomposition metrics such as ROC-AUC, but also with PR-AUC, Recall@K/Lift, and cost-sensitive analyses, which are more meaningful for imbalanced data. The results show that FT-Transformer achieved the strongest performance with ROC-AUC = 0.820 (vs. XGBoost: 0.752, TabNet: 0.760) and PR-AUC = 0.031 (approximately 3.9× above the random baseline of 0.008). It captured approximately 44% of fatal crashes in the riskiest 10% of cases, providing a ≈ 4.4-fold lift compared to random selection. Calibration analyses revealed that FT-Transformer produced more reliable risk scores: in the predicted probability band of 0.5-0.8, its observed positive rate reached the 8-15% range, representing a 4-7× elevation above the near-zero rates (0-2%) recorded for XGBoost and TabNet across the same probability range. These findings indicate that transformer-based tabular architectures offer consistent statistical, operational, and cost-sensitive advantages under extreme imbalance, supporting their use as decision-support tools in traffic safety management. To examine the generalizability of the framework beyond a single jurisdiction and a single time window, the analysis is complemented by (i) a temporal hold-out within Batman (training on 2013-2020, testing on 2021-2022) and (ii) external benchmarking on an independent publicly available rare-event crash corpus (n = 12,316; fatal rate = 1.28%) [61, 62]; the architectural ranking and rank-based operational gains are reproduced in both regimes.
While the liver has astonishing regenerative capabilities, its potential wanes in pathological conditions such as fibrosis, cirrhosis and carcinoma, posing significant global health challenges. The regenerative process is critically dependent on the regulated cellular signals, extracellular matrix (ECM), and its mechanical dynamics. Accordingly, hydrogel-based tissue engineering strategies replicate the liver ECM microenvironmental architecture, including bioactivity, biocompatibility, porosity, and stiffness. Studies have demonstrated that hydrogels, whether derived from natural polymers, synthetic materials, or decellularised liver tissue, can be fine-tuned in their properties. However, many fail to recapitulate the dynamic mechanical, immunological, and vascular cues required for regeneration. Studies have reported that the ECM-derived hydrogel helps preserve the phenotype (KRT, AAT, HNF4α) and functions (CYP activity, albumin & urea production) of hepatocytes. Therefore, dECM-based hydrogels are particularly important for cellular transplantation and the delivery of bioactive agents for liver regeneration. However, deviations from the normal stiffness range of 4-6 kPa may trigger pathological responses, such as hepatic stellate cell (HSC) differentiation into myofibroblasts, leading to liver fibrosis. Ineffective decellularisation can cause scaffold rejection during transplantation. Additionally, because vascularisation is critical given the liver's rich blood supply, endothelial cells tend to spread randomly within hydrogel scaffolds. The random spreading of endothelial cells can lead to the deformation of lobular zonation, which profoundly impacts hepatocyte functions and has been documented to affect conditions like hepatocellular carcinoma and alcoholic/non-alcoholic fatty liver disease (AFLD/NAFLD). In discussing hydrogel-based strategies, the article highlights the importance of addressing immunogenic concerns and matrix remodelling during decellularisation. It also argues that current studies lack integration of vascular-based zonation, which is fundamental to accurately mimicking the liver's native structural and functional architecture. This approach will facilitate the development of relevant patient-specific models.
The increased interest that high-entropy electrolytes (HEEs) are lately receiving makes their design (in terms of number of different components and ratio among them) challenging. Most of current efforts to bring some light into the combinatorial design space of HEEs rely on developing machine models but it is yet necessary identifying solvent descriptors that can accurately capture key solvent properties. Thus, there is an urgent need of alternative strategies offering convenient and powerful approaches for designing HEEs. Here, we hypothesized that deviations from ideality may indeed be a fingerprint of solvent mixtures with complex and nonadditive molecular interactions between components. While little attention has been paid to the nonlinear correlations in HEEs, deviations from ideality have been largely studied in deep eutectic solvents (DESs). The aim of this perspective is to demonstrate that these deviations serve as a definitive fingerprint of the solvation architecture of not only DESs but also HEEs, allowing these thermodynamic anomalies to be used as a proxy for determining the entropic landscape of some specific HEE compositions.
The endoplasmic reticulum (ER) is a central hub coordinating protein homeostasis and lipid metabolism in eukaryotic cells. In microalgae, which inhabit highly fluctuating environments, ER stress is increasingly recognized as a driver of lipid remodeling rather than a secondary metabolic consequence. This review synthesizes recent advances in ER stress signaling in microalgae, focusing on Chlamydomonas reinhardtii, and places these findings in a comparative eukaryotic context. Microalgae retain a conserved, IRE1-centered unfolded protein response (UPR) while lacking auxiliary branches found in animals and land plants. Activation of ER stress induces extensive reprogramming of membrane lipid composition, fatty acid desaturation, sterol metabolism, and triacylglycerol (TAG) accumulation. Notably, the Chlamydomonas IRE1/bZIP1 pathway functions to restrain excessive TAG accumulation, thereby prioritizing membrane adaptation and ER homeostasis. The graded and dynamic nature of this response likely compensates for the simplified single-sensor architecture by enabling flexible modulation of downstream outputs depending on stress intensity and duration. Importantly, ER stress responses exhibit distinct modes depending on stress severity: moderate stress promotes adaptive membrane stabilization, whereas severe or prolonged stress redirects membrane-derived fatty acids into TAG for sequestration. This lipid-centered adaptation contrasts with land plants, which stabilize membrane composition without substantial TAG accumulation, and with yeast and animals, where membrane biogenesis and neutral lipid storage occur in parallel, with distinct regulatory features. By integrating insights across eukaryotes, this review highlights ER stress as a framework for understanding lipid remodeling in microalgae and discusses how UPR manipulation may enable rational engineering of lipid production platforms.
The fungal pathogen Parastagonospora nodorum utilises necrotrophic effectors (NEs) to cause chlorosis and necrosis on wheat. NE expression is mediated by an assortment of transcription factors (TF), the most well-characterised of which is PnPf2. Orthologues of PnPf2 regulate virulence in phytopathogenic fungi across the Ascomycete fungal lineage, yet their protein architecture remains functionally uncharacterised. These orthologues are characterised by the archetypal N-terminal Zn2Cys6 zinc-finger DNA-binding domain, a conserved yet poorly characterised middle-homology region (MHR) and a C-terminal disordered region. We investigated the role of each of the three domains through PnPf2 truncation mutants in situ. Variable deletions of the C-terminal disordered region resulted in a reduction of SnToxA and SnTox3 expression and loss of virulence on SnTox3-selective wheat. The MHR domain is indispensable for host virulence, SnToxA and SnTox3 regulation, and also facilitates PnPf2 homodimerisation. Using the homodimer-mediating MHR as a bait in library-scale protein-protein interaction yeast-2-hybrid assays, we identified a putative COP9-signalosome protein PnCsn6 as a likely interacting partner. PnCsn6 is essential for disease symptoms during P. nodorum infection on wheat, and we observed NE dysregulation in PnCsn6-deletion mutants. This study provides the first domain-level functional characterisation of the virulence-regulating TF orthologue Pf2. Our results demonstrate the essential role of the MHR domain in mediating protein-protein interactions and effector regulation. We have also unveiled evidence that suggests PnCsn6 contributes to pathogenicity and may point to an important signalling pathway that could work in tandem with PnPf2 for fungal pathogenesis.
Atrophy of the tongue muscle without severe dysarthria is one of the clinical hallmarks of spinal and bulbar muscular atrophy (SBMA), a motor neuron disease caused by an androgene receptor defect. An operator-independent AI-based automatic segmentation of the tongue was applied to 3-D MRI data of the head in SBMA in order to quantify the tongue atrophy. Thirty-nine patients with SBMA and 51 age-matched healthy controls underwent MRI which were used for tongue volume quantification. A single triplanar convolutional neural network of U-Net architecture trained on axial, coronal, and sagittal planes was used for the segmentation of the tongue in MRI scans of the head, the resulting volumes were processed slice-wise across the three orientations and corrected for age. At the group level, a significant atrophy of the tongue was observed in SBMA when compared to controls (p < 0.05). Atrophy correlated well with total SBMA-functional rating scale and even more with bulbar subscores. In summary, the study employed an AI-assisted advanced imaging analysis to quantify the tongue morphology in individuals with SBMA in correlation to clinical bulbar function, suggesting this approach as a potential biomarker for disease assessment.
The Oxford Classic (OxC) prognostic signature classifies high-grade serous ovarian cancer (HGSOC) into five transcriptional programs, with epithelial-to-mesenchymal transition (EMT) marking poor prognosis. While successful in bulk transcriptomics, the spatial organisation of these programs within the tumour microenvironment remains unexplored. We developed the Signature-guided Zero-inflated Beta Variational Autoencoder (Sig-ZIB-VAE), a deep learning deconvolution method tailored for spatial transcriptomics data, and applied it to a large-scale HGSOC cohort comprising 94 tumours to quantify spatial cellular organisation. Prognostic significance was assessed using penalised Cox proportional hazards regression integrating clinical, molecular, and spatial features. Here we show that EMT cells form dense homotypic clusters broadly depleted from stromal and immune neighbourhoods, yet maintain selective monocyte co-localisation at cluster boundaries. EMT-high tumours display enhanced spatial reorganisation characterised by increased clustering and connectivity, forming locally concentrated mesenchymal-rich domains. Survival analysis confirms EMT-high status as an adverse prognostic factor. Critically, spatial metrics of immune cell organisation-particularly monocyte connectivity and clustering-provide substantially stronger prognostic discrimination than EMT proportion alone, demonstrating that tumour microenvironment architecture supersedes cellular composition in determining clinical outcomes in HGSOC. Ovarian cancer is often found late and can be hard to treat. The Oxford Classic is a test that looks at patterns of gene activity in cancer cells and can identify a more aggressive type of tumour cell linked to poorer survival. Until now, it was unclear whether how these tumour cells are arranged within the tumour also affects patient outcomes.In this study, we applied the Oxford Classic to tumour samples while also analysing where different cancer and immune cells are located. We found that tumours with more aggressive cancer cells were linked to worse survival, confirming that the Oxford Classic works in this new setting. Importantly, we also showed that how immune cells are physically arranged within the tumour gives extra information about patient outcomes, beyond simply counting cell types. This suggests that the spatial organisation of cells in ovarian tumours plays an important role in determining how the disease progresses.
Vaccination is one of the most effective public health interventions. Large language models (LLMs) show great capability for providing health information. This study compared the performance of ChatGPT 3.5 (G3E), ChatGPT 4o in English (G4E), Claude 3.0 (CDE), Gemini 1.5 (GME), and ChatGPT 4o in Italian (G4I) in delivering information on vaccination and preventive medicine. Mixed-method analysis evaluating large language models. Twenty-six expert-designed healthcare scenarios were used to evaluate each model through an adapted DISCERN-based instrument. Four experts independently rated outputs across six domains: Information Reliability, Information Quality, Medical Appropriateness, Impact on Vaccine Hesitancy, Potential for Behavioral Influence, and Overall Rating. Medians and interquartile ranges were calculated, and mixed-effects ordinal logistic regression models were applied to account for inter-rater variability. G4E showed the highest performance, with significant advantages in Overall Rating (OR = 2.17, 95% CI: 1.20-3.92, p = 0.010) and Medical Appropriateness (OR = 1.86, 95% CI: 1.04-3.33, p = 0.036). G4I outperformed others in Information Quality (OR = 1.76, 95% CI: 1.06-2.93, p = 0.030) but scored lower for vaccine hesitancy and behavioral influence. GME performed weaker across qualitative domains, with occasional generation issues, while CDE and G3E yielded intermediate, consistent results. Differences among LLMs reflect model architecture, training data, and language adaptation, influencing clarity, accuracy, and persuasive tone. These disparities highlight the need for domain-specific fine-tuning and language-sensitive optimization to enhance public health communication. LLMs show uneven performance in providing accurate and behaviorally effective vaccine information, underscoring the importance of evaluation and cautious integration into health communication strategies.
Per- and polyfluoroalkyl substances (PFAS), the so-called "forever chemicals," are emerging contaminants that severely disrupt soil ecosystems by rewiring microbial networks that sustain biogeochemical processes. This review deciphers the mechanisms underlying PFAS-induced microbial dysbiosis, revealing how these contaminants reconfigure community architecture, metabolic functions, and enzyme-mediated processes critical for biogeochemical cycling. It further integrates multi-omics approaches, spanning genomics to metabolomics, to elucidate molecular signatures and adaptive responses that govern microbial resilience and vulnerability across trophic hierarchies. Furthermore, the review examines PFAS biotransformation pathways, emphasising oxidoreductase-mediated mechanisms, kinetic bottlenecks, and catalytic constraints within complex soil matrices. By bridging microbial ecology with advanced material science, the review introduces a transformative paradigm of hybrid catalytic systems, including nanozyme-enabled transformations, engineered enzymes, and photocatalytic assemblies for targeted PFAS degradation. Thus, by linking microbial dysfunction with engineered catalytic innovation, the review offers a systems-level blueprint for sustainable and efficient strategies to restore PFAS-contaminated soils. Notably, this review highlights the urgent need for integrated multidisciplinary approaches to mitigate PFAS-induced ecological risks and advance sustainable soil restoration technologies.
Bioinspired neuromorphic computing offers an energy-efficient route to information processing beyond the von Neumann architecture. Wide-bandgap semiconductors such as diamond combine high carrier mobility with outstanding thermal and chemical robustness, yet the potential of hydrogen-terminated diamond (H-diamond) in neuromorphic computing remains largely unexplored. Here, we report an H-diamond-based optoelectronic synaptic transistor realized by engineering bulk defects, hydrogen-induced surface states, and interface traps. Under 365 nm ultraviolet (UV) illumination, the device displays tunable persistent photoconductivity governed by defect-assisted carrier trapping and delayed release, enabling the emulation of essential synaptic behaviors and logic operations. Furthermore, a spiking neural network constructed from the experimentally measured conductance states attains a recognition accuracy of 84.95% on the Fashion-MNIST dataset. This work establishes a foundational benchmark for diamond-based neuromorphic devices and underscores the promise of H-diamond for robust, UV-sensitive information processing systems.
Endometrial health is a key determinant of female fertility and successful pregnancy outcomes, making the accurate diagnosis of endometrial lesions essential for the success of assisted reproductive technology. While hysteroscopy remains the gold standard for uterine cavity evaluation, interpretation can vary based on clinical expertise. To address this, we developed a deep learning-based clinical decision support system to classify hysteroscopic images from high-resolution (4 K) videos into three categories: normal endometrium, endometrial polyps, and endometritis. Endometritis cases were classified based on hysteroscopic features suggestive of inflammation; no histopathological confirmation was obtained. Utilizing a dataset of 1500 expert-annotated images from 200 clinical videos, we applied transfer learning across four architectures: VGG-16, VGG-19, DenseNet-121, and EfficientNet-B0. Our results show that the models achieved classification accuracies between 85 and 89%, with DenseNet-121 demonstrating superior performance, specifically achieving a sensitivity of 93% and an AUC of 98.8% for polyp detection, alongside a precision of 90% for endometritis. Furthermore, Grad-CAM visualization confirmed that the networks focused on clinically relevant morphological features, enhancing model interpretability. These findings suggest that deep learning may serve as a supportive tool to assist clinicians in hysteroscopic analysis, pending validation on external datasets and with pathologically confirmed labels.
Designing tandem catalysts with well-defined interfacial architectures is of great significance for promoting multi-step electrochemical transformations, yet achieving synergistic regulation of dual active sites at the atomic level remains a formidable challenge. Herein, we develop an atomic-level engineering strategy to construct RuOx cluster-modified Cu-based nanowire array electrodes with abundant interfacial structures, which act as efficient tandem catalysts for sustained nitrite-ethanol paired electrolysis at ampere-level current densities. In situ spectroscopic analysis combined with theoretical calculations reveals that the atomically RuOx cluster serves as highly active water-activation sites, generating abundant active hydrogen/oxygen species that subsequently react with adsorbed nitrogen and carbon-containing intermediates, thereby enabling exceptionally favorable co-electrolysis kinetics. Impressively, a membrane electrode assembly flow electrolyzer constructed with RuOx@R-Cu/CF as both electrodes achieves >90% Faradaic efficiencies for NH3 and acetate over a wide current density window of 0.2-1.0 A cm-2, along with high yields of 5.86 mmol h-1 cm-2 (NH3) and 8.65 mmol h-1 cm-2 (acetate) at 1.0 A cm-2, and outstanding operational stability, significantly surpassing previously reported co-electrolysis systems.
Spontaneous coronary artery dissection (SCAD) is a major cause of myocardial infarction in young women without traditional cardiovascular risk factors (Hayes et al., 2018; Adlam et al., 2018 [1, 2]). Despite growing awareness, its biological underpinnings remain incompletely understood, and clinical management is largely based on observational evidence rather than mechanistic insight (Saw et al., 2014; Lettieri et al., 2015; Steg et al., 2024 [3-5]). To systematically integrate genomic, epitranscriptomic, proteomic, and metabolomic data in order to characterize the multi-omic architecture of SCAD and identify potential biomarkers and therapeutic targets. A systematic review was conducted in accordance with the PRISMA 2020 statement (Arbelo et al., 2023 [6]). PubMed/MEDLINE was searched for original studies investigating genomic and multi-omic features of SCAD. Data were extracted on study design, patient characteristics, identified variants, circulating biomarkers, and implicated biological pathways. Functional enrichment analysis was performed using the DAVID bioinformatics resource (Page et al., 2021 [7]). A total of 16 studies were included. Genome-wide association studies consistently identified susceptibility loci related to arterial structure and extracellular matrix integrity, including ADAMTSL4, PHACTR1/EDN1, LRP1, and FBN1 (Huang et al., 2009; Saw et al., 2020; Turley et al., 2020 [8-10]). Rare variant analyses further supported the role of genes involved in extracellular matrix remodeling and vascular smooth muscle cell function, including COL3A1, COL4A1/2, SMAD3, and TLN1 (Adlam et al., 2023; Turley et al., 2021, 2019; Carss et al., 2020; Zekavat et al., 2022; Wang et al., 2022 [11-16]), while ancestry-specific signals such as TSR1 variants were observed in distinct populations (Turley et al., 2023 [17]). Proteogenomic approaches linked genetic susceptibility loci to circulating proteins involved in matrix remodeling and inflammation, including cathepsin B and ECM1 (Maioli et al., 2010 [18]). Epitranscriptomic analyses identified differential microRNA expression profiles associated with vascular injury and repair pathways (Sun et al., 2019 [19]). SCAD is characterized by a complex, multi-layered biological architecture involving genetic susceptibility, extracellular matrix dysregulation, and vascular signaling pathways. Integration of multi-omic data provides novel insights into disease mechanisms and highlights potential biomarkers and targets for precision medicine approaches in SCAD.
T cell receptors (TCR) orchestrate adaptive immunity, yet the complex, repetitive architecture of the TCR loci has impeded systematic characterization of human genetic variation in the genes encoding the TCR. Using public long-read sequencing data from the Human Pangenome Reference Consortium and All of Us consortia spanning 2719 donors, we build a near-complete map of common alleles in TCR V, D, and J genes, revealing amino acid variation at almost every position within V genes. We observe allele frequency differences between populations for many individual TCR genes. We present evidence of natural selection on TCR genes, including signals of balancing selection and positive selection in the alpha chain locus. We find TCR allelic polymorphism alters core functional properties of T cells, including thymic fate commitment and cell-surface receptor abundance. Collectively, these findings position inherited variation in TCR genes as a key axis of immunological diversity that may shape interindividual differences in immune responses.
The hepatitis E virus (HEV) capsid protein, pORF2, mediates virion assembly, attachment and entry, yet the molecular mechanisms underlying these processes remain poorly defined. Structural studies have provided high-resolution views of pORF2 virus-like particles, revealing an architecture like that of the calicivirus VP1. However, while a paradigm has been established for calicivirus capsid dynamicity - environmentally triggered transitions that regulate receptor engagement, uncoating and immune evasion - comparable conformational plasticity has not yet been explored for HEV. At the same time, efforts to map pORF2 interactions with host factors have yielded several candidate attachment and entry molecules, but no definitive receptor. This review summarises the current knowledge of the structure, forms and host interactions of pORF2, contrasting it with the extensive body of work that has revealed the dynamic behaviour of the calicivirus capsid. This 'dynamic capsid lens' view of HEV may inspire new approaches to unresolved aspects of HEV entry biology. These include how pORF2 engages with host factors, how quasi-enveloped and non-enveloped particles differ functionally, and whether environmental cues encountered during gut-to-liver transit affect capsid conformation. To determine whether HEV, like its calicivirus relatives, exploits capsid dynamicity to establish infection, it will be key to integrate structural, biophysical and cell-based approaches.
Stigma toward mental illness remains a major barrier to social inclusion, help-seeking, and recovery. While previous studies have examined implicit and explicit stigma, underlying neurocognitive mechanisms remain poorly understood. This systematic review summarizes functional neuroimaging evidence on the neural correlates of mental illness stigma. Six studies were included (n = 251 participants), addressing self- and public stigma, as well as anti-stigma interventions. Key findings indicate that, regarding self-stigma, greater stigma resistance is associated with rostral-ventral medial prefrontal cortex (PFC) activation, suggesting that emotion regulation supports resilience to internalized stigma. In public stigma, reduced ventromedial PFC activation is linked to lower empathic concern, diminished self-referential processing, and outgroup categorization, whereas increased dorsomedial PFC activation supports impression formation, individuation, and higher willingness to recommend mental health treatment. Anterior insula and dorsal anterior cingulate cortex (ACC) engagement reflects heightened cognitive effort, conflict monitoring, and regulation of implicit biases. Overall, public stigma toward mental illness seems supported by a distributed neural architecture, including valuation-based social cognition (ventromedial PFC), impression formation and individuation (dorsomedial PFC), regulatory control (ventrolateral PFC), salience and conflict monitoring (anterior insula and dorsal ACC), and rapid affective processing (amygdala), flexibly recruited depending on task demands, target characteristics, and individual motivations. Intervention studies using immersive virtual reality and filmed social contact showed modulation of these circuits, leading to decreased public stigma and improvements in knowledge and behavioural intentions. These findings provide a neurobehavioural framework for understanding mental illness stigma, highlighting potential neural targets for evidence-based interventions to reduce stigma towards people with mental illness.