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BackgroundSepsis, caused by a dysregulated host response to infection, is a life-threatening condition linked to nearly one-fifth of deaths worldwide. As a biomarker of myocardial stress, N-terminal pro B-type natriuretic peptide (NT-proBNP) has gained attention for its potential association with clinical outcomes in sepsis patients. This study investigated its association with short- and long-term mortality in sepsis.MethodsThis retrospective observational cohort study utilized data from the Medical Information Mart for Intensive Care IV database. Sepsis patients on their first intensive care unit admission with NT-proBNP measured within 24 h were categorized into four groups based on their natural log-transformed levels. The primary endpoint was 30-day all-cause mortality (ACM), with secondary endpoints of 90-day and 1-year ACM. Cox models, Kaplan-Meier curves, restricted cubic splines (RCS), and subgroup analyses were performed to assess the relationship between log(NT-proBNP) and 30-day, 90-day, and 1-year ACM.ResultsThe study cohort consisted of 1237 patients, with 54.41% male. The 30-day, 90-day, and 1-year ACM rates were 27.41%, 29.99%, and 31.69%, respectively. Multivariate Cox analysis showed that log(NT-proBNP) was independently associated with higher risks of 30-day ACM (hazard ratio[HR] 1.12 [95% confidence interval (CI) 1.03-1.22]; P = 0.01), 90-day ACM (HR 1.12 [95% CI 1.03-1.22]; P = 0.005), and 1-year ACM (HR 1.11 [95% CI 1.03-1.20]; P = 0.007). These associations remained robust in sensitivity analyses. The RCS curves demonstrated a linear increase in ACM risk as log(NT-proBNP) levels rose. Subgroup analyses confirmed these associations across different patient groups.ConclusionElevated log(NT-proBNP) in critically ill sepsis patients are significantly and independently associated with higher 30-day, 90-day, and 1-year ACM, suggesting that NT-proBNP may serve as a valuable risk marker for both short- and long-term mortality in this population.

ObjectivesTo investigate the risk factors associated with thrombocytopenia in patients undergoing extracorporeal membrane oxygenation (ECMO) support.MethodsThis retrospective cohort study included 230 patients who received ECMO support during January 2018 to December 2024. Clinical data collected included age, gender, causes of ECMO, medical history, vital signs, laboratory test results, complications and outcomes. The patients were divided into the thrombocytopenia group and the control group based on whether the patient's platelet count was less than 150*10⁹/L. The differences in clinical data between the two groups were compared. Moreover, risk factors associated with thrombocytopenia were identified using univariate and multivariate regression analyses.ResultsIn this study, a total of 230 patients undergoing ECMO were involved. The mean age of patients was 52 ± 8 years with 119 (51.7%) males and 111 (48.3%) females. The age, sex distribution, reasons for ECMO, previous medical history, temperature, respiration rate, heart rate, mean arterial pressure, white blood cells, hemoglobin, C-reactive protein, platelets, total bilirubin, alanine aminotransferase, aspartate aminotransferase, prothrombin time, activated partial thromboplastin time, blood lactate between two groups showed no statistically significant differences (P > 0.05) while the APACHE II score (24(20,28) vs 21(17,24)) was higher in thrombocytopenia group (P < 0.05). The bleeding events were significantly higher (62.4% vs 15.7%), in-hospital survival (49.7% vs 62.9%) was lower, the duration of ICU stay (19(16,23) vs 18 (14,22)) was longer, and more platelet (30(10,40) vs 15 (10,20)) and plasma transfusions (600(400,800) vs 450 (200,600)) in thrombocytopenia group (P < 0.05). Logistic regression analysis showed that APACHE II score, rotational speed over the first 48 h, with sepsis, the heparin doseage before ECMO, use of continuous renal replacement therapy (CRRT) and intra-aortic balloon pump (IABP) during ECMO were risk factors associated with thrombocytopenia.ConclusionsThe incidence of thrombocytopenia in patients receiving ECMO is high and may be associated with poor prognosis. Patients with higher APACHE II score, higher rotational speed during the first 48 h, sepsis, higher heparin doseage before ECMO initiation, and the use of CRRT or IABP may at an increased risk of developing thrombocytopenia.

ObjectiveAnkle sprains, particularly lateral inversion injuries, commonly lead to chronic instability and often require surgical interventions; however, 25% of patients fail to return to sport (RTS) because of persistent physical or psychological barriers. This study aimed to identify demographic, surgical, rehabilitation, functional, and psychological factors influencing RTS in patients following lateral ankle ligament surgery.MethodsThis was a single-center prospective cohort study that included 132 patients (aged 28.23 ± 5.93 years) who underwent lateral ankle ligament surgery at the Fourth Medical Center of PLA General Hospital. The data included demographics, surgical type, rehabilitation information, functional outcomes and psychological readiness. Statistical analyses included univariate/multivariate logistic regression and receiver operating characteristic (ROC) curves to identify factors and optimal cutoffs for RTS.ResultsThe RTS rate was 52.27% (69/132), with a mean return time of 172 days. Compared with reconstruction, repair surgery demonstrated superior RTS rates (56.3% vs. 30.0%). The American Orthopedic Foot and Ankle Society (AOFAS) score73.5 (AUC = 0.92; sensitivity: 0.884, specificity: 0.587), visual analog scale (VAS) score≤3.5 (AUC = 0.78), and Ankle Ligament Reconstruction-Return to Sport after Injury (ALR-RSI) score≥62.9 (AUC = 0.64) emerged as critical thresholds. Multivariate analysis revealed three independent predictors influencing RTS timing: surgical repair type (p = 0.005), psychological readiness (p < 0.001), and inpatient rehabilitation (p = 0.031).ConclusionsIn this study, successful RTS hinges on repair techniques, functional recovery (AOFAS score ≥ 73.5), pain reduction (VAS score ≤ 3.5), and psychological readiness (ALR-RSI score ≥ 62.9). Clinicians should prioritize individualized, milestone-driven programs to optimize outcomes and reduce delayed recovery.

With the development of deep learning technology, the autonomous analytical performance of Traditional Chinese Medicine (TCM) inspections has greatly advanced in recent decades, particularly in the areas of tongue and face diagnosis. To improve the effectiveness of diagnosis and treatment in clinical practice, TCM doctors typically differentiate between TCM-based deficiency and excess based on patterns. Therefore, an accurate TCM-based deficiency and excess pattern differentiation system is required to support TCM doctors in their work, including online diagnosis and treatment, applications on major health platforms, and other situations. This study aimed to develop a TCM-based inspection characteristic extraction model based on convolutional neural networks to extract significant characteristics from the face, lips, tongue, and other areas. Based on TCM theory and the clinical expertise of doctors, mapping modules were created for TCM-based deficiency and excess. These two modules were combined to provide a thorough TCM-based deficiency and excess pattern differentiation system. The experimental results showed that the average accuracy for inspection characteristics, such as tongue body color, coating color, and coating thickness, as well as lip color reached 90% in tests on the gathered facial dataset. In addition, the average accuracy attained 81.67%.for the trained TCM-based deficiency and excess pattern differentiation system.

ObjectivesThis study aimed to develop and compare radiomics signatures derived from different renal regions on ultrasound images to assess fibrosis severity in chronic kidney disease (CKD) patients.MethodsA total of 146 CKD patients who underwent renal ultrasound and biopsy were enrolled. Radiomics features were extracted from the whole kidney, parenchyma, and mid-portion to generate region-specific signatures: radscore_whole, radscore_parenchyma, and radscore_mid-portion. Diagnostic performance in distinguishing mild from moderate-to-severe fibrosis was evaluated using receiver operating characteristic (ROC) curve analysis. Performance improvements were assessed via net reclassification improvement (NRI) and integrated discrimination improvement (IDI).ResultsThe radscore_mid-portion displayed the highest discriminatory accuracy, yielding an area under the ROC curve (AUC) of 0.74 (95% CI: 0.65-0.82), which exceeded both the radscore_whole (AUC = 0.61, 95% CI: 0.51-0.70; P = 0.035) and radscore_parenchyma (AUC = 0.66, 95% CI: 0.56-0.74; P = 0.181). Reclassification analysis confirmed the added diagnostic value of the mid-portion signature, with significant improvements compared with both the whole-kidney (NRI = 36.35%; IDI = 10.57%) and parenchyma signatures (NRI = 42.23%; IDI = 10.42%).ConclusionsRadiomics signatures from different renal regions offer varying diagnostic utility. The mid-portion-based signature demonstrated improved performance and added value in identifying moderate-to-severe renal fibrosis in CKD patients.

Amid severe challenges to global sustainable development, digital technology has emerged as a transformative force, providing innovative paradigms for climate governance and green transformation. Yet, the speed and depth of this transformation remain uneven across regions and sectors. This special collection focuses on the digital intelligence era, systematically exploring the crucial role of digital tools in enhancing environmental monitoring's accuracy, optimizing resource allocation's efficiency, and reducing green finance' cost. It further interrogates how these tools interact with institutional, behavioral, and geopolitical variables that condition their effectiveness. It also dissects digital tools' intrinsic mechanisms and synergistic effects in promoting the sustainable development of enterprises, industries, and regions, highlighting how data-driven approaches can bridge scale gaps and foster multi-level sustainability transitions. This special collection features eight papers providing crucial theoretical support and practical paths for building an intelligent, systematic, and fair global climate governance system. By integrating empirical findings with forward-looking perspectives, this body of work aims to inform policy, guide corporate strategy, and inspire further innovation in the pursuit of a digitally empowered sustainable future.

ObjectivesGrassland net primary productivity (NPP) in the Ili River Basin Grasslands, as Earth's largest terrestrial ecosystem, are crucial for global carbon cycling and ecological stability. Clarifying the spatiotemporal patterns and driving factors of grassland NPP is key to optimizing conservation strategies and enhancing productivity.MethodsUsing MODIS data (2000-2022) and the CASA model, this study estimated grassland NPP in the Ili River Basin, and explored its dynamic characteristics and responses to climate and human activities through trend analysis, correlation analysis, and the Hurst index.Results(1) NPP increased annually by 0.14% from 2000 to 2020, but decreased by 15.4% from 2020 to 2022, with an overall 12.95% decline over 23 years; extreme climates in 2008, 2014, and 2021 caused NPP to drop by 27.24%, 28.01%, and 21.31%, respectively, compared to the previous years. (2) High-NPP areas were concentrated in eastern mountainous regions (Nileke, Zhaosu, Tekes, etc.), while low-NPP areas were distributed in central urban zones and high-altitude regions, showing an "east-high-west-low" pattern. (3) Temperature was the main climatic driver (affecting 91,253 km²); human activities led to NPP decline in 55.94% of the area (far exceeding the 2.81% where NPP increased). (4) The Hurst index projected that 79.5% of the area would shift from decline to increase, 8% would continue to decline, and 9.81% would reverse from increase to decline.ConclusionsNPP showed a downward trend from 2000 to 2022 but is expected to rise significantly in the future, providing references for conservation efforts.

Background: Sudden sensorineural hearing loss is a prevalent ear disorder requiring acute intervention, characterized by unclear etiology and challenging therapeutic interventions. This study aims to identify potential biomarkers for sudden sensorineural hearing loss through metabolomic profiling.

Methods: We analyzed metabolomic data samples from a database associated with sensorineural hearing loss. Metabolomic analysis identified significantly differential metabolites in reversed-phase liquid chromatography and hydrophilic interaction liquid chromatography modes. We further validated abnormal metabolites using plasma samples from 51 sudden sensorineural hearing loss patients and 26 healthy controls in the case-control study.

Results: Metabolomic analysis revealed 75 and 76 significantly differential metabolites in reversed-phase liquid chromatography and hydrophilic interaction liquid chromatography modes, respectively. Integrating metabolomic and transcriptomic analyses, we validated four abnormal metabolites-triglyceride, lipase, S-adenosyl methionine, and cholesterol-in our cohort. In the serum of patients with sudden sensorineural hearing loss, the contents of triglyceride, lipase, S-adenosyl methionine, and cholesterol were significantly increased. These metabolites demonstrated significant discriminatory potential in distinguishing sudden sensorineural hearing loss patients from healthy controls.

Conclusions: Our findings underscore the promising utility of metabolomic profiling as a valuable approach for identifying potential biomarkers and unraveling the underlying mechanisms of sudden sensorineural hearing loss.

There are several factors that influence the mechanisms underlying human injuries in vehicle-to-pedestrian crashes (VTPCs). Traditional multibody system (MBS) models can simulate the entire VTPC process; however, they cannot fully and accurately assess the degree of injury to each part of the head. Therefore, a finite element (FE) model of the head with a complete brain structure is developed based on the 50th-percentile Chinese male data. First, the complete kinematic response of pedestrians in a VTPC is simulated using the MBS model, and the head kinematic response parameters that are most related to pedestrian injury are explored. An FE of the head model is subsequently established based on computed tomography (CT) and magnetic resonance imaging (MRI) data from a 50th-percentile Chinese male. Head-injury-related parameters are input into the head FE model, and orthogonal experiments are designed to analyze the head-to-windshield impact. The results show that impact velocity, position, and angle strongly affect the biological injury parameters of the head. These findings reveal the mechanisms of head injuries in pedestrians in VTPCs and the biomechanical dynamic response characteristics of the main parts of the head.

Currently, purely deep learning-based agents struggle to make optimal decisions within a short timeframe in problems with a vast decision-making space. Human planning knowledge is required to assist agents in making better decisions. This manuscript proposes a novel knowledge-guided and data-driven decision-making framework, utilizing hierarchical task network as the carrier of knowledge, deep learning as the trainer for data, and the Monte Carlo Tree Search as the connector between hierarchical task network and deep learning. The experiments on the MiniRTS environment validated that the proposed framework in this manuscript can replace humans in collecting high-quality data, and it can train neural networks that perform equally well as the compared network even with only 20% of the available data, which provide a new direction for future research.