Abstract: Domain Generation Algorithm(DGA) is widely used by botnets to evade detection by generating numerous pseudo-random domains to communicate with commandandcontrol servers. While existing Graph Neural Networks attempt to detect DGA botnets by exploiting the feature similarity of these domains to model semantic associations via similarity graphs, they are restricted to binary relationships, causing information decay during multi-hop propagation. To overcome this, we propose HyperDGA. Treating domains as nodes, HyperDGA utilizes K Nearest Neighbors to construct hyperedges, explicitly capturing high order group semantic correlations. Subsequently, a Local Topology Aggregation module employs multi-head node attention-based hypergraph convolution to dynamically assign distinct aggregation weights to intra hyperedge nodes, extracting fine-grained structural features. To mitigate the limited receptive field of hypergraph convolutions, a Global Node Association module integrates the selective state space model, Mamba, to capture long-range dependencies across all nodes. Experiments on two public datasets demonstrate that HyperDGA outperforms all baselines and achieves over 99% accuracy, validating the efficacy of high-order semantic modeling for DGA botnet detection.

Abstract: Mangrove wetlands are important coastal ecosystems and are increasingly vulnerable to heavy metal contamination. The accumulation of heavy metals in man-grove ecosystems is well studied; however, studies on the seasonal variations of heavy metals in mangrove wetlands are scarce. This study investigated heavy metal (Cd, Cr, Cu, As, Pb, and Zn) accumulation in surface sediments of six typical mangrove wet-lands (DZG, QLH, XCP, SYR, SBW, and XY) in Hainan Island, China, during wet and dry seasons. In addition, potential ecological concerns and relationships between sedimentary physicochemical parameters and metal accumulation were assessed. The findings demonstrated significant spatial differences in heavy metal accumulation, with higher concentrations in the northern localities and lower concentrations in the southern areas. There were notable seasonal fluctuations in heavy metal concentrations, with higher levels in the dry season. Risk assessment models exhibited that Cadmium (Cd) and Arsenic (As) were the principal contaminants of concern in most research sites with moderate levels of contamination and posed at least moderate ecological concerns in both wet and dry seasons. The overall ecological risk index indicated a moderate risk to the environment, especially in the dry season. The principal component analysis (PCA) and correlation analysis results indicated that the physicochemical properties of sediments, mainly total organic carbon (TOC), total phosphorus (TP), total nitrogen (TN), and salinity, had significant effects on the heavy metals accumulation in the mangrove sediments. The present study helps raise awareness of seasonal fluctuations in heavy metal pollutants and provides strategies for the prevention and monitoring of metal pollution in mangrove wetlands.

Abstract: Precise assessment of landslide potential in tectonically active mountain areas like Darjeeling Sikkim Himalaya (DSH) is a scientific challenge due to the complexity of different landslide conditioning factors that control the slope stability. Despite several studies for landslide susceptibility mapping, most of the conventional methods struggle to capture the nonlinear relationships and spatial heterogeneity that characterize landslides. Besides, the current use of pixel-based methods is insufficient to depict geomorphological units and slope-scale processes, thus limiting their effectiveness in boundary demarcation of landslide-prone areas. These limitations highlight the need for more robust machine learning frameworks that integrate geomorphology-based terrain segmentation with advanced machine learning models, which would not only facilitate modeling the multifaceted interactions among environmental components but also improve the understanding of the landslide driving forces. In this study, we have used slope unit based landslide susceptibility mapping with 4380 slope units integrated with 17 conditioning factors, and 8373 total updated inventories using six models Random Forest (RF), Generalized Additive Model (GAM), Categorical Boosting (CatBoost), Tabular Neural Network (TabNet), Bayesian Additive Regression Trees (BART), and Convolutional Neural Network (CNN). The model hyperparameters were optimized using Bayesian optimization, except for the BART model. Among the six models, RF (AUC = 0.848) and CatBoost (AUC = 0.846) were the best two performing models. Furthermore, SHAP analysis reveals that elevation, aspect, slope, distance to faults, NDVI, and proximity to roads and drainage networks are the main landslide controlling factors in DSH. The interaction analysis using SHAP indicates that the occurrence of landslides is controlled by nonlinear and threshold-dependent relations, especially among slope-rainfall, rainfall-soil moisture, and slope-distance to roads and faults, which represents a complex interaction between the hydrological triggering factor, geomorphic processes, tectonic activity, and human interventions.

Abstract:

Caryocar coriaceum Wittm. (Caryocaraceae) is a native Brazilian species predominantly distributed in Cerrado areas and transitional regions with the Caatinga in Northeastern Brazil, whose fruits exhibit significant nutritional, technological, and biofunctional potential. This review systematizes and critically analyzes the available scientific evidence regarding the fixed oil extracted from its fruits, addressing extraction methods, chemical composition, physicochemical parameters, nutritional value, technological applications, and the main bioactivities described in experimental models. Chromatographic and bromatological studies demonstrate that the oil presents a lipid profile characterized by the predominance of monounsaturated and saturated fatty acids, especially oleic acid and palmitic acid, in addition to the presence of carotenoids, phenolic compounds, and other bioactive lipophilic constituents. Available preclinical evidence indicates antioxidants, anti-inflammatory, wound-healing, gastroprotective, respiratory, anticonvulsant, and microbial resistance-modulating properties, suggesting potential applications in the food, pharmaceutical, cosmetic, and biotechnological fields. From the perspective of Food Science, the oil demonstrates characteristics compatible with lipid matrices of functional interest, although aspects related to oxidative stability, compositional standardization, sensory acceptability, and industrial scale-up remain insufficiently explored. Additionally, important limitations persist regarding the scarcity of systematic toxicological studies, the absence of clinical trials in humans, and the limited elucidation of the molecular mechanisms involved in the observed bioactivities. Thus, although C. coriaceum presents promising biotechnological potential, the advancement of its translational application will depend on multidisciplinary approaches capable of integrating chemical standardization, toxicological safety, and applied technological development.

Abstract: Hantaviruses are emerging zoonotic pathogens responsible for two severe clinical syndromes: (i) haemorrhagic fever with renal syndrome (HFRS) and (ii) hantavirus cardiopulmonary syndrome (HCPS), collectively causing more than 200,000 human cases annually worldwide. Despite their public-health importance, the molecular mechanisms governing the host response and the population-level dynamics of rodent- to-human spillover remain incompletely characterised. The timeliness of this frame- work is underscored by the April–May 2026 outbreak of Andes orthohantavirus aboard 9 the MV Hondius cruise ship – the first such cluster in a maritime setting, with three deaths reported across multiple countries (WHO Disease Outbreak News: https://www.who.int/emergencies/disease-outbreak-news/item/2026-DON599). This event revealed critical gaps in existing models that treat humans solely as dead-end spillover hosts. Here, we present an integrated computational study that combines three complementary analyses. Preliminarly, we performed the first phylogenetic analysis of such virus, idenifying as Orthoantavirus andensense the responsible for the vessel outbreak. Second, we performed a downstream transcriptomic analysis of Hantaan virus (HTNV)-infected human umbilical vein endothelial cells (HUVECs) using publicly available RNA-seq data (GEO accession GSE133751, n = 3 per group), identifying 184 upregulated and 19 downregulated evidencing the role of dominated by interferon-stimulated genes (ISGs), including CXCL10, CXCL11, MX2, DDX58, IRF7, STAT1, OASL, and CMPK2. We constructed a protein–protein interaction (PPI) network from STRING (176 nodes, 3,210 edges) and applied a composite network centrality score to rank regulatory hubs, identifying ISG15, IRF1, CXCL10, STAT1, and DDX58 as the most central nodes. Pathway enrichment analysis con- firms strong activation of interferon signalling (Reactome, p = 1.3×10⁻⁶³), antiviral defence (Gene Ontology, p = 3.8 × 10⁻⁵⁸), and NF-κB pathways, with concurrent suppression of ribosomal translation. We finally developed a coupled SEIRD epi-demiological model that explicitly represents rodent-to-rodent and rodent-to-human transmission with logistic rodent population growth. Preliminary simulation analysis demonstrates that reducing human exposure to rodent excreta is substantially more effective than rodent population control alone for reducing human disease burden, and that rodent control in isolation can paradoxically increase human cases through a dilution-like effect. The integrated framework provides molecular and epidemiological insights relevant to hantavirus surveillance, therapeutic target identification, and 35 public-health intervention design.

Abstract: Humanity faces not isolated problems but a PolyCrisis, which is a set of 26 tightly interwoven existential crises spanning ecological collapse, planetary overheating, chronic disease epidemics, institutional fragility and social breakdown. Each crisis amplifies the others through cascading feedback loops, and sixteen possess the independent capacity to cause human extinction. We are not entering an emergency, but we are already in a state of emergency. Multiple planetary boundaries have been transgressed, and climate tipping points are being crossed now. Extinction rates match historical great mass extinction events, while our food systems, primarily responsible for almost half these crises, simultaneously drive hunger, obesity and chronic diseases. This PolyCrisis is not the result of isolated failures, but the predictable outcome of Planet A, the Operating System of our mainstream civilization, characterized by economics of unbounded extraction and hoarding, violence-based and profit-based food systems, short-term thinking, and unlimited growth imperatives on a finite planet. Planet B is our proposed PolySolution framework, a complete alternative Operating System grounded in empirical reality and proven solutions. It integrates animal-free food systems releasing up to 5 billion hectares for rewilding, regenerative economics measuring non-violence and biocapacity, circular economy minimizing waste, technological restraint with democratic governance, seven-generation thinking, and PolyCommunity coordination, collaboration and co-creation of the PolySolution. It calls for the immediate emergency implementation of two planetary-scale MegaSolutions: a) Hungerless, implementing universal, free access to gourmet whole-foods, plant-based Vegan meals worldwide, eliminating hunger and accelerating food and health systems transformation, and b) Cool, halting planetary overheating through agricultural emissions elimination, massive rewilding for carbon sequestration, and comprehensive stabilization of the life-support systems of our planet.

Abstract: Existing unmanned aerial vehicle (UAV) urban logistics planning follows a sequential paradigm—depot siting first, routing second—that embeds a structural information loss. Straight-line distance screening systematically overestimates the feasible service radius of candidate depots, creating a blindzone of depot–demand pairs that appear reachable but prove operationally infeasible under road-network distances. We term this range-feasibility blindness and derive its analytical radius Δ=Rmax(α−1)/(2α), where α is the road-to-straight-line distance ratio. Empirical measurement across three Chinese urban districts confirms α∈[1.40,1.52] and blindzone radii exceeding 2.8 km, establishing the phenomenon as a systemic property of high-density urban road geometry. To eliminate this failure by construction, we formulate a feasibility-embedded location–routing mixed-integer linear programme (MILP) that enforces road-network range constraints simultaneously with depot-opening decisions, making blindzone configurations implicitly inadmissible. A structure-aware Adaptive Large Neighbourhood Search (ALNS) solves the model at practical scales. Benchmark experiments across all three cities show consistent cost reductions of 20.6–28.2% over sequential baselines, with gains increasing monotonically with instance scale. These results position joint optimisation as a necessary methodological shift for city-scale UAV infrastructure planning.

Abstract: The rapid advancement of Machine Learning (ML) has significantly transformed polymer science by enabling efficient prediction and design of polymer properties through high‑throughput screening. However, current methods still struggle with nonlinear Structure–Property Relationships (SPRs), limited dataset standardization, and computational inefficiency, which restrict prediction accuracy and interpretability. This study proposes a comprehensive ML‑based framework for predicting polymer properties and identifying SPRs. The approach integrates data preprocessing, molecular descriptor and topological index–based feature extraction, iterative feature selection, and XGBoost predictive modeling. Model hyperparameters are optimized using the Starfish Optimization Algorithm (SOA) to enhance performance and efficiency. Model interpretability is achieved through SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME), providing both global and local insights into the influence of molecular features on polymer properties. Experimental evaluation on the PolyOne dataset demonstrates strong predictive performance, with R² values exceeding 0.92, mean absolute error (MAE) below 0.08, and root mean square error (RMSE) under 0.12 for key physical and optical polymer properties. Overall, the proposed framework effectively balances accuracy, computational efficiency, and interpretability, offering a robust and practical tool for accelerating polymer design while enhancing understanding of molecular structure–property relationships.

Abstract: With the large-scale commercialization of 5G and rapid evolution of 6G wireless systems, planar interdigital bandpass filters (BPFs) have become the core passive components for low-power RF front-ends. However, state-of-the-art filter design methods either rely heavily on empirical trial-and-error with 8–10 simulation iterations, or fail to resolve the inherent trade-off between center frequency tuning and stopband performance degradation, which cannot meet the demands of rapid customized design for 5G/6G multi-band scenarios. In this paper, a symmetric five-resonator three-segment patch-type interdigital BPF is taken as the research object. Through theoretical derivation, full-wave electromagnetic simulation, parametric scanning and orthogonal experiments, the quantitative mapping between structural parameters and filter performance is established. Notably, the directional tuning mechanism of the resonator’s narrow segment width on the first stopband is first revealed, which realizes lossless stopband optimization without disturbing the center frequency. On this basis, a three-stage standardized design procedure is proposed, which reduces design iterations from 8–10 to 3, shortens the design cycle by over 70%, and achieves 100% compliance of core design indexes. This work provides an implementable, low-threshold engineering method for rapid customized design of planar interdigital BPFs for 5G/6G RF front-ends.

Abstract: This study aimed to analyze the temporal trends and spatial distribution of mortality from malignant central nervous system (CNS) neoplasms in Brazil from 2000 to 2023. An ecological, time-series study using Joinpoint regression for temporal analysis and Global and Local Moran’s I for spatial patterns. Data were retrieved from the Mortality Information System (SIM). A total of 187,551 deaths were recorded. The Average Annual Percent Change (AAPC) showed a significant growth of +2.1% (95% CI: 1.8 – 2.4). However, a significant inflection point was identified in 2013; between 2000–2013, the APC was +4.1% (p < 0.05), becoming non-significant (+0.8%) thereafter, likely reflecting methodological data inconsistencies in the national system. Spatially, high-high clusters were concentrated in the South and Southeast regions (Moran’s I p < 0.05), while the North and Northeast presented low-low clusters, suggesting significant underreporting. While mortality trends appear to increase, they are heavily influenced by regional diagnostic disparities and information system transitions. This is the first nationwide study to integrate spatio-temporal dynamics to highlight these inequities in Brazil.

Abstract: Background: Transthyretin amyloidosis (ATTR) is a rare, often underdiagnosed and undertreated, autosomal dominantly inherited, progressive disease that affects multiple systems of the body. It results from the extracellular accumulation of misfolded transthyretin (TTR) protein as insoluble amyloid fibrils, predominantly causing cardiomyopathy, polyneuropathy or mixed phenotypes. It can occur in a hereditary form (ATTRv) and in a wild-type form (ATTRwt), with over 150 different pathogenic mutations having been identified worldwide. The clinical presentation is highly variable, leading to a diagnostic delay of 2–5 years. Transthyretin (TTR) amyloidosis is an inherited disease for which recent advances in pathogenesis, diagnosis and treatment have revolutionized its management. Objectives: The aims of this review are to provide an update on the epidemiology and genotype-phenotype correlation, on current diagnostic techniques and on emerging and individualized treatments for this rare hereditary disease. Particular attention will be given to the early diagnosis Methods: A systematic literature search was conducted across major databases to identify studies addressing clinical characteristics, diagnostic modalities, and treatment outcomes in hereditary and wild-type ATTR amyloidosis. Registry data from THAOS and other multinational cohorts were analyzed to evaluate phenotypic variability across genotypes and geographic regions. Results: Clinical presentation of TTR related amyloidosis (h-Amyloid) can range from early onset to late onset with late onset having worse neurological and cardiac involvement at time of diagnosis. The Val30Met mutation is the most common TTR mutation worldwide, however patients with non-V30M mutations can have very different presentations of h-amyloidosis. Identifying “red flag” symptoms in a patient with suspicious clinical presentation can initiate correct diagnostic pathway. Non-invasive imaging, especially bone scintigraphy, has greatly facilitated the diagnosis of patients with Transthyretin related cardiac amyloidosis (ATTR-CM). First generation h-amyloid treatments or TTR stabilizers such as tafamidis have been shown to significantly improve survival in patients with h-amyloidosis. The second generation treatments such as RNA silencers (patisiran, vutrisiran, inotersen, eplontersen) have been shown to halt the disease progression. Present data from small to moderate-sized patient cohorts demonstrate that TTR-targeting therapy is associated with reduction of cardiovascular events and improvement in survival compared with current standard of care. Early recognition of key clinical features and application of a diverse diagnostic strategy, in conjunction with timely initiation of disease-modifying therapy, are critical to optimal management of patients with hereditary transthyretin (ATTR) amyloidosis. Conclusions: The therapeutic options have evolved and improved in recent years, and with current diagnostic tools, the opportunity to alter the natural history of a disease that was once invariably fatal is better than ever. Because the disease is systemic, a thorough, multidisciplinary approach to patient management is ideal.

Abstract: C-reactive protein (CRP) was discovered in 1930 by Tillett and Francis as a protein that reacts with C-polysaccharides to form precipitates in the serum of patients with pneumococcal infection. Subsequently, it was found to increase in the serum of patients with bacterial infections and rheumatic diseases, and it has since been widely recognized as a nonspecific biomarker of acute inflammation and utilized in clinical medicine. Meanwhile, CRP-like proteins are also present in the hemolymph of horseshoe crabs, and it has become clear that these proteins have long played a crucial role in the humoral innate immune response against foreign microorganisms. In recent years, advances in molecular analysis have revealed the details of the complex biological functions performed by CRP. Furthermore, with the development of highly sensitive CRP measurement methods, its importance as a biomarker is gaining attention not only in acute inflammatory diseases but also in chronic inflammatory diseases such as cardiovascular disease, diabetes, cancer and neurological disorders. New treatment strategies targeting CRP, based on recent findings, are also being explored.

Abstract: Mechanistic models in radiobiology have proliferated across FLASH ultra-high dose-rate radiotherapy, spatially fractionated radiation therapy (SFRT), and stereotactic body radiotherapy (SBRT), yet the field has not converged on unified mechanistic explanations despite decades of model development. We propose that this proliferation partly reflects a structural property of the model-observation pairing itself: clinically accessible measurements may be insufficient to uniquely recover the parameters governing the underlying biological dynamics. Using generating-series structural identifiability analysis, we examine four representative models spanning the temporal, spatial, magnitude, and adaptive-state dimensions of radiobiological response: the Pratx-Kapp radiolytic oxygen depletion model (FLASH), the McMahon kinetic-bystander model (SFRT), the LQ-L extension (SBRT), and the Gupta phenotypic-plasticity model of adaptive resistance. The Pratx-Kapp and McMahon models exhibit intrinsic non-identifiability under conventional surviving-fraction observation, while the LQ-L and Gupta models exhibit observation-dependent identifiability conditional on dose-range coverage and marker-panel richness. These findings suggest that increasing radiobiological complexity progressively exposes the limitations of fixed-parameter mechanistic descriptions under partial observability. As a constructive response, we propose, as a hypothesis, that adaptive latent-state inference frameworks operating over a coupled multi-layer organizational state may provide a complementary operational paradigm for radiobiology under uncertainty.

Abstract: Background and Objectives: Controlled hypotension during functional endoscopic sinus surgery (FESS) improves surgical field visibility but may pose a risk of subclinical cerebral hypoperfusion. Serum S100Β and neuron‑specific enolase (NSE) are established biomarkers of glial and neuronal injury and may reflect perioperative neuroprotection associated with different anesthetic regimens. This study evaluated the effect of four anesthetic protocols on perioperative brain biomarker release during FESS. Materials and Methods: In this single‑center, randomized, controlled trial, 88 adult patients (ASA I–III) undergoing FESS under moderately controlled hypotension (mean arterial pressure <55 mmHg) were allocated to one of four groups: propofol–remifentanil, propofol–remifentanil with ketamine–magnesium, sevoflurane–remifentanil, or sevoflurane–remifentanil with ketamine–magnesium. Serum S100Β and NSE concentrations were measured at three timepoints: early intraoperatively, during hypotension, and at the end of surgery. Biomarker data were analyzed using nested ANOVA and linear mixed‑effects models adjusted for relevant covariates. Secondary outcomes included recovery characteristics, surgical field quality, bleeding scores, and perioperative hemodynamics. Results: Baseline demographic and perioperative characteristics were comparable across groups. The group receiving sevoflurane–remifentanil combined with ketamine–magnesium showed the lowest S100B levels (p=0.01 compared to the propofol–remifentanil group; p=0.04 compared to the sevoflurane–remifentanil group). Additionally, NSE concentrations were markedly lower in both sevoflurane groups (sevoflurane–remifentanil and sevoflurane–remifentanil plus ketamine–magnesium) compared to the propofol–remifentanil group (p=0.003 and p=0.007, respectively). No intergroup differences were observed at baseline and surgical field quality, bleeding, and hemodynamic parameters did not differ significantly among groups. Recovery and extubation times were shortest with propofol–remifentanil, whereas ketamine–magnesium prolonged emergence. Conclusions: Anesthetic technique significantly influences perioperative brain biomarker release during FESS. Sevoflurane‑based regimens, with or without ketamine–magnesium, demonstrate more favorable neurobiological profiles under controlled hypotension, although propofol‑based anesthesia offers faster recovery.

Abstract: The emergence of vancomycin-intermediate Staphylococcus aureus (VISA) threatens the efficacy of this last-line antibiotic. The GraSR two component system is frequently mutated in VISA strains. Here, we demonstrate that the GraS(T136I) point mutation, identified in the clinical VISA isolate XN108, is a key determinant of reduced vancomycin susceptibility. Introducing this mutation into the susceptible strain Newman increased the vancomycin MIC from 1.5 to 4 mg/L, while its reversion in XN108 decreased the MIC from 12 to 8 mg/L. The mutation conferred common phenotypes, including thickened cell wall, decreased autolysis, and reduced cell surface negative charge via upregulation of the dltABCD operon and mprF. Notably, GraS(T136I) mutation also upregulated virulence genes (efb, hlb, sbi, hld) and enhanced hemolytic activity. Interestingly, despite this hypervirulence profile, the mutant showed impaired long term survival within macrophages. Our study reveals that a single GraSR mutation can co-regulate vancomycin resistance and virulence, offering new insights into the adaptation of S. aureus to antibiotic pressure.

Abstract: Background: Chronic low back pain (CLBP) is the leading cause of years lived with disability globally, affecting over 600 million individuals. Intervertebral disc degeneration (IVDD) is a principal structural contributor, yet conventional treatments, including pharmacotherapy, physical therapy, and surgical intervention, do not reverse the underlying degenerative pathology. Regenerative medicine has introduced a spectrum of biological therapies, ranging from cell-based mesenchymal stromal cells (MSCs) transplantation to cell-free modalities, including platelet-rich plasma (PRP), platelet lysate, MSC-derived extracellular vesicles (EVs), and MSC-derived secretomes. However, these approaches have largely been studied in isolation, without a unified framework to compare their respective advantages and limitations in CLBP secondary to IVDD. Accordingly, this narrative review aims to provide an integrated and comparative evaluation of these regenerative strategies within a single translational and clinical context. Methods: For this narrative review, PubMed, Scopus, and Web of Science were searched from January 2000 to January 2026 using terms combining regenerative modalities with intervertebral disc degeneration, and chronic low back pain. Randomized controlled trials (RCTs), prospective cohort studies, systematic reviews, and preclinical studies with translational relevance were included. Results: Intradiscal MSC therapy has demonstrated safety across multiple phase I–III trials, but two recent landmark RCTs (RESPINE and the Mesoblast phase III trial) failed to meet primary efficacy endpoints, highlighting the gap between preclinical promise and clinical outcomes. PRP has the largest clinical evidence base, with level II evidence supporting short- to medium-term pain relief for discogenic pain, although standardization remains a critical barrier. Platelet lysate, MSC-derived EVs, and MSC-derived secretomes show compelling preclinical data, including extracellular matrix restoration, anti-inflammatory modulation, and attenuation of nucleus pulposus cell apoptosis, but remain at early translational stages for spinal applications, with no completed RCTs. The hostile disc microenvironment (avascular, hypoxic, acidic, and nutrient-poor) poses unique challenges for all regenerative modalities, differing fundamentally from other musculoskeletal applications. Conclusions: The studies included in this narrative review suggest that no single regenerative modality has yet shown consistent and unequivocal efficacy for CLBP secondary to IVDD across clinical trials. Cell-free approaches offer manufacturing, scalability, and safety advantages over cell-based therapies, but lack clinical validation. Future progress requires standardized preparation protocols, disc-specific delivery systems, patient phenotyping strategies, and rigorously designed comparative clinical trials. This narrative review provides a framework for researchers and clinicians to evaluate these therapies in context rather than isolation.

Abstract: Avian metapneumovirus (aMPV) represents a serious respiratory pathogen of poultry and is associated with considerable economic losses in breeder flocks worldwide. Although horizontal transmission is well established, the contribution of vertical transmission remains poorly understood, especially under field conditions in chickens. In this study, we aimed to assess whether aMPV could be transmitted vertically in unvaccinated broiler breeder flocks that tested positive by PCR in Egypt. Therefore, 10 broiler breeder flocks (≥30 weeks) from seven Egyptian governorates were screened for aMPV subtypes A and B. From each flock, tracheal swabs were collected from breeder hens, along with 20 fertile eggs and 20 newly hatched chicks. All samples, including tracheal swabs, chicken tissues (trachea, lungs, reproductive tract, and spleen), eggshells, internal egg contents, and embryonic tissues were analyzed for aMPV RNA using subtype-specific RT-qPCR. All breeder flocks tested positive for aMPV subtype B, but not subtype A. No aMPV RNA was found in eggshells, internal egg contents, embryonic tissues, or tissues from newly hatched chicks. In conclusion, Despite PCR detection of aMPV in breeder hens, the absence of viral RNA in eggs and their progeny provides field evidence that vertical transmission of subtype B is unlikely to play a significant role in virus spread in commercial broiler breeder flocks. These results support horizontal transmission as the primary route of aMPV spread and highlight the need for further longitudinal and genomic studies to better elucidate aMPV transmission dynamics.

Abstract: Although SARS-CoV-2 has been extensively studied from clinical, virological, and diagnostic perspectives, the problem of accurate automatic semantic segmentation of SARS-CoV-2 particles in electron microscopy images remains inadequately explored. Existing studies have largely focused on virus detection, classification, morphometry, or conventional image analysis, while comparatively little attention has been paid to pixel-level delineation of viral structures using specialised deep learning segmentation frameworks. To address this gap, we propose here a deep learning system based on convolutional neural networks (CNNs) combined with image processing techniques to establish semantic segmentation tools for the automatic identification of SARS-CoV-2. Our approach utilises the super-Euclidean pixels method as an intermediate layer within the CNN for semantic segmentation. We then compare its performance against the gradient vector flow (GVF) and Poisson inverse gradient (PIG) segmenters. The proposed CNN model surpassed the traditional GVF and PIG segmentation models, achieving the following metrics (mean ± variance): Dice similarity coefficient (DSC) = 0.9345 ± 0.0006; intersection over union (IoU) = 0.8782 ± 0.0018; sensitivity/true positive rate (TPR) = 0.9373 ± 0.0018; specificity/true negative rate (SPC) = 0.9517 ± 0.0012; accuracy = 0.9449 ± 0.0004; area under the ROC curve (AUC) = 0.9446 ± 0.0431; and Cohen’s Kappa = 0.9137 ± 0.0011. This method enables virologists to employ an automatic CNN-based segmentation tool for detecting SARS-CoV-2 and demonstrates superiority over GVF and PIG.

Abstract: Background: The prognostic value of serial coronary artery calcium (CAC) progression remains uncertain in Asian populations, particularly among patients receiving statin therapy. We evaluated whether CAC progression predicts major adverse cardiovascular events (MACE) in a Taiwanese cohort and whether this association differs by statin use. Methods: We retrospectively studied 1,942 individuals who underwent two cardiac computed tomography scans for CAC scoring at a tertiary medical center in Taiwan between 2006 and 2021. CAC progression was defined as an annualized Agatston score increase of ≥20 units/year. The primary outcome was MACE, defined as acute myocardial infarction, stroke, or cardiovascular death. Predictors of CAC progression were assessed using logistic regression. Associations between CAC progression and MACE were evaluated using Cox models with propensity score–based inverse probability weighting; 1,621 participants with complete covariate data were included in weighted analyses. Results: CAC progression occurred in 397 participants (20.4%). Independent predictors included male sex, hypertension, fasting glucose, lipid parameters, and baseline CAC score. CAC progression was associated with a higher risk of MACE, with increasing event rates across higher categories of annualized CAC change (p for trend < 0.0001). This association was consistent across clinical subgroups and was observed in both statin and non-statin users, without a significant CAC progression × statin interaction (p = 0.163). Conclusions: In this Asian serial CAC cohort, CAC progression was strongly associated with future MACE and may serve as a marker of residual cardiovascular risk, including among statin-treated patients. Serial CAC assessment may support dynamic risk stratification, but prospective studies are needed to determine whether progression-guided management improves outcomes.

Abstract: Against the backdrop of escalating global climate risks, this study systematically investigates the effects, boundary conditions, and interactive mechanisms of two core policy instruments—green finance and environmental regulation—on agricultural supply chain resilience. Using panel data of China’s Shanghai and Shenzhen A-share listed agribusiness firms from 2010 to 2025 and a two-way fixed effects model, we find: First, climate risk serves as a critical external pressure driving resilience building, yet its positive impact is conditional on a “capacity threshold”—only significant for firms with high resilience and large scale. Second, the two policy instruments exhibit heterogeneous structural thresholds: green finance demonstrates a “supporting-the-weak” effect, enhancing resilience primarily in small and medium-sized enterprises (SMEs) with low resilience, but is constrained by an “institutional–technological” double threshold. In contrast, environmental regulation displays a “scale bias”, with its statistically significant positive effect limited to large firms. Third, climate risk negatively moderates the effectiveness of green finance: under high-risk conditions, firms tend to divert green funds toward short-term relief, eroding long-term resilience investment, and this “policy failure” risk is particularly pronounced among SMEs. Fourth, mechanism tests rule out the traditional mediation channel of alleviating financing constraints; moreover, the two policy instruments have not yet formed significant synergistic effects under the current institutional framework. This study extends the application boundaries of the resource-based view and dynamic capabilities theory in high-risk contexts, provides micro-level empirical evidence on policy instrument implementation biases in heterogeneous market structures, and offers theoretical support and practical references for developing climate-smart agricultural supply chain policies.