Search Results (4,621)

Background and Objectives: The relationship between psychiatric disorders and systemic inflammation remains incompletely understood. Increasing evidence suggests that inflammatory processes may play a role in the biological mechanisms underlying suicidal behavior. This study aimed to investigate the association between classical inflammatory markers and hemogram-derived inflammatory indices in patients who attempted suicide by oral drug overdose. Materials and Methods: This retrospective observational comparative study included 343 patients hospitalized following a suicide attempt by oral medication overdose and 421 age- and sex-matched healthy individuals. Serum C-reactive protein (CRP), albumin levels, complete blood count parameters, and derived inflammatory indices, including the CRP-to-albumin ratio (CAR), neutrophil-to-lymphocyte ratio (NLR), systemic immune–inflammation index (SIII), platelet-to-lymphocyte ratio (PLR), and monocyte-to-lymphocyte ratio (MLR), were analyzed. Results: Patients with suicide attempts had significantly higher CRP, leukocyte, neutrophil, and monocyte levels compared to the comparison group. CAR, NLR, SIII, and MLR values were also significantly elevated, whereas PLR did not differ between groups. ROC analysis demonstrated that CAR showed the highest discriminative ability for suicide attempt, with high sensitivity and specificity. Conclusions: Hemogram-derived inflammatory indices, particularly CAR, were significantly associated with suicide attempts. These easily accessible and low-cost biomarkers may provide additional biological insight into suicide risk assessment. Further prospective studies are needed to confirm these findings. Full article

Background: The diaphragm is the primary respiratory muscle, and its proper function is essential for efficient breathing. Respiratory muscle weakness is a common complication that can hinder the withdrawal of mechanical ventilation. This weakness not only negatively affects patients’ quality of life but also represents an economic challenge for healthcare systems, as it significantly increases medical costs due to prolonged hospitalization and the need for additional procedures to manage associated complications. Ultrasonography has emerged as a precise technique for assessing diaphragmatic function through measurements such as diaphragmatic excursion and thickening fraction, with the right hemidiaphragm being the most suitable for evaluation. However, several studies have shown that diaphragmatic ultrasound measurements vary considerably in both healthy individuals and patients, mainly due to the lack of standardization of body position during assessment. Therefore, it is necessary to investigate how patient posture influences diaphragmatic ultrasound measurements in order to standardize protocols, improve diagnostic accuracy, and support reliable clinical decision-making. We employed ultrasonography to determine the influence of changes in body position on diaphragmatic excursion in a healthy population from the city of Cali. Methods: A descriptive cross-sectional study was conducted in 36 healthy adults aged 18 to 65 years, distributed into sex and age groups. Diaphragmatic excursion was assessed using a 3.5–5 MHz ultrasound transducer. Participants were evaluated in five body positions: supine at 0°, and head-of-bed inclinations of 30°, 45°, 70°, and 90°. Results: A progressive increase in diaphragmatic excursion was observed from the supine position (0°) up to 70° inclination. The 70° inclination showed the greatest diaphragmatic mobility as measured by ultrasonography. This finding suggests the existence of an optimal intermediate position in which biomechanical conditions and intra-abdominal pressure allow more efficient diaphragmatic contraction. Conclusions: The results of this study demonstrate that changes in body position significantly influence diaphragmatic excursion in healthy individuals, with a trunk inclination of 70° yielding the greatest diaphragmatic mobility. These findings support the importance of considering body posture as a key determinant in the functional assessment of the diaphragm using ultrasonography. Full article

In vitro models of the human nasal cavity are crucial for understanding the deposition dynamics of nasally administered drugs. Three-dimensional (3D) printing offers a powerful method for creating patient-specific, anatomically precise models for such experimental purposes. Background/Objectives: This study details the complete workflow for the development, design, and fabrication of a sectioned nasal cavity model intended for droplet deposition analysis of nasal sprays. Methods: A digital nasal cavity model was derived from medical imaging data and optimized for computer-aided design (CAD) operations. It was segmented into five therapeutically relevant regions: nasal vestibule, olfactory area, middle and upper turbinates, lower turbinate, and nasopharynx. Sections were 3D-printed in polypropylene for chemical compatibility, and a carbon fiber-reinforced fixation frame ensured precise alignment and airtight assembly. Results: Functional validation confirmed the model’s functional relevance through comparative deposition studies using automated actuation and high-performance liquid chromatography (HPLC) based regional quantification. Two devices with distinct spray characteristics (characterized separately by laser diffraction, plume geometry, and spray pattern imaging) were tested under varied administration conditions. The study demonstrated the model’s ability to discriminate between products, establishing a solid foundation for future investigations incorporating additional variables. Conclusions: Overall, the developed methodology provides a cost-effective and replicable platform for producing anatomically accurate, sectioned nasal cavity models. The newly developed in vitro system is well suited for detailed, region-specific analysis of nasal spray deposition, offering a valuable tool for pharmaceutical research and development. Full article

Background/Objective: Hand hygiene is a cornerstone of infection prevention; however, compliance is inconsistent in intensive care units (ICUs), particularly in resource-constrained settings. This study evaluated whether a low-cost, multimodal quality improvement intervention could improve process-level hand hygiene compliance using routine, episode-based audits embedded in the ICU practice. Methods: We conducted a single-cycle Plan-Do-Study-Act quality improvement project in a 12-bed mixed medical–surgical ICU in Pakistan (December 2023–January 2024). Hand hygiene performance was assessed using the unit’s routine weekly episode-based audit protocol, aligned with the WHO Five Moments framework. A targeted multimodal intervention comprising education, point-of-care visual reminders, audit feedback, and leadership engagement was implemented between the pre- and post-intervention phases (four weeks each). Non-applicable moments were scored as “compliant by default” according to the institutional protocol. A sensitivity analysis was performed excluding these moments to calculate pure adherence. Compliance proportions were summarized using exact 95% Clopper–Pearson confidence intervals without inferential testing. Results: A total of 942 audit episodes (471 per phase) generated 4710 moment-level assessments were generated. Composite hand hygiene compliance increased from 63.1% pre-intervention to 82.0% post-intervention [absolute increase: 18.9 percentage points (pp)]. Sensitivity analysis excluding non-applicable moments demonstrated pure adherence improvement from 54.2% to 82.5% (+28.3 pp), confirming a genuine behavioral change rather than a measurement artifact. Compliance improved across all five WHO moments, with the largest gains in awareness-dependent moments targeted by the intervention: before touching the patient (+27.0 pp) and after touching patient surroundings (+40.0 pp). Week-by-week compliance remained stable within both phases, without immediate post-intervention decay. Conclusions: A pragmatic, low-cost multimodal intervention embedded in routine ICU workflows was associated with substantial short-term improvements in hand hygiene compliance over a four-week observation period, particularly for awareness-dependent behaviors. Episode-based audit systems can support directional process monitoring in resource-limited critical care settings without the need for electronic surveillance. However, its long-term sustainability beyond one month and generalizability to other settings remain unknown. Sensitivity analyses are essential when using “compliant by default” scoring to distinguish adherence patterns from measurement artifacts. Full article

Tackling poor mental health and wellbeing among healthcare workers remains a high priority for the National Health Service (NHS). This study evaluated the social value of the Coming to Our Senses mindfulness-based programme, designed to support the mental health of workers in high-stress medical environments, for NHS healthcare workers in Wales. Respondents (N = 39) to an online survey were assessed for mental health social value at baseline and one-month follow-up using the Short Warwick–Edinburgh Mental Wellbeing Scale (SWEMWBS) and the Social Value Bank (SVB). Social return on investment (SROI) ratios were calculated by dividing the change in mental health social value, health resource service use, and productivity costs by the programme delivery costs. Social value generated per respondent was £1890.05 using SWEMSWBS and £5775.97 using SVB. Cost savings in health resource and productivity were £9.41 and £79.10 per respondent, respectively. The programme delivery cost was £463.63 per respondent. Overall, including sensitivity analysis, the programme yielded a positive SROI of £2.35–£4.27:£1 using SWEMWBS or £6.82–£12.65:£1 using SVB. These findings suggest that the Coming to Our Senses programme may be effective in generating positive social value by improving self-reported mental health and wellbeing among NHS healthcare workers in Wales. Full article

After large-scale disasters, swift and robust humanitarian logistics are crucial to provide timely assistance to injured people and displaced individuals. This study proposes a bi-objective optimization model for humanitarian logistics network design to simultaneously consider the facility location-allocation decisions, along with the transportation operation issues under uncertainty. The framework addresses the needs of both severely and mildly injured casualties and homeless populations. A hybrid robust optimization approach is accordingly developed that incorporates scenario-based, box-type, and polyhedral uncertainty representations to handle the uncertainty of factors such as casualty volume, travel times, facility failures, and demands for resources. More recently, machine learning methods have been applied to classify casualties and displaced individuals with respect to their geographic distribution and severity, further improving demand estimates and operational efficacy. This study seeks to develop a data-driven and robust optimization framework for designing humanitarian logistics networks under uncertainty, enabling decision-makers and emergency planners to gain insights into enhancing casualty evacuation, medical treatment, and shelter allocation in disaster response operations. The case of the Kermanshah earthquake in Iran is used for assessing the applicability of the model. The computational experiments and comparative analyses conducted show that the developed model exhibits high efficiency and robustness. The results are useful for guiding disaster preparedness and strategic decisions in humanitarian logistics. Besides operational performance, the model optimizes sustainability in the area of emergency response based on cost efficiency and social fairness, as underlined by SDGs 3 and 11. Full article

Heterogeneous electronic medical record (EMR) systems and institution-specific data structures continue to limit interoperability and large-scale utilization of healthcare data. Although Health Level Seven (HL7) Fast Healthcare Interoperability Resources (FHIR) has been adopted as an international standard, existing conversion approaches often require extensive preprocessing, high implementation costs, or deep system-specific expertise, restricting their applicability, particularly in small and medium-sized hospitals. To address these constraints, we propose a lightweight EMR-to-HL7 FHIR conversion library optimized for small- and medium-sized healthcare providers that operate with limited system resources. Methods: The library adopts a modular architecture comprising data preprocessing, reference management, structural transformation using transform maps, terminology translation, and validation modules. The proposed approach was implemented using the HL7 Application Programming Interface (HAPI) FHIR and evaluated with anonymized EMR data extracted from multiple hospitals in South Korea, with performance and validation results compared against a conventional HAPI FHIR client-based conversion method. Results: This study proposes a standardized FHIR-based medical data conversion library that enables the efficient transformation of diverse EMR data structures into interoperable FHIR. The proposed library achieved approximately 30% lower single-request conversion latency compared to a conventional HAPI FHIR client-based conversion pipeline under identical hardware and runtime conditions. Conclusions: The proposed conversion method provides a lightweight and adaptable solution for EMR-to-FHIR transformation, improving interoperability with reduced implementation effort and supporting scalable medical data exchange across diverse healthcare environments. Full article

Ant Colony Optimization (ACO) is a widely adopted metaheuristic for solving complex combinatorial problems; however, performance is often deteriorated by premature convergence and limited exploration in later iterations. Eclipse Randomness–Ant Colony Optimization (ER-ACO) is introduced as a lightweight ACO variant in which an exponentially fading randomness factor is integrated into the state-transition mechanism. Strong early-stage exploration is enabled, and a smooth transition to exploitation is induced, improving convergence behavior and solution quality. Low computational overhead is maintained while exploration and exploitation are dynamically balanced. ER-ACO is positioned within real-time healthcare logistics, with a focus on Emergency Medical Services (EMS) routing and hospital resource scheduling, where rapid and adaptive decision-making is critical for patient outcomes. These systems face dynamic constraints such as fluctuating traffic conditions, urgent patient arrivals, and limited medical resources. Experimental evaluation on benchmark instances indicates that solution cost is reduced by up to 14.3% relative to the slow-fade configuration ($\gamma =1$) in the 20-city TSP sweep, and faster stabilization is indicated under the same iteration budget. Additional comparisons against Standard ACO on TSP/QAP benchmarks indicate consistent improvements, with unchanged asymptotic complexity and negligible measured overhead at the tested scales. TSP/QAP benchmarks are used as controlled proxies to isolate algorithmic behavior; EMS deployment is treated as a motivating application pending validation on EMS-specific datasets and formulations. These results highlight ER-ACO’s potential as a lightweight optimization engine for smart healthcare systems, enabling real-time deployment on edge devices for ambulance dispatch, patient transfer, and operating room scheduling. Full article

Foreign matter accumulating on catheters during ascites paracentesis in cancer patients can interfere with the procedure. The paracentesis site must be visually inspected by patients or medical staff. We propose a monitoring method using sensors, as they enable real-time, automatic status detection. The proposed design integrates a sensor into the drainage tube to detect liquid flow using the Lorentz force. The sensor consists of a permanent magnet, a yoke, and a signal processing circuit. Mu-metal shields the magnet to prevent interference with surrounding circuits. Physiological saline solution is used as a substitute for bodily fluids. Sensor performance was evaluated via finite element analysis. The Lorentz force generated an average voltage of 11.07 μV when liquid was present, enabling detection of the flow status. The proposed sensor is non-invasive and features a clip design, allowing attachment and detachment from the drainage tube for reuse. Non-invasiveness ensures safety from infection, and reusability can reduce medical costs. This study proposes a sensor for monitoring peritoneal puncture status. By detecting liquid flow using the Lorentz force, the system enables real-time monitoring during the procedure. Full article

This research investigates the effects of corona plasma treatment on the mechanical properties of jute/epoxy-reinforced composites, particularly within biomedical application contexts. Plant Fibre Composites (PFCs) are attractive for medical devices and scaffolds due to their environmental friendliness, renewability, cost-effectiveness, low density, and high specific strength. However, their applications are often constrained by inferior mechanical performance arising from poor bonding between the plant fibre used as the reinforcement and the synthetic resin or polymer serving as the matrix. This study addresses the challenge of improving the weak interfacial bonding between plant fibre and synthetic resin in a 2/2 twill-weave-woven jute/epoxy composite material. The surface of the jute fibre is modified for better adhesion with the epoxy resin through plasma treatment, which exposes the jute fibre to controlled plasma energy and utilises dry air (plasma only), argon (Ar) (argon gas with plasma), and nitrogen (N₂) (nitrogen gas with plasma) at two different distances (25 mm and 35 mm) between the plasma nozzle and the fibre surface. In this context, “equilibrium” refers to the optimal combination of plasma power, treatment distance, and gas environment that collectively determines the degree of fibre surface modification. The results indicate that all plasma treatments improve the interlaminar shear strength in comparison to untreated samples, with treatments at 35 mm using N₂ gas showing a 35.4% increase in shear strength. Conversely, plasma treatment using dry air at 25 mm yields an 18.3% increase in tensile strength and a 35.7% increase in Young’s modulus. These findings highlight the importance of achieving an appropriate equilibrium among plasma intensity, treatment distance, and fibre–plasma interaction conditions to maximise the effectiveness of plasma treatment for jute/epoxy composites. This research advances sustainable innovation in biomedical materials, underscoring the potential for improved mechanical properties in environmentally friendly fibre-reinforced composites. Full article

Electromagnetic interference (EMI) shielding materials are essential for ensuring the reliable operation of electronic devices and safeguarding human health, yet conventional metal-polymer materials are non-biodegradable, energy-intensive, and difficult to recycle. This study prepared a biodegradable paper-based shielding material; renewable cellulose filter paper was employed as the sole substrate, and graphene was integrated to construct an electromagnetic shielding network. A low-cost paper-based electromagnetic shielding preparation method was developed, and the performance of the material was analyzed in electromagnetic shielding applications. Samples were fabricated through a simple impregnation-evaporation-lamination process. It has a thickness of 1 mm for single layers and a maximum conductivity of 21.3 S/m. The influence of sample thickness on electromagnetic shielding in the X-band (8.2–12.4 GHz) was investigated, when the graphene filter cake loading reached 20 wt%, the SE_T values for triple-layer electromagnetic shielding papers reach 36 dB at 8.2 GHz and 33 dB at 12.4 GHz. A phone box for indoor environments and a card holder with anti-radio-frequency identification (RFID) functionality were designed. Furthermore, achievable design solutions for an EMI shielding wallpaper in medical and artistic installations were proposed. Full article

Out-of-distribution (OOD) detection is essential for ensuring the reliability of machine learning models deployed in safety-critical applications. Existing methods often rely solely on statistical properties of feature distributions while ignoring the geometric structure of learned representations. We propose TopoAD, a topology-aware OOD detection framework that leverages Euler Characteristic Curves (ECCs) extracted from intermediate convolutional activation maps and fuses them with standardized energy scores. Specifically, we employ a computationally efficient superlevel-set filtration with a local estimator to capture topological invariants, avoiding the high cost of persistent homology. Furthermore, we introduce task-adaptive aggregation strategies to effectively integrate multi-scale topological features based on the complexity of distribution shifts. We evaluate our method on CIFAR-10 against four diverse OOD benchmarks spanning far-OOD (Textures), near-OOD (SVHN), and semantic shift scenarios. Our results demonstrate that TopoAD-Gated achieves superior performance on far-OOD data with 89.98% AUROC on Textures, while the ultra-lightweight TopoAD-Linear provides an efficient alternative for near-OOD detection. Comprehensive ablation studies reveal that cross-layer gating effectively captures multi-scale topological shifts, while threshold-wise attention provides limited benefit and can degrade far-OOD performance. Our analysis demonstrates that topological features are particularly effective for detecting OOD samples with distinct structural characteristics, highlighting TopoAD’s potential as a sustainable solution for resource-constrained applications in texture analysis, medical imaging, and remote sensing. Full article

Deep vision foundation models such as DINOv3 offer strong visual representation capacity, but their direct deployment in medical image segmentation remains difficult due to the limited availability of annotated clinical data and the computational cost of full fine-tuning. This study proposes an adaptation framework called StrDiSeg that integrates lightweight bottleneck adapters between selected transformer layers of DINOv3, enabling task-specific learning while preserving pretrained knowledge. An attention-enhanced U-Net decoder with multi-scale feature fusion further refines the representations. Experiments were performed on two publicly available ischemic stroke lesion segmentation datasets—AISD (Non Contrast CT) and ISLES22 (DWI). The proposed method achieved Dice scores of 0.516 on AISD and 0.824 on ISLES22, outperforming baseline models and demonstrating strong robustness across different clinical imaging modalities. These results indicate that adapter-based fine-tuning provides a practical and computationally efficient strategy for leveraging large pretrained vision models in medical image segmentation. Full article

Aging is accompanied by progressive gastrointestinal structural and functional decline, increased intestinal permeability, dysbiosis, and impaired mucosal immunity, collectively elevating susceptibility to infections, chronic inflammation, and multimorbidity. These age-related changes are further exacerbated by polypharmacy, metabolic disorders, and lifestyle factors, positioning the gastrointestinal tract as a central driver of systemic physiological decline. Gut-centered interventions have emerged as critical strategies to mitigate these vulnerabilities and support healthy aging. Dietary modulation, prebiotic and probiotic supplementation, and microbiota-targeted approaches have demonstrated efficacy in improving gut microbial diversity, enhancing short-chain fatty acid production, restoring epithelial integrity, and modulating immune signaling in older adults. Beyond nutritional strategies, non-nutritional interventions such as molecular hydrogen and medical ozone offer complementary mechanisms by selectively neutralizing reactive oxygen species, reducing pro-inflammatory signaling, modulating gut microbiota, and promoting mucosal repair. Hydrogen-based therapies, administered via hydrogen-rich water or inhalation, confer antioxidant, anti-inflammatory, and cytoprotective effects, while ozone therapy exhibits broad-spectrum antimicrobial activity, enhances tissue oxygenation, and stimulates epithelial and vascular repair. Economic considerations further differentiate these modalities, with hydrogenated water positioned as a premium wellness product and ozonated water representing a cost-effective, scalable option for geriatric gastrointestinal care. Although preclinical and early clinical studies are promising, evidence in older adults remains limited, emphasizing the need for well-designed, age-specific trials to establish safety, dosing, and efficacy. Integrating dietary, microbiota-targeted, and emerging non-nutritional gut-centered interventions offers a multimodal framework to preserve gut integrity, immune competence, and functional health, potentially mitigating age-related decline and supporting overall health span in older populations. Full article

Neural network quantization has become established as a key strategy for transitioning medical imaging models from research environments to clinical devices and resource-constrained edge platforms; however, the available evidence remains fragmented and focused on highly heterogeneous use cases. This study presents a systematic review of 72 studies on quantization applied to medical images, following PRISMA guidelines, with the aim of characterizing the relationship among quantization technique, network architecture, imaging modality, and execution environment, as well as their impact on latency, memory footprint, and clinical deployment. Based on a structured variable matrix, we analyze—through tailored visualizations—usage patterns of Post-Training Quantization (PTQ), Quantization-Aware Training (QAT), mixed precision, and binary/low-bit schemes across frameworks such as PyTorch V 2.6.0, TensorFlow 2.19.0, and TensorFlow Lite, executed on server-class GPUs, edge/embedded devices, and specialized hardware. The results reveal a strong concentration of evidence in PyTorch/TensorFlow pipelines using INT8 or mixed precision on GPUs and edge platforms, contrasted with limited attention to PACS/RIS interoperability, model lifecycle management, energy consumption, cost, and regulatory traceability. We conclude that, although quantization can approximate real-time performance and reduce memory footprint, its clinical adoption remains constrained by integration challenges, model governance requirements, and the maturity of the hardware–software ecosystem. Full article