Search Results (7,337)

SCN4A encodes the skeletal-muscle voltage-gated sodium channel NaV1.4. Pathogenic variation in this gene produces fundamentally different disease mechanisms, including dominant alpha-pore gain of function, dominant S4 gating-pore currents, and reduced channel availability, with severe biallelic loss of function causing congenital myopathy and fetal hypokinesia. This heterogeneity makes variant interpretation difficult because formal variant classification, the direction of channel dysfunction, and patient-level disease attribution are closely related but distinguishable conclusions. In this review, we synthesize the clinical, genetic, electrophysiological, structural, and functional evidence relevant to SCN4A variant interpretation, with particular attention to missense variants of uncertain significance. We examine how inheritance, channel topology, phenotype, population data, segregation, RNA evidence, regional and residue-level context, and mechanism-matched functional assays can be integrated within the ACMG/AMP framework. Published pathogenic-enriched regions, gnomAD regional missense constraint, and same-residue observations may contribute to variant interpretation when the requirements of an applicable ACMG/AMP criterion are met; when they have not been specifically validated or calibrated for SCN4A, they are best used as contextual information to prioritize additional evidence generation. Based on this literature, we organize these evidence domains into a practical phenotype-first, mechanism-informed workflow for SCN4A variant interpretation. This workflow is not intended as an alternative to or extension of ACMG/AMP but as an SCN4A-specific application of established variant-interpretation principles in a gene associated with multiple inheritance patterns and directionally distinct disease mechanisms. After technical confirmation of a candidate variant, as well as phenotype and inheritance context guide selection of the relevant disease model, appropriately validated functional evidence may contribute directly to ACMG/AMP classification, while functional mechanism and patient-level disease attribution are documented as related but distinguishable interpretative outputs. The same approach applies when a variant is identified through genotype-first sequencing: interpretation should return to deliberate phenotyping, inheritance assessment, electrophysiological characterization, and consideration of the differential diagnosis before disease causality is inferred. Full article

Adolescence is marked by significant emotional changes, which may be intensified by the presence of a rare respiratory disease. The purpose of this study was to explore the psychological outcomes and challenges associated with these conditions during adolescence. Following the PRISMA guidelines, a systematic review was conducted in Web of Science, ProQuest and Scopus databases, identifying 677 articles. Following screening, 18 studies met the inclusion criteria and three additional eligible studies were identified through manual snowball searching, resulting in 21 studies included in the final synthesis. Most studies focused on symptoms of anxiety and depression as key psychological variables. Some also assessed health-related quality of life, psychological comorbidities, treatment adherence, disease knowledge and perceived self-efficacy. Most studies focused on adolescents with cystic fibrosis, whereas evidence concerning primary ciliary dyskinesia was considerably more limited (20 studies included participants with cystic fibrosis and 3 with primary ciliary dyskinesia; 1 study included both conditions). Findings regarding anxiety, depressive symptoms, and health-related quality of life were heterogeneous across studies. Despite the adequate methodological quality of these studies in general, the findings highlight inconsistent conclusions in the literature, with no clear consensus on the extent or nature of the psychological impact. There is a clear need for further high-quality research to better understand the psychological difficulties and consequences associated with rare respiratory diseases during adolescence. Regular psychological assessment may be considered part of multidisciplinary care to identify psychological difficulties and support the well-being of adolescents with CF and PCD. The main limitations were the heterogeneity of study populations and methodologies, the predominance of cross-sectional designs, and the restriction of the search to three databases. Full article

Chronic low back pain (CLBP) is a leading cause of disability and is characterized by marked clinical heterogeneity. Structural imaging remains fundamental for diagnosis and treatment planning, yet anatomical abnormalities alone do not fully explain differences in pain, disability, physical performance, or treatment response. Increasing evidence implicates paraspinal muscle morphology, composition, neuromuscular behavior, endurance, and physical conditioning as complementary determinants of spinal function. This narrative review synthesizes current evidence within the concept of spinal muscle health, defined here as a multidimensional, region-specific construct reflecting the structural, compositional, neuromuscular, and functional capacity of the muscles contributing to spinal control and load management. The construct is not proposed as a new diagnosis or as a substitute for established concepts such as sarcopenia, myosteatosis, or deconditioning; rather, it provides an integrative framework for considering complementary muscle-related domains that cannot be represented adequately by a single imaging or performance measure. We review muscle-specific and level-specific findings, measurement approaches, sources of heterogeneity, longitudinal and interventional evidence, and the interaction of muscle-related features with systemic and psychosocial factors. Current evidence supports associations between selected paraspinal muscle abnormalities and CLBP, particularly for multifidus composition, but findings vary substantially across muscles, spinal levels, populations, and measurement methods. Most evidence remains cross-sectional, and causal direction is uncertain. Clinical improvement after exercise may occur without consistent normalization of imaging-derived muscle composition. Accordingly, spinal muscle health is best regarded at present as a research and clinical reasoning framework rather than a validated diagnostic phenotype. Standardized measurement, longitudinal studies, and intervention trials linking changes in muscle-related parameters to patient-centered outcomes are required before routine clinical implementation. Full article

Urban vitality (UV) is the core index to measure the quality and sustainability of urban development. Accurately analyzing the complex association mechanism between UV and built environment (BE) is critical to urban planning practice. Focusing on the main urban area of Chengdu, this study integrates eight categories of multi-source data, including nighttime light data, WorldPop population distribution data, street view images, and POI data, to construct a four-dimensional UV evaluation system and identify 26 BE factors. Firstly, the UV level is quantified by objective weighting methods. Secondly, an XGBoost model combined with a SHAP framework is adopted to investigate the nonlinear association between UV and BE factors. Finally, a spatial autocorrelation model, SHAP spatial visualization and clustering methods are employed to reveal the spatial pattern of UV and the spatial heterogeneity of the association between UV and BE. The results indicate: (1) Various elements of the BE show a significant nonlinear association and threshold effect for UV. Catering services and public transit services are the core factors for UV prediction, with their combined contribution accounting for 37.47%. (2) UV shows obvious spatial differentiation and agglomeration characteristics. It presents a spatial pattern with a gradual decline from the core to the periphery. (3) The association between UV and BE presents spatial heterogeneity, and the predictive contribution logic differs distinctly across different concentric rings. The study conclusions provide a scientific basis for UV improvement and BE optimization in Chengdu. Full article

Measles virus infection couples nonlinear processes across biological scales, including within-host viral amplification, immune-cell depletion, delayed adaptive control, persistent viral RNA, heterogeneous host severity and vaccination-dependent population spread. A multiscale mathematical framework is developed by coupling a seven-variable within-host ordinary differential equation model to a stochastic cellular automaton. The within-host system extends a four-variable measles immunodynamics core by including IFN-$\gamma$-dominant and IL-17-associated immune responses, persistent viral RNA and neutralizing antibodies. Six host archetypes are represented as structured parameter perturbations of this common dynamical core. The principal novelty is an explicit cross-scale coupling operator that separates genuinely ODE-derived host descriptors from hybrid epidemiological mapping rules and independently specified population-level contact and susceptibility assumptions, allowing within-host heterogeneity to propagate transparently into a spatial stochastic epidemic model. An explicit ODE-to-cellular-automaton map translates within-host trajectories into infectious timing, daily infectivity profiles and an illustrative ODE-informed severity-to-death transition mapping used internally by the cellular automaton. The mortality map depends on viral burden, infectious duration, IFN-$\gamma$ deficit, cumulative infectivity and an immune-deficit–infectivity interaction term. Population simulations show a nonlinear reduction in attack rate with increasing vaccination coverage, reduced modeled death burden under targeted high-risk in silico perturbations and additional suppression under reactive vaccination campaigns. A direct local cellular-automaton secondary-infection estimate is reported instead of interpreting cumulative infectivity burden as a reproduction number. A targeted contact-structure sensitivity further shows that matching the expected local direct-secondary-infection potential does not imply equivalent population-level attack rates, emphasizing that the quantitative CA outcomes are geometry specific. Sobol sensitivity analysis with convergence up to $N_{\mathrm{base}}=4096$ identifies core viral and immune parameters as dominant drivers of within-host and multiscale outputs. The framework provides an explicit dynamical-systems approach for coupling differential-equation immunodynamics to spatial stochastic population models in mathematical biology. Full article

Excessive abdominal fat deposition reduces feed efficiency and edible carcass yield in chickens, yet its developmental and regional specialization remains poorly understood. We generated 93 bulk RNA sequencing (RNA-seq) libraries from 15 fat-associated anatomical regions at embryonic day 16 (E16) and 16 regions at 2 weeks post-hatch (2w) in six Xinghua chickens. Exploratory single-nucleus RNA sequencing was additionally performed on abdominal region (AR) tissue from one individual at each stage. Repeated-measures-aware analysis identified developmental stage as the major source of transcriptomic variation, with 4556 genes expressed at higher levels at E16 and 4528 at 2w. E16-associated programs were enriched for proliferation, morphogenesis, and structural development, whereas postnatal programs were dominated by immune and stimulus-response processes. Stage-specific weighted gene co-expression network analysis (WGCNA) identified substantial regional heterogeneity. AR showed stage-dependent signatures, including higher developmental and morphogenetic programs and lower lipid-metabolic programs at E16, and higher cell-cycle-related and lower developmental and fatty-acid-metabolic programs at 2w. Single-nucleus analysis identified adipose-lineage and multiple non-adipocyte populations, and cross-omics mapping showed that AR-associated signatures were distributed across multiple cellular compartments. These results suggest stage-dependent, multicellular transcriptional specialization of chicken AR tissue. Full article

Platform-listed destinations are widely used to represent urban activity opportunities, yet their correspondence with realized activity may vary across local contexts. This study examines destination–activity representation gaps in four bounded local activity systems in Guangzhou, China. Mobile positioning activity, platform-listed destinations, population-positioning surfaces, roads, land cover, and street-view imagery were integrated at a 25 m grid scale. A spatially out-of-fold ridge model estimated activity from destination context and within-area percentiles of observed and destination-predicted activity were used to identify contrasting representation gap types. Destination correspondence was modest overall and spatially uneven across the four cases. Activity-rich/destination-light cells were more prevalent in the two island/peripheral cases, while their intraday profiles resembled those of destination-supported high-activity cells. These cells also showed lower population co-location on average, while contiguous patches revealed substantial local heterogeneity. Roads and land cover provided limited transferable discrimination, while selected raw street-view elements modestly improved annual activity prediction within the central sample. The findings show that platform-listed destinations and realized activity are related but non-equivalent representations and that their divergence varies across local activity systems. Full article

Background: Evidence to guide risk stratification after ICD implantation in adults with congenital heart disease (ACHD) is limited. We sought predictors of appropriate ICD intervention in this population. Methods: Consecutive ACHD patients implanted with an ICD (2009–2023) were analysed retrospectively. Time to first appropriate ICD intervention was modelled from a single, consistently defined start of observation. Because only 24 events were available, candidate predictors were pre-specified and each model limited to two covariates; sparse predictors used Firth penalisation, and multiplicity was addressed by the Benjamini–Hochberg false discovery rate procedure. Results: Among 56 patients (73.2% male; 22 primary and 34 secondary prevention), appropriate intervention occurred in 24 (42.9%) over a median follow-up of 51.9 months. Atrial fibrillation (HR 2.75, 95% CI 1.22–6.23) and radiographic cardiomegaly (HR 3.99, 95% CI 1.28–12.40) showed the largest unadjusted associations, whereas individual congenital lesions and disease-severity descriptors did not. No predictor remained significant after Benjamini–Hochberg correction (q ≈ 0.10). The very high hazard ratios for sparsely represented thyroid disorders (hypothyroidism, n = 2; hyperthyroidism, n = 4) were unstable under penalised estimation and are reported as exploratory. A pre-specified two-variable model (atrial fibrillation plus radiographic cardiomegaly) showed modest discrimination (optimism-corrected Harrell’s C-index 0.63). Conclusions: In a heterogeneous real-world ACHD cohort, a history of atrial fibrillation and radiographic cardiomegaly were associated with appropriate ICD intervention. Given the small number of events and the sparsity of several predictors, these findings should be regarded as hypothesis-generating and require validation in larger multicentre ACHD cohorts. Full article

The increasing frequency of wildfires has made large-scale collective emergency evacuation increasingly critical. However, existing studies provide limited understanding of how information structures shape the interaction between individual risk perception and collective evacuation behavior. This study develops a collective evolutionary game-based evacuation framework under ambiguous and clear information conditions. Under ambiguous information, individual heterogeneity in risk sensitivity, mobility, and resource endowment is incorporated into social interaction payoffs. Under clear information, observable evacuation consequences, including travel time, risk exposure, and congestion effects derived from route-choice interactions, are incorporated into evacuation utility. Numerical simulations examine the evolutionary characteristics of collective evacuation behavior under different information conditions and population scales. The results show that social interactions play an important role in shaping evacuation decisions under ambiguous information, while congestion effects and route-choice interactions influence evacuation utility under large-scale demand. Sensitivity analyses further demonstrate that congestion representation affects evacuation utility across different population scales. These findings highlight the importance of considering information structure, individual heterogeneity, and collective interactions in evacuation modeling. Emergency management should therefore improve risk communication, evacuation capacity, and congestion mitigation strategies. This study provides theoretical insights into collective evacuation decision-making under heterogeneous information conditions. Full article

Background: Undifferentiated carcinoma with osteoclast-like giant cells (UCOGC) is a rare pancreatic malignancy with striking morphology. Its prognosis appears more heterogeneous than its high-grade histology would suggest. Larger clinicopathologic and population-based studies suggest that UCOGC may behave more favorably than conventional PDAC. However, the prognostic impact of an associated PDAC component remains unresolved. Methods: Six cases of pancreatic UCOGC, diagnosed between 2019 and 2024, were retrospectively identified and selected for immunohistochemical and molecular analysis. We retrieved available clinical, radiologic, and follow-up data from electronic medical records. We performed targeted next-generation sequencing on individually macro-dissected UCOGC and PDAC components. Results: The cohort included four females and two males, with a median age of 68.5 years. All tumors were associated with conventional PDAC, and, in this series, only two cases were correctly diagnosed at biopsy; others were interpreted as PDAC or sarcomatoid carcinoma, with a definitive UCOGC diagnosis made only at resection. Immunoreactivity for cytokeratin was observed in the epithelial component, while the undifferentiated and giant cell components showed vimentin and CD68 immunoreactivity, respectively. NGS revealed co-mutations in TP53 and KRAS in 4 cases. Pathogenic mutations in APC, BRCA2, BRAF, GNAS, PIK3CA, RB1, SMAD4, and CDKN2A/B loss were also detected. concordant pathogenic variants Paired analysis of macro-dissected components from the same tumor (n = 4) demonstrated concordant pathogenic variants, providing supportive evidence of a shared clonal relationship. Two patients received neoadjuvant chemotherapy, and systemic therapy was administered in five patients. Three patients were alive during the last follow-up, including two long-term survivors. Conclusions: Pancreatic UCOGC shares key genetic alterations with PDAC, and paired molecular analysis provides supportive evidence of a shared clonal relationship between the UCOGC and ductal components. The variable clinical outcomes observed in this small series are descriptive and hypothesis-generating and require validation in larger cohorts. Full article

Background: Chronic disease management, an ageing global population, and the aftermath of the COVID-19 pandemic have pushed real-time, continuous health monitoring from a research curiosity toward routine clinical practice. Artificial intelligence (AI), the Internet of Medical Things (IoMT), edge computing, and next-generation wireless networks are converging to enable systems that sense, interpret, and act on physiological data outside the traditional hospital setting. Methods: This is a narrative review, not a PRISMA-guided systematic or scoping review; it synthesises 75 sources (a mixture of primary studies, systematic/scoping reviews, and meta-analyses), individually verified against their published record. The topics span IoMT system architecture and security, wearable and implantable sensing, deep learning for electrocardiogram (ECG), fall-related and human-activity signal analysis, edge and TinyML deployment, federated learning, blockchain-based health-record security, medical imaging diagnostics, explainable AI (XAI), continuous glucose monitoring, 5G/6G-enabled telemonitoring, digital twins, consumer-grade and contactless cardiac sensing, neurological and mental health monitoring, and the materials, regulatory, and acute care infrastructure surrounding real-time deployment. Studies were organised into a five-layer architectural taxonomy spanning perception, edge, network, cloud, and application layers. Results: Reported accuracies for deep learning models on ECG arrhythmia classification range from 91% to 99.5% across the reviewed studies, but the figures come from different datasets, class definitions, and validation protocols and are therefore not directly comparable; within this heterogeneous evidence, edge-deployed models report accuracies in the 85–96% range at substantially reduced power budgets. Deep-learning-based fall detection and chest radiograph classification are each reported, in the individual studies reviewed, to outperform threshold-based or classical alternatives, though this has not been established through head-to-head comparison across the full evidence base. Federated learning and blockchain are discussed as technical mechanisms that can contribute to data privacy and record integrity; neither constitutes regulatory compliance with frameworks such as HIPAA or the GDPR on its own. Persistent obstacles identified across the reviewed literature include dataset heterogeneity, limited external clinical validation, energy-constrained edge hardware, low clinician trust in opaque models, and fragmented interoperability standards. Conclusions: The evidence reviewed here is consistent with, but does not by itself establish, a layered, privacy-preserving, and explainable architecture that couples lightweight on-device inference with federated or blockchain-secured cloud learning as a design direction for future real-time healthcare monitoring systems. Future work should prioritise standardised benchmarking, prospective clinical validation, and regulatory-aligned data–governance frameworks. Full article

The escalating global burden of neurodegenerative and neuropsychiatric disorders is deeply intertwined with cumulative environmental toxicant exposure. To accurately capture the prodromal and subclinical impacts of this multi-chemical “pollutome”, research is shifting from symptom-based assessments toward the use of objective, quantifiable biological indicators. Guided by the PRISMA-ScR framework, this scoping review systematically analyzed literature across PubMed, Scopus, Web of Science, and Embase databases without temporal restrictions. The final selection included 52 human observational studies that evaluated the relationship between environmental stressors and objective neurobiological markers across diverse geographical populations and life stages. The synthesized evidence reveals that varied environmental insults—including ambient air pollution (e.g., fine particulate matter, PM_2.5), heavy metals, agrochemicals, and persistent organic contaminants—frequently correlate with measurable alterations in fluid biomarkers. These exposures are primarily associated with variations in markers of axonal damage (neurofilament light chain), astrocytic reactivity (glial fibrillary acidic protein), potential microglial dysfunction (soluble triggering receptor expressed on myeloid cells 2), and cytostructural alterations (Tau proteins and amyloid-beta). The literature highlights distinct windows of vulnerability, spanning from early-life epigenetic modifications (DNA methylation) to adult neurovascular injury. Mechanistically, despite their chemical heterogeneity, the available evidence suggests that these pollutants may converge on shared pathophysiological pathways defined by blood–brain barrier disruption and chronic, self-perpetuating neuroinflammation. Understanding environmental neurotoxicity may benefit from moving beyond traditional single-pollutant approaches toward a broader exposome framework. Future epidemiological research should integrate high-dimensional human biomonitoring with artificial intelligence and machine learning architectures. This computational integration would be essential to decode non-linear multi-pollutant interactions and accelerate the deployment of targeted, early-stage public health interventions. Full article

Although BRCA1 and BRCA2 remain the backbone for germline DNA testing of patients with breast cancer (BC) or ovarian cancer (OC), there are a number of other genes with proven or potential clinical significance. PALB2 is associated with an elevated risk of BC, whereas pathogenic variants (PVs) in RAD51C, RAD51D, and BRIP1 are mainly relevant to OC development. Some germline findings are helpful in guiding therapeutic decisions: for example, PALB2, RAD51C, and RAD51D germline PVs render tumors sensitive to PARP inhibitors (PARPi), whereas cancers arising in PTEN heterozygotes are likely to be responsive to AKT down-regulators. CHEK2, ATM, BLM, and NBN PVs result in only a two-fold or even lower excess of cancer risk. However, the incorporation of these genes in DNA testing panels may occasionally lead to the identification of individuals with biallelic germline inactivation; these patients have a severe disease phenotype and thus require intensive medical intervention. The accumulation of data on “non-BRCA” BC- and OC-predisposing genes is complicated due to the rarity of their alterations, significant interethnic variations in population frequency of relevant PVs, heterogeneity of disease subtypes, etc. The use of extended gene panels, which pool together both clinically validated cancer-associated genes and “candidate” genes with potential but unproven significance, is likely to be a prevailing diagnostic approach in the next few years; therefore, proper attitudes towards accumulation and interpretation of genetic data are important. Full article

Microbiome diversity varies significantly across countries and social groups due to geographic, ethnic, lifestyle, and dietary factors, with populations in traditional and rural settings tending to harbor more diverse microbial communities than those in industrialized urban environments. In Saudi Arabia, the influx of migrant workers from regions with distinct health and ecological backgrounds represents a public health concern. In particular, these workers may carry novel microbial species from their places of origin, thereby increasing the risk of infectious diseases spreading in the local population. This may also result in disruption of gut microbial homeostasis, thereby elevating risks related to infection, metabolic disorders, and disease susceptibility. Furthermore, the potential emergence of drug-resistant or locally uncommon microbial strains is particularly concerning. In this study, bacterial diversity was assessed in 28 fecal samples collected from migrant workers living in Saudi Arabia, using 16S rRNA gene sequencing targeting the V3–V4 regions. Operational taxonomic units (OTUs) were clustered at 97% sequence similarity, and alpha diversity indices (Shannon, Simpson, Chao1, ACE) were calculated to evaluate microbial richness and evenness. The results revealed considerable inter-individual variability, with Segatella copri, Peptostreptococcus, and Lachnospiraceae identified as dominant taxa across multiple samples. Principal component analysis (PCA) demonstrated distinct patterns of inter-individual variation, indicating compositional heterogeneity within the cohort. Functional predictions using PICRUSt2 suggested enrichment in metabolic pathways related to carbohydrate and amino acid processing. These findings offer a comprehensive characterization of the gut microbiome structure and predicted functionality in a population characterized by diverse geographic origins. Full article

Background: The timing of oxytocin for slow labour progress rests on custom rather than evidence, and existing syntheses pool clinically distinct populations. Objectives: We restricted this systematic review to nulliparous women in established active-phase labour with a trial-specified diagnosis of slow progress, comparing early versus delayed or withheld intravenous oxytocin. Methods: Cochrane review CD007123 was updated by searching MEDLINE, Embase and CENTRAL; counts were recomputed from primary reports. Results: Three trials (1102 women) were eligible. Early oxytocin did not alter caesarean delivery (three trials, 1102 women; risk ratio [RR] 0.86, 95% confidence interval [CI] 0.63–1.16), an interval excluding neither benefit nor harm. Uterine hyperstimulation, defined differently in the two contributing trials, was more frequent with early oxytocin (two trials, 472 women; RR 2.51, 95% CI 1.04–6.06), but 92% of the weight came from one trial and the association did not survive Hartung–Knapp or leave-one-out analysis. No difference was demonstrated for instrumental or spontaneous delivery, Apgar < 7, neonatal admission or haemorrhage. Labour was shorter by two hours (two trials, 1042 women; mean difference −2.17 h, 95% CI −3.54 to −0.80), with substantial heterogeneity. Certainty was low or very low. Conclusions: Early oxytocin may shorten labour; its effects on caesarean delivery and other outcomes remain uncertain. Full article