Search Results (2,732)

Background: Wilson’s disease (WD) is a rare autosomal recessive disorder caused by mutations in the ATP7B gene, leading to copper accumulation in the liver and brain. Given the clinical heterogeneity of the disease, this study aimed to characterize the mutational spectrum of ATP7B and explore genotype–phenotype correlations in Turkish patients. Methods: Whole-exome sequencing (WES) was performed in 17 Turkish patients clinically diagnosed with WD. Variants were annotated and evaluated using five in silico prediction tools (REVEL, CADD, PolyPhen, SIFT, MutationTaster). Copy number variation (CNV) analysis was conducted using the CLC Genomics Server (Version 22.0.2). Results: A total of 14 distinct ATP7B variants were identified, comprising 12 missense, 1 nonsense, and 1 frameshift mutation. Variant distribution showed some phenotype-specific patterns: four variants were found more frequently in hepatic cases and three in neurological cases, although no statistically significant or consistent correlation between genotype and clinical presentation could be established. The most frequent mutation was p.His1069Gln, present in both phenotypes. All missense variants were predicted to be pathogenic by at least three computational tools, with high concordance among platforms. No pathogenic CNVs were detected. Conclusions: This study expands the mutational landscape of ATP7B in Turkish patients with WD and supports the utility of WES combined with in silico tools for accurate variant classification. The results emphasize the genetic heterogeneity of WD and suggest possible associations between certain mutations and clinical phenotypes. Full article

The pre-main-sequence evolution of the chemically peculiar (CP) stars on the upper main sequence is still a vast mystery and not well understood. Our analysis of young associations and open clusters aims to find (very) young CP stars to try to put a lower boundary on the age of such objects. Using three catalogues of open clusters and associations, we determined membership probabilities using HDBSCAN. The hot stars from this selection were submitted to synthetic $\Delta a$ photometry, spectral, and light curve classification to determine which ones are CP stars and candidates. Subsequently, we used spectral energy distribution fitting and emission line analysis to check for possible PMS CP stars. The results were compared to the literature. We detected 971 CP stars and candidates in 217 clusters and associations. A relatively large fraction, ∼10% of those, show characteristics of PMS CP stars. This significantly expands the known list of candidate PMS CP stars, bringing us closer to solving the mystery of their origin. Full article

Background: The pronator teres (PT) muscle, although traditionally described as a constant two-headed forearm flexor, demonstrates considerable morphological variability. These variants play a crucial role in the pathogenesis of pronator syndrome, a rare but clinically significant entrapment of the median nerve in the proximal forearm. Despite growing interest, there is no widely adopted classification integrating anatomy, imaging, and surgical relevance. This review summarises and contextualises current classifications of the pronator teres in relation to median nerve entrapment, with emphasis on their anatomical, imaging, and surgical relevance. Methods: We performed a narrative review of the literature using PubMed, Scopus, and Web of Science (1960–2025). Studies were included if they reported cadaveric findings, imaging features, or clinical management of PT-related median nerve entrapment. Representative anatomical and clinical sources were analysed to synthesise a morphology-based framework. Results: We identified three morphological types of the PT: Type I (median nerve between humeral and ulnar heads, 74%), Type II (beneath both heads, 12%), and Type III (beneath the humeral head only, 14%). Each type demonstrates distinct entrapment mechanisms and imaging features. Dynamic ultrasound and advanced MRI sequences, particularly MR neurography, have been reported to improve diagnostic confidence but remain underutilised. Published reports describe differing management approaches by type, with variable outcomes. Tables and summary boxes compile previously published findings on entrapment potential, imaging pitfalls, and surgical approaches by type. Conclusions: This review summarises existing classifications linking PT variability to median nerve entrapment. Such integration may have potential clinical relevance but requires further empirical validation. Future studies should standardise imaging protocols, validate electrodiagnostic correlations, and explore functional classifications incorporating clinical, radiological, and anatomical data. Full article

Accessory breasts denote the formation of extra breast tissue along the milk line, and are known to be more prevalent among Black and Asian populations, affecting both genders. This first-ever study aimed to determine the genetic aetiology of accessory breasts in a multiplex family, where all female siblings present with bilateral accessory breasts. The study also ascertained secondary findings (SFs) responsible for comorbidities. Clinical data and saliva samples were obtained from all family members. Ultrasound and histopathology confirmed the diagnosis. Whole-exome sequencing was conducted on DNA samples obtained from the saliva, with variant calling conducted utilising the Sentieon workflow. Variant classification was based on American College of Medical Genetics and Genomics guidelines. After segregation analysis, 12 candidate genes emerged. Among these, PRSS50 and FANCC emerged as top candidates, being implicated in breast diseases. However, two variants in FANCC (c.360del; p.His120GlnfsTer24 and c.355_358del; p.Ser119IlefsTer24) were selected as the most probable causal variants because of the role of this gene in hereditary breast and ovarian cancer syndromes. The remaining ten genes were reported as potentially accounting for comorbidities segregating with accessory breasts. Reported SFs involve TTR and RYR1. In conclusion, pathogenic variants in FANCC cause familial accessory breasts. These novel observations impact pathophysiology, genetic counselling, and personalised medicine. Full article

Freshwater mussels in the genera Fusconaia, Pleurobema, and Pleuronaia are similar in their external shell morphology, which has made the identification and classification of species within these genera difficult and led to many taxonomic revisions. Large samples (N = 464) of select mussel species in these genera were collected from 2012 through 2014, primarily in the upper Tennessee River basin of Tennessee and Virginia, USA. Mitochondrial ND1 and nuclear ITS1 DNA sequences were analyzed to assess phylogenetic relationships among taxa. Ten species were verified as phylogenetically distinct at ND1, two of which were cryptic and previously unrecognized species. Described herein as Pleurobema parmaleei and Pleuronaia estabrookianus, each species clade was diverged at this gene region by ~3.0% from the respective closest congener. The nuclear ITS1 gene region’s nucleotide-site insertion/deletion (indel) patterns were analyzed as single mutational events rather than as fifth character states or missing data. Most species, including these two, were phylogenetically distinct at the ITS1 region when incorporating indels into analyses, but some estimated interspecific pairwise distances were lower than corresponding intraspecific estimates. Among morphological traits assessed for each species, differences in foot color and gravidity characteristics illustrated differences between phylogenetically recognized species and their closest congeners. Due to the limited known geographical distributions of these two cryptic species, each may require protection under the U.S. Endangered Species Act. While this study collected large sample sizes for each species, many streams in the basin remain unsampled and could potentially contain populations of these species or additional cryptic species. Full article

This article develops a method for detecting malware based on the multi-scale recurrent architecture (time-aware multi-scale LSTM) with salience gating, multi-headed attention, and a sequential statistical change detector (CUSUM) integration. The research aim is to create an algorithm capable of effectively detecting malicious activities in behavioural data streams of executable files with minimal delay and ensuring interpretability of the results for subsequent use in forensic audit and cyber defence systems. To implement the task, deep learning methods (training LSTM models with dynamic consideration of time intervals and adaptive attention mechanisms) and sequence statistical analysis (CUSUM, Kulback–Leibler divergence, and Wasserstein distances), as well as regularisation approaches to improve the model stability and explainability, were used. Experimental evaluation demonstrates the proposed approaches’ high efficiency, with the neural network model achieving competitive indicators of accuracy, recall, and classification balance with a low level of false positives and an acceptable detection delay. Attention and salience profile analysis confirmed the possibility of interpreting signals and early detection of abnormal events, which reduces the experts’ workload and reduces the number of false positives. This study introduces the new hybrid architecture development that combines the advantages of recurrent and statistical methods, the theoretical properties formalisation of gated cells for long-term memory, and the proposal of a practical approach to the model solutions’ explainability. The developed method implementation, implemented in the specialised software product form, is shown in a forensic audit. Full article

Gait analysis is essential in the evaluation of neuromuscular and musculoskeletal disorders; however, traditional approaches based on expert visual observation remain subjective and often lack consistency. Accurate and objective assessment of gait impairments is critical for early diagnosis, monitoring rehabilitation progress, and guiding clinical decision-making. Although Gait Energy Images (GEI) have become widely used in automated, vision-based gait analysis, they are limited in capturing boundary details and time-resolved motion dynamics, both critical for robust clinical interpretation. To overcome these limitations, we introduce four novel gait representation maps: the time-coded gait boundary image (tGBI), color-coded GEI (cGEI), time-coded gait delta image (tGDI), and color-coded boundary-to-image transform (cBIT). These representations are specifically designed to embed spatial, temporal, and boundary-specific features of the gait cycle, and are constructed from binary silhouette sequences through straightforward yet effective transformations that preserve key structural and dynamic information. Experiments on the INIT GAIT dataset demonstrate that the proposed representations consistently outperform the conventional GEI across multiple machine learning models and classification tasks involving different numbers of gait impairment categories (four and six classes). These findings highlight the potential of the proposed approaches to enhance the accuracy and reliability of automated clinical gait analysis. Full article

With increasingly scarce spectrum resources, frequency-domain signal overlap interference has become a critical issue, making multi-user modulation classification (MUMC) a significant challenge in wireless communications. Unlike single-user modulation classification (SUMC), MUMC suffers from feature degradation caused by signal aliasing, feature redundancy, and low inter-class discriminability. To address these challenges, this paper proposes a collaborative “separation–recognition” framework. The framework begins by separating overlapping signals via a band partitioning and FastICA module to alleviate feature degradation. For the recognition phase, we design a dual-branch network: one branch extracts prior knowledge features, including amplitude, phase, and frequency, from the I/Q sequence and models their temporal dependencies using a bidirectional LSTM; the other branch learns deep hierarchical representations directly from the raw signal through multi-scale convolutional layers. The features from both branches are then adaptively fused using a gated fusion module. Experimental results show that the proposed method achieves superior performance over several baseline models across various signal conditions, validating the efficacy of the dual-branch architecture and the overall framework. Full article

We investigated differences in circulating cell-free DNA (cfDNA) methylation between patients with cancer and those presenting with severe, nonspecific symptoms. Plasma cfDNA from 229 patients was analyzed, of whom 37 were diagnosed with a wide spectrum of cancer types within 12 months. Samples underwent enzymatic conversion, library preparation, and enrichment using the NEBNext workflow and Twist pan-cancer methylation panel, followed by sequencing. Methylation analysis was performed with nf-core/methylseq. Differentially methylated regions (DMRs) were identified with DMRichR. Machine learning with cross-validation was used to classify cancer and controls. The classifier was applied to an external validation set of 144 controls previously unseen by the model. Cancer samples showed higher overall CpG methylation than controls (1.82% vs. 1.34%, p < 0.001). A total of 162 DMRs were detected, 95.7% being hypermethylated in cancer. Machine learning identified 20 key DMRs for classification between cancer and controls. The final model achieved an AUC of 0.88 (83.8% sensitivity, 83.8% specificity), while mean cross-validation performance reached an AUC of 0.73 (57.1% sensitivity, 77.5% specificity). The specificity of the classifier on unseen control samples was 79.2%. Distinct methylation differences and DMR-based classification support cfDNA methylation as a robust biomarker for cancer detection in patients with confounding conditions. Full article

Newborn screening (NBS) for spinal muscular atrophy (SMA) and severe combined immunodeficiency (SCID) faces challenges. Accurate and precise SMN1 and SMN2 copy number determination, confirmed by two orthogonal methods, are vital for SMA prognostication and treatment. Single SMN1 copy detection also enables the further feasibility to screen for compound heterozygotes. In SCID, low-level T-cell receptor excision circle (TREC) quantification by quantitative PCR is imprecise, necessitating replicates for reliable results. An assay with enhanced accuracy, precision, and high throughput is warranted for NBS SMA and SCID. False positive of SMN1 deletions due to allele dropout are also a potential pitfall in PCR-based methods. We evaluated a first-tier fully automated quadruplex droplet digital PCR (ddPCR) assay detecting SMN1, SMN2, TREC, and RPP30 using dried blood spots together with a second-tier Sanger sequencing to exclude SMN1 allele dropout. Five proficiency test samples and six patient samples with known SMN1 and SMN2 copy numbers confirmed by multiplex ligation-dependent probe amplification were used for accuracy evaluation with full concordance. The ddPCR assay showed high precision for SMN1 and SMN2 (<7% coefficient of variation (CV) for ≥0 copy) and TREC (14.6% CV at 37 copies/µL blood). Second-tier Sanger sequencing identified all SMA cases with homozygous deletions. Accuracy for TREC classification was concordant with 10 proficiency samples. The reference interval of TREC concentration was established for newborns ≥ 34 weeks (n = 1812) and the 2.5th percentile was 57 copies/µL blood. A two-tiered approach with fully automated quadruplex ddPCR and Sanger sequencing delivers accurate and precise quantitation for NBS SMA and SCID, enabling early treatment and counseling. Full article

Accurate traffic light detection and classification are fundamental for autonomous vehicle (AV) navigation and real-time traffic management in complex urban environments. Existing systems often fall short of reliably identifying and classifying traffic light states in real-time, including their flashing modes. This study introduces FlashLightNet, a novel end-to-end deep learning framework that integrates the nano version of You Only Look Once, version 10m (YOLOv10n) for traffic light detection, Residual Neural Networks 18 (ResNet-18) for feature extraction, and a Long Short-Term Memory (LSTM) network for temporal state classification. The proposed framework is designed to robustly detect and classify traffic light states, including conventional signals (red, green, and yellow) and flashing signals (flash red and flash yellow), under diverse and challenging conditions such as varying lighting, occlusions, and environmental noise. The framework has been trained and evaluated on a comprehensive custom dataset of traffic light scenarios organized into temporal sequences to capture spatiotemporal dynamics. The dataset has been prepared by taking videos of traffic lights at different intersections of Starkville, Mississippi, and Mississippi State University, consisting of red, green, yellow, flash red, and flash yellow. In addition, simulation-based video datasets with different flashing rates—2, 3, and 4 s—for traffic light states at several intersections were created using RoadRunner, further enhancing the diversity and robustness of the dataset. The YOLOv10n model achieved a mean average precision (mAP) of 99.2% in traffic light detection, while the ResNet-18 and LSTM combination classified traffic light states (red, green, yellow, flash red, and flash yellow) with an F1-score of 96%. Full article

Filoviruses are among the most lethal viral human pathogens known, with significant relevance to public health, yet their evolutionary history remains poorly resolved. This study applied a multiprotein molecular phylogenetic approach to investigate the evolutionary and temporal dynamics of the family Filoviridae. Amino acid sequences from the proteome and seven individual proteins (NP, VP35, VP40, GP, VP30, VP24, L) were analyzed using MEGA 12, with RelTime inference anchored on uniform calibrations, and integration of epidemiological data (cases, fatalities, case fatality). The phylogenetic reconstructions revealed robust topologies for most proteins, though selective pressures on GP, VP30 and VP40 generated more variable patterns. Temporal inferences supported the classification of filoviruses into three groups: an ancestral lineage (>1 MYA, fish- and reptile-associated), an intermediate lineage (BCE–1 MYA, bat-associated), and a contemporary lineage (CE, ebolaviruses and marburgviruses). VP30 and VP40 showed consistent associations with epidemiological outcomes in Orthoebolavirus zairense, suggesting their interplay may underlie enhanced dispersal and virulence. Contrariwise, Orthoebolavirus restonense emerged as a natural counterpoint for comparison with other potential human pathogenic filoviruses. Taken together, these findings highlight that filoviral evolution is intrinsically linked not only to viral biology but also to the ecology and history of their hosts. Full article

Red-bed desertification represents a critical form of land degradation in subtropical regions, yet the coupled soil–vegetation processes remain poorly understood. This study integrates Sentinel-2 vegetation indices with soil fertility gradients to assess vegetation–soil interactions in the Nanxiong Basin of South China. By combining Normalized Difference Vegetation Index (NDVI)-based vegetation classification with comprehensive soil property analyses, we aim to uncover the spatial patterns and driving mechanisms of degradation. The results revealed a clear gradient from intact forests to exposed red-bed bare land (RBBL). NDVI classification achieved an overall accuracy of 77.8% (κ = 0.723), with mixed forests being identified most reliably (97.1%), while Red-Bed Bare Land (RBBL) exhibited the highest omission rate. Along this gradient, soil organic matter, available nitrogen, and phosphorus declined sharply, while pH shifted from near-neutral in forests to strongly acidic in bare lands. Principal component analysis (PCA) identified a dominant fertility axis (PC1, explaining 56.7% of the variance), which clustered forested sites in nutrient-rich zones and isolated RBBL as the most degraded state. The observed vegetation–soil pattern aligns with a “weathering–transport–exposure” sequence, whereby physical disintegration and selective erosion during monsoonal rainfall drive organic matter depletion, soil thinning, and acidification, with human disturbance further accelerating these processes. To our knowledge, this study is the first to directly couple PCA-derived soil fertility gradients with vegetation patterns in red-bed regions. By integrating vegetation indices with soil fertility gradients, this study establishes a process-based framework for interpreting red-bed desertification. These findings underscore the utility of remote sensing, especially NDVI classification, as a powerful tool for identifying degradation stages and linking vegetation patterns with soil processes, providing a scientific foundation for monitoring and managing land degradation in monsoonal and semi-arid regions. Full article

This review analyzed approximately 115 peer-reviewed studies published between 2010 and 2025, focusing on molecular subtyping and circulating tumor DNA (ctDNA)-guided approaches in Diffuse Large B-Cell Lymphoma (DLBCL). Evidence was synthesized from retrospective cohorts, prospective clinical trials, and translational studies, highlighting how molecular heterogeneity, clonal evolution, and the tumor microenvironment complicate classification and treatment. While molecular subtypes such as MCD, BN2, EZB, A53, and ST2 have improved prognostication, their routine use in clinical practice remains limited due to cost, complexity, and restricted access to sequencing platforms. Tumor-informed ctDNA assays show promise for minimal residual disease (MRD) monitoring and adaptive therapy, yet their predictive power for CAR-T therapy, bispecific antibodies, and checkpoint inhibitors is still incompletely understood. Overall, the literature converges on the need for integrated strategies combining ctDNA, molecular subtyping, and immune microenvironment analysis to personalize frontline therapy. Full article

Mild cognitive impairment (MCI), a transitional stage between normal aging and Alzheimer’s disease (AD), comprises three potential trajectories: reversion, stability, or progression. Accurate prediction of these trajectories is crucial for disease modeling and early intervention. We propose a novel analytical framework that integrates a healthy control–AD difference template (HAD) with a large-scale Granger causality algorithm based on long short-term memory networks (LSTM-lsGC) to construct effective connectivity (EC) networks. By applying principal component analysis for dimensionality reduction, modeling dynamic sequences with LSTM, and estimating EC matrices through Granger causality, the framework captures both symmetrical and asymmetrical connectivity, providing a refined characterization of the network alterations underlying MCI progression and reversion. Leveraging graph-theoretical features, our method achieved an MCI subtype classification accuracy of 84.92% (AUC = 0.84) across three subgroups and 90.86% when distinguishing rMCI from pMCI. Moreover, key brain regions, including the precentral gyrus, hippocampus, and cerebellum, were identified as being associated with MCI progression. Overall, by developing a symmetry-aware effective connectivity framework that simultaneously investigates both MCI progression and reversion, this study bridges a critical gap and offers a promising tool for early detection and dynamic disease characterization. Full article