Search Results (710)

Background: Sodium–glucose cotransporter-2 inhibitors (SGLT2is), initially developed as antihyperglycemic agents, have emerged as multifunctional therapeutics with profound cardiorenal and metabolic benefits. Their unique insulin-independent mechanism, targeting renal glucose reabsorption, distinguishes them from conventional antidiabetic drugs. Mechanisms and Clinical Evidence: SGLT2is induce glycosuria, reduce hyperglycemia, and promote weight loss through increased caloric excretion. Beyond glycemic control, they modulate tubuloglomerular feedback, attenuate glomerular hyperfiltration, and exert systemic effects via natriuresis, ketone utilization, and anti-inflammatory pathways. Landmark trials (DAPA-HF, EMPEROR-Reduced, CREDENCE, DAPA-CKD) demonstrate robust reductions in heart failure (HF) hospitalizations, cardiovascular mortality, and chronic kidney disease (CKD) progression, irrespective of diabetes status. Adipose Tissue and Metabolic Effects: SGLT2is mitigate obesity-associated adiposopathy by shifting macrophage polarization (M1 to M2), reducing proinflammatory cytokines (TNF-α, IL-6), and enhancing adipose tissue browning (UCP1 upregulation) and mitochondrial biogenesis (via PGC-1α/PPARα). Modest weight loss (~2–4 kg) occurs, though compensatory hyperphagia may limit long-term effects. Emerging Applications: Potential roles in non-alcoholic fatty liver disease (NAFLD), polycystic ovary syndrome (PCOS), and neurodegenerative disorders are under investigation, driven by pleiotropic effects on metabolism and inflammation. Conclusions: SGLT2is represent a paradigm shift in managing T2DM, HF, and CKD, with expanding implications for metabolic syndrome. Future research should address interindividual variability, combination therapies, and non-glycemic indications to optimize their therapeutic potential. Full article

In wind tunnel tests for aerospace and bridge engineering, the accurate prediction of Mach number remains a core challenge to ensure the reliability of airflow dynamics characterization. Pure data-driven models often fail to meet high-precision prediction requirements due to the lack of physical mechanism constraints and insufficient generalization capability. This paper proposes a physical information-based long short-term memory network (P-LSTM), which constructs a physical loss function by embedding isentropic flow equations from gas dynamics, thereby constraining the Mach number prediction solution space within the physically feasible domain. This approach effectively balances the neural network’s ability to capture temporal features with the interpretability of physical mechanisms. Aiming at the scarcity of data in new wind tunnel scenarios, an adaptive weight transfer learning method (AWTL) is further proposed, realizing efficient knowledge transfer across different-scale wind tunnels via cross-domain data calibration, adaptive source-domain weight reweighting, and target-domain fine-tuning. Experimental results show that the P-LSTM method achieves a 50.65–62.54% reduction in RMSE, 48.00–54.05% in MAE, and 47.88–73.68% in MD compared with traditional LSTM for Mach number prediction in the 0.6 m continuous wind tunnel flow field. The AWTL model also outperforms the direct training model significantly in the 2.4 m continuous wind tunnel, with RMSE, MAE, and MD reduced by 85.26%, 95.12%, and 71.14%, respectively. These results validate that the proposed models achieve high-precision Mach number prediction with strong generalization capability. Full article

In the original randomised Dietary Intervention to Stop Coronary Atherosclerosis (DISCO-CT) trial, a 12-month Dietary Approaches to Stop Hypertension (DASH) project led by dietitians improved cardiovascular and metabolic risk factors and reduced platelet chemokine levels in patients with coronary artery disease (CAD). It is unclear whether these benefits are sustained. Objective: To determine whether the metabolic, inflammatory, and clinical benefits achieved during the DISCO-CT trial are sustained six years after the structured intervention ended. Methods: Ninety-seven adults with non-obstructive CAD confirmed in coronary computed tomography angiography were randomly assigned to receive optimal medical therapy (control group, n = 41) or the same therapy combined with intensive DASH counselling (DASH group, n = 43). After 301 ± 22 weeks, 84 individuals (87%) who had given consent underwent reassessment of body composition, meal frequency assessment, and biochemical testing (lipids, hs-CRP, CXCL4, RANTES and homocysteine). Major adverse cardiovascular events (MACE) were assessed. Results: During the intervention, the DASH group lost an average of 3.6 ± 4.2 kg and reduced their total body fat by an average of 4.2 ± 4.8 kg, compared to an average loss of 1.1 ± 2.9 kg and a reduction in total body fat of 0.3 ± 4.1 kg in the control group (both p < 0.01). Six years later, most of the lost body weight and fat tissue had been regained, and there was a sharp increase in visceral fat area in both groups (p < 0.0001). CXCL4 decreased by 4.3 ± 3.0 ng/mL during the intervention and remained lower than baseline values; in contrast, in the control group, it initially increased and then decreased (p < 0.001 between groups). LDL cholesterol and hs-CRP levels returned to baseline in both groups but remained below baseline in the DASH group. There was one case of MACE in the DASH group, compared with four cases (including one fatal myocardial infarction) in the control group (p = 0.575). Overall adherence to the DASH project increased by 26 points during counselling and then decreased by only four points, remaining higher than in the control group. Conclusions: A one-year DASH project supported by a physician and dietitian resulted in long-term suppression of the proatherogenic chemokine CXCL4 and fewer MACE over six years, despite a decline in adherence and loss of most anthropometric and lipid benefits. It appears that sustained systemic reinforcement of behaviours is necessary to maintain the benefits of lifestyle intervention in CAD. Full article

Traditional unmanned aerial vehicle (UAV) detection and tracking methods have long faced the twin challenges of high cost and poor efficiency. In real-world battlefield environments with complex backgrounds, occlusions, and varying speeds, existing techniques struggle to track small UAVs accurately and stably. To tackle these issues, this paper presents an enhanced YOLOv8N-Drone-based algorithm for improved target tracking of small UAVs. Firstly, a novel module named C2f-DSFEM (Depthwise-Separable and Sobel Feature Enhancement Module) is designed, integrating Sobel convolution with depthwise separable convolution across layers. Edge detail extraction and multi-scale feature representation are synchronized through a bidirectional feature enhancement mechanism, and the discriminability of target features in complex backgrounds is thus significantly enhanced. For the feature confusion problem, the improved lightweight Context Anchored Attention (CAA) mechanism is integrated into the Neck network, which effectively improves the system’s adaptability to complex scenes. By employing a position-aware weight allocation strategy, this approach enables adaptive suppression of background interference and precise focus on the target region, thereby improving localization accuracy. At the level of loss function optimization, the traditional classification loss is replaced by the focal loss (Focal Loss). This mechanism effectively suppresses the contribution of easy-to-classify samples through a dynamic weight adjustment strategy, while significantly increasing the priority of difficult samples in the training process. The class imbalance that exists between the positive and negative samples is then significantly mitigated. Experimental results show the enhanced YOLOv8 boosts mean average precision (Map@0.5) by 12.3%, hitting 99.2%. In terms of tracking performance, the proposed YOLOv8 N-Drone algorithm achieves a 19.2% improvement in Multiple Object Tracking Accuracy (MOTA) under complex multi-scenario conditions. Additionally, the IDF1 score increases by 6.8%, and the number of ID switches is reduced by 85.2%, indicating significant improvements in both accuracy and stability of UAV tracking. Compared to other mainstream algorithms, the proposed improved method demonstrates significant advantages in tracking performance, offering a more effective and reliable solution for small-target tracking tasks in UAV applications. Full article

CT scanners are essential tools in modern medical imaging. Sudden failures of their X-ray tubes can lead to equipment downtime, affecting healthcare services and patient diagnosis. However, existing prediction methods based on a single model struggle to adapt to the multi-stage variation characteristics of tube lifespan and have limited modeling capabilities for temporal features. To address these issues, this paper proposes an intelligent prediction architecture for CT tubes’ remaining useful life based on a dual-branch neural network. This architecture consists of two specialized branches: a residual self-attention BiLSTM (RSA-BiLSTM) and a multi-layer dilation temporal convolutional network (D-TCN). The RSA-BiLSTM branch extracts multi-scale features and also enhances the long-term dependency modeling capability for temporal data. The D-TCN branch captures multi-scale temporal features through multi-layer dilated convolutions, effectively handling non-linear changes in the degradation phase. Furthermore, a dynamic phase detector is applied to integrate the prediction results from both branches. In terms of optimization strategy, a dynamically weighted triplet mixed loss function is designed to adjust the weight ratios of different prediction tasks, effectively solving the problems of sample imbalance and uneven prediction accuracy. Experimental results using leave-one-out cross-validation (LOOCV) on six different CT tube datasets show that the proposed method achieved significant advantages over five comparison models, with an average MSE of 2.92, MAE of 0.46, and R² of 0.77. The LOOCV strategy ensures robust evaluation by testing each tube dataset independently while training on the remaining five, providing reliable generalization assessment across different CT equipment. Ablation experiments further confirmed that the collaborative design of multiple components is significant for improving the accuracy of X-ray tubes remaining life prediction. Full article

Background/Objectives: Conventional ketogenic diets, although effective for weight loss, often contain high total and saturated fat intake, which leads to increased low-density lipoprotein cholesterol (LDL-C). Thus, the Healthy Ketogenic Diet (HKD) was developed to address these concerns. It emphasizes calorie restriction, limiting net carbohydrate intake to 50 g per day, prioritizing unsaturated fats, and reducing saturated fat intake. However, adherence to the HKD remains a challenge in urban, time-constrained environments. Therefore, this pilot randomized controlled trial aimed to investigate the effects of Healthy Ketogenic Diet Ready-To-Eat (HKD-RTE) meals (provided for the first month only) versus HKD alone on weight loss and metabolic parameters among overweight adults. Methods: Multi-ethnic Asian adults (n = 50) with a body mass index (BMI) ≥ 27.5 kg/m² were randomized into the HKD-RTE group (n = 24) and the HKD group (n = 26). Both groups followed the HKD for six months, with the HKD-RTE group receiving HKD-RTE meals during the first month. Five in-person workshops and mobile health coaching through the Nutritionist Buddy Keto app helped to facilitate dietary adherence. The primary outcome was the change in body weight at 6 months. Linear regression was performed on the change from baseline for each continuous outcome, adjusting for demographics and relevant covariates. Logistic regression was performed on binary weight loss ≥ 5%, adjusting for demographics and relevant covariates. Results: In the HKD group, participants’ adherence to the 50 g net carbohydrate target was 15 days, while that in the HKD-RTE group was 19 days over a period of 30 days. Participants’ adherence to calorie targets was 21 days in the HKD group and 23 days in the HKD-RTE. The average compliance with the HKD-RTE meals provided in the HKD-RTE group was 55%. The HKD-RTE group experienced a greater percentage weight loss at 1 month (−4.8 ± 3.0% vs. −1.8 ± 6.2%), although this was not statistically significant. This trend continued up to 6 months, with the HKD-RTE group showing a greater percentage weight reduction (−8.6 ± 6.8% vs. −3.9 ± 8.6%; p = 0.092). At 6 months, the HKD-RTE group had a greater reduction in total cholesterol (−0.54 ± 0.76 mmol/L vs. −0.05 ± 0.56 mmol/L; p = 0.283) and LDL-C (−0.43 ± 0.67 mmol/L vs. −0.03 ± 0.52 mmol/L; p = 0.374) compared to the HKD group. Additionally, the HKD-RTE group exhibited greater reductions in systolic blood pressure (−8.3 ± 9.7 mmHg vs. −5.3 ± 11.0 mmHg), diastolic blood pressure (−7.7 ± 8.8 mmHg vs. −2.0 ± 7.0 mmHg), and HbA1c (−0.3 ± 0.5% vs. −0.1 ± 0.4%) than the HKD group (not statistically significant for any). Conclusions: Both HKD-RTE and HKD led to weight loss and improved metabolic profiles. The HKD-RTE group tended to show more favorable outcomes. Short-term HKD-RTE meal provision may enhance initial weight loss, with sustained long-term effects. Full article

The effects of long-term adjustments in body weight on the lipid balance in patients with severe obesity are not well understood. This study aimed to evaluate a non-invasive lipidomic approach to identifying biomarkers that could help predict which patients may require additional therapies before and after weight loss. Using mass spectrometry, 275 lipid species were analysed in non-obese controls, patients with severe obesity, and patients one year after bariatric surgery. The results showed that severe obesity disrupts lipid pathways, contributing to lipotoxicity, inflammation, mitochondrial stress, and abnormal lipid metabolism. Although weight loss improved these disturbances, surgery did not fully normalise the lipid profiles of all patients. Outcomes varied depending on their baseline liver health and genetic differences. Persistent alterations in cholesterol handling, membrane composition, and mitochondrial function were observed in partial responders. Elevated levels of sterol lipids, glycerophospholipids, and sphingolipids emerged as markers of complete metabolic recovery, identifying candidates for targeted post-surgical interventions. These findings support the use of lipidomics to personalise obesity treatment and follow-up. Full article

In this study, multicomponent PLA-based biocomposites were developed. In particular, both native fibrous cellulose and cellulose with modified morphology obtained through ball milling treatments were incorporated into the polyester matrix in combination with an oligomeric plasticizer, specifically a lactic acid oligomer (OLA). The resulting materials were analyzed in terms of their morphology, thermal and mechanical properties over time, water vapor permeability, and degradation under soil burial conditions in comparison to neat PLA and unplasticized PLA/cellulose composites. The cellulose phase significantly affected the mechanical properties and enhanced their long-term stability, addressing a common limitation of PLA/plasticizer blends. Additionally, water vapor permeability increased in all composites. Finally, the ternary systems exhibited a significantly higher degradation rate in soil burial conditions compared to PLA, evidenced by larger weight loss and reduction in the molecular weight of the PLA phase. The degradation rate was notably influenced by the morphology of the cellulose phase. Full article

Background: Malnutrition and unintended weight loss are frequent in cancer patients and linked to poorer outcomes. However, data on long-term weight trajectories, particularly comparing different cancer entities, remain limited. Methods: In this retrospective, multicenter study, we analyzed 145 patients diagnosed with either head and neck cancer (HNC; n = 48) or high-grade B-cell lymphoma (HGBCL; n = 97). Body weight, C-reactive protein (CrP), albumin, and modified Glasgow Prognostic Score (mGPS) were assessed at diagnosis and at 3, 6, 9, and 12 months. Clinically relevant weight loss was defined as >5% from baseline. Survival analyses were performed for HGBCL patients. Results: Weight loss was common in both cohorts, affecting 32.2% at 3 months and persisting in 42.3% at 12 months. Nearly half of HNC patients had sustained >5% weight loss at one year, whereas HGBCL patients were more likely to regain weight, with significantly higher rates of weight gain at 6 and 12 months (p = 0.04 and p = 0.02). At baseline, HGBCL patients showed elevated CrP and lower albumin compared to HNC (both p < 0.001). Weight loss at 6 months was significantly associated with reduced overall survival in HGBCL (p < 0.01). Both Δweight at 6 months and mGPS emerged as useful prognostic indicators. Conclusions: This study reveals distinct patterns of weight change and systemic inflammation between HNC and HGBCL patients during the first year after diagnosis. These findings highlight the need for entity-specific nutritional monitoring and tailored supportive care strategies extending into survivorship. Prospective studies integrating body composition analyses are warranted to better guide long-term management. Full article

Background/Objectives: After diagnosis, it is common for women with breast cancer to gain weight, which is associated with worse clinical outcomes. However, traditional measures such as body weight, BMI, and waist circumference do not detect key changes in body composition, such as fat redistribution or muscle loss. The objective of this exploratory study was to assess the evolution of body composition and muscle strength after one year of treatment, and their relationship with metabolic biomarkers. Methods: Prospective observational study in newly diagnosed breast cancer patients. Body composition was assessed using bioelectrical impedance analysis (BIA) and ultrasound (US); muscle strength was measured by handgrip dynamometry. Biomarkers analyzed included glucose, insulin, Homeostatic Model Assessment of Insulin Resistance (HOMA-IR), glycosylated hemoglobin (HbA1c), total cholesterol (and its fractions), triglycerides, C-reactive protein (CRP), 6-interleukin (IL-6), vitamin D, myostatin, and fibroblast growth factor 21 (FGF-21). Results: Sixty-one women (mean age 58 years) were included. After one year, fat mass and related parameters significantly increased, while skeletal muscle mass and muscle strength decreased. Sarcopenic obesity prevalence rose from 1.16% to 4.9%. No significant changes were found in biomarkers, but positive correlations were observed between fat parameters and insulin, HOMA-IR, and triglycerides, and negative correlations with HDL-cholesterol. Conclusions: BIA and US can detect unfavorable changes in body composition that are not reflected in conventional measurements. At one year post-diagnosis, women showed increased fat accumulation, muscle loss, and reduced strength, even without significant metabolic biomarker changes. Further research is warranted to elucidate the long-term clinical implications of these findings and the external validity in larger cohorts. Full article

In this paper, we aim to provide a new algorithm for managing financial risk in portfolios containing multiple high-volatility assets. We assess the variability of volatility with the Volterra model, and we construct an estimator of the Value-at-Risk (VaR) function using quantile regression. Because of its long-memory property, the Volterra model is particularly useful in this domain of financial time series data analysis. It constitutes a good alternative to the standard approach of Black–Scholes models. From the weighted asymmetric loss function, we construct a new estimator of the VaR function usable in Multi-Functional Volterra Time Series Model (MFVTSM). The constructed estimator highlights the multi-functional nature of the Volterra–Gaussian process. Mathematically, we derive the asymptotic consistency of the estimator through the precision of the leading term of its convergence rate. Through an empirical experiment, we examine the applicability of the proposed algorithm. We further demonstrate the effectiveness of the estimator through an application to real financial data. Full article

Obesity is a growing global health concern with widespread impacts on physical, psychological, and social well-being. Clinically, it is a major driver of type 2 diabetes (T2D), cardiovascular disease (CVD), non-alcoholic fatty liver disease (NAFLD), and cancer, reducing life expectancy by 5–20 years and imposing a staggering economic burden of USD 2 trillion annually (2.8% of global GDP). Despite its significant health and socioeconomic impact, earlier obesity medications, such as fenfluramine, sibutramine, and orlistat, fell short of expectations due to limited effectiveness, serious side effects including valvular heart disease and gastrointestinal issues, and high rates of treatment discontinuation. The advent of glucagon-like peptide-1 (GLP-1) receptor agonists (e.g., semaglutide, tirzepatide) has revolutionized obesity management. These agents demonstrate unprecedented efficacy, achieving 15–25% mean weight loss in clinical trials, alongside reducing major adverse cardiovascular events by 20% and T2D incidence by 72%. Emerging therapies, including oral GLP-1 agonists and triple-receptor agonists (e.g., retatrutide), promise enhanced tolerability and muscle preservation, potentially bridging the efficacy gap with bariatric surgery. However, challenges persist. High costs, supply shortages, and unequal access pose significant barriers to the widespread implementation of obesity treatment, particularly in low-resource settings. Gastrointestinal side effects and long-term safety concerns require close monitoring, while weight regain after medication discontinuation emphasizes the need for ongoing adherence and lifestyle support. This review highlights the transformative potential of incretin-based therapies while advocating for policy reforms to address cost barriers, equitable access, and preventive strategies. Future research must prioritize long-term cardiovascular outcome trials and mitigate emerging risks, such as sarcopenia and joint degeneration. A multidisciplinary approach combining pharmacotherapy, behavioral interventions, and systemic policy changes is critical to curbing the obesity epidemic and its downstream consequences. Full article

The environmental detriments of the growing global production and overconsumption of beef, including greenhouse gas emissions, deforestation, and biodiversity loss, are well-documented. However, public awareness of how dietary choices affect the environment remains limited. This study examines sustainability views on beef consumption and the potential for behavioral change as a step toward more sustainable intake levels. An observational web-based survey was conducted (n = 1367) to assess respondents’ current beef intake frequency, views on beef consumption related to planetary health, tropical deforestation, greenhouse gas emissions, and climate change, and willingness to modify beef consumption behavior. Chi-square tests were used for group comparisons, and weighted average scores were applied to rank levels of resistance to reducing beef intake. Environmental concern related to beef consumption was associated with greater beef cutback intentions and lower long-term intake reduction resistance amongst beef eaters. Beef eaters who strongly agreed that global beef consumption negatively impacts the environment were considerably more likely to express intentions to reduce their long-term beef intake compared to those who strongly disagreed (94.4% vs. 19.6%). Overall, 76.6% of beef eaters indicated wanting to eat less beef or phase it out entirely (30.7% reduce, 29.4% minimize, 16.6% stop), with only 23.4% of them intending to keep their consumption unchanged. Compelling messages that help translate awareness into action, such as the #NoBeefWeek concept explored in this study, may support individuals in adopting more sustainable food choices. These cross-national findings provide evidence for a ‘knowledge–intent’ gap in sustainable diet research, with relevance for health communicators and policymakers. Future research could examine the factors and motivations influencing decisions to modify beef consumption, including the barriers to achieving sustainable consumption levels and the role of suitable alternatives in facilitating this transition. Full article

Background: In recent years, changes in climate conditions and long-term continuous cropping have led to the increased occurrence of Verticillium wilt in various cotton-growing regions, causing significant economic losses in cotton production. Research has shown that volatile substances are closely linked to plant disease resistance; however, studies on their roles in the response of cotton to Verticillium wilt, including their relationship with gene regulation, are limited. Methods: In this study, the transcriptomes and metabolomes of Xinluzao 57 (a highly susceptible Verticillium wilt variety) and 192,868 (a highly resistant Verticillium wilt variety) were sequenced at different time points after inoculation with Verticillium wilt. Results: A total of 21,911 commonly differentially expressed genes (DEGs) were identified within and between the materials, and they were clustered into eight groups. Significant annotations were made in pathways related to amino acids and anthocyanins. Metabolomics identified and annotated 26,200 volatile metabolites across nine categories. A total of 158 differentially accumulated metabolites (DAMs) were found within and between the materials; three clusters were identified, and the 10 metabolites with the most significant fold changes were highlighted. Weighted gene coexpression network analysis (WGCNA) revealed that 13 genes were significantly correlated with guanosine, 6 genes were correlated with 2-deoxyerythritol, and 32 genes were correlated with raffinose. Conclusions: Our results provide a foundation for understanding the role of volatile substances in the response of cotton to Verticillium wilt and offer new gene resources for future research on Verticillium wilt resistance. Full article

In the aluminum electrolysis production process, the traditional cell control method based on cell voltage and series current can no longer meet the goals of energy conservation, consumption reduction, and digital-intelligent transformation. Therefore, a new digital cell control technology that is centrally dependent on various process parameters has become an urgent demand in the aluminum electrolysis industry. Among them, the real-time online measurement of alumina concentration is one of the key data points for implementing such technology. However, due to the harsh production environment and limitations of current sensor technologies, hardware-based detection of alumina concentration is difficult to achieve. To address this issue, this study proposes a soft-sensing model for alumina concentration based on a long short-term memory (LSTM) neural network optimized by a weighted average algorithm (WAA). The proposed method outperforms BiLSTM, CNN-LSTM, CNN-BiLSTM, CNN-LSTM-Attention, and CNN-BiLSTM-Attention models in terms of predictive accuracy. In comparison to LSTM models optimized using the Grey Wolf Optimizer (GWO), Harris Hawks Optimization (HHO), Optuna, Tornado Optimization Algorithm (TOC), and Whale Migration Algorithm (WMA), the WAA-enhanced LSTM model consistently achieves significantly better performance. This superiority is evidenced by lower MAE and RMSE values, along with higher R² and accuracy scores. The WAA-LSTM model remains stable throughout the training process and achieves the lowest final loss, further confirming the accuracy and superiority of the proposed approach. Full article