Search Results (53)

Background: The hallux interphalangeal ossicle (HIO) is commonly considered as an incidental anatomical variant; however, its biomechanical role remains poorly understood. This study aimed to investigate the influence of HIO on hallux joint biomechanics. Methods: A cross-sectional correlational study was conducted, including 419 feet (218 individuals). The presence of HIO was assessed using ultrasound imaging. Range of motion (ROM) of the metatarsophalangeal joint (MTPJ) and interphalangeal joint (IPJ) were evaluated under both open kinetic chain and dynamic conditions. Statistical comparisons between HIO and non-HIO groups were made, and receiver operating characteristic (ROC) curve analysis was used to assess the discriminative capacity of IPJ ROM. Results: HIO was present in 48% of cases and was bilateral in all participants. Individuals with HIO exhibited significantly greater IPJ extension under both open kinetic chain and dynamic conditions (p < 0.05). No significant differences were observed in MTPJ ROM between groups. A positive, albeit variable, relationship was found between ossicle size and IPJ extension. ROC analysis demonstrated moderate discriminative ability of IPJ ROM for detecting HIO (sensitivity 63.2%, specificity 54.6%). Conclusions: The presence of HIO is associated with increased IPJ extension, suggesting a measurable influence on hallux biomechanics. These findings support the notion that the HIO is a biomechanically relevant structure rather than purely incidental an asymptomatic anatomical variant. Increased IPJ extension may represent an early functional adaptation with potential clinical implications. Full article

Leveraging Artificial Intelligence (AI) ethically in connected healthcare systems requires a quantifiable framework that measures not only outcome correctness, but also the clarity, auditability, and ethical acceptability of model explanations in high-stakes clinical and cybersecurity workflows. This manuscript first presents a narrative review of ethical risks and countermeasures in Healthcare Internet of Things (HIoT) and explains why existing performance metrics are insufficient for trustworthy deployment. We then formalize a quantitative metric called Ethical Explainability (E_e) as a composite index integrating (1) a Human Agreement Ratio (HAR), capturing concordance between AI recommendations (and their rationale) and a calibrated expert consensus, and (2) an Entropy Reduction Index (ERI), capturing the proportional reduction in expert uncertainty after receiving an explanation, operationalized via probability-elicitation questionnaires mapped to Shannon entropy. Designed for HIoT security monitoring, E_e links transparency with governance-ready evidence of trustworthiness for human–AI collaboration. Full article

Sleep disorders, such as insomnia, obstructive sleep apnea (OSA), narcolepsy, REM sleep behavior disorder, and circadian rhythm disturbances, represent a rapidly expanding global health burden that is strongly associated with cardiovascular, metabolic, neurological, and psychiatric diseases. Advancements in wearable sensing technologies and Internet of Medical Things (IoMT) infrastructures have expanded the possibilities for continuous, home-based sleep assessment beyond conventional polysomnography laboratories. These Sleep Health Internet of Things (S-HIoT) systems combine multimodal physiological sensing (EEG, ECG, SpO₂, respiratory effort and actigraphy) with wireless communication and cloud-based analytics for automated sleep-stage classification and disorder detection. Nonetheless, the digitization of sleep medicine brings about significant cybersecurity concerns. The constant transmission of sensitive biomedical information makes S-HIoT networks open to anomalous traffic flows, signal manipulation, replay attacks, spoofing, and data integrity violation. Existing studies mostly focus on analyzing physiological signals and network intrusion detection independently, resulting in a systemic vulnerability of cyber–physical sleep monitoring ecosystems. With the aim of addressing this empirical deficiency, this review integrates emerging advances (2022–2026) in the AI-assisted categorization of sleep phases and IoMT anomaly detector designs on the finer analysis of CNN, LSTM/BiLSTM, Transformer-based systems, and a component part of federated schemes and the lightweight, edge-deployable intruder assessor models available. The aim of this study is to uncover a gap in the literature: integrated architectures to trade off audiences of faithfulness of physiological modeling with communication-layer security. To counter it, we present a single framework to include CNN-based spatial feature extraction, Bidirectional Long Short-Term Memory (BiLSTM)-based temporal models and Random Forest-based ensemble classification using a dual task-learning approach. We propose a multi-objective optimization framework to jointly optimize the performance of sleep-stage prediction and that of network anomaly detection. Performance on publicly available datasets (Sleep-EDF and CICIoMT2024) confirms that hybrid integration can be tailored to achieve high accuracy [99.8% sleep staging; 98.6% anomaly detection] whilst being characterized by low inference latency (<45 ms), which is promising for feasibility in real-time deployment in view of targeting edge devices. This work presents a comprehensive framework for developing secure, intelligent, and clinically robust digital sleep health ecosystems by bridging chronobiological signal modeling with cybersecurity mechanisms. Furthermore, it highlights future research directions, including explainable AI, federated secure learning, adversarial robustness, and energy-aware edge optimization. Full article

Background/Objectives: Insect-derived lipids are emerging as circular-economy feed ingredients, but their implementation in ruminant diets requires robust safety assessment beyond productive endpoints. This study evaluated genome integrity in 26 lactating Valdostana Red Pied cows fed concentrates containing either hydrogenated palm fat (HPF; n = 13) or black soldier fly oil (Hermetia illucens oil, HIO; n = 13) for 50 days. Methods: Peripheral blood lymphocytes were analyzed using Sister Chromatid Exchanges (SCE), reflecting replication-associated chromosomal instability, and the alkaline Comet assay, quantifying primary DNA damage at the single-cell level (Tail DNA and Olive tail moment) at T0 (the day before the start of the two experimental diets), T1 (30 d) and T2 (50 d). Results: Baseline SCE frequencies were comparable between groups. Over time, SCE values decreased in both groups, but a significant reduction occurred only in HIO at day 50, with lower SCE frequency than HPF (5.73 ± 0.11 vs. 6.29 ± 0.13; p = 0.002). Comet tail DNA showed a significant time effect (T0 vs. T1: mean difference = 179,846.6; p < 0.001; T0 vs. T2: mean difference = 138,395.2; p = 0.012), with diet-dependent modulation. In fact, in HIO, tail DNA decreased from 387,886 ± 94,606 (T0) to 147,006 ± 30,592 (T1; p < 0.001), remained lower at day 50 (155,723 ± 29,357; p = 0.024), and was lower than HPF at both T1 (p = 0.006) and T2 (p = 0.009). Olive tail moment also decreased over time (T0 vs. T1: mean difference = 1.925 × 10¹⁵; p = 0.008; T0 vs. T2: mean difference = 1.676 × 10¹⁵; p = 0.025), and it differed between diets at day 50 in favor of HIO (5.99 × 10¹⁵ ± 5.45 × 10¹⁴ vs. 7.26 × 10¹⁵ ± 5.98 × 10¹⁴; p = 0.017). Conclusions: Overall, no evidence of genotoxicity was observed in cows fed HIO; conversely, the results support compatibility with genome stability and suggest a modest time-dependent improvement detectable mainly after prolonged supplementation. Full article

Direct methods provide a model-free approach to solving the crystallographic phase problem and deliver unbiased atomic structures. However, conventional iterative projection algorithms such as Hybrid Input–Output (HIO) face two critical challenges: discontinuous density modification at the protein-solvent boundary and inaccurate molecular envelope reconstruction that fails to account for trapped solvent, particularly in crystals with solvent content approaching the lower limits of direct phasing applicability. We introduced four continuous iterative projection algorithms, including our improved continuous version, which implements smooth density modification at protein-solvent interfaces. To address envelope inaccuracy, we developed a two-step refined reconstruction scheme using sequential large-radius and small-radius Gaussian filters to identify trapped solvent molecules within surface cavities and internal channels. This scheme enhances the performance of both continuous and classical algorithms, including HIO, the difference map, and our improved versions. Benchmarking on 28 protein structures (solvent contents 55–78%, resolutions 1.46–3.2 Å, reported R-factor less than 0.22) showed that the refined envelope scheme increased average success rates of continuous algorithms by 45.7% and classical algorithms by 60.5%. The performance of continuous algorithms and improved classical algorithms proved comparable to the well-established HIO algorithm, forming a top-tier group that exceeded other classical algorithms. Integrating a genetic algorithm co-evolution strategy further enhanced average success rates by approximately 2.5-fold and accelerated convergence through population-wide information sharing. Although the success rate correlates with solvent content, our strategy improved success probability at any given solvent level, extending the practical boundaries of direct methods. The high success rate enabled averaging of multiple independent solutions, which reduced mean phase error by approximately 6.83° and yielded atomic models with backbone root-mean-square deviation (RMSD) typically below 0.5 Å relative to structures reported in the Protein Data Bank (PDB). This work introduces novel algorithms, a refined envelope reconstruction methodology, and an effective optimization strategy with genetic algorithm evolution. The complete framework enhances the capability and reliability of direct methods for phasing protein crystals with limited solvent content and provides a toolkit for addressing challenging cases in structural biology. Full article

Direct methods for solving protein crystal structures from X-ray diffraction data provide an essential approach for validating predicted models while avoiding external model bias. Nevertheless, traditional iterative projection algorithms, including the widely used Difference Map (DiffMap), are often limited by modest phase retrieval success rates. To address this limitation, we introduce a novel Hybrid Difference Map (HDM) algorithm that synergistically combines the strengths of DiffMap and the Hybrid Input–Output (HIO) method through six distinct iterative update rules. HDM retains an optimized DiffMap-style relaxation term for fine-grained density modulation in protein regions while adopting HIO’s efficient negative feedback mechanism for enforcing the solvent flatness constraint. Using the transmembrane photosynthetic reaction center 2uxj as a test case, the first HDM formula, HDM-f1, successfully recovered an atomic-resolution structure directly from random phases under a conventional full-resolution phasing scheme, demonstrating the robust phasing capability of the approach. Systematic evaluation across 22 protein crystal structures (resolution 1.5–3.0 Å, solvent content ≥ 60%) revealed that all six HDM variants outperformed DiffMap, achieving 1.8–3.5× higher success rates (average 2.8×), performing on par with or exceeding HIO under a conventional phasing scheme. Further performance gains were achieved by integrating HDM with advanced strategies: resolution weighting and a genetic algorithm-based evolutionary scheme. The genetic evolution strategy boosted the success rate to nearly 100%, halved the median number of iterations required for convergence, and reduced the final phase error to approximately 35° on average across test structures through averaging of multiple solutions. The resulting electron density maps were of high interpretability, enabling automated model building that produced structures with a backbone RMSD of less than 0.5 Å when compared to their PDB-deposited counterparts. Collectively, the HDM algorithm suite offers a robust, efficient, and adaptable framework for direct phasing, particularly for challenging cases where conventional methods struggle. Our implementation supports all space groups providing an accessible tool for the broader structural biology community. Full article

The Internet of Things (IoT) has influenced the healthcare industry by enabling real-time monitoring, data-driven decision-making, and automation of medical activities. IoT in healthcare comprises a network of interconnected medical devices, sensors, and software systems that gather, analyse, and transmit patient data, enhancing the efficiency, accuracy, and accessibility of healthcare services. Despite its benefits, the deployment and impact of IoT in healthcare vary between countries due to differences in healthcare infrastructure, regulatory frameworks, and technical advancements. This review highlights how IoT technologies underpin the efficiency of EHR and HIE systems by enabling continuous data flow, interoperability, and real-time patient care. It also addresses the problems involved with IoT adoption, including data privacy concerns, interoperability issues, high implementation costs, and cybersecurity dangers. Additionally, the paper examines future trends in IoT healthcare, including 5G integration, AI-enhanced healthcare analytics, blockchain-based security solutions, and the creation of energy-efficient IoT medical equipment. Through an analysis of worldwide trends and obstacles, this research offers suggestions for policies, methods, and best practices to close the digital healthcare gap and make sure that healthcare solutions powered by the IoT are available, safe, and effective everywhere. Full article

Background: Cardiac arrest is a time-critical medical emergency during which prompt and effective drug delivery plays a key role in patient outcomes. Current resuscitation guidelines recommend intravenous (IV) access as the first-line route, with intraosseous (IO) access recommended as an alternative when IV access is delayed or not feasible. Although the endotracheal (ET) route was previously included in resuscitation protocols, it is no longer recommended. This study aims to evaluate the pharmacokinetic (PK) and pharmacodynamic (PD) effects of resuscitation drugs administered through different injection sites and under varying hemodynamic conditions in in vivo animal models. Methods: PubMed, CENTRAL and ClinicalTrials.gov were searched up to August 2025 for studies comparing different injection sites for the same drug (adrenaline/epinephrine, amiodarone, lidocaine and vasopressin) during CPR. Study selection, data extraction, and quality assessments were performed independently by two reviewers. Frequentist random-effects models were used to calculate mean differences and odds ratios (ORs) with 95% confidence intervals (CIs). Results: Fourteen prospective experimental studies (sample sizes ranging from 15 to 49 animals) conducted on swine were included. For epinephrine under normovolemia, humeral IO (HIO) access achieved significantly higher maximum concentrations (C_max; p = 0.0238) and a shorter time to the maximum concentration (T_max; p < 0.01) compared to IV, translating into faster return of spontaneous circulation (ROSC) (p = 0.0681). Under hypovolemia, IV access proved superiority over IO for epinephrine administration (MD = +382.80 ng/mL; p = 0.0022). The time to ROSC was significantly shorter with sternal IO (SIO) compared to tibial IO (TIO) (p = 0.0109). For amiodarone and vasopressin, no consistent or statistically significant differences were observed between administration routes, and in several cases, the findings were based on a single study. Conclusions: The injection site significantly influences the PK and PD of epinephrine during cardiac arrest. Proximal IO routes may offer advantages under normovolemic conditions, while IV access appears superior in cases of hypovolemic shock. Further research is needed to guide optimal drug delivery in varying hemodynamic conditions during cardiac arrest. Full article

To ensure the normal water status of apple rootstock softwood cuttings during the initial stage of cutting, a drought stress monitoring model was designed. The model is optimized based on the YOLOv11n-seg instance segmentation model, using the leaf curl degree of cuttings as the classification basis for drought-stress grades. The backbone structure of the IMYOLOv11n-seg model is improved by the C3K2_CMUNeXt module and the multi-head self-attention (MHSA) mechanism module. The neck part is optimized by the KFHA module (Kalman filter and Hungarian algorithm model), and the head part enhances post-processing effects through HIoU-SD (hierarchical IoU–spatial distance filtering algorithm). The IMYOLOv11-seg model achieves an average inference speed of 33.53 FPS (frames per second) and the mean intersection over union (MIoU) value of 0.927. The average recognition accuracies for cuttings under normal water status, mild drought stress, moderate drought stress, and severe drought stress are 94.39%, 93.27%, 94.31%, and 94.71%, respectively. The IMYOLOv11n-seg model demonstrates the best comprehensive performance in ablation and comparative experiments. The automatic humidification system equipped with the IMYOLOv11n-seg model saves 6.14% more water than the labor group. This study provides a design approach for an automatic humidification system in protected agriculture during apple rootstock cutting propagation. Full article

This study investigated the influence of CO₂ levels (20–40%: M20, M30, and M40) in high-oxygen modified atmosphere packaging (Hi-O₂ MAP) on microbial communities and quality attributes of chilled goat meat stored at 4 °C for 12 days. Alpha diversity indices (Chao1, ACE, Simpson, and Shannon) revealed a significant decline in microbial diversity over time, with storage duration exerting a greater impact than packaging conditions. Nonetheless, MAP played a crucial role in shaping microbial profiles, with air packaging (AP) showing the most distinct community, while M40 differed notably from M20 and M30, particularly by day 12, as shown by beta diversity analysis using principal coordinates analysis (PCoA). Proteobacteria and Firmicutes dominated microbial composition, with Pseudomonas and Brochothrix linked to spoilage in AP, while MAP, especially M40, favored the growth of Lactococcus, Acinetobacter, and Vagococcus, enhancing microbial stability. Despite pathogen levels remaining within safe limits, AP exceeded the spoilage threshold (TVC > 7.00 log colony-forming unit (CFU)/g), whereas all MAPs extended shelf life, with M40 most effectively suppressing microbial growth (p < 0.05). Interestingly, metagenomic functional profiling revealed that elevated CO₂ levels (>30%) altered metabolic pathways, shifting spoilage mechanisms from protein degradation in AP to carbohydrate metabolism in MAP, potentially influencing odor and texture attributes. MAP, particularly M40, also reduced protein and lipid degradation and oxidation, as indicated by lower total volatile base nitrogen (TVB-N), thiobarbituric acid reactive substances (TBARSs), and shear force, suggesting better prevention of increased meat hardness and the development of undesirable odors and flavors, although high CO₂ negatively affected redness. Overall, M40 provided the greatest microbial stability and shelf life extension, highlighting the potential of optimized CO₂ levels in Hi-O₂ MAP to preserve goat meat quality and regulate spoilage dynamics. Full article

Drop-in fuels are renewable alternatives that can be integrated into an existing fuel infrastructure without modification. Among these, fuels synthesized from hydroprocessed renewable lipids have garnered significant attention owing to their compatibility with petroleum-based diesel. In this study, we investigated the feasibility of hydrodeoxygenated insect oil (HIO), derived from black soldier fly larvae (Hermetia illucens; BSFL), as a renewable drop-in fuel for a diesel blend. The optimal growth conditions for BSFL were studied to maximize lipid production, and the extracted insect oil was subjected to hydrodeoxygenation (HDO) via catalytic reaction. The HIO was blended with commercial diesel at ratios of 5–30%, and its fuel properties were compared with commercial diesel. A detailed fuel property analysis was conducted for the 5% blend to evaluate its suitability as a diesel fuel. Characterization of the blended fuels’ physicochemical properties was carried out to assess the potential of insect-derived fuels for diesel applications. Full article

This study aims to improve the accuracy of fire source detection, the efficiency of path planning, and the precision of firefighting operations in drone swarms during fire emergencies. It proposes an intelligent firefighting technology for drone swarms based on multi-sensor integrated path planning. The technology integrates the You Only Look Once version 8 (YOLOv8) algorithm and its optimization strategies to enhance real-time fire source detection capabilities. Additionally, this study employs multi-sensor data fusion and swarm cooperative path-planning techniques to optimize the deployment of firefighting materials and flight paths, thereby improving firefighting efficiency and precision. First, a deformable convolution module is introduced into the backbone network of YOLOv8 to enable the detection network to flexibly adjust its receptive field when processing targets, thereby enhancing fire source detection accuracy. Second, an attention mechanism is incorporated into the neck portion of YOLOv8, which focuses on fire source feature regions, significantly reducing interference from background noise and further improving recognition accuracy in complex environments. Finally, a new High Intersection over Union (HIoU) loss function is proposed to address the challenge of computing localization and classification loss for targets. This function dynamically adjusts the weight of various loss components during training, achieving more precise fire source localization and classification. In terms of path planning, this study integrates data from visual sensors, infrared sensors, and LiDAR sensors and adopts the Information Acquisition Optimizer (IAO) and the Catch Fish Optimization Algorithm (CFOA) to plan paths and optimize coordinated flight for drone swarms. By dynamically adjusting path planning and deployment locations, the drone swarm can reach fire sources in the shortest possible time and carry out precise firefighting operations. Experimental results demonstrate that this study significantly improves fire source detection accuracy and firefighting efficiency by optimizing the YOLOv8 algorithm, path-planning algorithms, and cooperative flight strategies. The optimized YOLOv8 achieved a fire source detection accuracy of 94.6% for small fires, with a false detection rate reduced to 5.4%. The wind speed compensation strategy effectively mitigated the impact of wind on the accuracy of material deployment. This study not only enhances the firefighting efficiency of drone swarms but also enables rapid response in complex fire scenarios, offering broad application prospects, particularly for urban firefighting and forest fire disaster rescue. Full article

To address the challenges faced by fuel cell hybrid electric tractors (FCHETs) equipped with a battery and supercapacitor, including the complex coordination of multiple energy sources, low power allocation efficiency, and unclear optimal energy consumption, this paper proposes two energy management strategies (EMSs): one based on hierarchical instantaneous optimization (HIO) and the other based on multi-dimensional dynamic programming with final state constraints (MDDP-FSC). The proposed HIO-based EMS utilizes a low-pass filter and fuzzy logic correction in its upper-level strategy to manage high-frequency dynamic power using the supercapacitor. The lower-level strategy optimizes fuel cell efficiency by allocating low-frequency stable power based on the principle of minimizing equivalent consumption. Validation using a hardware-in-the-loop (HIL) simulation platform and comparative analysis demonstrate that the HIO-based EMS effectively improves the transient operating conditions of the battery and fuel cell, extending their lifespan and enhancing system efficiency. Furthermore, the HIO-based EMS achieves a 95.20% level of hydrogen consumption compared to the MDDP-FSC-based EMS, validating its superiority. The MDDP-FSC-based EMS effectively avoids the extensive debugging efforts required to achieve a final state equilibrium, while providing valuable insights into the global optimal energy consumption potential of multi-energy source FCHETs. Full article

Cationic porous organic polymers have a unique advantage in removing radioactive iodine from the aqueous phase because iodine molecules exist mainly in the form of iodine-containing anions. However, halogen anions will inevitably be released into water during the ion-exchange process. Herein, we reported a novel and easy-to-construct zwitterionic hypercrosslinked polymer (7AIn-PiP)-containing cationic pyridinium-type group, uncharged pyridine-type group, pyrrole-type group, and even an electron-rich phenyl group, which in synergy effectively removed 94.2% (456 nm) of I₂ from saturated I₂ aqueous solution within 30 min, surpassing many reported iodine adsorbents. Moreover, an I₂ adsorption efficiency of ~95% can still be achieved after three cyclic evaluations, indicating a good recycling performance. More importantly, a unique dual 1,3-dipole was obtained and characterized by ¹H/¹³C NMR, HRMS, and FTIR, correlating with the structure of 7AIn-PiP. In addition, the analysis of adsorption kinetics and the characterization of I₂@7AIn-PiP indicate that the multiple binding sites simultaneously contribute to the high affinity towards iodine species by both physisorption and chemisorption. Furthermore, an interesting phenomenon of inducing the formation of HIO₂ in unsaturated I₂ aqueous solution was discovered and explained. Overall, this work is of great significance for both material and radiation protection science. Full article

The 6-electron electrochemical reduction of IO₃⁻ to I⁻ represents a breakthrough for the development of next-generation redox flow batteries, offering substantially higher energy densities for oxidizer storage. Our study reveals that on a glassy carbon (GC) electrode in acidic electrolytes, HIO₃ undergoes an autocatalyzed electrochemical reduction to I⁻. This process is mediated by the formation of a thin iodine layer on the electrode, acting as an intermediate and a catalyst. Under steady-state conditions, the iodine layer forms via a comproportionation reaction (HIO₃ + I⁻ + 5H⁺ = I_2 (s) + 3H₂O). Initially, the iodine layer is generated through the slow direct electrochemical reduction of HIO₃ on pristine GC. Once established, this layer significantly enhances the rate of iodate reduction. On voltammetry curves, it is clearly observable as a step-wise current surge to reach a plateau. The limiting current density on the GC seemingly aligns with the Levich equation, varying with the RDE rotation rate. Earlier, we demonstrated the electrochemical oxidation of I⁻ back to HIO₃ using an H₂/HIO₃ flow cell, showcasing a full cycle that underpins the feasibility of this approach for energy storage. This study advances the understanding of iodate electroreduction and underscores its role in enhancing the capacity of next-generation energy storage systems. Full article