Search Results (10,972)

Background: Systemic inflammation plays a central role in determining postoperative outcomes in patients undergoing isolated coronary artery bypass grafting with cardiopulmonary bypass. Traditional inflammatory indices such as the neutrophil-to-lymphocyte ratio and the platelet-to-lymphocyte ratio have prognostic value; however, their dynamic behavior during cardiopulmonary bypass remains insufficiently characterized. More comprehensive indices, including the systemic immune-inflammation index and the systemic inflammatory response index, may help characterize early intraoperative inflammatory activity; however, their prognostic relevance should be regarded as exploratory and requires prospective validation. Methods: This retrospective nested case–control study included 245 patients who underwent isolated coronary artery bypass grafting, and intraoperative inflammatory indices during cardiopulmonary bypass were evaluated. Because of the nested case–control design, mortality cases were intentionally overrepresented to improve statistical power; therefore, the observed mortality rate does not reflect the true institutional mortality rate. Inflammatory indices (NLR, PLR, SII, and SIRI) were calculated at induction, at the 5th, 45th, and 90th minutes during cardiopulmonary bypass, and in the early postoperative period. Associations between these indices and in-hospital mortality were evaluated using univariate and multivariable logistic regression analyses. Predictive performance was assessed using receiver operating characteristic (ROC) curve analysis and the area under the curve (AUC). Results: The final enriched analytical sample consisted of 51 mortality cases and 194 randomly sampled surviving controls. During cardiopulmonary bypass, inflammatory indices, particularly at the 5th minute, were significantly higher in patients who experienced mortality (p < 0.001 for all major indices). SII demonstrated the strongest predictive performance at the 5th minute (AUC = 0.790), followed by SIRI (AUC = 0.765), PLR (AUC = 0.687), and NLR (AUC = 0.681). In multivariable analysis, SII and SIRI measured at the 5th minute remained independent predictors of mortality. The addition of 5th-minute SII to the limited study-specific clinical model, which included age, ejection fraction, and preoperative creatinine, improved exploratory discrimination for in-hospital mortality (with AUC increasing from 0.698 to 0.797). Conclusions: Early intraoperative assessment of inflammatory indices during cardiopulmonary bypass may provide additional prognostic information in patients undergoing coronary artery bypass grafting. Composite indices, particularly SII and SIRI, showed stronger exploratory discrimination than traditional inflammatory markers in this enriched analytical sample. However, these findings should be considered hypothesis-generating and require prospective external validation before use in perioperative risk stratification or clinical decision-making can be recommended. Full article

Reliable assessment of rockburst tendency is essential for maintaining the stability and operational safety of deep underground excavations. However, the complex coupling among stress conditions, lithological characteristics, and structural features of rock masses introduces significant uncertainty into rockburst prediction. Conventional evaluation approaches relying on individual indices frequently produce inconsistent classifications and are often insufficient to represent actual rockburst behavior. To address this issue, a hybrid evaluation framework integrating unascertained measure theory, cloud-based uncertainty analysis, and a game-theoretic weighting strategy was developed in this study. Four representative parameters, including the strain energy storage index (W_et), geostress index (S), rock quality designation (RQD), and rock mass integrity factor (K_v), were adopted to characterize the energy-storage capability, stress environment, and structural condition of the surrounding rock mass. The conventional unascertained measure approach was further enhanced using the normal cloud model to describe the uncertain mapping relationship between quantitative measurements and qualitative rockburst classifications. In addition, a combination weighting scheme incorporating AHP, entropy weight (EW), and CRITIC methods was established to improve the stability and rationality of index weighting. The developed framework was subsequently applied to a deep phosphate mine in China. The calculated comprehensive weights of the four evaluation parameters were 0.1982, 0.3446, 0.2173, and 0.2399, respectively, demonstrating that the stress-related parameter has the greatest influence on rockburst evaluation. The results indicate that the investigated rock masses generally exhibit moderate-to-strong rockburst tendency. The shallow and moderately deep zones exhibited relatively high rockburst potential, while the ultra-deep dolomite formations mainly showed a moderate tendency due to the development of joints and fractures, which weakened the integrity of the deep rock mass. The proposed framework provides an effective and practical approach for preliminary hazard assessment, rockburst risk zoning, and prevention strategy design in deep mining engineering. Full article

Flood risks are escalating under climate change, necessitating advanced methods to improve runoff prediction and multivariate flood-risk assessment. In this study, a physics–XGBoost-based Bayesian model averaging with bivariate copulas (PXB-BVC) framework was developed by integrating the Soil and Water Assessment Tool (SWAT), the Hydrologiska Byråns Vattenbalansavdelning (HBV) model, Extreme Gradient Boosting (XGBoost), Bayesian model averaging (BMA), and bivariate copulas. Spatially detailed underlying surface parameters including 30 m land-use data derived from the 2000 China land-use remote sensing monitoring data were pre-processed and reclassified using ArcGIS to support spatially explicit hydrological simulation. The framework was applied to the Xiangxi River Basin (XXRB), China, under four general circulation models and three shared socioeconomic pathways. PXB-BVC improved daily runoff simulation by combining process-based hydrological information with nonlinear machine learning correction, achieving Nash–Sutcliffe efficiency (NSE) values of 0.95 during calibration and 0.89 during validation. Future runoff generally increased from the near-term to the late-century period, with stronger changes under SSP585 and Sen slopes reaching up to 0.46 m³ s⁻¹ yr⁻¹, although the magnitude and significance of trends varied among GCMs. The dependence structures among flood peak, flood volume, and flood duration showed non-stationary behavior under future climate forcing, with Kendall’s tau for peak–volume pairs mostly ranging from 0.6 to 0.8. The revised bivariate return-period analysis further indicates that inferred flood-risk changes depend on the joint risk definition. Under SSP245 and ACCESS-ESM1–5, OR-type joint return periods show that representative near-future 50-year events may become more frequent in 2061–2100, whereas AND-type return periods show weaker and less uniform changes among flood-characteristic pairs. Conditional probability analysis also indicates enhanced compound risk under high-emission conditions: given an extreme peak flow, the probability of accompanying high flood volume increases from 0.23 to 0.56, while the probability of prolonged duration increases from 0.18 to 0.45. These results demonstrate that the PXB-BVC framework can support non-stationary multivariate flood-risk assessment and provide useful information for climate-resilient water-resource management and infrastructure planning. Full article

Background/Objectives: Surgical site infection (SSI) remains a clinically important complication after spinal surgery. This study developed and assessed machine learning approaches for predicting postoperative SSI using routinely collected preoperative clinical variables, with emphasis on calibration and clinical applicability. Methods: In this retrospective single-center study, four prediction models were developed in patients undergoing spinal surgery: logistic regression, random forest, gradient boosting, and XGBoost. Model training used five-fold stratified cross-validation, and performance was evaluated using a hold-out internal test set. Performance was assessed using the area under the receiver operating characteristic curve (AUC), area under the precision–recall curve (AUPRC), sensitivity, precision, F1 score, Brier score, and calibration slope. SHAP analysis was performed to evaluate model interpretability. Results: The incidence of SSI was 16.6%. In cross-validation, discrimination performance was broadly comparable across models, with logistic regression showing the highest observed AUC (0.814) and AUPRC (0.484). In the hold-out test set, the same model showed the highest AUC (AUC 0.806, 95% CI 0.757–0.852) and the highest sensitivity (0.758). Calibration performance varied across models. SHAP analysis identified C-reactive protein, hemoglobin, albumin, and white blood cell count as the most influential predictors. Perioperative variables provided only modest incremental predictive value. Conclusions: Machine learning models showed acceptable performance for predicting SSI after spinal surgery. Logistic regression demonstrated performance comparable to that of the evaluated machine learning models, suggesting that conventional statistical approaches may remain clinically useful in structured datasets. Preoperative clinical and laboratory variables were the major contributors to prediction, supporting their use for routine preoperative risk stratification. Full article

A decade ago, several publications summarized the state of knowledge on Colombia’s non-marine turtle species, and in 2015, the Colombian Red Book of Reptiles assessed their national conservation status, yielding information to guide research and conservation priorities in this biodiverse country. However, a recent initiative has produced or updated the global extinction risk assessments of Latin American non-marine turtle species. We summarize the current state of knowledge on Colombian populations of these species and propose new research and conservation priorities. The number of threatened turtle species in Colombia and the magnitude of the threats they face are greater than previously thought. Despite advances in research on the Colombian populations of these species, knowledge biases exist across species, with many important aspects of their life histories and population trends poorly understood. Given the speed of habitat loss and degradation, the current levels of exploitation, and the lack of enforcement of the legislation that protects them, we predict continued declines in population densities and distributions. We emphasize the need for more life-history studies and monitoring of population trends and threats to assign a more realistic category of national extinction risk and request the implementation of conservation legislation and the establishment of conservation programs. Full article

Background: Multiple organ dysfunction syndrome (MODS) is a serious complication and a leading cause of death in patients undergoing heart valve surgery. The main aim of the present study was to assess the predictive capacity of selected perioperative parameters, including Troponin T levels, to predict the occurrence of postoperative MODS and 30-day mortality in patients undergoing heart valve surgery. Methods: This prospective study included a group of patients with hemodynamically severe symptomatic valvular heart disease who underwent valve surgery. The primary endpoint was postoperative multiple organ dysfunction syndrome (MODS), defined as the dysfunction of at least two organs/systems, including cardiogenic shock, perioperative stroke, respiratory failure requiring prolonged mechanical ventilation, and/or postoperative acute kidney injury requiring renal replacement therapy. The secondary endpoint was death during the 30-day follow-up. Logistic regression was used to assess the relationships between the variables. Results: In total, 739 patients undergoing valvular heart surgery were included in this study. The primary end point was observed in 45 patients. Preoperative hemoglobin level (p = 0.01), red cell distribution width (RDW) (p = 0.001) and troponin T level measured on the first day after surgery (TnT II) (p < 0.001) were independent predictors of the primary endpoint. EuroSCORE II (p = 0.002) and TnT II (p < 0.001) were independent predictors of 30-day mortality. Conclusions: MODS is a clinical condition that is associated with a high risk of death. Troponin T levels measured within the first 24 h postoperatively may be useful in predicting postoperative MODS and 30-day mortality in patients undergoing heart valve surgery as a complement to commonly used risk calculators. Full article

Background/Objectives: Total or partial pancreatectomy is associated with significant metabolic stress and high risk of postoperative malnutrition. Accurate estimation of resting energy expenditure (REE) is essential, as predictive equations may not reflect true energy needs. Methods: A prospective study among patients undergoing total or partial pancreatectomy for pancreatic tumors was conducted. REE was measured by indirect calorimetry (mREE) and compared with the Harris–Benedict and Schofield equations and the weight-based approaches (25 and 30 kcal/kg). Agreement was assessed using linear regression and Bland–Altman analysis; accuracy indices included ±10%, Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE). Results: In 26 patients (mean age, 66.7 ± 8.7 years; 53.8% male) undergoing pancreatic resection (17 pancreaticoduodenectomies, 8 distal pancreatectomies, 1 total pancreatectomy), 60% were at preoperative malnutrition risk. The median measured REE was 1484 kcal/day, rising to 1706 kcal/day after activity adjustment (×1.15) within 14 postoperative days. At 3–6 months postoperatively, patients demonstrated significant declines in nutritional status with a median body weight reduction of −7.3% and a decrease in BMI of −2 kg/m². The 30 kcal/kg method showed the lowest accuracy (MAPE 23.2%, RMSE 416 kcal/day) and overestimated energy needs. Harris–Benedict underestimated mREE in 61.5% of cases, while the 25 kcal/kg approach showed more balanced performance. Conclusions: Postoperative energy expenditure in patients undergoing pancreatic resection appeared elevated relative to predictive equations. Predictive equations lack reliability, favoring indirect calorimetry for precision. Sustained weight loss underscores the need for prolonged nutritional surveillance. Full article

Objective: Maternal blood microbiome signatures during pregnancy have been linked to adverse birth outcomes. We conducted a pilot study to examine whether they are also associated with early childhood growth in offspring and to explore maternal metabolites as potential mediators of these relationships. Methods: This study included 50 mother-child dyads from a prospective pregnancy cohort. Children were selected based on distinct body mass index (BMI) growth trajectories from birth to 4 years, including 25 children in a rising-high-BMI trajectory and 25 in a low-BMI trajectory. Maternal plasma collected during the second trimester underwent 16S rRNA gene sequencing for microbial profiling and an untargeted metabolomics analysis. Microbial diversity indices were compared between groups. Multivariable logistic regression models assessed associations between microbial taxa and BMI trajectories with adjustment for covariates. Mediation analyses evaluated whether maternal metabolites mediated observed associations. Results: Higher maternal blood microbial α-diversity was observed among mothers of children in the rising-high-BMI trajectory. Greater abundance of Gammaproteobacteria/Proteobacteria (class/phylum) was associated with lower odds of membership in the rising-high-BMI trajectory, whereas Bacteroidia/Bacteroidota and Actinobacteria/Actinobacteriota were associated with a greater risk. Mediation analyses identified several maternal metabolites that potentially linked prenatal microbial taxa to child growth outcomes. Key mediators included metabolites involved in benzoate metabolism (e.g., 4-vinylphenol sulfate for taxa Gammaproteobacteria/Proteobacteria), lipid metabolism (e.g., 1-linoleoyl-GPG (18:2) for Bacteroidia/Bacteroidota), glutathione metabolism (cysteinylglycine disulfide for Bacteroidia/Bacteroidota), branched-chain amino acid metabolism (3-hydroxy-2-ethylpropionate for Bacteroidia/Bacteroidota), histidine metabolism (imidazole propionate for Actinobacteria/Actinobacteriota), and TCA cycle (glutaconate for Actinobacteria/Actinobacteriota). These pathways are implicated in oxidative stress, adipocyte differentiation, insulin signaling, and energy metabolism, processes that are highly relevant to obesity development. Conclusion: Findings suggest that prenatal blood microbial signatures may influence early childhood growth through metabolic pathways related to obesity. These pilot study findings support further investigation into the role of prenatal blood microbial signatures in child development and health outcome prediction in larger studies. Full article

The Yili River Valley in Xinjiang is characterized by complex geological structures and frequent geological hazards, which seriously threaten local lives, property, and infrastructure. Improving the accuracy and interpretability of geological hazard assessment is therefore of great significance. To address this, nine factors, including elevation, distance from fault, and slope, were selected to construct a WOE-BPNN-SHAP coupled model. The weights of evidence (WOE) method was first used for factor correlation testing and to optimize the input of the BP neural network. The evaluation accuracies of WOE, WOE-DNN, and WOE-BP models were then compared, and the SHAP model was introduced to analyze the coupling relationships among factors. Results show that the WOE-BP model achieves the best predictive performance, with an AUC of 83.65%. Areas of extremely high-risk account for 8.63% of the study area, while higher-risk areas account for 15.39%. Elevation (1688–2847 m), distance from fault (<3000 m), precipitation (192.6–290.8 mm), and slope (>16°) are identified as the main driving factors. This coupled method provides a new technical approach for regional geological hazard assessment and offers a theoretical basis for disaster prevention, mitigation, and resilience building in the Yili River Valley. Full article

Background: Insect stings, particularly those caused by Hymenoptera such as wasps and bees, are frequent triggers of severe allergic reactions and anaphylaxis, yet the ability to predict short-term risk periods based on environmental conditions has not been systematically evaluated. Meteorological factors influence both insect activity and human exposure, highlighting a relevant gap in preventive risk assessment. Methods: This exploratory single-center study was conducted in Bochum, Germany, an urban region within the Rhine-Ruhr metropolitan area. A 17-year retrospective dataset (2005–2022) of clinically treated Hymenoptera-related sting events was analyzed to explore time-resolved, weather-informed patterns using artificial intelligence (AI)-based machine learning. The study emphasizes methodological feasibility and pattern identification rather than clinical prediction. Daily weather parameters were transformed into expert-informed indicators capturing current-season and carry-over environmental conditions. A multilayer perceptron (MLP) was trained to identify periods of increased sting occurrence, and model performance was evaluated primarily using recall to capture rare-event signals. Results: A total of 346 clinically significant sting events were recorded. Weather variables showed strong spatial coherence across four stations and were associated with intra-seasonal clustering of sting events rather than absolute annual incidence. Exploratory analyses suggested that earlier seasonal onset correlated with higher sting counts (Pearson R = −0.52; p = 0.037). Weekly aggregation improved model performance compared with daily prediction. The cross-validated MLP showed moderate recall (0.431) and high specificity (0.86), supporting exploratory risk stratification; however, post hoc benchmarking did not demonstrate consistent superiority over simpler baseline approaches. Conclusions: This study combines a long-term clinical insect sting dataset with high-resolution meteorological data to explore time-resolved, weather-informed risk patterns using machine learning. The findings demonstrate the technical feasibility of exposure-based risk stratification in a rare-event setting. However, benchmarking showed that the MLP did not consistently outperform simpler baseline approaches for binary warning of elevated-risk periods. This proof-of-concept should therefore be interpreted as exploratory and not as a stand-alone warning system, supporting further external validation in larger, multi-center cohorts before clinical, public health, or digital health implementation can be considered. Full article

Artificial intelligence (AI) is increasingly recognized as a transformative technology in healthcare, with growing evidence supporting its applicability across time-critical clinical environments. This perspective aims to evaluate the integration of AI and machine learning (ML) into pediatric emergency departments (PEDs) across three core domains: clinical decision support, stakeholder engagement, and medical education. Within clinical decision support, ML architectures have demonstrated high predictive performance across several high-acuity clinical scenarios, including triage stratification, pediatric traumatic brain injury risk classification, early sepsis detection and clinical deterioration prediction, and dermatological assessment. Model interpretability and real-world implementability remain critical prerequisites for clinical adoption, with explainability methods representing fundamental instruments to enhance transparency and stakeholder trust. Regarding stakeholder engagement, the triadic dynamic among clinicians, caregivers, and patients defines a unique communication challenge in PEDs, with large language models (LLMs) showing preliminary utility; however, stakeholder-inclusive model validation and robust data privacy protections for minors remain key challenges, particularly regarding legal ambiguities of LLM deployment in clinical pipelines. In medical education, AI-driven simulation platforms and LLM-generated adaptive curricula represent promising tools for competency-based training across pediatric emergency scenarios. Future directions emphasize the imperative of prospective multicenter validation in pediatric-specific cohorts, rigorous data quality standards addressing conformance, completeness, and plausibility, and the development of pediatric-tailored governance frameworks. Real-world implementation will require the systematic involvement of all stakeholders—including children, caregivers, clinicians, developers, and institutions—as co-designers of equitable, transparent, and safe AI systems for this uniquely vulnerable population. Full article

Background: Pectus excavatum (PEX) is the most common congenital chest wall deformity and may result in cardiac compression and arrhythmias. The relationship between structural severity and exercise-induced electrocardiographic (ECG) abnormalities in pediatric patients remains unclear. Methods: We performed a retrospective study of patients aged 10–19 years that underwent standardized preoperative evaluation for PEX between 2015 and 2021, including ECG, transthoracic echocardiography (TTE), computed tomography (CT), and cardiopulmonary exercise testing (CPET). PEX severity was assessed using the Haller index (HI), while right ventricular (RV) compression was evaluated on CT. Tricuspid valve annular size (TVAS) on TTE was used as a surrogate marker of RV compression. Exercise-induced ECG abnormalities, including premature ventricular complexes (PVCs), were analyzed and correlated with HI, RV compression, and TVAS. Results: Among 124 patients (85% male; median age 15 years), 33% exhibited exercise-induced ECG abnormalities, most commonly PVCs (24% overall). PVC occurrence was not associated with Haller index severity (p = 0.35) but was significantly associated with RV compression on CT (92.6% vs. 62.1%, OR 7.64, p = 0.02). Patients with ECG abnormalities had significantly smaller TVAS compared to those without (1.98 ± 0.31 cm vs. 2.09 ± 0.33 cm, p = 0.04). Although PVCs were more frequent in patients with TVAS z-score ≤ −2.0, this did not reach statistical significance. Conclusions: Exercise-induced ventricular ectopy in pediatric PEX is associated with right ventricular compression rather than structural severity as defined by HI. Echocardiographic measures such as TVAS may serve as noninvasive markers of clinically significant compression. These findings highlight the importance of cardiac–thoracic relationships in predicting arrhythmic risk and suggest a potential for reversibility with surgical correction. Full article

Background/Objectives: Immune checkpoint inhibitors (ICIs) have broadened treatment options for non-small cell lung cancer (NSCLC), but many patients show limited response. Baseline biomarkers and indices, including C-reactive protein (CRP), albumin, Neutrophil-to-Lymphocyte Ratio (NLR), Glasgow Prognostic Score (GPS), Prognostic Nutrition Index (PNI), and Advanced Lung Cancer Inflammation Index (ALI), may predict treatment outcomes. This study evaluated whether these biomarkers associate with three-month mortality and disease progression assessed by disease control rate (DCR) using RECIST. Methods: We conducted a retrospective cohort study of consecutive patients with NSCLC from the OncoLifeS biobank (2015–2020). Three-month mortality was defined by status alive or deceased at three months after treatment start. DCR was based on CT-assessments using RECIST. Univariate and multivariable logistic regression models with backward selection and bootstrapping were developed to test biomarker associations with three-month mortality and DCR. Sensitivity analysis was performed to compare results with the standard definition of objective response rate (ORR). Results: Among 505 patients, 421 were alive and 84 deceased; 297 were responders and 208 non-responders. In the final progression model, higher GPS was associated with increased odds of 3-month progression, whereas higher ALI (OR 0.99, 95% CI 0.97–1.00) and higher PNI (OR 0.93, 95% CI 0.87–0.99) were associated with decreased odds of 3-month progression. Higher ALI (OR 0.97, 95% CI 0.94–0.99), and higher PNI (OR 0.83, 95% CI 0.78–0.89) were associated with lower odds of 3-month mortality. The mortality-model showed an AUC of 0.82 and 0.73 for the disease progression model. Sensitivity analysis with the standard RECIST definition revealed similar results. Conclusions: Higher GPS was associated with increased risk of progression, whereas higher PNI and ALI were associated with lower risk of progression and mortality. These findings warrant external validation before clinical implementation. Full article

Cardiac surgery remains a high-risk, resource-intensive domain in which perioperative complications significantly influence clinical outcomes, institutional performance, and healthcare expenditure. Despite advances in technique and protocol standardization, contemporary perioperative management largely relies on static risk stratification and reactive quality assessment. This narrative review synthesizes the current evidence on artificial intelligence (AI) and machine learning for perioperative risk prediction in cardiac surgery, spanning acute kidney injury, mortality, prolonged mechanical ventilation, postoperative atrial fibrillation, and intensive care unit deterioration, and critically appraises the methodological limitations, validation gaps, and fairness concerns that constrain clinical translation. Across these applications, predictive models have demonstrated incremental discrimination over conventional risk scores, yet remain predominantly endpoint-specific, single-institution, and disconnected from prospective clinical implementation. Building on this evidence, we propose Preventive Cardiovascular Intelligence (PCInt) as one possible organizing framework that integrates predictive analytics, dynamic risk trajectory modeling, and structured quality improvement methodologies, and we outline how such a framework might be operationalized across the surgical lifecycle. PCInt is presented as a conceptual proposal requiring prospective validation rather than as a validated system. We conclude by discussing implementation barriers, regulatory and ethical considerations, and priorities for future research toward anticipatory, value-based perioperative cardiovascular care. Full article

Increasing power density of electrical devices in electrified transportation is an irreversible trend which involves power electronic-type supply, higher voltage and temperature. However, fast converter-switch rise times, high modulation and carrier frequencies, harmonics, and increased design field and temperature constitute potential causes of accelerated electrothermal aging of insulation, especially if harmful phenomena, as partial discharges (PDs), incept. This paper focuses on solving issues related to PD monitoring under power electronics waveforms, dealing with effective and automatic tools for noise rejection and for the identification of the type of source generating PD, the latter being fundamental for quality control, diagnostic and condition maintenance. It is shown that innovative techniques are available, which allow PD to be measured even under fast switching (rise time) and high frequency, separating, in the time domain, PD pulses from switching noise. This approach can be carried out automatically by the PD detector software presented here, not requiring experts for measurement management and, thus, making it a feasible tool also for on-line PD monitoring and condition-based maintenance. PD monitoring results from accelerated aging tests on a motor under pulse-width modulation (PWM supply) are presented. In order to assess the insulation health condition, progressive degradation of the motor is quantified using a dynamic health index (DHI), primarily based on key PD parameters, i.e., PD magnitude, repetition rate, and likelihood of discharge type (surface or internal). The proposed DHI approach not only provides meaningful metrics for translating PD data into a diagnostic tool, but it also offers insights into residual life estimation and failure risk prediction. Full article