Search Results (30,040)

Objective: This study aimed to develop a nomogram prediction model for predicting 14-day in-hospital mortality in patients with acute myocardial infarction (AMI) and ventricular septal rupture (VSR). Methods: Clinical data of 86 hospitalized patients (44 survivors and 42 non-survivors within 14 days) were retrospectively collected in Nanjing First Hospital from 1 March 2015 to 7 August 2025. Lasso regression and multivariable logistic regression were used to identify predictors, which were subsequently incorporated into the nomogram development. The model performance was assessed using area under the receiver operating characteristic curve (AUC), calibration plots, decision curve analysis (DCA), and clinical impact curves, with internal validation via 1000 bootstrap resamples. Results: Analysis of lasso regression and multivariable logistic regression analysis identified WBC count (OR = 1.31, 95% CI: 1.01–1.28, p = 0.040), D-dimer level (OR = 1.18, 95% CI: 1.01–1.38, p = 0.043), early revascularization (OR = 0.22, 95% CI: 0.06–0.88, p = 0.032), ventilatory support (OR = 3.48, 95% CI: 1.07–11.29, p = 0.038), and infection (OR = 3.97, 95% CI: 1.02–15.42, p = 0.047) as independent predictors of 14-day mortality for patients. Based on the results, a prediction nomogram model was constructed. The model achieved an area under the receiver operating characteristic curve (AUC) of 0.866 (95% CI: 0.785–0.946), with sensitivity of 0.857 (95% CI: 0.751–0.963) and specificity of 0.818 (95% CI: 0.704–0.932). Calibration plots demonstrated acceptable agreement between predicted and observed probabilities; decision curve analysis (DCA) and clinical impact curve further confirmed its net benefit and clinical utility. By 1000 bootstrap resampling iterations, the model demonstrated an apparent AUC of 0.864, 95% CI: 0.776–0.938, confirming reasonable discriminative performance. Conclusions: In summary, this study developed a clinical interpretable nomogram to estimate short-term (14-day) in-hospital mortality risk in patients with AMI-VSR; it provides a robust and interpretable tool for predicting short-term in-hospital mortality. Full article

Against the backdrop of high-quality urbanization in cities, the rapid expansion of metro networks has led to severe spatial mismatches in land use around station areas, which seriously restricts the full exertion of the comprehensive benefits of the transit-oriented development (TOD) model. Taking 139 operational metro stations in Xi’an in 2024 as the research sample, this study constructs a multi-objective land use optimization model with the richness of public services, transportation accessibility and population distribution balance as the three core maximization objectives. A hierarchically adaptive improved NSGA-III algorithm is proposed, with the following four key technical optimizations implemented: multi-dimensional adaptive reference point adjustment, design of real-integer hybrid coding genetic operators, construction of an enhanced multi-criteria environmental selection mechanism, and dynamic regulation of algorithm iteration. Experimental results show that the performance of the improved algorithm is significantly superior to that of the traditional NSGA-III algorithm: the values of the three core objectives are increased by 59.58%, 12.94% and 7.35% respectively compared with the original data; the algorithm achieves stable convergence after 25 iterations, with the convergence efficiency improved by 30%. The obtained Pareto optimal front features good uniformity (U = 0.92) and coverage (C = 0.95), and all the 80 non-dominated solutions meet all constraint conditions, with the solution set highly coupled with the urban functional zoning and spatial planning of Xi’an. This study proposes a zoned, prioritized and phased hierarchical land use optimization strategy for the areas around metro stations in Xi’an. The research findings provide a replicable research framework and methodological reference for the TOD practice and land use optimization of metro station areas in other rapidly urbanizing central cities in China and developing countries worldwide with the characteristic of rapid rail transit expansion. Full article

With the rapid development of Intelligent Transportation Systems (ITSs), traffic prediction, a crucial component of ITSs, has garnered growing scholarly attention. The appli-cation of deep learning into traffic prediction has emerged as a prominent research direction, especially amid the rapid advancement of pretrained large language models (LLMs), which offer substantial benefits in time-series analysis through cross-modal knowledge transfer. In response to this advancement, this study introduces an innovative model for traffic flow prediction, designated as WGLLM. To capture spatiotemporal characteristics inherent in traffic flow data, this model incorporates a sequence embedding layer constructed on the stationary wavelet transform (SWT) and long short-term memory (LSTM), in conjunction with a spatial embedding layer founded on graph convolutional networks (GCNs). Additionally, a fully connected layer is utilized to integrate embeddings into the LLMs for comprehensive global dependency analysis. To verify the effectiveness of the proposed approach, experiments were carried out on two real traffic flow datasets. The experimental results demonstrate that WGLLM achieves superior predictive performance compared to multiple mainstream baseline models, accompanied by a significant enhancement in prediction accuracy. Full article

The management of waste bentonite slurry (WBS) produced during slurry shield TBM excavation involves environmental and operational challenges from the perspective of developing a more sustainable tunnelling construction process. In this study, the potential reuse of WBS as a complete replacement for bentonite in two-component grout formulations used for TBM backfilling is explored. A comprehensive laboratory testing program is conducted, in which the effects of WBS on the properties of two-component grout (unit weight, viscosity, bleeding, gel time, and mechanical strength) are assessed after various curing times, and the outcomes are compared with standard values commonly given in technical specifications. WBS produced from two different commercial bentonites is investigated. The results show that while the first formulation exhibits rapid setting and irregular gelation, the mix derived from the second bentonite demonstrates superior mechanical performance, increasing compressive strength by up to 40%. This enhancement is primarily governed by a physical filler effect, where fine soil particles optimize packing density and refine the microstructure. Consequently, the incorporation of selected types of WBS into a two-component grout could be a practicable approach, since it offers benefits in terms of mechanical performance, although careful mix design would be required to manage workability. This study shows how tunnelling can become more sustainable by reusing excavation waste and transforming it into a useful by-product. Full article

Phosphogypsum (PG), a major by-product of the phosphate industry, has potential for improving acidic and nutrient-poor red soils, yet its agronomic benefits and heavy metal risks require systematic evaluation. A field experiment was conducted with five treatments, CK (soil only), GT (50% modified phosphogypsum, MPG), TT (40% MPG), ZT (50% phosphorite tailings), and DT (25% MPG + 25% lake sediment), to assess their effects on soil properties, enzyme activities, peanut growth, yield, quality, and heavy metal accumulation. All amendments improved soil structure, moisture retention, nutrient availability, and enzymatic activities. Peanut pod and kernel yields increased under all treatments, with DT achieving the greatest improvements (29.89% and 40.88%, respectively), whereas ZT showed the weakest response (1.91% and 6.26%). DT also achieved the highest soil quality index, and performed best in both yield improvement and root development. Although Cd accumulation increased under DT, heavy metal concentrations in peanut kernels remained below national food safety limits. Overall, DT was identified as the most effective amendment for enhancing red soil fertility and peanut productivity, while long-term monitoring of Cd bioavailability is recommended to ensure sustainable and safe application. Full article

Background: Salmonella enterica subsp. enterica serovar Gallinarum biovar Pullorum (Salmonella Pullorum) is a specific avian pathogen responsible for Pullorum disease, causing substantial economic losses to the global poultry industry. With the rising restrictions on antibiotic use, probiotics have emerged as promising therapeutic alternatives. The Bacillus subtilis group, including B. amyloliquefaciens and B. subtilis, is a collection of closely related species that has been widely used as a probiotic due to its broad-spectrum antimicrobial activity and other benefits. However, how the probiotics-derived antibacterial phenotype contributes to infection control is still unclear. Methods: In this study, we used two different antibacterial phenotype strains, B. amyloliquefaciens and B. subtilis, to treat S. Pullorum infections. The spores of two strains (10⁷ CFUs) were supplemented daily for 21 days. Results: The reduction in body weight gains and the severity of S. Pullorum-induced symptoms were ameliorated. Compared to B. subtilis, B. amyloliquefaciens exhibited a stronger host protection effect, manifested in a greater reduction in the bacterial load of S. Pullorum in organs throughout the infection. Furthermore, both strains enhanced cecal microbiota diversity, suppressed infection-associated taxa, and promoted beneficial genera. Conclusions: Our findings demonstrate that probiotic Bacillus can alleviate S. Pullorum infection and improve growth performance in poultry, especially the antimicrobial phenotype contributing to pathogen clearance. This work provides crucial insights for developing effective, probiotic-based strategies against Pullorum disease. Full article

Background: Ventricular septal rupture (VSR) following acute myocardial infarction (AMI) creates an abrupt left-to-right shunt that can progress to cardiogenic shock (CS). Once CS develops, mortality increases dramatically and delayed repair becomes less feasible. Intra-aortic balloon pumps (IABPs) are widely used to facilitate delayed repair; however, whether initiating IABP before CS onset improves survival remains unclear. Methods: We retrospectively analyzed 124 patients with AMI-related VSR (2009–2024), categorized by IABP timing relative to CS onset (defined as first catecholamine administration) into pre-CS, post-CS, and no-IABP groups. The primary outcome was all-cause mortality within 90 days after AMI onset. Kaplan–Meier curves and Cox proportional hazards models were applied, with subgroup analyses by CS status. Results: The 90-day survival rate was 68.2% in the pre-CS IABP group, 14.3% in the post-CS group, and 35.1% in the no-IABP group. Pre-CS IABP was associated with significantly lower mortality compared with no-IABP (adjusted HR = 0.401, 95% CI 0.174–0.925, p = 0.032) and post-CS IABP (adjusted HR = 0.369, 95% CI 0.149–0.910, p = 0.030). In the CS subgroup, IABP use did not improve survival (19.4% vs. 17.6%, p = 0.365). Among non-CS patients, IABP use was independently associated with lower mortality (85.7% vs. 50.0%, p = 0.027; adjusted HR = 0.178, 95% CI 0.040–0.801, p = 0.025). Conclusions: Given the retrospective design and limited sample size, these findings are hypothesis-generating. Early IABP use was associated with improved short-term survival, an effect not observed once CS had developed. These findings support early risk stratification to identify high-risk patients who may benefit from timely hemodynamic support. Full article

Background: Pemphigus diseases are rare autoimmune blistering disorders mediated by pathogenic autoantibodies directed mainly against desmoglein 1 and desmoglein 3. Although most cases are considered idiopathic, external triggers that can disrupt immune tolerance have been described. Vaccination has been discussed as a potential precipitating factor in autoimmune skin diseases. However, the relationship between vaccination and the induction of pemphigus-related autoantibodies has not been comprehensively summarized. Methods: We conducted a narrative review of all available studies published in the last 25 years identified through medical databases, excluding studies on COVID-19 vaccinations. Reports describing either new-onset pemphigus or exacerbation of preexisting pemphigus with a temporal association to vaccination were included. Clinical characteristics, vaccine type, latency period, direct immunofluorescence findings, and ELISA results for desmoglein autoantibodies were analyzed. In addition, we present our own clinical-laboratory experience illustrating this issue. Results: The current evidence consists predominantly of case reports and small case series. Published cases describe pemphigus vulgaris and pemphigus foliaceus occurring after vaccinations against influenza, hepatitis B, tetanus, diphtheria, pertussis, rabies, and other routinely administered immunizations. The latency period most often ranged from several days to a few weeks. Immunopathological findings were consistent with classical pemphigus diseases, including intercellular IgG deposits in the epidermis and circulating autoantibodies against desmoglein 1 and/or desmoglein 3. Our patient was a 78-year-old woman who developed cutaneous form of pemphigus vulgaris, diagnosed with direct immunofluorescence (DIF) and multiplex ELISA, 10 days after diphtheria–tetanus–pertussis vaccination. The patient had a positive family history of autoimmune blistering disease, namely mucous membrane pemphigoid. Conclusions: Based on the currently available evidence, a direct causal relationship between vaccination and pemphigus diseases cannot be established. Nevertheless, accumulated clinical and serological observations suggest that vaccination may act as a triggering factor in genetically or immunologically predisposed individuals, possibly by amplifying pre-existing subclinical autoreactive immune responses. Further population-based and mechanistic studies are required to clarify this association, while the overall benefits of vaccination remain substantial. Full article

Atherosclerosis remains the leading cause of death worldwide and imposes a major healthcare burden. Physiologically, elimination of cholesterol from the arterial wall depends on reverse cholesterol transport (RCT). RCT requires access to HDL and apolipoprotein A-I (ApoA-I) to lesional macrophages/foam cells. The endothelial glycocalyx is a dynamic and injury-sensitive layer of proteoglycans and glycosaminoglycans (including hyaluronan). It contributes to vascular barrier properties, leukocyte adhesion, mechanotransduction, and macromolecular transport. In atherosclerosis, glycocalyx structure and function are altered; this may facilitate entry/retention of atherogenic lipoproteins and may also alter transport conditions relevant to cholesterol efflux pathways. This article presents a mechanistic hypothesis: short, transient, systemic hyaluronidase exposure could temporarily remodel glycocalyx/extracellular matrix components and thereby facilitate conditions permissive for regulated transport processes relevant to RCT. However, the proposed link between glycocalyx remodeling and improved lesional cholesterol efflux remains theoretical. Direct in vivo evidence that the endothelial glycocalyx is a dominant barrier limiting HDL- or ApoA-I-mediated cholesterol efflux from plaque macrophages is currently limited. Moreover, glycocalyx degradation is widely associated with endothelial dysfunction, increased permeability, inflammation, and thrombosis, all of which could aggravate rather than ameliorate atherosclerosis. Human pharmacokinetic data indicate a very short plasma half-life of circulating hyaluronidase activity, suggesting that any systemic enzymatic effect is brief. Nevertheless, the biological consequences of repeated degradation–regeneration cycles, especially in high-risk states such as diabetes, inflammation, oxidative stress, or chronic kidney disease, remain incompletely understood. Evidence supporting clinical benefit in atherosclerosis is currently limited to heterogeneous animal experiments, historical uncontrolled reports, and a small number of anecdotal case observations, whereas randomized trials have only been performed in other settings such as acute myocardial infarction and do not establish efficacy for plaque regression. We therefore provide a balanced evaluation of knowns, uncertainties, alternative interpretations, potential risks, dosing unknowns, and a translational research agenda including mechanistic preclinical studies, biomarker development, imaging, and carefully designed early-phase clinical investigation. Full article

This study investigates the thermo-mechanical response of geocell-reinforced concrete pavements through scaled model tests and three-dimensional finite element analyses. Static, thermal, traffic, and coupled temperature–loading tests were conducted to clarify the deformation evolution, strain distribution, and damage-related response of the reinforced structure. The results show that, under static loading, pavement settlement evolves through three stages, namely initial compaction, plastic development, and stable strengthening, indicating progressive mobilization of geocell confinement. Under thermal loading, slab strain exhibits pronounced spatial and temporal non-uniformity, and the slab center is identified as the thermally sensitive zone. Under coupled temperature–loading conditions, both strain and settlement show a non-monotonic response near 1.1–1.3 kN, suggesting a potential damage-initiation range. Post-test crack observations further provide direct qualitative evidence that local cracking damage occurred in the slab under representative loading conditions. Under traffic loading, permanent deformation accumulates with load repetitions and is highly sensitive to load amplitude, indicating a load-sensitive transition in cumulative deformation behavior rather than a definitive fatigue threshold. Numerical results further show that geocell reinforcement reduces central settlement by 17.4% relative to plain concrete pavement and by 7.6% relative to doweled pavement, while producing a smoother deflection basin and a more uniform stress distribution. Parametric analyses indicate that the optimum geocell height is approximately one-third of the slab thickness; beyond this range, the marginal reinforcement benefit decreases. Overall, the results demonstrate that geocell reinforcement can effectively improve load transfer, deformation compatibility, and thermo-mechanical stability of concrete pavements under the investigated conditions. Full article

Treatment for large critical-sized bone defects and impaired fracture healing remain challenging. Clinically used protein-based osteoinductive factors, such as recombinant bone morphogenetic proteins (BMPs), can be effective; however, they are costly and limited by stability, dose-delivery issues, and safety concerns. Preclinical small molecules offer an alternative because they are chemically stable, scalable to manufacture, and readily integrated for systemic administration or localized release from scaffolds, hydrogels, cements, and implant coatings. With an emphasis on delivery formats and mechanistic themes, this review examines small molecules that have been shown to improve bone regeneration in preclinical models, contrasting those of biological origin with synthetic and repurposed compounds. Across studies, these selected compounds promote osteoblast commitment, differentiation, and matrix mineralization via BMP/Smad signaling and Wnt/beta-catenin (β-catenin) activation, often through glycogen synthase kinase-3 beta (GSK-3β) inhibition or relief of pathway antagonism or Hedgehog (Hh) pathway stimulation. Beyond osteoinduction, several candidates address issues that commonly limit repair, including angiogenesis, oxidative stress, inflammatory tone, osteoimmune regulation, and suppression of osteoclast-mediated resorption. Direct head-to-head comparisons are rare across both classes and reporting heterogeneity complicates interpretation. Key translational gaps include limited cytotoxicity and immunologic profiling, dose and release optimization, durability of benefit, and insufficient evaluation of rational combinations. More rigorous in vivo studies, including larger animal models and standardized outcome metrics, are needed to prioritize promising candidates and guide clinical development. Full article

Accurate wind power forecasting is essential for efficient wind farm operation and reliable grid dispatch. This study proposes a site-adaptive forecasting framework that integrates machine learning, Tree-structured Parzen Estimator (TPE)-based Bayesian hyperparameter optimization, and SHapley Additive exPlanations (SHAP) for interpretability. Using real-world meteorological and power generation data from two wind farms, we first perform joint-distribution feature analysis to characterize statistical relationships between key inputs and power output, supporting model development and interpretation. TPE optimization is then applied to six benchmark models (CatBoost, Extra Trees, GBM, LightGBM, TabNet, and XGBoost). The optimized Extra Trees model achieves the best performance at Site 1 (R² = 0.965, RMSE = 3.872 kW, MAE = 2.333 kW), whereas the optimized XGBoost model performs best at Site 2 (R² = 0.921, RMSE = 3.049 kW, MAE = 1.382 kW), demonstrating the effectiveness of TPE tuning and the strong predictive capability of tree-ensemble learners. SHAP analysis further reveals heterogeneous drivers across sites: Site 1 benefits from synergistic wind-speed contributions across multiple heights, while Site 2 is primarily governed by hub-height wind speed. Overall, the proposed framework achieves both high accuracy and robust interpretability for multi-site wind power forecasting. Full article

Karst pile foundation cavity treatment requires grouting materials with suitable flowability, stability, strength, and cost-effectiveness, while large quantities of waste mudstone generated by tunnel excavation in Guangxi, China, also require sustainable valorization. In this study, tunnel-excavated mudstone from a tunnel project in Guangxi, China, was used as the primary raw material to develop a solidified grouting material for karst pile foundation cavity treatment. Uniform experimental design, stepwise nonlinear regression, response surface analysis, and multi-objective optimization were employed to evaluate the effects of key mix parameters and determine the optimal formulation. The results showed that the optimal slurry was obtained at a cementitious material-to-mudstone ratio of 0.16, an admixture-to-cementitious material ratio of 0.06, a water-to-solid ratio of 0.63, and the slag powder content-to-cementitious materials ratio of 0.34. In addition, the anti-dispersion performance improved by 87.78%, and compared with conventional cement-soil, C25 concrete, and C30 concrete, the CO₂ emissions were reduced by 37%, 67.4%, and 68.6%, respectively, with the material cost being 73.8% lower than that of traditional cement mortar. These results indicate that the proposed material has promising engineering applicability and demonstrates significant economic and environmental benefits, as well as the valorization potential of tunnel-excavated mudstone. Full article

The integration of automated learning and video analysis enables the development of intelligent systems that can operate effectively in uncertain scenarios. These systems can autonomously identify dominant motion dynamics, depending on the theoretical framework used for representation and the learning process used for pattern identification. Current literature offers a state-based approach to describe the key temporal and spatial relationships required to understand motion dynamics. An important aspect of this approach is determining when the number of positively learned rules from a given information source is sufficient to detect dominant motion in automatic surveillance scenarios. This is crucial, as it affects both the variability of movements that monitored subjects can exhibit within the camera’s field of view and the resources needed for effective implementation. This study addresses these gaps through a grammar-based sufficiency criterion, which posits that learning is complete when production rule growth stabilizes, under the assumption of system stationarity. The stability criterion evaluates whether the most probable rules are learned over time, and whenever a high-growth rule is added, it is used to update the criterion. We outline several benefits of having a formal criterion for determining when a symbolic surveillance system has a robust model that explains the observed motion dynamics. Our hypothesis is that a correct model can consistently account for the majority of motion dynamics over time in an automated learning process. The proposed approach is evaluated by modeling motion dynamics in several scenarios using the SEQUITUR algorithm as input and computing the probability of stability along the learning curve, which indicates when the model reaches a steady state of consistent learning. Experimental validation was conducted in real-world scenarios under varying acquisition conditions. The results show that the proposed method achieves robust modeling performance, with accuracy values ranging from 83.56% to 95.92% in dynamic environments. Full article

The impact of skin pigmentation on the accuracy of optical biomedical devices has gained increased attention since the COVID-19 pandemic, particularly following evidence of oximetry measurement bias in dark-skinned individuals. Meanwhile, many computational models utilising the Monte Carlo (MC) technique have been developed as a cost-effective and scalable method for investigating these effects. Hence, this review explores the application of the MC technique in modelling skin pigmentation, focusing specifically on how melanin in the epidermis is represented across different studies. First, the biological mechanisms of pigmentation and current stratification methods are outlined to contextualise the variability in skin tone, followed by the principles of MC modelling, including photon scattering, absorption, reflection, and detection. Following a screening and exclusion process, 50 studies were evaluated in terms of how melanin concentration and distribution are incorporated into MC models and their applications, revealing a range of approaches that include analytical equations, experimental optical property measurements, or hybrid methods. The benefits and limitations of each approach is discussed, in addition to emerging advancements such as heterogeneous melanin distribution and the relation between optical properties and skin colour classification scales. Overall, the review outlines the current methodological approaches utilised for skin pigmentation modelling and offers a reference framework for researchers seeking to improve the representation of skin pigmentation in MC-based optical simulations. Full article