Search Results (48)

Hypertrophic cardiomyopathy (HCM), the most common inherited cardiomyopathy, represents a paradigmatic condition for precision cardiovascular medicine. Once regarded as a monogenic autosomal dominant disorder driven by rare sarcomeric variants, HCM is now recognized as a genetically complex disease characterized by incomplete penetrance, variable expressivity, and heterogeneous clinical trajectories. This review summarizes current evidence on the evolving genetic architecture of HCM, emphasizing the predominant role of definitively validated sarcomeric genes, particularly MYBPC3 and MYH7, and the clinical value of gene panel expansion. Phenotypic variability reflects interactions among variant classes, gene-specific mechanisms, and modifying factors. Differences between missense and truncating variants, haploinsufficiency and poison-peptide effects, allelic imbalance, and age-dependent penetrance contribute to diverse disease expression. Emerging data further support oligogenic inheritance and polygenic modulation, with genome-wide association studies and polygenic risk scores elucidating their contribution to disease susceptibility and variability, especially in genotype-negative patients and carriers of rare variants. We also address genes with emerging evidence and underrecognized pathogenic mechanisms, including deep intronic and splice-altering variants that may explain part of the missing heritability. The importance of distinguishing phenocopies is highlighted, advocating for phenotype-anchored diagnostic pathways integrating clinical assessment, multimodality imaging, and targeted genetic testing. Overall, contemporary data support a targeted, gene-validity-driven approach to genetic testing, where molecular findings primarily inform diagnosis and cascade screening, while risk stratification remains phenotype-led and longitudinal. Future progress will depend on integrative models combining rare variants, polygenic background, imaging, and biomarkers to translate genetic complexity into actionable precision care. Full article

Background and Objectives: Burnout is highly prevalent among emergency and acute care healthcare workers (HCWs), yet biological correlates remain debated because candidate biomarkers are strongly shaped by circadian timing, shift work, sleep loss, and overlapping affective symptoms. We mapped post-2018 evidence of biological biomarkers assessed alongside validated burnout measures in emergency department (ED), emergency medical services (EMS), and related acute care settings. Specifically, we asked whether reproducible biological correlates of burnout can be identified in emergency and acute-care healthcare workers when biomarker endpoint class and sampling context are systematically considered. Materials and Methods: We conducted a systematic scoping review with evidence mapping (PRISMA-ScR). PubMed/MEDLINE and the MDPI platform were searched for English-language studies published from 2018 onward (through January 2026). Eligible quantitative studies enrolled ED/EMS or acute care HCWs, assessed burnout using validated instruments, and reported at least one biological biomarker. Evidence was charted by biomarker domain and endpoint class (basal measures, stress reactivity paradigms, and chronic indices such as hair-based markers). Results: Overall, 19 studies were included in mapping/synthesis. Biomarker selection clustered around the hypothalamic–pituitary–adrenal axis (cortisol; n = 10/19), with fewer studies focused on autonomic function (heart rate variability; n = 2/19) and immune–inflammatory markers (n = 2/19), and single-study coverage for oxidative stress (n = 1/19), cardiometabolic candidates (n = 1/19), cellular aging (n = 1/19), neuroglial/multi-system candidates (n = 1/19), and feasibility-oriented multi-marker designs (n = 1/19). Reported associations with burnout were heterogeneous in direction and magnitude, but were more interpretable when endpoint class, timing anchors, and shift/sleep-related covariates were explicitly reported. Rates of confounder adjustment were low across studies (e.g., only 3/19 reported multivariable adjustment, and none systematically measured sleep or circadian factors), substantially limiting interpretability. Conclusions: The 2018+ literature does not support a single reproducible biomarker for burnout in emergency and acute care workforces. Evidence instead suggests multi-system dysregulation that is highly sensitive to endpoint class, sampling timing, and contextual confounding. Future studies should prioritize timing-anchored repeated-measures protocols across shift and recovery windows, jointly model sleep/circadian factors and depressive symptoms, and evaluate multi-marker panels and intervention responsiveness. Full article

Surface defects in industrial production are complex and diverse. Therefore, deep learning-based defect detection models must consistently adapt to newly emerging defect categories. The trained models generally suffer from catastrophic forgetting as they learn new defect categories. To address this issue, we propose a selective distillation for incremental defect detection (SD-IDD) model based on GFLv1. Specifically, three selective distillation strategies are proposed, including high-confidence classification distillation, dual-stage cascaded regression distillation, and Intersection over Union (IoU)-driven difficulty-aware feature distillation. The high-confidence classification distillation aims to preserve critical discriminative knowledge of old categories within semantic confusion regions of the classification head, reducing interference from low-value regions. Dual-stage cascaded regression distillation focuses on high-quality anchors through geometric prior coarse filtering and statistical fine filtering, utilizing IoU-weighted KL divergence distillation loss to accurately transfer localization knowledge. IoU-driven difficulty-aware feature distillation adaptively allocates distillation resources, prioritizing features of high-difficulty targets. These selective distillation strategies significantly mitigate catastrophic forgetting while enhancing the detection accuracy of new classes, without requiring access to old training samples. Experimental results demonstrate that SD-IDD achieves superior performance, with mAP_old of 58.2% and 99.3%, mAP_new of 69.0% and 97.3%, and mAP_all of 63.6% and 98.3% on the NEU-DET and DeepPCB datasets, respectively, surpassing existing incremental detection methods. Full article

As a widely used catalyst class, transition metal oxides (TMOs) face the challenges of detrimental nanoparticle agglomeration. The newly developing two-dimensional (2D) covalent triazine frameworks (CTFs) offer a promising solution as catalyst supports, capable of yielding composites with excellent dispersibility and synergistic catalytic enhancement. Building on this, and employing a hydroxylation functional modification strategy, this article introduces a binary oxide system to construct a CTF/CuO–NiO composite that exhibits excellent catalytic performance for the thermal decomposition of ammonium perchlorate (AP). Specifically, polyvinyl alcohol (PVA) was first employed to introduce -OH anchoring sites onto the CTF surface. A subsequent co-precipitation yielded a uniform dispersion of CuO–NiO nanoparticles across the functionalized CTF support. DSC analysis revealed that incorporating merely 2 wt% of the CTF/CuO–NiO composite into AP significantly alters its high-temperature decomposition (HTD) peak temperature, shifting it from 404.6 °C to 332.1 °C. This work highlights the construction of a binary oxide system through an effective dispersion strategy to enhance the synergistic catalytic performance of CTF-based composites. Full article

Biopolymer adhesives are moving toward frontline use in medicine and manufacturing as the limitations in some petrochemical systems, including cytotoxicity, challenges in wet adhesion for specific families of synthetic resins and formaldehyde emissions associated with amino-formaldehyde materials are becoming increasingly difficult to accept. This review integrates mechanisms, material classes and quantitative performance across biopolymer-based adhesives. We focus on architectures that combine permanent covalent anchoring with reversible, energy-dissipating bonds and on how functional group density, crosslink density, microstructure and additives act as design knobs for wet performance, durability and degradation. Across biomedical applications, chitosan, alginate, gelatin and related hydrogels achieve wet lap-shear strengths on the order of tens of kilopascals, cut liver-bleeding times by roughly half, provide strong antibacterial activity and close diabetic wounds by about 92 percent by day 14. Thermoresponsive alginate–gelatin sealants exceed clinically relevant burst pressures and microneedle patches withstand more than 120 mmHg while sealing arteries in under a minute. In industrial settings, dialdehyde-based starch resins deliver 0.83 to 1.05 MPa dry shear and maintain strength after water immersion while meeting stringent emission classes, and silane-modified nanocellulose in urea–formaldehyde markedly reduces free formaldehyde without sacrificing the internal bond. We conclude by identifying priorities for standardized wet testing, and lifetime matching of strength and degradation that can support large-scale clinical and industrial translation. Full article

Aim: To propose a standardized clinical protocol for cone-specific neuromodulation that classifies therapeutic filters for selective stimulation of S-, M-, and L-cones and translates optical and safety parameters into condition-specific frameworks for amblyopia, strabismus, and ADHD. Methods: Previously characterized spectral filters were re-evaluated using published transmittance and cone-excitation data to identify a reduced set of monochromatic and combined options with meaningful cone bias. These were integrated with $\alpha$-opic metrology, international photobiological and flicker standards, and condition-specific neurophysiological evidence to define reproducible ranges for wavelength, corneal illuminance, exposure timing, temporal modulation, and safety verification. Results: The protocol consolidates eleven monochromatic and six combined filters into operational classes mapped onto mechanistic profiles for amblyopia, esotropia, exotropia, vertical deviations, and exploratory ADHD applications. All time frames and applications are presented as methodological anchors rather than efficacy claims. Conclusions: This work provides a structured, safety-anchored framework intended to guide protocol design and comparability in future cone-specific neuromodulation trials; therapeutic benefit must be demonstrated in prospective clinical studies. Full article

Long-tailed image classification remains challenging for vision–language models. Head classes dominate training while tail classes are underrepresented and noisy, and short prompts with weak text supervision further amplify head bias. This paper presents TASA, an end-to-end framework that stabilizes textual supervision and enhances cross-modal fusion. A Semantic Distribution Modulation (SDM) module constructs class-specific text prototypes by cosine-weighted fusion of multiple LLM-generated descriptions with a canonical template, providing stable and diverse semantic anchors without training text parameters. Dual-Space Cross-Modal Fusion (DCF) module incorporates selective-scan state–space blocks into both image and text branches, enabling bidirectional conditioning and efficient feature fusion through a lightweight multilayer perceptron. Together with a margin-aware alignment loss, TASA aligns images with class prototypes for classification without requiring paired image–text data or per-class prompt tuning. Experiments on CIFAR-10/100-LT, ImageNet-LT, and Places-LT demonstrate consistent improvements across many-, medium-, and few-shot groups. Ablation studies confirm that DCF yields the largest single-module gain, while SDM and DCF combined provide the most robust and balanced performance. These results highlight the effectiveness of integrating text-driven prototypes with state–space fusion for long-tailed classification. Full article

Osteoarthritis (OA) lacks disease-modifying therapies, in part because key features of the joint microenvironment remain underappreciated. One such feature is localized acidosis, characterized by sustained reductions in extracellular pH within the cartilage, meniscus, and the osteochondral interface despite near-neutral bulk synovial fluid. We synthesize current evidence on the origins, sensing, and consequences of joint acidosis in OA. Metabolic drivers include hypoxia-biased glycolysis in avascular cartilage, cytokine-driven reprogramming in the synovium, and limits in proton/lactate extrusion (e.g., monocarboxylate transporters (MCTs)), with additional contributions from fixed-charge matrix chemistry and osteoclast-mediated acidification at the osteochondral junction. Acidic niches shift proteolysis toward cathepsins, suppress anabolic control, and trigger chondrocyte stress responses (calcium overload, autophagy, senescence, apoptosis). In the nociceptive axis, protons engage ASIC3 and sensitize TRPV1, linking acidity to pain. Joint cells detect pH through two complementary sensor classes: proton-sensing GPCRs (GPR4, GPR65/TDAG8, GPR68/OGR1, GPR132/G2A), which couple to G_s, G_q/11, and G_12/13 pathways converging on MAPK, NF-κB, CREB, and RhoA/ROCK; and proton-gated ion channels (ASIC1a/3, TRPV1), which convert acidity into electrical and Ca²⁺ signals. Therapeutic implications include inhibition of acid-enabled proteases (e.g., cathepsin K), pharmacologic modulation of pH-sensing receptors (with emerging interest in GPR68 and GPR4), ASIC/TRPV1-targeted analgesia, metabolic control of lactate generation, and pH-responsive intra-articular delivery systems. We outline research priorities for pH-aware clinical phenotyping and imaging, cell-type-resolved signaling maps, and targeted interventions in ‘acidotic OA’ endotypes. Framing acidosis as an actionable component of OA pathogenesis provides a coherent basis for mechanism-anchored, locality-specific disease modification. Full article

Public transport agencies must triage thousands of multilingual complaints every day, yet the cost of training and serving fine-grained sentiment analysis models limits real-time deployment. The proposed “one encoder, any facet” framework therefore offers a reproducible, resource-efficient alternative to heavy fine-tuning for domain-specific sentiment analysis or opinion mining tasks on digital service data. To the best of our knowledge, we are the first to test this paradigm on operational multilingual complaints, where public transport agencies must prioritize thousands of Russian- and Kazakh-language messages each day. A human-labelled corpus of 2400 complaints is embedded with five open-source universal models. Obtained embeddings are matched to semantic “anchor” queries that describe three distinct facets: service aspect (eight classes), implicit frustration, and explicit customer request. In the strict zero-shot setting, the best encoder reaches 77% accuracy for aspect detection, 74% for frustration, and 80% for request; taken together, these signals reproduce human four-level priority in 60% of cases. Attaching a single-layer logistic probe on top of the frozen embeddings boosts performance to 89% for aspect, 83–87% for the binary facets, and 72% for end-to-end triage. Compared with recent fine-tuned sentiment analysis systems, our pipeline cuts memory demands by two orders of magnitude and eliminates task-specific training yet narrows the accuracy gap to under five percentage points. These findings indicate that a single frozen encoder, guided by handcrafted anchors and an ultra-light head, can deliver near-human triage quality across multiple pragmatic dimensions, opening the door to low-cost, language-agnostic monitoring of digital-service feedback. Full article

Unsupervised feature selection aims to characterize the cluster structure of original features and select the optimal subset without label guidance. However, existing methods overlook fuzzy information in the data, failing to model cluster structures between data effectively, and rely on squared error for data reconstruction, exacerbating noise impact. Therefore, a robust unsupervised feature selection algorithm based on fuzzy anchor graphs (FWFGFS) is proposed. To address the inaccuracies in neighbor assignments, a fuzzy anchor graph learning mechanism is designed. This mechanism models the association between nodes and clusters using fuzzy membership distributions, effectively capturing potential fuzzy neighborhood relationships between nodes and avoiding rigid assignments to specific clusters. This soft cluster assignment mechanism improves clustering accuracy and the robustness of the graph structure while maintaining low computational costs. Additionally, to mitigate the interference of noise in the feature selection process, an adaptive fuzzy weighting mechanism is presented. This mechanism assigns different weights to features based on their contribution to the error, thereby reducing errors caused by redundant features and noise. Orthogonal tri-factorization is applied to the low-dimensional representation matrix. This guarantees that each center represents only one class of features, resulting in more independent cluster centers. Experimental results on 12 public datasets show that FWFGFS improves the average clustering accuracy by 5.68% to 13.79% compared with the state-of-the-art methods. Full article

Background/Objectives: Oncogenic KRAS drives ~30% of solid tumours, yet the only approved G12C-specific drugs benefit ≈ 13% of KRAS-mutant patients, leaving a major clinical gap. We sought mutation-agnostic natural ligands from Ziziphus lotus, whose stereochemically rich phenolics may overcome this limitation by occupying the SI/II (Switch I/Switch II) groove and locking KRAS in its inactive state. Methods: Phytochemical mining yielded five recurrent phenolics, such as (+)-catechin, hyperin, astragalin, eriodictyol, and the prenylated benzoate amorfrutin A, benchmarked against the covalent inhibitor sotorasib. An in silico cascade combined SI/II docking, multi-parameter ADME/T (Absorption, Distribution, Metabolism, Excretion, and Toxicity) filtering, and 100 ns explicit solvent molecular dynamics simulations. Pharmacokinetic modelling predicted oral absorption, Lipinski compliance, mutagenicity, and acute-toxicity class. Results: Hyperin and astragalin showed the strongest non-covalent affinities (−8.6 kcal mol⁻¹) by forging quadridentate hydrogen-bond networks that bridge the P-loop (Asp30/Glu31) to the α3-loop cleft (Asp119/Ala146). Catechin (−8.5 kcal mol⁻¹) balanced polar anchoring with entropic economy. ADME ranked amorfrutin A the highest for predicted oral absorption (93%) but highlighted lipophilic solubility limits; glycosylated flavonols breached Lipinski rules yet remained non-mutagenic with class-5 acute-toxicity liability. Molecular dynamics trajectories confirmed that hyperin clamps the SI/II groove, suppressing loop RMSF below 0.20 nm and maintaining backbone RMSD stability, whereas astragalin retains pocket residence with transient re-orientation. Conclusions: Hyperin emerges as a low-toxicity, mutation-agnostic scaffold that rigidifies inactive KRAS. Deglycosylation, nano-encapsulation, or soft fluorination could reconcile permeability with durable target engagement, advancing Z. lotus phenolics toward broad-spectrum KRAS therapeutics. Full article

This study examines the political and elite motives behind Colombo’s ‘world-class city’ initiative and its impact on public housing in underserved communities. Informed by interviews with high-ranking government officials, including urban planning experts and military officers, this study examines how President Rajapaksa’s elite-driven postwar Sri Lankan government leveraged military capacities within the neoliberal developmental framework to transform Colombo’s urban space for political and economic goals, often at the expense of marginalized communities. Applying a contextual discourse analysis model, which views discourse as a constellation of arguments within a specific context, we critically analyzed interview discussions to clarify the rationale behind the militarized approach to public housing while highlighting its contradictions, including the displacement of underserved communities and the ethical concerns associated with compulsory relocation. The findings suggest that Colombo’s postwar public housing program was utilized to consolidate authoritarian control and promote speculative urban transformation, treating public housing as a secondary aspect of broader political and economic agendas. Anchored in militarized urban governance, these elite-driven strategies failed to achieve their anticipated economic objectives and deepened socio-spatial inequalities, raising serious concerns about exclusionary and undemocratic planning practices. The paper recommends that future urban planning strike a balance between economic objectives and principles of spatial justice, inclusion, and participatory governance, promoting democratic and socially equitable urban development. Full article

Virus-like nanoparticles (VNPs) based on Moloney murine leukemia virus represent a well-established platform for the expression of heterologous molecules such as cytokines, cytokine receptors, peptide MHC (pMHC) and major allergens, but their application for inducing protective anti-viral immunity has remained understudied as of yet. Here, we variably fused the wildtype SARS-CoV-2 spike, its receptor-binding domain (RBD) and nucleocapsid (NC) to the minimal CD16b-GPI anchor acceptor sequence for expression on the surface of VNP. Moreover, a CD16b-GPI-anchored single-chain version of IL-12 was tested for its adjuvanticity. VNPs expressing RBD::CD16b-GPI alone or in combination with IL-12::CD16b-GPI were used to immunize BALB/c mice intramuscularly and subsequently to investigate virus-specific humoral and cellular immune responses. CD16b-GPI-anchored viral molecules and IL-12-GPI were well-expressed on HEK-293T-producer cells and purified VNPs. After the immunization of mice with VNPs, RBD-specific antibodies were only induced with RBD-expressing VNPs, but not with empty control VNPs or VNPs solely expressing IL-12. Mice immunized with RBD VNPs produced RBD-specific IgM, IgG_2a and IgG₁ after the first immunization, whereas RBD-specific IgA only appeared after a booster immunization. Protein/peptide microarray and ELISA analyses confirmed exclusive IgG reactivity with folded but not unfolded RBD and showed no specific IgG reactivity with linear RBD peptides. Notably, booster injections gradually increased long-term IgG antibody avidity as measured by ELISA. Interestingly, the final immunization with RBD–Omicron VNPs mainly enhanced preexisting RBD Wuhan Hu-1-specific antibodies. Furthermore, the induced antibodies significantly neutralized SARS-CoV-2 and specifically enhanced cellular cytotoxicity (ADCC) against RBD protein-expressing target cells. In summary, VNPs expressing viral proteins, even in the absence of adjuvants, efficiently induce functional SARS-CoV-2-specific antibodies of all three major classes, making this technology very interesting for future vaccine development and boosting strategies with low reactogenicity. Full article

This study examines students’ perceptions of the subjects geography, mathematics, and computer science in integrated science, technology, engineering, and mathematics (STEM) lessons. Although the importance of an integrated approach in STEM education is emphasized, researchers are not clear about whether students perceive connections between the subjects on the one hand and subject-specific working methods and content in integrated lessons on the other. Data was collected in an integrated teaching unit on the sustainability of apples using an open-ended digital questionnaire in to two ninth grade classes in Hamburg, Germany (n = 38); this data was analyzed using qualitative content analysis. The results reveal that students perceive the subjects differently, but similarities can also be identified. While subject-specific content is perceived—such as the use of maps in geography, the calculation of volumes in mathematics, and Dijkstra’s algorithm in computer science—methodological connections, such as calculating, analyzing diagrams, or solving problems, are anchored across disciplines. This suggests that the subject-specific contents are not lost in integrating lessons, and that connections among the subjects are, to a certain extent, promoted. Full article

Convolutional neural networks (CNNs) and their application in animal husbandry have in-depth mathematical expressions, which usually revolve around how well they map input data such as images or video frames of animals to meaningful outputs like health status, behavior class, and identification. Likewise, computer vision and deep learning models are driven by CNNs to act intelligently in improving productivity and animal management for sustainable animal husbandry. In animal husbandry, CNNs play a vital role in the management and monitoring of livestock’s health and productivity due to their high-performance accuracy in analyzing images and videos. Monitoring animals’ health is important for their welfare, food abundance, safety, and economic productivity. This paper aims to comprehensively review recent advancements and applications of relevant models that are based on CNNs for livestock health monitoring, covering the detection of their various diseases and classification of their behavior, for overall management gain. We selected relevant articles with various experimental results addressing animal detection, localization, tracking, and behavioral monitoring, validating the high-performance accuracy and efficiency of CNNs. Prominent anchor-based object detection models such as R-CNN (series), YOLO (series) and SSD (series), and anchor-free object detection models such as key-point based and anchor-point based are often used, demonstrating great versatility and robustness across various tasks. From the analysis, it is evident that more significant research contributions to animal husbandry have been made by CNNs. Limited labeled data, variation in data, low-quality or noisy images, complex backgrounds, computational demand, species-specific models, high implementation cost, scalability, modeling complex behaviors, and compatibility with current farm management systems are good examples of several notable challenges when applying CNNs in animal husbandry. By continued research efforts, these challenges can be addressed for the actualization of sustainable animal husbandry. Full article