Search Results (1,164)

Introduction: Community-based day center programs may support healthy ageing by promoting functional ability, mental well-being, and social participation among older adults, but real-world evidence from Latin America remains limited. Objective: We aimed to examine changes in functional status, mental health, and quality of life among older adults participating in the CEDIAM program in the Maule Region of Chile in 2022 and 2023. Methods: Pre–post observational study using routinely collected data from 15 CEDIAM centers. The 2022 and 2023 datasets were analyzed as independent cohorts. Functional status was assessed with the Barthel Index, the Lawton and Brody scale, and the Timed Up and Go test; mental health with the Mini-Mental State Examination and the 15-item Geriatric Depression Scale; and quality of life with the EuroQol-5D visual analogue scale. Paired comparisons, category-transition analyses, and multivariable logistic regression models of improvement were performed. Results: Baseline samples included 894 participants in 2022 and 897 in 2023. In 2022, all continuous outcomes improved significantly (all p ≤ 0.001). In 2023, the Barthel Index, the Timed Up and Go test, and the Geriatric Depression Scale improved (all p < 0.0001), and the EuroQol-5D visual analogue scale also improved (p < 0.01), whereas the Lawton and Brody scale (p = 0.204) and the Mini-Mental State Examination (p = 0.725) did not. Category-transition analyses showed significant improvements in basic activities of daily living and mobility in both cohorts (both p < 0.001), while significant categorical changes in instrumental activities of daily living, global cognition, depressive symptoms, and self-rated quality of life were observed only in 2022 (all p ≤ 0.01). Rural residence was associated with higher odds of improvement in basic activities of daily living (OR 1.62, 95% CI 1.17–2.25; p = 0.004), whereas age ≥75 years was associated with lower odds of improvement in depressive symptoms (OR 0.56, 95% CI 0.41–0.76; p < 0.001) and self-rated quality of life (OR 0.65, 95% CI 0.45–0.94; p = 0.023). Conclusions: Participation in CEDIAM was associated with favorable changes, particularly in basic functional status and mobility, although responses varied across outcomes and participant subgroups. Full article

This paper proposes an advanced metering infrastructure (AMI)-informed hierarchical energy management framework for coordinated operation of electric vehicles (EVs), photovoltaic (PV) systems, and battery energy storage systems (BESS) in campus microgrids. The proposed two-layer architecture integrates a soft actor–critic (SAC) deep reinforcement learning (DRL) agent in the upper layer with a receding horizon model predictive control (MPC) optimizer in the lower layer. The key novelty is an AMI-to-control pipeline that transforms historical 15 min smart-meter measurements into operational flexibility features and embeds them into a hierarchical SAC–MPC architecture, where the DRL layer provides adaptive coordination and the MPC layer enforces grid, storage, and EV-service constraints. The proposed framework using the real-world Pecan Street data (15 min resolution) of 73 homes across Austin, Texas and California (2014–2019) achieves a 53.1% cost reduction and a 25.7% peak demand reduction when compared with uncontrolled charging, and the proposed framework outperforms MPC-only (50.9%), DRL-only (−5.2%), and rule-based (5.1%) baselines. The statistically significant contributions of network-aware constraints, demand-response activation, and predictive look-ahead horizon are statistically significant (n = 10 independent runs) contributions (p = 0.001). The state representation informed by AMI offers directional cost improvement (+8.4%, p = 0.055) with 11% faster convergence of training. The zero network constraint violation is observed in all evaluation scenarios and the average MPC solve time is around 150 ms, which is much less than the 15 min sampling period. Sensitivity analyses show that the hierarchical DRL–MPC architecture remains computationally feasible across EV penetration, seasonal, and forecast-uncertainty scenarios. However, BESS provided no net economic benefit under the evaluated energy-only TOU tariff, increasing weekly cost by $15.25 and peak grid demand by 14.2 kW. Break-even analysis indicates that demand charges of approximately $9.9/kW per month are required for BESS to become cost-effective in the proxy system, highlighting that storage value depends strongly on tariff design and peak-demand objective formulation. Full article

Tourism destinations increasingly operate through digital platforms, real-time data, and online connectivity tools, yet service coordination among stakeholders remains constrained. This study analyses how tourism logistics information is created, updated, verified, shared, and used in service coordination at coastal destinations in Vietnam’s South Central Coast subregion. The study adopts a multi-stakeholder qualitative case study design, in which qualitative data from 42 participants provide the main source of evidence, while a supplementary descriptive survey of 358 participants is used only to contextualise and clarify selected issues within the sample. The findings show that existing digital tools and information channels support information search, transaction confirmation, operational coordination, and service feedback. However, coordination at the destination and inter-provincial levels remains limited when information flows are fragmented across stakeholders, channels or platforms, data or procedures, and local or regional spaces. On this basis, the study conceptualises the Tourism Logistics Information System (TLIS) as a socio-technical, inter-organisational, and coordination-oriented structure in which the value of information depends on the extent to which data are updated, verified, shared, and translated into coordinated action among stakeholders. The paper contributes to research on smart destinations, destination governance, and tourism supply chains by clarifying information fragmentation (IF) as a mechanism that constrains service coordination in inter-provincial coastal destination contexts. Full article

Background and Objectives: Faricimab, the first bispecific antibody targeting VEGF-A and angiopoietin-2, has demonstrated durable efficacy in pivotal phase 3 trials for neovascular age-related macular degeneration (nAMD) and diabetic macular edema (DME). Real-world data on treatment-naïve patients managed with fixed-interval maintenance protocols, particularly from South-Eastern Europe, remain limited. This pilot study evaluated 24-month outcomes of intravitreal faricimab in treatment-naïve nAMD and DME, using a standardized four-injection loading phase followed by fixed every-16-week (Q16W) maintenance. Materials and Methods: This study conducted a retrospective, observational, single-center pilot cohort study of 20 consecutive treatment-naïve eyes (9 nAMD, 11 DME). All patients received four monthly loading injections followed by a fixed every-16-week (Q16W) maintenance schedule, supplemented by discretionary additional injections for residual or recurrent disease activity (215 injections total; mean 10.75 ± 0.79 per patient; range 9–12). Primary outcomes were changes in central foveal thickness (CFT) and best-corrected visual acuity (BCVA; Snellen lines with ETDRS letter equivalents) at months 4 and 24. Prespecified secondary analyses included bootstrap 95% confidence intervals, a linear mixed-effects model with a time × disease-group interaction, Bayesian credible intervals with weakly informative priors, false-discovery-rate (FDR) correction, and a minimum detectable effect-size analysis. Results: All 20 eyes completed 24-month follow-up. In nAMD, mean CFT decreased by 186.9 ± 71.9 µm (35.9%; bootstrap 95% CI 148.1–236.0; p < 0.001; d = 2.60), and BCVA improved by 3.89 ± 0.78 Snellen lines (~19 ETDRS letters; 95% CI 3.44–4.33; p < 0.001; d = 4.97). In DME, CFT decreased by 197.7 ± 65.7 µm (39.3%; 95% CI 162.5–237.3; p < 0.001; d = 3.01), and BCVA improved by 4.55 ± 1.04 lines (~23 ETDRS letters; 95% CI 4.00–5.09; p < 0.001; d = 4.39). All 20 eyes (100%) achieved ≥ 3 Snellen lines gain and ≥20% CFT reduction; 80% reached final BCVA ≥ 7 lines. A linear mixed-effects model showed a significant time effect (p < 0.001) but no time × group interaction (CFT p = 0.84; BCVA p = 0.51), indicating concordant trajectories across diseases. Bayesian analysis with weakly informative priors yielded posterior P(|d| > 0.8) ≥ 0.99 for all primary outcomes. After FDR correction, all pre-specified primary comparisons remained significant. The minimum detectable effect size with the realized sample sizes (Cohen’s d ≈ 0.66 combined, 1.07 nAMD, 0.94 DME at 80% power) was substantially below all observed effect sizes. No ocular or systemic adverse events were recorded. Conclusions: In this small, single-center, treatment-naïve pilot cohort, a fixed Q16W faricimab maintenance schedule with discretionary additional injections was associated with durable anatomical and functional improvements over 24 months in both nAMD and DME, with no adverse events recorded across 215 injections. Given the limited sample, these findings should be regarded as hypothesis-generating. The high responder rates likely reflect the cohort’s substantial baseline visual impairment (mean baseline BCVA ~20/120–20/200), which provides greater absolute capacity for measurable gain than in higher-acuity registration trial populations. These pilot data support fixed-interval faricimab as a logistically feasible candidate strategy in resource-constrained settings and should be confirmed in larger multicenter cohorts using standardized ETDRS acuity assessment. Full article

Vision-based fire detection models are often limited by the insufficient diversity of annotated fire and smoke images, particularly in terms of fire location, flame scale, smoke density, ignition cause, and indoor scene context. This study investigates whether generative AI-based synthetic images can expand fire-image diversity and improve object detection-based fire detection performance. Real fire images were combined with conventional augmented images and synthetic images generated using ChatGPT-4.o and ChatGPT-5.5. The generated images were constructed using multivariable prompts considering fire location, scale, and cause, and unsuitable samples were screened using a pretrained fire detection model. YOLOv8n, YOLOv11n, and RT-DETR were trained under 48 dataset–detector conditions and evaluated using fixed validation and test datasets. The results showed that generated-image-based training generally maintained or improved detection performance compared with the original and conventional augmentation conditions. In particular, selected ChatGPT-4.o-based YOLOv11 conditions showed statistically supported improvements over matched augmentation conditions, with increases of +0.052 in Precision, +0.031 in Recall, +0.065 in mAP@0.5, and +0.038 in mAP@0.5:0.95. LPIPS and t-SNE analyses indicated that the generated images formed structured perceptual and feature-space distributions relative to real fire images. Scenario-based inference using location-specific video frames also showed stable model responses in several complex indoor fire environments. These findings suggest that validated generative AI-based images can supplement the limited visual diversity of real fire datasets and improve the robustness of vision-based fire detection models. Full article

This study systematically characterizes Do It Yourself (DIY) and open-source wheeled robotic platforms used in higher education and academic competitions. It also analyzes Robot Operating System (ROS)-based designs with respect to real-time performance and multi-sensor integration, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines. A total of 20 high-quality studies were identified across five major digital libraries (Dimensions, Web of Science, SpringerLink, ScienceDirect, and IEEE Xplore), which were searched on 12 January 2026. Eligibility was restricted to peer-reviewed English-language studies published between 2005 and 2026 that explicitly implement ROS-based wheeled platforms in higher education contexts. Results were synthesized through qualitative analysis using a structured data extraction form implemented in the Parsifal systematic review platform. Methodological quality and risk of bias were assessed using a structured appraisal checklist. The results show a dominant trend toward distributed dual-processor architectures, which separate low-level real-time control from high-level processing. Most platforms target an accessible price range of 50€ to 500€ for open-source and DIY platforms. ROS has emerged as the standard middleware, enabling multi-sensor integration and supporting digital twin workflows. There is also a clear shift toward open-source hardware and Three-Dimensional (3D)-printed modular designs, which reduce production costs. However, challenges remain, including software obsolescence and the lack of maintenance plans. The findings highlight the need for interoperable reference architectures and automated deployment workflows to ensure long-term sustainability. Evidence is limited by heterogeneity, inconsistent reporting, and small sample sizes, which introduce risks of bias and imprecision. This review was formally registered with protocols.io. Full article

Chub Mackerel (Scomber japonicus) is an important pelagic fishery resource in the Northwest Pacific, and its fishing-ground distribution is strongly influenced by dynamic marine environmental conditions. This study aimed to evaluate how environmental information at different temporal scales affects the prediction of high-catch fishing grounds and to identify environmental-variable contributions. Fishery logbook data from Chinese light purse seine vessels during 2014–2022 were combined with marine environmental variables to construct four feature sets: instantaneous features (E1), multi-temporal-scale fusion features (E2), short-term features with 7-day rolling means (E3), and long-term features with 30-day rolling means (E4). Deep Forest, random forest, XGBoost, LightGBM, and CatBoost were evaluated using nested spatial group cross-validation, and SHapley Additive exPlanations (SHAP) was applied to interpret model predictions. The results showed that, after historical environmental information was added, AUC values increased for most models, and the multi-temporal-scale fusion features performed better in metrics related to high-catch sample identification; therefore, the hypothesis proposed in this study was supported in the overall trend. Model comparisons showed that Deep Forest performed relatively stably under E2, E3, and E4, whereas RF performed relatively well under E1. Short-term environmental features helped improve overall fishing-ground discrimination, whereas multi-temporal-scale fusion was more favorable for identifying high-catch samples. Time-lag correlation and SHAP analyses indicated that short-term environmental changes, longer-term background conditions, and seasonal signals jointly provided information for model prediction. This study may provide a reference for real-time fishing-ground prediction and fishery management. Full article

Background/Objectives: Novel vision-language models (VLMs) can integrate patient textual data with image data to support medical diagnosis. Recent studies reported conflicting results regarding the performance of multimodal VLMs compared to other models and physician performance. This systematic review aims to assess the diagnostic performance of multimodal VLMs integrating both patient textual and image data across diverse real-world hospital settings. Methods: We performed comprehensive searches of eight resources, including Embase, MEDLINE, and SCOPUS, on 17 December 2025. Eligible studies reporting diagnostic performance of VLMs integrating both image and patient history textual data from real-world adult patients compared to that of other models and physicians were included. The review adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The Prediction model study Risk Of Bias Assessment Tool + AI (PROBAST + AI) was used to assess the quality and risk of bias. The study protocol was registered in the PROSPERO database (CRD420251244054). This review received no external funding. Results: We screened 11,026 records, of which 18 studies met the inclusion criteria. Six studies comparing multimodal and unimodal models demonstrated the consistent superiority of the multimodal models. Four studies evaluating VLM accuracy as standalone agents compared with physician performance reported conflicting evidence. One study assessing VLMs as a clinical copilot demonstrated higher accuracy from the group of physicians using VLM assistance. A meta-analysis could not be performed due to the heterogeneity across study populations and outcomes. The majority of the studies were assessed as having a high risk of bias due to dataset quality. Primary limitations identified across studies include small sample size, a lack of external validation, and the need for prospective clinical deployment studies. No study provided documented considerations regarding model safety or data security. Conclusions: This systematic review suggests that multimodal VLMs consistently outperform unimodal models with access to only image or text. While model performance as standalone agents compared to humans remains inconclusive, a copilot model has demonstrated high diagnostic accuracy. Given substantial methodological concerns across studies, cautious interpretation is required, No firm clinical recommendation can be made regarding the use of standalone VLMs. Further research employing high-quality datasets is needed to ensure the reliability and clinical applicability of future VLMs. Full article

Lysine lactylation (Kla) is a recently discovered post-translational modification implicated in energy metabolism, cellular reprogramming, and disease progression. Here, we train the existing ProteinBERT-based predictor PBertKla on an integrated multi-source dataset and augment it with a lightweight machine-learning (ML) layer over sequence-derived features to predict Kla sites; on a common blind test set, the resulting model (PBertKla + ML) reaches an area under the receiver operating characteristic curve (AUROC) of 0.9126 on the integrated set and is statistically indistinguishable from the strongest available tool (Auto-Kla, DeLong p = 0.74) while significantly exceeding a recent ProtBert-based method (PCBert-Kla, p = 4 × 10⁻¹⁵). Two elements support this result. First, to train and benchmark the model, we assembled and released the largest curated Kla dataset to date, Multi (26,034 samples compiled from nine published sources through a 9-step quality-control pipeline), as a community resource. Second, we validated the model under a leakage-controlled protocol: re-training the complete pipeline under protein-level, 40%-identity homology, and leave-one-study-out splits—each verified to have zero train–test overlap—maintained ≈0.90 AUROC, only 0.6–1.5 percentage points (pp) below the random-split value, confirming genuine generalization rather than memorization. Ablation and SHapley Additive exPlanations (SHAP) analyses locate the predictive signal primarily in the ProteinBERT metafeature, with the ML layer adding a modest but real increment (+0.63 pp over PBertKla alone on Multi; no significant gain on the smaller hepatocellular carcinoma (HCC) set). Finally, an exploratory AlphaFold-based structural case study of FAM210A illustrates how predicted Kla sites distribute across ordered and disordered regions, without claiming a quantitative structure–probability relationship. All trained weights and code are publicly available. Full article

Background/Objectives: Coronary artery disease (CAD) is a leading cause of cardiovascular morbidity and mortality worldwide. Type 2 diabetes mellitus (T2DM) further increases CAD risk through metabolic disturbances and endothelial dysfunction. Adropin, S100A1, and SERCA2b are important regulators of endothelial function, energy metabolism, and calcium homeostasis. This study aimed to investigate the gene and protein expression levels of these biomarkers in CAD patients with and without T2DM. Methods: Gene and protein expression levels of adropin (ENHO), S100A1, and SERCA2b were evaluated in peripheral blood samples obtained from healthy controls (n = 50), CAD patients (n = 46), and CAD patients with T2DM (CAD+T2DM) (n = 40). Gene expression was determined using real-time PCR, while protein levels were measured with ELISA. Additionally, in silico bioinformatics analyses, such as protein–protein interaction networks and pathway enrichment analyses, were performed to explore potential molecular relationships among these biomarkers. Results: Adropin and ENHO gene expression levels were significantly lower in CAD patients and inversely related to the SYNTAX score. S100A1 levels were also reduced, and SERCA2b gene expression was significantly decreased, especially in the CAD+T2DM group. Bioinformatics analyses revealed that these molecules participate in interconnected pathways related to calcium signaling, cardiac muscle contraction, and metabolic regulation. Conclusions: These findings demonstrate links between altered levels of adropin, S100A1, and SERCA2b and CAD with or without T2DM. However, these observations are preliminary and need validation in larger prospective studies and mechanistic research before drawing definitive conclusions about their clinical utility, disease progression, or prognostic value. Full article

Background/Objectives: Advanced hormone receptor-positive (HR+), epidermal growth factor 2-negative (HER2−) breast carcinoma (BC) patients receive frontline therapy with cyclin-dependent tyrosine kinase 4/6 inhibitors + endocrine therapy (ET). At progression, the best management includes mutational analysis for ESR-1, allowing second-line therapy with elacestrant. The aim of this study was to evaluate the efficacy and safety of elacestrant in an Italian real-world setting. Methods: A multicenter, observational study with a mixed retrospective and prospective design was conducted in 13 medical oncology units across Italy. The study population included adult patients with HR+/HER2− locally advanced or metastatic breast cancer with an activating ESR1 mutation documented by liquid biopsy and progressing after at least one line of endocrine therapy containing a CDK4/6 inhibitor. Mutational analysis of plasma was performed using next-generation sequencing with a multigene panel that included ESR1, PIK3CA, AKT, and PTEN. The sample size was calculated according to the two-stage Simon design. Toxicity was classified according to CTCAE version 5.0 criteria. Survival analyses were conducted using the Kaplan–Meier method. Results: At the time of analysis, 39 evaluable patients were enrolled, all female and Caucasian, with a median age of 67 years (range 41–89). The efficacy analysis documented an overall ORR of 28% and a disease control rate of 56%. The median duration of response was 6+ months (95% CL: 3.5–10.6 m). Median overall survival was not reached with a median follow-up of 10 months. The toxicity profile was overall favorable: grade ≥2 asthenia was the most frequent adverse event (23%), followed by gastrointestinal toxicity, which was generally mild. No treatment-related toxicity was reported in 64% of patients. Dose reductions were necessary in 15% of cases, while permanent treatment discontinuation due to toxicity occurred in only 4%. Conclusions: The results of this Italian multicenter observational study confirm the efficacy and tolerability of elacestrant in HR+/HER2− metastatic breast cancer with ESR1 mutation, in a real-world context consistent with the data from the pivotal EMERALD study and with real-world data present in the literature. Full article

Background/Objectives: Sex estimation is a cornerstone of research and practice in bioarchaeology and forensic anthropology. However, morphological and metric methods are often hampered by population-specific variation, subjectivity in assessment, and taphonomy. This study compares morphological analysis and ancient DNA (aDNA)-based sex assessment in a 19th-century Portuguese sample to evaluate the accuracy of osteological (anthropological) criteria. Methods: This study analysed 37 skeletons from the Venerável Ordem Terceira da Nossa Senhora do Carmo burial grounds in Porto. Sex estimation was based on (1) the bioanthropological assessment of morphological traits of the os coxae and the skull (2) through aDNA analysis using a multi-marker approach, including real-time PCR (qPCR) targeting autosomal loci, the amelogenin locus, a Y-chromosomal INDEL, and Y-STRs. aDNA was extracted via a non-destructive protocol. Results: Whilst anthropological analysis was possible on all 37 individuals, estimation of sex through aDNA analysis was possible for 26 individuals. A 20% discordance rate was found between morphological and aDNA results. Many individuals morphologically classified as “possible female” or “indeterminate” were genetically identified as male. Genetic analysis resolved most cases that biological anthropologists concluded were “indeterminate”. Conclusions: The high discordance in the Carmo sub-sample may indicate reduced skeletal sexual dimorphism, with males exhibiting skeletal traits typically associated with females, suggesting a sample-specific reduction in sexual dimorphism likely influenced by environmental, nutritional, and/or genetic stressors. A limitation of this study is its small sample size: only 26 of 37 individuals yielded usable genetic results, and only a portion of these individuals provided sufficient data for a direct comparison between morphological and genetic data. Nevertheless, these findings highlight the risk that applying generalised osteological standards relying solely on morphology can lead to systematic misclassification, emphasising the need for a critical, multidisciplinary approach to sex estimation. Full article

High-dimensional covariance estimation remains a fundamental challenge when the number of variables (p) substantially exceeds the sample size (n). In such settings, the sample covariance matrix is unstable, singular, and heavily contaminated by estimation noise. Although shrinkage estimators improve stability and thresholding methods promote sparsity, each approach alone may introduce bias or lose structural information. This study proposes a Noise-Adjusted Shrinkage Covariance (NASC) framework as a post-processing enhancement strategy for shrinkage-based covariance estimators. The framework first stabilizes the covariance structure through shrinkage toward a structured target, then suppresses noise-induced small covariance entries via thresholding, and finally applies a stabilization step to ensure positive definiteness of the resulting estimator. Sensitivity analyses were conducted to investigate the effects of the shrinkage and thresholding parameters, and the Monte Carlo simulations were subsequently performed using the best-performing parameter configuration. The simulation results showed that shrinkage alone may not sufficiently suppress entrywise noise, whereas NASC-adjusted estimators improved upon their corresponding shrinkage baselines in many scenarios, with the strongest gains observed for sparse covariance structures and for shrinkage estimators that do not explicitly suppress entrywise estimation noise. Improvements were more limited for highly optimized shrinkage estimators. Real-data analyses were conducted on the SRBCT and colon cancer benchmark datasets. On the SRBCT dataset, numerical stability and positive-definiteness properties were examined, while LOOCV-LDA classification performance without prior feature selection or dimensionality reduction was evaluated on the colon cancer dataset. The results suggest that NASC provides a computationally simple and numerically stable extension to classical shrinkage covariance estimation methods for high-dimensions. Full article

Introduction: Pedestrians lying on the road—collapsed through medical emergency, intoxication, or displacement following a prior collision—represent a disproportionately lethal and underaddressed category in road traffic safety. Forensic database analyses derived from Japan’s national police records document a fatality rate of 33.0% for collisions involving pedestrians lying on the road, more than double the rate for upright pedestrian collisions. Standard Advanced Driver-Assistance Systems (ADAS) yield a True Positive Rate (TPR) of only 21.4% for detecting pedestrians lying on the road under night conditions—a classification gap of 73.3 percentage points. Methods: In simulation trials, we evaluated the Advanced Falling Object Detection System (AFODS—where “falling object” denotes the low-profile human form at road level, distinguishing the prone pedestrian from the upright postures addressed by conventional ADAS) on a composite dataset of 3200 annotated fall events and 12,000 negative samples (training/validation), with 320 independent controlled simulation trials used for performance evaluation, spanning real-world, forensic-reconstruction, and Total Human Body Model for Safety (THUMS)-validated synthetic scenarios. No physical prototype has been evaluated; all performance data are derived from simulation, and 37.5% of positive samples are synthetically generated. These simulation conditions represent a first feasibility demonstration pending real-world hardware validation. This paper introduces three original contributions absent from prior work: a three-stage quantitative injury-risk model, a formal ISO 26262 Hazard Analysis and Risk Assessment (HARA), and a medicolegal SHAP interpretability framework. The injury-risk model translated detection latency via impact velocity to Head Injury Criterion (HIC) and estimated fatal injury probability (AIS ≥ 5); these model outputs should be interpreted as exploratory estimates pending ATD validation. Reporting follows principles consistent with the TRIPOD statement. Results: Under clear daytime conditions, AFODS demonstrated a TPR of 98.2% (95% CI: 97.4–98.8%) in simulation, decreasing to 95.6% under night dry-road conditions and 89.4% under night rain. The system achieved an AUC of 0.981 and a mean end-to-end latency of 46.5 ms, representing a 76.8 percentage-point improvement in simulation over the monocular RGB baseline (p < 0.001). The injury-risk model projects a reduction in estimated fatal head injury probability from 66.2% (Monte Carlo mean) (no detection, 50 km/h full-speed impact) to 0.7% under AFODS worst-case night/rain conditions, and to ≈0% under clear daytime simulation conditions. Conclusions: A 73.3 percentage-point classification gap places pedestrians lying on the road outside the effective detection envelope of current ADAS, compounded by the systematic exclusion of non-upright postures from regulatory test protocols and benchmark datasets. AFODS supports proof-of-concept feasibility under simulation conditions. Three translational steps are required: prototype validation on real-world hardware using instrumented Anthropomorphic Test Devices (ATDs); prone-posture biomechanical injury modelling using HIC and BrIC criteria; and regulatory extension of pedestrian AEB test standards to non-upright scenarios. Full article

The re-emergence of Coxsackievirus A6 (CV-A6) as a predominant pathogen in hand, foot, and mouth disease (HFMD) underscores the need for ongoing molecular surveillance to clarify local evolutionary dynamics. This study aimed to characterize the genetic features of CV-A6 strains circulating in Baotou, Inner Mongolia, from 2023 to 2024. Throat swabs collected from HFMD patients were screened using real-time quantitative PCR; the VP1 region and complete genomes of representative CV-A6-positive samples were amplified and sequenced. Phylogenetic and recombination analyses were subsequently performed. Among 266 clinical specimens, 169 (63.53%) tested positive for enterovirus, of which 146 (86.39%) were identified as CV-A6. The local epidemic displayed an autumn–winter seasonality and predominantly affected children aged 4–6 years. Phylogenetic reconstruction of 133 VP1 sequences revealed that all Baotou CV-A6 isolates belonged to subgenotype D3c, and analysis of complete genomes identified a predominant recombinant form. These findings demonstrate that the D3c subgenotype, characterized by a specific recombinant structure, was responsible for HFMD outbreaks in Baotou during the study period, providing essential molecular evidence for regional public health strategies and vaccine development. Full article