Search Results (572)

Background: A complex interplay between viral activity and host immune responses drives the progression of liver fibrosis in chronic hepatitis B. The T helper 17 (Th17) immune pathway, which produces the pro-inflammatory cytokine interleukin-17A (IL-17A), has been implicated in hepatic fibrogenesis. However, the relationship between IL-17A levels, IL-17A G197A (rs2275913) gene SNP, and the degree of liver fibrosis across different phases of the natural history of chronic hepatitis B remains insufficiently explored. Methods: This study employed an analytical observational design with a cross-sectional approach in treatment-naïve patients with chronic hepatitis B. The degree of liver fibrosis was assessed using liver elastography. IL-17A (rs2275913) gene SNP was analysed using Real-Time PCR, while serum IL-17A levels were measured using enzyme-linked immunosorbent assay. Statistical analyses included Spearman’s correlation, the contingency coefficient, the Chi-square test, the Kruskal–Wallis test, and the Mann–Whitney test, with a significance level set at p < 0.05. Results: A total of 76 patients with chronic hepatitis B were included in this study. The phase of disease progression was significantly associated with the degree of liver fibrosis (p = 0.016). Median IL-17A levels increased in parallel with fibrosis severity (p = 0.003), with a particularly significant association observed during the R phase (p = 0.002). However, no significant association was found between the IL-17A G197A (rs2275913) gene SNP and either liver fibrosis severity or serum IL-17A levels. Conclusions: Elevated serum IL-17A levels were associated with greater liver fibrosis severity, particularly during the reactivation phase of chronic hepatitis B. These findings suggest a potential relationship between IL-17A-mediated immune responses and liver fibrosis in patients with chronic hepatitis B. 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: Pulmonary arterial hypertension (PAH) is a rare and progressive disorder characterized by increased pulmonary vascular resistance and vascular remodeling. Genetic polymorphisms, epigenetic modifications, and ion channel dysregulation are increasingly recognized as key contributors to disease pathogenesis. Anoctamin-1 (ANO1/TMEM16A), a calcium-activated chloride channel, plays a critical role in vascular tone regulation. Objective: This study aimed to investigate the association between ANO1 gene polymorphisms (rs7127129 and rs2509153), promoter methylation status, and serum chloride levels in patients with idiopathic pulmonary arterial hypertension (IPAH), congenital heart disease (CHD), and chronic thromboembolic pulmonary hypertension (CTEPH). Methods: A total of 106 IPAH patients, 40 CHD patients, and 30 CTEPH patients, together with 125 healthy controls, were included. The control group had a comparable age distribution, with a balanced sex ratio, whereas females predominated in all three PH groups. Genotyping was performed using TaqMan-based real-time PCR. Promoter methylation was analyzed using bisulfite conversion followed by quantitative real-time PCR. Serum chloride levels were measured using an ion-selective electrode method. Results: No significant association was observed between rs7127129 and rs2509153 polymorphisms and IPAH or CTEPH (p > 0.05). However, rs7127129 showed a significant association with CHD (p < 0.05). After excluding hypertensive patients, both polymorphisms remained significantly associated with CHD. Serum chloride levels differed significantly among groups (p < 0.001), with higher levels observed particularly in the CTEPH and CHD groups compared to controls, while IPAH patients exhibited intermediate but still elevated levels relative to controls. In contrast, promoter methylation levels were significantly lower in all patient groups compared to controls. An inverse relationship between chloride levels and methylation status was observed. Conclusions: ANO1 polymorphisms are not major determinants of IPAH or CTEPH but may contribute to CHD susceptibility. Increased serum chloride levels, together with decreased promoter methylation, suggest a potential mechanistic link between ion channel dysregulation and epigenetic alterations in pulmonary hypertension. Further large-scale and functional studies are warranted. Full article

The Tua River is a tributary of the Douro River in the North of Portugal used as a spawning ground for potamodromous fish, namely the Iberian barbel (Luciobarbus bocagei, Steindachner, 1864). Although access to this tributary became severely constrained after the construction of the Foz Tua Hydroelectric Facility (AHFT), fish continued to use the remaining accessible 1.1 km stretch of the Tua River below the dam, especially during their spawning season. Therefore, this study presents the monitoring of migratory fish influx downstream of the AHFT and associated mitigation measures. Fixed and mobile surveys, using an ARIS 1800 sonar, and focused on Iberian barbel were conducted between March and July, from 2023 to 2025. In 2023, fixed sonar monitoring recorded 100,289 individuals, showing a progressive increase over the sampling period, while mobile surveys confirmed high local concentrations (2083 individuals) and temporal fluctuations. In 2024, total counts rose substantially to 182,216 individuals (fixed surveys) and 2656 individuals (mobile surveys), with a peak in early May followed by a gradual reduction in these numbers. In 2025, the highest abundance was observed, with 196,935 individuals (fixed surveys) and 5441 individuals (mobile surveys), alongside higher variability between monitoring campaigns. Overall, these results suggest an intensifying pattern of fish accumulation downstream of the AHFT during the sampled periods, with recurring seasonal peaks. As a method to mitigate massive accumulation of fish downstream of this dam, in 2024 and 2025, a near real-time detection and mitigation protocol was implemented. This protocol identifies an initial “trigger” and a sequential methodology that recognizes possible massive accumulation scenarios, followed by the application of an adaptive operational management measure (e.g., ecological flow regulation) by the AHFT. The application of these measures effectively contributed to reducing fish accumulation during the critical periods. In conclusion, the results highlight a consistent increase in migratory fish accumulation, over the study period, downstream of the AHFT. The successful application of adaptive measures demonstrates that the implemented strategy seems to be effective so far and provides a strong basis for future management actions. Full article

Background and Objectives: Anemia is a common complication of chronic kidney disease (CKD) and is associated with reduced quality of life, accelerated disease progression, and increased cardiovascular risk. Sodium–glucose cotransporter-2 inhibitors (SGLT2is) have demonstrated significant renal and cardiovascular benefits, and clinical trials have reported improvements in hematologic parameters during treatment. However, real-world evidence regarding their longitudinal effects on hemoglobin (Hb) and iron metabolism in patients with CKD remains limited. Materials and Methods: We conducted a pre–post analysis of 118 adult patients with CKD stages 1–4 treated with SGLT2is (empagliflozin or dapagliflozin) at the University Clinical Center of Serbia between January 2024 and June 2025. Patients received either agent at 10 mg once daily for 18 months. Hb, ferritin, C-reactive protein (CRP), albumin (Alb), daily proteinuria (Prt), and estimated glomerular filtration rate (eGFR) were assessed at baseline and at 18 months. Ferritin was adjusted for inflammatory and nutritional status using a residualization model incorporating CRP and Alb. Changes between the two time points were analyzed using repeated-measures general linear models (GLMs). Results: In unadjusted analyses, mean Hb increased modestly from 136.5 ± 17.9 g/L at baseline to 138.8 ± 18.9 g/L at follow-up (p = 0.028), while median ferritin decreased from 102.2 µg/L to 89.9 µg/L (p = 0.011). After adjustment for CRP and Alb, ferritin levels remained unchanged (p = 0.752). Repeated-measures analyses showed no significant longitudinal effect of time on Hb or ferritin and no significant interaction between time and SGLT2i type. Baseline eGFR, Prt, sex, and baseline ferritin significantly influenced longitudinal hematologic trajectories. Conclusions: SGLT2i therapy was associated with modest increases in Hb levels over 18 months, while inflammatory status remained stable and no significant reduction in ferritin levels was observed after adjustment for inflammatory and nutritional factors. Longitudinal Hb and ferritin trajectories did not differ significantly between empagliflozin and dapagliflozin, while baseline kidney function, Prt, iron status, and sex significantly influenced hematologic outcomes. Although causal inference is limited by the absence of a control group, these findings suggest a possible favorable effect of SGLT2is on anemia-related parameters in patients with CKD. Full article

Anterior cruciate ligament (ACL) injuries remain a leading cause of morbidity in athletic populations, with 70–80% occurring through non-contact mechanisms driven by biomechanical risk factors including knee valgus (>10°), low knee flexion (<30°), tibial internal rotation (>20°), and loading asymmetry (>15°), yet implementation of evidence-based neuromuscular training (which reduces injury risk by 50–70%) remains limited due to barriers in identifying at-risk individuals through accessible field-based screening. This narrative review synthesizes motion analysis technologies spanning laboratory-based optical systems (marker-based), wearable inertial measurement units (IMUs), computer vision and marker-less pose estimation, force plate and pressure-sensitive insole systems, and integrated drone-based field assessment platforms to address this critical gap. We present a three-tier clinical screening framework that progresses from basic anthropometric and single-plane video analysis to multi-modal biomechanical assessment using real-time kinematic feedback. As an illustrative example of emerging field-deployable technology, an integrated drone-based motion capture and smart insole system combining 4K video capture, AI-driven 3D motion reconstruction, and plantar pressure mapping is described to demonstrate how laboratory-quality biomechanical assessment can be achieved in ecologically valid field settings. This evidence-based review addresses current gaps between laboratory research and practical field deployment, with emphasis on cost-effectiveness, accessibility, and clinical utility for ACL injury prevention in diverse sporting environments. Full article

Earthquake-induced landslides represent a critical threat to transportation infrastructure in tectonically active mountainous regions, particularly in tropical volcanic settings where weak, highly weathered geomaterials dominate. This study develops an integrated framework that directly links physically based seismic threshold modelling with real-time landslide monitoring and operational early warning. The approach is demonstrated in the Cugenang area of Cianjur Regency, West Java, Indonesia, which was severely impacted by the moment magnitude (Mw) 5.6 earthquake in 2022. Slopes composed of highly weathered pyroclastic deposits [Plasticity Index (PI) = 54–68%; porosity > 60%] exhibit low shear strength and high sensitivity to seismic loading. Limit equilibrium analysis using the Morgenstern–Price method that combines the influence of seismic loading and groundwater conditions suggests that a horizontal seismic coefficient (kh) of approximately 0.06, corresponding to a Peak Ground Acceleration (PGA) of about 0.12 gravitational acceleration (g), is a critical threshold for initial landsliding. This comparatively low threshold challenges commonly reported values and demonstrates that slope failure in tropical volcanic terrains can occur under moderate ground shaking, reinforcing the need for site-specific hazard characterisation. The derived thresholds are operationalised within a multi-sensor early warning system integrating Micro-Electro-Mechanical Systems (MEMS) accelerometers and inclinometer measurements. Three hazard levels—Normal (<0.06 g), Alert (0.06–0.12 g), and Emergency (≥0.12 g)are combined with deformation thresholds [<10 milimeter (mm), 10–30 mm, >30 mm] to capture progressive failure processes and minimise false alarms. By coupling geotechnical modelling and real-time monitoring, this study provides a transferable and scalable framework for enhancing infrastructure resilience in landslide-prone regions. Full article

Diabetic foot ulcers represent a severe complication of diabetes mellitus, affecting millions of adults worldwide and often leading to hospitalization and amputation. Diabetic neuropathy increases the risk of plantar injuries, while the lack of continuous monitoring and delayed detection contributes to the progression of these lesions. This study presents a low-cost smart insole system for continuous plantar pressure monitoring and screening of plantar pressure patterns associated with diabetic neuropathy. The system integrates piezoresistive sensors distributed across key regions of the foot, connected to a low-power ESP32 microcontroller for data acquisition. Measurements are transmitted via Bluetooth Low Energy to a mobile application that enables real-time visualization, user management, and storage in a MySQL database for historical data consultation. Data processing employs a convolutional neural network configured to classify plantar pressure patterns between non-diabetic individuals and diabetic patients presenting neuropathic alterations. System validation demonstrated 88% accuracy, 88% recall, and 87% F1-score in classifying plantar pressure patterns. The results confirm that the combination of low-cost hardware and open-source software constitutes a viable and scalable solution for screening biomechanical alterations associated with diabetic foot complications. Full article

Assessing motor symptoms in Parkinson’s disease (PD) is challenging due to the progressive evolution of the condition and the variability of symptoms, which are not fully captured by periodic clinical visits. In this context, wearable sensors and machine learning (ML) have emerged as a viable path toward objective and continuous monitoring, although achieving robust generalization to free-living conditions remains a challenge. This work explores the egg-beating task, a simple everyday activity, as a digital approach for PD motor assessment using smartwatch-based inertial measurements and ML techniques. Twenty-two individuals with PD and sixteen healthy controls (HC) completed a one-minute egg-beating task while wearing a smartwatch equipped with tri-axial accelerometer and gyroscope sensors. Data were recorded both under supervised clinical conditions and during unsupervised home sessions. Time- and frequency-domain features were extracted from the inertial signals, and models trained exclusively on supervised recordings were then tested on supervised, unsupervised, and combined data. PD participants showed systematically lower movement amplitude, slower oscillation frequency, and a progressive drop in signal energy over the course of the task, all of which align with the characteristic features of bradykinesia. The support vector machine achieved the best overall performance, reaching 90% accuracy in distinguishing PD from healthy controls under supervised conditions, with a reduction of less than 4% when applied to unsupervised data. These results support the egg-beating task as a practical and ecologically valid method for real-world motor assessment, with potential for future use in remote monitoring and longitudinal assessment. Full article

Cardiovascular disease (CVD) remains the leading global cause of death, with approximately 17.9 million mortalities annually. Studies have shown that adopting healthy behaviors, i.e., a balanced diet, regular physical activity, and weight management, can reduce CVD risk. However, evaluating their long-term impact requires extensive data collection and analysis, which are both time-consuming and challenging. This study developed a novel mathematical framework integrating an agent-based model (ABM) to simulate CVD risk progression and established clinical guidelines into synthetic training data for machine learning (ML) classification. The ML model was trained entirely on synthetic data generated from World Health Organization/International Society of Hypertension cardiac risk indications, and validated using outcomes from a NetLogo simulation. The workflow does not use real patient data; instead, the expected simulation results serve as a reference to assess the ML model and synthetic data. The ABM, designed in NetLogo, exchanges agent characteristics with a trained ML model to classify individuals into appropriate CVD risk levels based on lifestyle and clinical parameters. The simulation indicated measurable risk progression (5–12%) by year 20 in individuals with both smoking and diabetes. A combined effect of high dietary intake and low physical activity showed over 20% risk increase, demonstrating the model’s capacity to capture dynamic risk interactions. The relationship between CVD risk and systolic blood pressure was also effectively reproduced. Additional scenarios confirmed the alignment of model outcomes with real-world trends, showing model self-consistency, identifying critical thresholds and population-level risk shifts through detailed tabular analysis. Beyond confirming known associations, the findings support the internal consistency of the model, highlighting its potential as a simulation based tool for studying cardiovascular risk patterns and supporting risk monitoring within controlled settings. Full article

Attenuated total reflection time-domain spectroscopy (ATR-TDS) in the terahertz regime was employed to investigate the dielectric response of water–acetone mixtures over the full molar concentration range. The ATR configuration enabled stable measurements in a controlled and nearly closed environment, minimizing acetone evaporation and allowing reliable characterization of this highly volatile binary system. The complex dielectric function, retrieved in the 0.4–1.6 THz range, was analyzed by means of a double Cole–Cole model, which provided a more consistent description of the mixtures than a simple Debye-based approach. A strongly nonlinear dependence on composition was observed, with the highest sensitivity in the water-rich region, where even small amounts of acetone produced a marked change in both the real and imaginary parts of the dielectric function. The extracted parameters indicate that acetone primarily suppresses the slow, cooperative relaxation channel associated with the hydrogen-bond network of water, whereas the faster channel remains comparatively less affected, consistent with its more local intermolecular origin. The evolution of the Kirkwood–Fröhlich correlation factors and of the broadening parameters further supports a progressive transition from a highly correlated hydrogen-bonded liquid to a structurally heterogeneous and weakly cooperative dipolar environment. These results demonstrate that THz ATR-TDS is a sensitive tool for probing intermolecular reorganization in aqueous binary mixtures, providing a physically grounded framework for the detection of acetone and other volatile hydrogen-bond-active species in water-based systems. Full article

Robotic assistive devices, such as exoskeletons, are increasingly employed in walking rehabilitation. Therefore, the measurement of both movement kinematics and cognitive workload is important to understand this human–robot interaction in real-world contexts. To address this need this study presents the validation of a framework integrating inertial motion capture (Xsens) and eye-tracking sensor (Pupil Neon) within a Mixed Reality (Meta Quest 3) architecture. We developed an overground dual-task paradigm in which holographic numbers appear in the user’s peripheral vision. This setup actively stimulates visuospatial attention while quantifying kinematic and cognitive output. To validate the framework, the protocol has been tested on 30 healthy subjects across repeated exoskeleton training sessions. Statistical analyses revealed that the Coefficient of Multiple Correlation (CMC) and Spectral Arc Length (SPARC), calculated on the shank angular velocity, together with the Step Length Variability, exhibited significant time effects (p < 0.01), mapping the transition toward automated gait. Concurrently, pupillometric data demonstrated a measurable reduction in neurocognitive demand; specifically, the Task-Evoked Pupillary Response (TEPR) decreased significantly across progressive training sessions (p < 0.05). With this work, we validated a measurement protocol that aims to provide a novel methodology for objectively evaluating motor and cognitive adaptation in wearable assistive devices. Full article

Short-term mortality following the last dose of immune checkpoint inhibitors (ICIs) is an increasingly recognized real-world outcome measure, yet its clinical predictors remain poorly characterized. This multicenter retrospective study included 458 consecutive patients with advanced melanoma, non-small cell lung cancer, or renal cell carcinoma who received ICIs at four tertiary centers in Turkey between 2018 and 2023. The primary endpoint was 30-day mortality after the final ICI dose. Among 458 patients, 71 (15.5%) died within 30 days. Multivariable logistic regression identified ECOG performance status ≥ 2, number of metastatic sites ≥ 3, and log-transformed C-reactive protein-to-albumin ratio (log-CAR) as independent predictors of 30-day mortality in Model 1 (AUC 0.954), while ECOG PS ≥ 2, brain metastasis, metastatic sites ≥ 3, and log-NLR were independent predictors in Model 2 (AUC 0.912). In the lung cancer subgroup, log-CAR and NLR remained independent predictors while ECOG PS did not. Patients who died within 30 days had significantly shorter progression-free survival (1.18 vs. 4.63 months) and overall survival (2.30 vs. 14.39 months) compared with survivors. These findings suggest that routine assessment of inflammatory and nutritional biomarkers alongside tumor burden parameters may help identify patients at high risk of early mortality and inform the timing of supportive care in ICI-treated populations. Full article

Manufacturing systems are undergoing a fundamental transition as efficiency-driven optimisation paradigms prove increasingly inadequate for meeting net-zero, resource-efficiency, and resilience objectives. Digital twins have emerged as a central enabler of this transition, offering continuously coupled physical–digital representations capable of real-time monitoring, prediction, and control. Recent advances in artificial intelligence have accelerated this evolution, transforming digital twins from static simulation artefacts into adaptive, learning-enabled systems embedded within cyber–physical manufacturing environments. However, this shift has also exposed critical challenges related to trust, interpretability, scalability, and sustainability alignment. This review provides a critical synthesis of AI-enabled digital twin research with a specific focus on manufacturing and additive manufacturing systems. It examines the progression from physics-based and data-driven twins toward hybrid AI–physics architectures that balance predictive performance with physical consistency and explainability. Beyond technical performance, the review reframes digital twins as decision-making infrastructures whose value depends on how effectively they integrate energy consumption, material efficiency, carbon intensity, and lifecycle impacts into optimisation and control logic. Particular attention is given to real-time optimisation, predictive maintenance, and intelligent asset management, highlighting persistent gaps in uncertainty propagation, cross-scale coordination, and sustainability-aware governance. The review further identifies structural barriers to large-scale industrial adoption, including data interoperability fragmentation, platform lock-in, organisational resistance, and regulatory ambiguity surrounding AI-driven decisions. Synthesising insights across domains, it argues that many current digital twin implementations remain technically sophisticated yet strategically conservative, reinforcing throughput-centred objectives rather than enabling systemic decarbonisation and circularity. The paper concludes by outlining future research directions and policy-relevant opportunities, emphasising the need for digital twins that reason across timescales, objectives, and lifecycle boundaries. By aligning manufacturing intelligence with measurable sustainability outcomes, AI-enabled digital twins can move from incremental efficiency gains toward transformative impact in net-zero and circular manufacturing systems. Full article

Obesity remains a major global health concern associated with increased cardio-metabolic risk and healthcare burden. L-Carnitine plays a central role in mitochondrial fatty acid transport and has been investigated as a potential adjunct in weight management. This study evaluated the real-world effectiveness and tolerability of L-Carnitine L-Tartrate supplementation in adults with overweight and class I obesity. In this prospective, single-center, uncontrolled observational study, 50 adults (BMI 25–35 kg/m²) newly initiated on L-Carnitine L-Tartrate 2000 mg/day were followed for 8 weeks in a non-comparative, real-world setting, alongside standard lifestyle advice. The primary outcome was the mean change in body weight from baseline. Secondary outcomes included anthropometric measures, body composition parameters assessed by bioelectrical impedance analysis, quality of life using the RAND 36-Item Health Survey, global satisfaction, and safety. Over 8 weeks, mean body weight in this cohort decreased from 73.69 ± 7.73 kg at baseline to 67.36 ± 7.87 kg at Week 8 (mean reduction: 6.33 kg; 8.59%; p < 0.001. Over the follow-up period, we observed reductions in waist circumference (−2.38 cm), hip circumference (−2.96 cm), total fat mass (−3.90 kg), and visceral fat (−39.91%) (all p < 0.001) in within-subject analyses. Quality of life shows progressive improvement over time. No adverse events or treatment discontinuations were reported. In this exploratory, single-arm, real-world observational study, initiation of L-Carnitine L-Tartrate supplementation alongside routine lifestyle advice was associated with reductions in body weight, central adiposity, and improvements in patient-reported outcomes over 8 weeks. While the uncontrolled study design warrants cautious interpretation, these findings provide supportive real-world evidence and generate a basis for future controlled studies to further evaluate the therapeutic potential of L-Carnitine L-Tartrate. Full article