Search Results (2,200)

Polyhydroxyalkanoates (PHAs), a family of biodegradable polyesters produced through microbial fermentation of carbon-rich residues, are emerging as attractive alternatives to petroleum-based plastics. Their appeal lies in their exceptional biocompatibility, inherent biodegradability, and tunable physicochemical properties across diverse applications. These materials are environmentally friendly not just at the end of their life, but throughout their entire production–use–disposal cycle. This mini-review presents an update on the expanding biomedical relevance of PHAs, with emphasis on their utility in tissue engineering and drug delivery platforms. In addition, current clinical evaluations and regulatory frameworks are briefly discussed, underscoring the translational potential of PHAs in meeting unmet medical needs. As the healthcare sector advances toward environmentally responsible and patient-focused innovations, PHAs exemplify the convergence of waste valorization and biomedical progress, transforming discarded resources into functional materials for repair, regeneration, and healing. Full article

Background: Recent advances in artificial intelligence (AI) have produced ChatGPT-4o, a multimodal large language model (LLM) capable of processing both text and image inputs. Although ChatGPT has demonstrated usefulness in medical examinations, few studies have evaluated its image analysis performance. Methods: This study compared GPT-4o and GPT-4 using public questions from the 116th–118th Japanese National Medical Licensing Examinations (JNMLE), each consisting of 400 questions. Both models answered in Japanese using simple prompts, including screenshots for image-based questions. Accuracy was analyzed across essential, general, and clinical questions, with statistical comparisons by chi-square tests. Results: GPT-4o consistently outperformed GPT-4, achieving passing scores in all three examinations. In the 118th JNMLE, GPT-4o scored 457 points versus 425 for GPT-4. GPT-4o demonstrated higher accuracy for image-based questions in the 117th and 116th exams, though the difference in the 118th was not significant. For text-based questions, GPT-4o showed superior medical knowledge, clinical reasoning, and ethical response behavior, notably avoiding prohibited options. Conclusion: Overall, GPT-4o exceeded GPT-4 in both text and image domains, suggesting strong potential as a diagnostic aid and educational resource. Its balanced performance across modalities highlights its promise for integration into future medical education and clinical decision support. Full article

Background: Integrated medical information systems process large volumes of sensitive clinical data and are exposed to persistent cyber threats. Artificial intelligence (AI) is increasingly used for anomaly detection and incident response, yet its systemic effect on the dynamics of security indicators is not fully quantified. Aim: To develop and numerically study a nonlinear dynamical model describing the joint evolution of system vulnerability, threat activity, compromise level, AI detection quality, and response resources in a medical data protection context. Method: A five-dimensional system of ordinary differential equations was formulated for variables $V$, $T$, $C$, $D$, $R$. Parameters characterize appearance and elimination of vulnerabilities, attack intensity, AI learning and degradation, and resource consumption. The corresponding Cauchy problem $V\left(0\right)=0.5$, $T\left(0\right)=0.6$, $C\left(0\right)=0.1$, $D\left(0\right)=0.4$, $R\left(0\right)=0.8$ was solved on $\left[0,200\right]$ numerically using a fourth-order Runge–Kutta method. Results: Numerical modelling showed convergence to a favourable steady regime. On the interval t ∈ [195, 200] the mean values were $V=0.0073$, $T=0.3044$, $C=7.7·{10}^{-5}$, $D=0.575$, $R=19.99$. Thus, the initial 10% compromise is reduced by more than 99.9%, while AI detection quality stabilizes at around 0.58, and response capacity increases 25-fold. Conclusions: The model quantitatively confirms that the integration of AI detection and a managed response capacity enables the system to reach a stable state with virtually zero compromised medical data even with non-zero threat activity. Full article

This study evaluates the impact of capillary refill time (CRT) modulation on photoplethysmography (PPG) signals for improved non-invasive continuous blood pressure (CBP) estimation. Data from 21 healthy participants were collected, applying a standardized 9 N pressure for 15 s to induce CRT during 6-min sessions. PPG signals were segmented into 252 paired 30-s intervals (CRT-modulated and standard). Three machine learning models—ResNetCNN, LSTM, and Transformer—were validated using leave-one-subject-out (LOSO) and non-LOSO methods. CRT modulation significantly enhanced accuracy across all models. ResNetCNN showed substantial improvements, reducing mean absolute error (MAE) by up to 35.6% and mean absolute percentage error (MAPE) by up to 40.6%. LSTM and Transformer models also achieved notable accuracy gains. All models met the Association for the Advancement of Medical Instrumentation (AAMI) criteria (mean error < 5 mmHg; standard deviation < 8 mmHg). The findings suggest CRT modulation’s strong potential to improve wearable CBP monitoring, especially in resource-limited settings. Full article

Background: Acute appendicitis, a frequent pediatric surgical emergency, requires distinguishing simple from complicated cases for treatment decisions. Current tools, such as clinical scores and ultrasound, are sometimes ineffective. This study evaluates the biomarkers: neutrophils to lymphocytes ratio (NLR), monocytes to lymphocytes ratio (MLR), platelet-to-lymphocyte ratio (PLR), neutrophils to monocytes ratio (NMR), neutrophils to platelet ratio (NPR), pan-immune-inflammation value (PIV) ratio, and C-Reactive Protein (CRP) to lymphocytes ratio (CLR) for differentiation between simple and complicated appendicitis. Methods: A retrospective study of 878 pediatric patients (<18 years) who underwent appendectomy (2018–2024) at a tertiary medical center, with appendicitis classified as simple (SA, n = 696) or complicated (CA, n = 182) using intraoperative findings. Biomarkers were calculated from preoperative blood counts and CRP. Diagnostic accuracy was assessed using Mann–Whitney U tests, ROC curves, and logarithmic regression. Results: Patients with CA had higher neutrophils counts (13.61 ± 4.92 vs. 11.39 ± 4.29 K/μL), monocytes counts (1.23 ± 1.41 vs. 0.95 ± 0.48 K/μL), platelet counts (294.31 ± 72.73 vs. 270.15 ± 72.08 K/μL), CRP levels (88.55 ± 97.75 vs. 27.15 ± 44.74 mg/L), and elevated biomarker ratios as compared to those with SA: NLR (≥10.15, OR = 2.45), MLR (≥0.645, OR = 2.78), PLR (≥224.38, OR = 2.502), NMR (≥6.38, OR = 2.34), NPR (≥0.0405, OR = 1.876), PIV (≥2433.85, OR = 3.348), and CLR (≥11.77, OR = 5.935), all at p < 0.01. CLR demonstrated the highest accuracy (AUC = 0.772, sensitivity 78%, specificity 62.6%), outperforming established biomarkers, followed by PIV (AUC = 0.679). NPR was the least effective marker (AUC = 0.569). Conclusions: CLR, a promising biomarker, can aid in distinguishing complicated from simple appendicitis in children, and may offer accessible tools for resource-limited settings. Full article

Background: Carpal tunnel syndrome (CTS) is a common peripheral neuropathy with increasing prevalence and economic burden. This study aimed to analyze recent trends in CTS treatment patterns, healthcare utilization, and costs within the dualized healthcare system in Korea, using nationwide claim data. Methods: This cross-sectional study used data from the Korean Health Insurance Review and Assessment Service National Patient Sample (HIRA-NPS) between 2010 and 2017. Patients with a primary diagnosis of CTS (KCD-10: G56.0) were included. Descriptive analyses were performed to examine trends in patient characteristics, healthcare utilization, treatment patterns, and medical costs in Western and Korean medicine. Results: A total of 29,112 patients with CTS were analyzed. In Western medicine, diagnostic tests accounted for the highest expenditure, particularly X-ray, nerve conduction studies, and electromyography. Over time, X-ray utilization increased, while nerve conduction and electromyography tests decreased. The proportion of surgical treatment declined from 11.28% in 2010 to 8.55% in 2017, whereas Korean medicine use increased from 9.41% to 15.08%, mainly consisting of acupuncture and related procedures. Conclusions: Korea exhibited a lower CTS surgery rate than other countries, alongside a rising trend in Korean medicine utilization. These findings underscore the distinctive dual healthcare system in Korea and highlight the need for prospective studies to assess the long-term effectiveness of Korean medicine-based conservative treatments. Additionally, the results may inform national health policy decisions, including insurance coverage and resource allocation for CTS management. Full article

Background/Objectives: In low-resource settings, diagnostic delays and limited specialist access worsen health inequalities, making hypertensive disorders of pregnancy (HDPs) defined by new-onset blood pressure ≥ 140/90 mmHg after 20 weeks of gestation, with or without proteinuria, a major cause of maternal morbidity and mortality. This study evaluated the diagnostic effectiveness of a rural-applicable clinical model for detecting HDPs in a real-world population from Hospital General San Felipe (Tegucigalpa, Honduras). Methods: A cross-sectional diagnostic accuracy study was conducted on 147 consecutive pregnant women in February 2025. Clinical documentation from the initial appointment defined HDP. We modeled HDP risk using penalized logistic regression and common factors such maternal age, gestational age, blood pressure, BMI, primary symptoms, semi-quantitative proteinuria, and medical history. Median imputation was utilized for missing numbers and stratified five-fold cross-validation assessed performance. We assessed AUROC, AUPRC, Brier score, calibration, and operational utility at a data-driven threshold. Results: Of patients, 27.9% (41/147) had HDP. The model had an AUROC of 0.614, AUPRC of 0.461 (cross-validation averages), and Brier score of 0.253. The threshold with the highest F1-score (0.474) had a sensitivity of 0.561, specificity of 0.679, positive predictive value of 0.404, and negative predictive value of 0.800. HDP had higher meaning systolic/diastolic/mean arterial pressure (130.7/82.9/98.9 vs. 120.5/76.1/90.9 mmHg) and ordinal proteinuria (0.59 vs. 0.36 units). Conclusions: The model had moderate but clinically meaningful discriminative performance using low-cost, commonly obtained variables, excellent calibration, and a good negative predictive value for first exclusion. These findings suggest modification of predictors, a larger sample size, and clinical usefulness assessment using decision curves and process outcomes, including quick referral and prophylaxis. This approach aligns with contemporary developments in the 2023–2025 European Society of Cardiology (ESC) and 2024 American Heart Association (AHA) guidelines, which emphasize earlier identification and risk-stratified management of hypertensive disorders during pregnancy as a cornerstone of women’s cardiovascular health. Full article

Image noise degrades image quality in applications such as medical imaging, surveillance, and remote sensing, where real-time processing and high accuracy are critical. Software-based denoising can be flexible, but often suffers from latency and low throughput when deployed on embedded or edge systems. A Field Programmable Gate Array (FPGA)-based system offers parallelism and lower latency, but the existing work typically focusses on fixed architectures rather than scalable framework supporting multiple filter models. This paper presents a high-performance, resource-efficient FPGA-based framework for real-time noise reduction. The modular, pipelined architecture integrates median and adaptive filters, managed by a state machine-based control unit to enhance processing efficiency. Implemented on a Cyclone V FPGA using Quartus Prime 22.1std, the system provides scalability through adjustable Random Access Memory (RAM) and supports multiple denoising algorithms. Tested on Leena images with salt-and-pepper noise, it processes 10% noise in 1.724 ms in a simulated environment running at 800 MHz; it was compared with Python version 3.11.2 with the OpenCV-library version 4.8.076 on a general-purpose Central Processing Unit (CPU) (0.0201 ms). The proposed solution demonstrates low latency and high throughput, making it well-suited for embedded and edge computing applications. Full article

Background and Objectives: Hospital length of stay (LOS) after breast cancer surgery is a key indicator of postoperative recovery, healthcare quality, and hospital resource utilization. Traditional statistical approaches have identified general correlates of LOS but remain limited in predictive accuracy, particularly in heterogeneous real-world surgical populations. Machine learning (ML) models may offer improved performance by capturing nonlinear interactions among clinical, pathological, and operative factors. This study aimed to evaluate ML algorithms for LOS prediction and to identify determinants of prolonged hospitalization in a contemporary breast cancer cohort. Materials and Methods: We conducted a retrospective cross-sectional study of 198 consecutive breast cancer patients who underwent surgery between January 2022 and December 2023 at a single tertiary care center. Clinical, pathological, and surgical data were extracted from electronic medical records. Three regression models—multiple linear regression, Random Forest, and Gradient Boosting—were trained to predict continuous LOS, and three classification models were applied to prolonged LOS (≥10 days). Model performance was assessed using mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (R²), and area under the curve (AUC). Feature importance was analyzed for the best-performing model. Results: The median LOS was 7 days (IQR 5–10), ranging from 1 to 26 days. Breast-conserving surgery showed the shortest LOS (median 3 days), while mastectomy with immediate reconstruction resulted in the longest stays (median 8 days). Random Forest regression achieved the lowest prediction error (MAE 2.31 days; RMSE 2.82; R² = 0.37), outperforming Gradient Boosting and substantially surpassing linear regression (MAE 8.63 days; R² = –8.17). Key predictors included age, surgical complexity, reconstruction modality, BMI, implant capacity, and tumor burden. Classification models yielded modest AUCs (0.545–0.589) with low sensitivity, indicating limited discriminative performance for dichotomized LOS outcomes. Conclusions: Machine-learning models, particularly Random Forest, substantially improve LOS prediction compared with classical regression and provide clinically meaningful insights into the drivers of hospitalization after breast cancer surgery. Continuous LOS modeling is more informative than binary thresholds. These findings support integrating ML-based tools into perioperative planning, resource allocation, and patient counseling in breast surgical care. Full article

Background and Objectives: Type 2 diabetes mellitus (T2DM) is increasing in prevalence worldwide, placing a substantial burden on healthcare systems, particularly in resource-limited settings. In Yemen, limited screening and diagnostic capacity contribute to delayed detection and management. Prediabetes, a reversible state of dysglycemia, carries significant cardiovascular risk and frequently progresses to diabetes. Early identification of both conditions is vital for prevention and public health planning. Materials and Methods: This cross-sectional study, conducted from July 2024 to May 2025 in three medical centers in Ibb Governorate, Yemen, assessed 1045 adults aged 18–60 years without known diabetes or prediabetes. Glycaemic status was classified according to the 2025 American Diabetes Association criteria. Undiagnosed diabetes was defined using three diagnostic combinations: FBS + OGTT, FBS + HbA1c, and OGTT + HbA1c. Results: The prevalence of undiagnosed diabetes was 8.4% (FBS + OGTT) and 9.76% (FBS + HbA1c or OGTT + HbA1c). Prediabetes prevalence was 23.4%, 14.7% and 26.4% based on FBS, OGTT, and HbA1c, respectively. Females represented a higher proportion of undiagnosed diabetes and prediabetes cases. Age was significantly associated with glycemic status across all tests, while gender showed significant associations with FBS and HbA1c. Family history of chronic disease was significantly associated with HbA1c-based classification. Approximately 8–10% of adults in Ibb had undiagnosed diabetes, while up to one-quarter had prediabetes. Age and family history were key predictors of dysglycaemia. Conclusions: These findings highlight the need for targeted, multi-marker screening and early intervention strategies, particularly in relatively stable regions of conflict-affected settings, to prevent progression to diabetes and reduce long-term complications and healthcare burden. Full article

The Internet of Medical Things (IoMT) interconnects medical devices, software applications, and healthcare services through the internet to enable the transmission and analysis of health data. IoMT facilitates seamless patient care and supports real-time clinical decision-making. The IoMT faces substantial security threats due to limited device resources, high device interconnectivity, and a lack of standardization. In this paper, we present an Intrusion Detection System (IDS) called An Empirical Distribution Ranking-Based Fuzzy J48 Classifier for Multiclass Intrusion Detection in IoMT Networks (EDR-FJ48) to distinguish between regular traffic and multiple types of security threats. The proposed IDS is built upon the J48 decision tree algorithm and is designed to detect a wide range of attacks. To ensure the protection of medical devices and patient data, the system incorporates a fuzzy IF-THEN rule inference module. In our approach, fuzzy rules are formulated based on the fuzzified values of selected features, which capture the statistical behavior of the input observations. These rules enable interpretable and transparent decision-making and are applied before the final classification step. We thoroughly evaluated our methodology through extensive simulations using three publicly available datasets, such as WUSTL-EHMS-2020, CICIoMT2024, and ECU-IoHT. The results exhibit exceptional accuracy rates of 99.68%, 98.71%, and 99.43%, respectively. A comparative analysis against state-of-the-art models in the existing literature, based on metrics including accuracy, precision, recall, F1-score, and time complexity, reveals that our proposed method achieves superior results. This evidence suggests that our method constitutes a robust solution for mitigating security threats in IoMT networks. Full article

Objective: The objectives of this study were to characterize the clinical, hormonal, and extended biomarker profile of infertile men in a Moroccan context, based on a retrospective single-center study, and to assess the relevance of selected markers for initial andrological assessment. Methods: This descriptive, retrospective, single-center study included 1399 men consulting for infertility between January and December 2024 in a specialized center. Collected data encompassed lifestyle habits, medical history, semen parameters (WHO 2021 criteria), sperm DNA fragmentation (TUNEL assay), nuclear decondensation, and hormonal assays (FSH, testosterone, and inhibin B) available in a subset of 156, 56, and 26 patients (for FSH, testosterone, and inhibin B, respectively). Associations with oligozoospermia were explored using univariate logistic regression analysis. Results: The mean age was 39.0 ± 8.0 years; 57% presented with primary infertility, and 82.8% were active smokers. A sperm concentration <16 M/mL was observed in 31.6% of patients. Among the 156 patients analyzed, high FSH levels were observed in 24% of cases. As for inhibin B, among the 26 patients evaluated, a decrease in levels was observed in 38% of cases. Pathological DNA fragmentation was found in 9.6%. In univariate analysis, oligozoospermia was significantly associated with elevated FSH (OR = 7.25; 95% CI: 3.15–16.70), varicocele (OR = 1.81), and smoking (OR = 0.66). Conclusion: This is the first large-scale Moroccan study integrating advanced biomarkers into the assessment of male infertility. The observed associations between elevated FSH, sperm DNA fragmentation, and varicocele support the development of a simplified andrological triage strategy, particularly relevant in resource-limited settings. Full article

Evacuated blood collection tubes are widely used in clinical and laboratory settings due to their simplicity and reliability. However, their performance is influenced by factors such as ambient pressure, temperature, tube design, and procedural conditions. This study systematically investigates and quantifies these effects on draw volume and filling dynamics, with a particular emphasis on high-altitude applications. A combination of theoretical modeling, experimental validation, and qualitative analysis was employed to identify critical parameters and assess their significance. The results demonstrate that standard tubes designed for sea-level conditions, particularly those with low fill ratios, may exhibit substantial deviations in draw volume at high altitudes. Factors such as blood temperature and venous pressure were found to have a considerable impact, while others, such as material creep, were negligible under typical conditions. By consolidating and analyzing these effects, this study provides a valuable resource for manufacturers and medical personnel, offering valuable insights to improve the design and use of evacuated blood collection tubes. The findings emphasize the importance of considering environmental conditions during production and clinical application, particularly for high-altitude scenarios. Future work should refine the models and expand testing under realistic conditions to enhance reliability and applicability. Full article

Knowledge distillation (KD) enables the training of lightweight yet effective models, particularly in the visual domain. Meanwhile, reinforcement learning (RL) facilitates adaptive learning through environment-driven interactions, addressing the limitations of KD in handling dynamic and complex tasks. We propose a novel two-stage framework integrating Knowledge Distillation with Reinforcement Learning (KDRL) to enhance model adaptability to complex data distributions, such as remote sensing and medical imaging. In the first stage, supervised fine-tuning guides the student model using logit and feature-based distillation. The second stage refines the model via RL, leveraging confidence-based and cluster alignment rewards while dynamically reducing reliance on task loss. By combining the strengths of supervised knowledge distillation and reinforcement learning, KDRL provides a comprehensive approach to address the dual challenges of model efficiency and domain heterogeneity. A key innovation is the introduction of auxiliary layers within the student encoder to evaluate and reward the alignment of the characteristics with the teacher’s cluster centers, promoting robust feature learning. Our framework demonstrates superior performance and computational efficiency across diverse tasks, establishing a scalable design for efficient model training. Across remote sensing benchmarks, KDRL boosts the lightweight CLIP/ViT-B-32 student to 69.51% zero-shot accuracy on AID and 80.08% on RESISC45; achieves state-of-the-art cross-modal retrieval on RSITMD with 67.44% (I→T) and 74.76% (T→I) at R@10; and improves DIOR-RSVG visual-grounding precision to 64.21% at Pr@0.9. These gains matter in real deployments by reducing missed targets and speeding analyst search on resource-constrained platforms. Full article

Background/Objectives: The increasing rate of complex patients with multiple chronic somatic and/or mental disorders in modern medicine is challenging, necessitating special management programs. The aim of the present study was to identify clinical variables and the use of health services associated with the primary-physician-driven inclusion of complex patients in the “Team Management for Complex Patients” (TMCP) special program. Methods: Using validated electronic medical records of a nationwide health maintenance organization, a case–control study was performed. The study compared parameters before enrollment of complex patients included in the TMCP program with those of complex patients during the same time period who were not included, and were matched using a propensity score for age, sex, socioeconomic status, place of residence, ethnicity, smoking status, physical activity, and the balance before the day of enrollment for the major body measurements and laboratory results. Results: The control group was well-balanced, except for the South region and no physical activity. Several respiratory, cardiac, gastrointestinal, neurological, inflammatory and autoimmune diseases were significantly more common among patients included in the TMCP program than among those not included. Complex patients included in the program presented significantly higher previous rates of attending outpatient urgent care centers, visiting hospital emergency departments, hospitalization, and medication use. Conclusions: Although limited by subjective inclusion criteria and potential confounding, the present comparative study identified clinical variables associated with the identification of complex patients for enrollment into a special managed program. These associations may inform future work to develop and validate criteria to support physician decision-making in selecting complex patients for managed programs and designing healthcare resources for patients who need them most. We currently meticulously follow the outcomes of the patients included in the special managed program. Full article