Search Results (1,597)

Background/Objectives: Bail-out stenting remains a procedural challenge for percutaneous coronary intervention (PCI) performed with drug-coated balloons (DCBs). No dedicated bedside tool is currently available to predict this event. We aimed to develop and internally validate a bedside Bail-Out Risk Score. Methods: We analyzed patients treated with DCBs between 2021 and 2025. Predictors of bailout stenting were identified through univariate analysis, and variables with p < 0.10 were entered into a multivariable logistic regression model. Regression coefficients were then transformed into integer points using the Sullivan method. Model performance was evaluated by AUC-ROC, calibration, and bootstrap internal validation (B = 1000). Results: A total of 352 patients (399 de novo lesions) were treated with DCB-only PCI. Bail-out stenting occurred in 14.5% of lesions (58/399). Independent predictors of bail-out stenting were prior CABG (OR 4.29, p = 0.002), proximal lesion location (OR 2.99, p = 0.003), and diffuse disease (OR 2.18, p = 0.018). Prior PCI (OR 0.44, p = 0.009) and lipid-lowering therapy (OR 0.42, p = 0.029) were protective, while LAD involvement showed a non-significant trend (OR 1.57, p = 0.137). The model demonstrated moderate discrimination (AUC = 0.734; optimism-corrected AUC = 0.704) and excellent calibration (intercept = 0.000, slope = 1.000). The final score (range −4 to +8) stratified lesions into low (≤−1), intermediate (0–3), and high (≥3) risk groups, with progressively higher predicted probabilities (≤9%, 13–37%, and ≥49%). Conclusions: The Bail-Out Risk Score provides a practical and reliable bedside tool to estimate procedural risk during stentless PCI. Full article

This study develops a compact elasticity-based framework for assessing how autonomous truck adoption influences corridor-level performance, freight demand, modal competition, and CO₂ emissions in multimodal freight Intelligent Transportation Systems. The model specifies the constant elastic (log-linear) responses of traffic performance and generalized costs to adoption, enabling the closed-form characterization of system-level rebound and road–rail reallocation effects. The analytical results show that an internal adoption threshold $P^{*}$ emerges, defined by $d\widehat{E}/dP=0$, which separates a beneficial regime (efficiency gains dominate) from an adverse regime (rebound and modal shift dominate). Comparative statics indicate that $P^{*}$ decreases with stronger ITS capability A and increases with rebound intensity R and the road–rail carbon intensity contrast K. Numerical experiments across representative corridor contexts illustrate induced demand effects exceeding 25% under high-rebound conditions and threshold ranges around $P^{*}\approx 0.3$–0.4 for plausible parameters. The results provide interpretable guidance for coordinating autonomous truck deployment with intermodal logistics design and decarbonization strategies. Full article

High-quality agricultural products from the Lambayeque region have contributed to the growth of Peru’s agro-export sector and increased international trade. However, the need for agricultural exports to be more resilient and sustainable is demonstrated by the fact that markets are still concentrated, logistical costs are high, and global demand is constantly shifting. The purpose of this study is to use a gravity-based trade model and market intelligence techniques to analyse the agricultural exports from the Lambayeque region between 2015 and 2024. Using official data from the World Bank, AZATRADE, CEPII, and MINCETUR, we employed a quantitative explanatory approach. The results show that the concentration of businesses has significantly decreased while the value of exports has increased steadily. The Herfindahl–Hirschman Index increased from 6209 in 2015 to 1349 in 2024, and export destinations have become slightly more diverse. Exports are negatively impacted by geographic distance, but free trade agreements greatly benefit them. There is a lot of export potential in markets like Finland, Indonesia, Austria, Bolivia, and Vietnam. However, Israel and Hong Kong appear to be full. Overall, the findings indicate that Lambayeque’s export performance has improved, but it still runs the risk of becoming overly focused on a single sector. Long-term sustainability of the region’s agricultural exports depends on enhancing logistical infrastructure, bolstering market intelligence, and promoting regional diversity. Full article

Background/Objectives: Accurate and reproducible grading of lumbar spinal stenosis (LSS) is clinically critical for guiding treatment decisions and patient management, yet manual assessment remains challenging due to imaging variability and inter-observer subjectivity. To address these limitations, this study aimed to evaluate the generalizability of deep learning–based feature extraction methods—VGG19, ConvNeXt-Tiny, and DINOv2—combined with classical machine learning classifiers for automated multi-grade LSS assessment. Automated grading enables objective, reproducible, and scalable assessment of lumbar spinal stenosis severity, addressing key limitations of manual interpretation. Methods: Axial MRI images were processed using pretrained VGG19, ConvNeXt-Tiny, and DINOv2 models to extract deep features. Logistic Regression, Support Vector Machine (SVM), and LightGBM were trained on internal datasets and externally validated using MRI data from the University of Phayao Hospital. Performance was assessed using accuracy, precision, recall, F1-score, confusion matrices, and multi-class ROC curves. Results: VGG19-based features yielded the strongest external performance, with Logistic Regression achieving the highest accuracy (0.9556) and F1-score (0.9558). External validation further demonstrated excellent discrimination, with AUC values ranging from 0.994 to 1.000 across all severity grades. SVM (0.9333 accuracy) and LightGBM (0.9222 accuracy) also performed well. ConvNeXt-Tiny showed stable cross-model performance, while DINOv2 features exhibited reduced generalizability, especially with LightGBM (accuracy 0.6222). Most classification errors occurred between adjacent grades. Conclusions: Deep convolutional features—particularly VGG19—combined with classical machine learning classifiers provide robust and generalizable LSS grading across external MRI data. Despite advances in modern architectures, CNN-based feature extraction remains highly effective for spinal imaging and represents a practical pathway for clinical decision support. Full article

Background/Objectives: Given the high prevalence of obesity and abdominal obesity in Indigenous adults from Sonora (IAS) and its strong association with diet, this study evaluates the association of dietary inflammatory index (DII) with obesity and abdominal obesity and its indicators, such as body mass index (BMI) and waist circumference (WC), respectively. Methods: This cross-sectional study included data from 559 adults across two Indigenous populations (Seris and Yaquis) collected in two separate studies. Obesity and abdominal obesity were classified according to the definitions established by the World Health Organization and the International Diabetes Federation. The DII was calculated with data from population-specific food frequency questionnaires. Multiple linear regression was used to assess the association between the DII variable (expressed as both numeric and categorical) and BMI and WC, separately; multiple logistic regression was used to evaluate the association between obesity and abdominal obesity. Results: The prevalence of obesity and abdominal obesity was 34.1% and 78.2%, respectively. There was a positive association between the DII and BMI (DII as numeric: β = 0.53, p = 0.001; tertile3 of DII vs. tertile1: β = 1.86, p = 0.001) and WC (DII as numeric: β = 1.15, p = 0.002; tertile3 of DII vs. tertile1: β = 3.81, p = 0.005). Similar results were found for both types of obesity. Conclusions: Higher DII scores were associated with increased obesity indicators (BMI and WC) and a higher risk of obesity and abdominal obesity in IAS. Promoting anti-inflammatory diets represents a feasible approach for preventing non-communicable diseases. Full article

National Tuberculosis Reference Laboratories (NTRLs) are central to tuberculosis (TB) control programs. Between 2018 and 2024, the Republic of Congo, a country of 6 million inhabitants, achieved a transformative strengthening of its TB diagnostic system, coordinated by the NTRL. Strategic investments, supported mainly by international partners, enabled a substantial decentralization of services, expanding the diagnostic network from 38 to 113 diagnostic and testing centers and increasing GeneXpert sites from 3 to 31. The expansion of the diagnostic network and specimen referral system was associated with a reduced structural gap in diagnostic coverage by extending access to GeneXpert testing to a larger number of peripheral and previously underserved centers. Critically, the establishment of a BSL-3 laboratory and the deployment of advanced assays like Xpert MTB/XDR ended the reliance on overseas testing by introducing in-country capacity for multidrug-resistant and pre-extensively drug-resistant TB detection. These systemic improvements were associated with significant positive outcomes, including an annual molecular testing surging from 11,609 in 2022 to over 27,000 in 2024 and bacteriological confirmation rates rising from 34 to 73%. This comprehensive laboratory systems strengthening, which also facilitated cross-programmatic initiatives like HIV and Mpox testing integration, underscores how sustained investment in infrastructure, logistics, and quality management is fundamental to improving case detection, surveillance, and progress toward the WHO End TB Strategy milestones. Full article

The healthy evolution of an entrepreneurial ecosystem relies on the symbiotic relationships among its diverse internal actors. This study addresses a gap in entrepreneurial ecosystem research, which has predominantly focused on two-agent models, by constructing a tripartite symbiotic evolution model that incorporates entrepreneurial ventures, incubation chains, and customers. Based on the Logistic and Lotka-Volterra models, the research identifies the system’s equilibrium points and their stability conditions. Simulations reveal evolutionary paths from parasitism and commensalism to mutualism. A comparative case study of SenseTime (Shanghai, China) and Lanma Technology (Shanghai, China) validates these findings. The comparison shows that an influx of multiple agents, coupled with the core venture’s ability to strengthen key symbiotic coefficients, drives the ecosystem towards a dynamic multi-agent symbiosis in the post-optimization phase. Conversely, the failure to establish these robust reciprocal value flows leads to ecosystem fragility. The results indicate that: (1) Multi-agent entrepreneurial ecosystems are complex systems where symbiotic units form adaptive relationships for value creation, adhering to market laws. (2) The system’s equilibrium depends on symbiotic coefficients, leading to four modes—independent coexistence, parasitism, commensalism, and mutualism—with mutualism being the optimal state. (3) The contrasting cases further demonstrate that the evolution towards mutualism is not automatic but hinges on the core venture’s strategic agency in constructing and strengthening synergistic pathways with forward and backward linkages. This study provides a theoretical model for understanding the evolutionary mechanisms of entrepreneurial ecosystems and offers practical insights for optimizing ecosystem governance. Full article

Studies have rarely examined the effects of changes in legal anti-doping knowledge (LADK) on doping tendencies in athletes. This study aimed to evaluate the effectiveness of a structured educational intervention focused on LADK and to analyze how LADK changes affect elite athletes’ doping tendency. The participants were athletes (n = 310; 156 females; 24.1 ± 4.2 years of age), all actively competing at the senior national or international level in either individual (N = 119) or team sports (N = 191), tested on sociodemographic-, sport-, doping-factors (including doping tendency—DT), and LADK. Participants were randomly divided into an experimental group (E: N = 140) and a control group (C: N = 170). The E group participated in a structured educational program on LADK. A pre- and posttest design was used to evaluate changes in LADK (dependent variable). Logistic regression was calculated to evaluate the association between LADK and binarized DT (negative vs. neutral/positive DT). Factorial ANOVA for repeated measurements revealed significant improvement in LADK in the E group, with significant ANOVA effects for time (F test = 35.8, p < 0.05) and time × group interaction (F test = 12.27, p < 0.05). The logistic regression did not reveal significant correlations between LADK and DT. Further studies exploring younger athletes, as well as long-term, multidimensional interventions, are warranted. Full article

The circadian cycle regulates metabolism in response to external stimuli, such as light exposure, sleep schedules, and eating patterns. However, misalignment between internal biological rhythms and social demands can compromise food choices, potentially leading to overweight and obesity. This research aimed to assess how a person’s chronotype links to social jet lag (SJL), which in turn would relate to their nutritional status and food consumption patterns as a university student. 617 students from a State University located in the State of Paraná, Brazil, completed a cross-sectional research study that collected sociodemographic information/anthropometrics by means of an online survey. It included self-reported height/weight data and dietary habits. The Munich Chronotype Questionnaire (MCTQ) was utilized to determine each participant’s chronotype classification and SJL calculation. Researchers found that nearly half of the students (49.3%) displayed an Intermediate Chronotype, which is associated with a diet that contained elements of the “Mixed” Diet, meaning there are equal portions of healthy food (Fresh Fruits, Beans, etc.) and unhealthy foods (Sweetened Beverages). The multivariate logistic regression analyses identified age as a significant predictor of obesity risk (OR: 1.15, p < 0.001), while dietary habits such as fruit consumption played a protective role. Additionally, having a breakfast protected them from being classified as obese compared to those who did not eat breakfast (OR = 0.59). Contrary to expectations, late-night supper was not a statistically significant predictor in the adjusted model. Predictors of an Intermediate chronotype included being male and eating morning snacks regularly. The results of this study suggest that students with an intermediate chronotype will predictably have skewed eating patterns, such as skipping breakfast and eating late—both of which affect obesity risks. Nutritional strategies for university students should focus on promoting circadian regularity and optimizing meal timing. Full article

Environmental, Social, and Governance (ESG) factors have become central to international trade, transforming how firms, industries, and governments engage in global markets. This study conducts a systematic literature review to synthesize current knowledge on the ESG–trade nexus. Using content analysis, three key thematic clusters were identified: (i) ESG in supply chains and logistics, (ii) ESG in export performance and international competitiveness, and (iii) ESG and trade within geopolitics, energy, and resource security. The synthesis reveals that ESG has evolved from a voluntary corporate initiative into a structural determinant of global competitiveness, resilience, and legitimacy. Building on these findings, the study proposes an integrative ESG–Trade framework, which conceptualizes ESG as a multidimensional governance ecosystem comprising (i) institutional and regulatory, (ii) technological and operational, and (iii) geopolitical and strategic dimensions. This framework explains how sustainability regulations, digital transformation, and global political economy dynamics co-evolve to shape trade flows and industrial upgrading. The study highlights the need for greater regulatory coherence and strategic ESG integration while offering a foundation for future interdisciplinary and empirical research on sustainable trade governance. Full article

Background/Objectives: Pediatric trauma systems require accurate metrics for evaluating triage decisions. Undertriage occurs when an injured child requires pediatric trauma center resources but is treated at a center lacking those resources. Current undertriage definitions utilize mortality-based scores, including the Injury Severity Score (ISS) > 15 or the International Classification of Disease (ICD) Injury Severity Score (ICISS). However, resource-based metrics like the ICD Critical Care Severity Score (ICASS) may be preferable in children. This study evaluated the relationship of ISS, ICISS and ICASS to the need for pediatric trauma resources (NFPTCR) to derive a more empiric definition of undertriage. Methods: The American College of Surgeons Trauma Quality Improvement Program database was queried for patients aged ≤ 15 years old. NFPTCR was defined as blood product transfusion within 4 h, invasive procedure for cardiopulmonary stabilization/contamination/bleeding within 72 h, initial admission to intensive care unit (ICU) or ICU stay ≥ 3 days, intubation, mechanical ventilation and general anesthesia ≤ 5 years old, or physical child abuse. ICASS and ICISS were derived from 2014 to 2018 datasets and applied to the 2019 dataset. The ability of ISS, ICISS and ICASS to distinguish NFPTCR patients was assessed using multivariable logistic regression and receiver–operator characteristic (ROC) analysis. Results: Out of 97,773 children, 15,985 (16%) were NFPTCR+. ISS, ICISS and ICASS had areas under the curve of 0.760, 0.701 and 0.812 for NFPTCR+, respectively (all p < 0.001). ISS had 36% sensitivity at 15; whereas ICASS had 95%, 93% and 89% sensitivity at 5, 10 and 15, respectively. Conclusions: ICASS was superior to ISS and ICISS for identifying NFPTCR. Consideration should be given to redefining pediatric trauma undertriage based on resource-based metrics, like ICASS. Full article

Considering the escalating international geopolitical tensions and the ensuing great power maneuvers, China’s oil supply faced unprecedented threats. To safeguard against these risks and harness domestic resources more effectively, addressing the stability of refined oil supply had become an urgent imperative. The complex network theory is integrated into oil product delivery logistics, accounting for transportation volumes, distances, and node importance. Through simulation, we evaluated each scheme’s efficacy using a case study from a province in northwest China. The results demonstrate notable improvements in network robustness across all four strategies. The key node focuses on protection measures emerged as the most effective, followed by the oil depot resource optimization strategy and the network topology optimization strategy, in descending order. By mitigating the risks stemming from international uncertainties, our strategies ensured the timely supply of refined oil products, thereby upholding the stable functioning of the national economy. Full article

Objectives: To develop and validate a transthoracic lung ultrasound (TLUS)-integrated clinical nomogram for predicting pulmonary arterial hypertension (PAH) in patients with connective tissue disease-associated interstitial lung disease (CTD-ILD). Methods: This multicenter retrospective study included 550 patients with CTD-ILD from the Second Affiliated Hospital of Fujian Medical University and 169 external cases from the Xijing Hospital, Fourth Military Medical University. Patients were randomly divided into a training cohort (n = 385) and an internal validation cohort (n = 165); the external dataset served as a testing cohort. Demographic, physiological, laboratory, pulmonary function, and TLUS data were collected. Univariate and multivariate logistic regression analyses identified independent predictors of PAH, which were used to construct a nomogram model. Discrimination was assessed using receiver operating characteristic (ROC) curves and area under the curve (AUC) values. Calibration, decision curve analysis (DCA), and clinical impact curves (CIC) were performed to evaluate model accuracy and clinical utility. Results: Five independent predictors were identified: respiratory rate, diffusing capacity of the lung for carbon monoxide (DLCO% predicted), TLUS score, red blood cell (RBC) count, and brain natriuretic peptide (BNP). The model achieved excellent discrimination with AUCs of 0.952 (95% confidence interval [CI]: 0.927–0.977) in the training cohort, 0.935 (95% CI: 0.885–0.985) in the validation cohort, and 0.874 (95% CI: 0.806–0.942) in the testing cohort, outperforming individual predictors. Calibration plots showed close agreement between predicted and observed probabilities, while DCA and CIC confirmed strong clinical benefit and applicability across all thresholds. Conclusions: This TLUS-integrated nomogram provides a noninvasive and reliable tool for individualized PAH risk assessment in CTD-ILD patients. By combining ultrasound findings with physiological and laboratory markers, the model enables accurate detection of high-risk cases and may assist clinicians in optimizing surveillance and management strategies. Full article

Empirical debates about a “crisis of trust” highlight long-lived pockets of high trust and deep distrust in institutions, as well as abrupt, shock-induced shifts between the two. We propose a probabilistic model in which such phenomena emerge endogenously from social learning on hierarchical networks. Starting from a discrete model on a directed acyclic graph, where each agent makes a binary adoption decision about a single assertion, we derive an effective influence kernel that maps individual priors to stationary adoption probabilities. A continuum limit along hierarchical depth yields a degenerate, non-conservative logistic–diffusion equation for the adoption probability $u(x,t)$, in which diffusion is modulated by $(1-u)$ and increases the integral of u rather than preserving it. To account for micro-level uncertainty, we perturb these dynamics by multiplicative Stratonovich noise with amplitude proportional to $u(1-u)$, strongest in internally polarised layers and vanishing at consensus. At the level of a single depth layer, Stratonovich–Itô conversion and Fokker–Planck analysis show that the noise induces an effective double-well potential with two robust stochastic phases, $u\approx 0$ and $u\approx 1$, corresponding to persistent distrust and trust. Coupled along depth, this local bistability and degenerate diffusion generate extended domains of trust and distrust separated by fronts, as well as rare, Kramers-type transitions between them. We also formulate the associated stochastic partial differential equation in Martin–Siggia–Rose–Janssen–De Dominicis form, providing a field-theoretic basis for future large-deviation and data-informed analyses of trust landscapes in hierarchical societies. Full article

This study aimed to determine the degree of diversification in exports of fresh/chilled salmon and the level of international competitiveness of Norway, Sweden, Chile, and the United Kingdom over 2020–2024, using the Herfindahl–Hirschman Index (HHI) and the normalized revealed comparative advantage (NRCA). A quantitative, descriptive approach was adopted, drawing on annual Trade Map data for HS subheading 030214. HHI series were constructed by country–destination and NRCA series by country–market, and both were examined through univariate analysis. The findings showed that Norway exhibited low concentration levels and strong, stable advantages in Saudi Arabia, Türkiye, and Russia, whereas Sweden displayed moderate but rising concentration, supported by high advantages in Belgium, the United Kingdom, Germany, and Italy. In contrast, Chile and the United Kingdom recorded persistently high HHI values, with pronounced advantages concentrated in a limited number of markets (Brazil in Chile’s case; France and Chinese Taipei in the UK’s) and intra-product positions or comparative disadvantages in China, the United States, and Mexico. The study concludes that the combination of geographic diversification and positive NRCA enhances export resilience, while extreme specialization increases vulnerability to demand and regulatory shocks. It is recommended that Chile and the United Kingdom further develop diversification strategies toward markets where NRCA is neutral or negative, and that Norway and Sweden consolidate their advantages through investments in sustainability, traceability, and logistics. Further multivariate research incorporating macroeconomic and firm-level variables is also suggested. Full article