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Background:This study is a prospective cohort study. It aims to investigate the relationship between blood gas parameters and neonatal respiratory outcomes and to develop a prognostic prediction model.Methods:A total of 163 preterm newborns who satisfied the diagnostic criteria outlined in the European Guidelines for the Prevention and Treatment of NRDS-2010 from January 2022 to January 2025 were included. The baseline data of mothers and newborns were collected, and the blood gas parameters were dynamically monitored at 6, 12, 24, and 48 h after birth, including pH, oxygen partial pressure (PaO2), carbon dioxide partial pressure (PaCO2), lactic acid (Lac), and oxygenation index (OI), as well as PaO2/fraction of inspired oxygen (FiO2). Elastic net regression and the Boruta algorithm were used to screen predictive variables, and a multivariate Cox proportional hazards regression model was established. The performance of the model was evaluated using a time-dependent receiver operating characteristic (ROC) curve, Bootstrap resampling calibration curve, and decision curve analysis (DCA).Results:The poor prognosis group (n = 30) experienced a higher rate of maternal pregnancy comorbidities (50.0% vs. 26.3%; p = 0.011), had a smaller gestational age (29.4 weeks; p = 0.019), lower birth weight (1412.5 g; p < 0.001) and 5-minute Apgar score (p = 0.034), and a higher need for initial mechanical ventilation (53.3% vs. 27.1%; p = 0.005). Dynamic monitoring revealed significant acidosis in the early phase (6 hours), which remained at persistently low levels even at 48 hours. The OI progressively increased, oxygenation efficiency declined, and lactate clearance was markedly delayed. Elastic net regression (optimized λ = 0.1759 via 10-fold cross-validation) and Boruta algorithm screening identified core variables for inclusion in a multivariate Cox regression. Meanwhile, △OI_24 h (hazard ratio (HR) = 1.82, 95% confidence interval (CI) 1.51–2.21; p < 0.001) and Lac_48 h (HR = 1.95, 95% CI: 1.40–2.73; p < 0.001) were identified as independent risk factors. The model predicted a 7-day poor prognosis with an area under the ROC curve of 0.96 (95% CI 0.92–1.00). A 1000 Bootstrap validation model demonstrated high concordance between predicted and actual risks. The DCA indicated that the model provided a significant clinical net benefit compared to intervention or no intervention strategies when the risk threshold exceeded 0.15.Conclusions:△OI_24 h and Lac_48 h serve as core early warning indicators for poor prognosis in NRDS. The model was constructed using elastic net regression and the Boruta algorithm, demonstrating robust predictive performance and clinical utility, and providing a basis for early risk stratification.

The aim of this study was to identify the influencing factors for all-cause mortality in elderly patients with intertrochanteric and femoral neck fractures and to construct predictive models. This study retrospectively collected elderly patients with intertrochanteric fractures and femoral neck fractures who underwent hip fractures surgery in the Third Hospital of Hebei Medical University from January 2020 to December 2022. Cox proportional hazards regression is used to explore the association between fractures type and mortality. Boruta algorithm was used to screen the risk factors related to death. Multivariate logistic regression was used to determine the independent risk factors, and a nomogram prediction model was established. The ROC curve, calibration curve and DCA decision curve were drawn by R language, and the prediction model was established by machine learning algorithm. Among the 1373 patients. There were 6 variables that remained in the model for intertrochanteric fractures: age (HR 1.048, 95% CI 1.014-1.083, p = 0.006), AMI (HR 4.631, 95% CI 2.190-9.795, P < 0.001), COPD (HR 3.818, 95% CI 1.516-9.614, P = 0.004), CHF (HR 2.743, 95% CI 1.510-4.981, P = 0.001), NOAF (HR 1.748, 95% CI 1.033-2.956, P = 0.037), FBG (HR 1.116, 95% CI 1.026-1.215, P = 0.011). There were 3 variables that remained in the model for femoral neck fractures: age (HR 1.145, 95% CI 1.097-1.196, P < 0.001), HbA1c (HR 1.264, 95% CI 1.088-1.468, P = 0.002), BNP (HR 1.001, 95% CI 1.000-1.002, P = 0.019). The experimental results showed that the model has good identification ability, calibration effect and clinical application value. Intertrochanteric fractures is an independent risk factor for all-cause mortality in elderly patients with hip fractures. By constructing a prognostic model based on machine learning, the risk factors of mortality in patients with intertrochanteric fractures and femoral neck fractures can be effectively identified, and personalized treatment strategies can be developed.

Landslide susceptibility assessment has been a comprehensive tool for decision makers. However, the efficacy of susceptibility model depends on factor selection and the scientific trustworthiness of the results yielded is varying. This research was objectified to select the factors for model construction through an ensemble of genetic algorithm and Boruta algorithm. 1,888 landslides and 1,888 non-landslides points were collected and randomly split into 70:30 ratio for model training and validation purpose. Twenty selected environmental factors were utilized for model construction. Six advanced machine learning models, Sparse Partial Least Square, Bayesian Generalized Linear Model, Neural Network with Principal Component Analysis, Multivariate Adaptive Regression Spline, Boosted Decision Tree and Extreme Gradient Boosting, were used for susceptibility map preparation with their hyperparameters optimized through Particle Swarm Optimization. The models attained astounding prediction results with testing dataset having AUCROC score of 0.84, 0.85, 0.89, 0.89, 0.87, and 0.95 respectively. Following AUCROC, the model performances were validated through the Quality Sum Index (Q’s), which resulted highest quantification for XGBoost model (3.54), which proved the model excellence. The model’s discrimination capability was quantified through Kolmogorov-Smirnov (KS) statistics, which showed XGBoost as the most efficient model having a KS value of 95.8%, following which came the MARS model with KS value of 65.9%. Furthermore, the uncertainty of the model was computed and confidence map (CNFM) was generated for actual susceptibility map. The regional policy makers for disaster mitigation will be greatly benefitted from the findings of this research.

[This corrects the article DOI: 10.3389/fimmu.2023.1247131.].

Background Sepsis is a severe form of systemic inflammatory response syndrome that is caused by infection. Sepsis is characterized by a marked state of stress, which manifests as nonspecific physiological and metabolic changes in response to the disease. Previous studies have indicated that the stress hyperglycemia ratio (SHR) can serve as a reliable predictor of adverse outcomes in various cardiovascular and cerebrovascular diseases. However, there is limited research on the relationship between the SHR and adverse outcomes in patients with infectious diseases, particularly in critically ill patients with sepsis. Therefore, this study aimed to explore the association between the SHR and adverse outcomes in critically ill patients with sepsis. Methods Clinical data from 2312 critically ill patients with sepsis were extracted from the MIMIC-IV (2.2) database. Based on the quartiles of the SHR, the study population was divided into four groups. The primary outcome was 28-day all-cause mortality, and the secondary outcome was in-hospital mortality. The relationship between the SHR and adverse outcomes was explored using restricted cubic splines, Cox proportional hazard regression, and Kaplan‒Meier curves. The predictive ability of the SHR was assessed using the Boruta algorithm, and a prediction model was established using machine learning algorithms. Results Data from 2312 patients who were diagnosed with sepsis were analyzed. Restricted cubic splines demonstrated a \"U-shaped\" association between the SHR and survival rate, indicating that an increase in the SHR is related to an increased risk of adverse events. A higher SHR was significantly associated with an increased risk of 28-day mortality and in-hospital mortality in patients with sepsis (HR > 1, P < 0.05) compared to a lower SHR. Boruta feature selection showed that SHR had a higher Z score, and the model built using the rsf algorithm showed the best performance (AUC = 0.8322). Conclusion The SHR exhibited a U-shaped relationship with 28-day all-cause mortality and in-hospital mortality in critically ill patients with sepsis. A high SHR is significantly correlated with an increased risk of adverse events, thus indicating that is a potential predictor of adverse outcomes in patients with sepsis.

Background The prevalence of obesity-associated insulin resistance (IR) is increasing along with the increase in obesity rates. In this study, we compared the predictive utility of four alternative indexes of IR [triglyceride glucose index (TyG index), metabolic score for insulin resistance (METS-IR), the triglyceride/high-density lipoprotein cholesterol (TG/HDL-C) ratio and homeostatic model assessment of insulin resistance (HOMA-IR)] for all-cause mortality and cardiovascular mortality in the general population based on key variables screened by the Boruta algorithm. The aim was to find the best replacement index of IR. Methods In this study, 14,653 participants were screened from the National Health and Nutrition Examination Survey (2001–2018). And TyG index, METS-IR, TG/HDL-C and HOMA-IR were calculated separately for each participant according to the given formula. The predictive values of IR replacement indexes for all-cause mortality and cardiovascular mortality in the general population were assessed. Results Over a median follow-up period of 116 months, a total of 2085 (10.23%) all-cause deaths and 549 (2.61%) cardiovascular disease (CVD) related deaths were recorded. Multivariate Cox regression and restricted cubic splines analysis showed that among the four indexes, only METS-IR was significantly associated with both all-cause and CVD mortality, and both showed non-linear associations with an approximate “U-shape”. Specifically, baseline METS-IR lower than the inflection point (41.33) was negatively associated with mortality [hazard ratio (HR) 0.972, 95% CI 0.950–0.997 for all-cause mortality]. In contrast, baseline METS-IR higher than the inflection point (41.33) was positively associated with mortality (HR 1.019, 95% CI 1.011–1.026 for all-cause mortality and HR 1.028, 95% CI 1.014–1.043 for CVD mortality). We further stratified the METS-IR and showed that significant associations between METS-IR levels and all-cause and cardiovascular mortality were predominantly present in the nonelderly population aged < 65 years. Conclusions In conjunction with the results of the Boruta algorithm, METS-IR demonstrated a more significant association with all-cause and cardiovascular mortality in the U.S. population compared to the other three alternative IR indexes (TyG index, TG/HDL-C and HOMA-IR), particularly evident in individuals under 65 years old.

Hydraulic systems are advanced in function and level as they are used in various industrial fields. Furthermore, condition monitoring using internet of things (IoT) sensors is applied for system maintenance and management. In this study, meaningful features were identified through extraction and selection of various features, and classification evaluation metrics were presented through machine learning and deep learning to expand the diagnosis of abnormalities and defects in each component of the hydraulic system. Data collected from IoT sensor data in the time domain were divided into clusters in predefined sections. The shape and density characteristics were extracted by cluster. Among 2335 newly extracted features, related features were selected using correlation coefficients and the Boruta algorithm for each hydraulic component and used for model learning. Linear discriminant analysis (LDA), logistic regression, support vector classifier (SVC), decision tree, random forest, XGBoost, LightGBM, and multi-layer perceptron were used to calculate the true positive rate (TPR) and true negative rate (TNR) for each hydraulic component to detect normal and abnormal conditions. Valve condition, internal pump leakage, and hydraulic accumulator data showed TPR performance of 0.94 or more and a TNR performance of 0.84 or more. This study’s findings can help to determine the stable and unstable states of each component of the hydraulic system and form the basis for engineers’ judgment.

The poor prognosis of sepsis warrants the investigation of biomarkers for predicting the outcome. Several studies have indicated that PANoptosis exerts a critical role in tumor initiation and development. Nevertheless, the role of PANoptosis in sepsis has not been fully elucidated. We obtained Sepsis samples and scRNA-seq data from the GEO database. PANoptosis-related genes were subjected to consensus clustering and functional enrichment analysis, followed by identification of differentially expressed genes and calculation of the PANoptosis score. A PANoptosis-based prognostic model was developed. experiments were performed to verify distinct PANoptosis-related genes. An external scRNA-seq dataset was used to verify cellular localization. Unsupervised clustering analysis using 16 PANoptosis-related genes identified three subtypes of sepsis. Kaplan-Meier analysis showed significant differences in patient survival among the subtypes, with different immune infiltration levels. Differential analysis of the subtypes identified 48 DEGs. Boruta algorithm PCA analysis identified 16 DEGs as PANoptosis-related signature genes. We developed PANscore based on these signature genes, which can distinguish different PANoptosis and clinical characteristics and may serve as a potential biomarker. Single-cell sequencing analysis identified six cell types, with high PANscore clustering relatively in B cells, and low PANscore in CD16+ and CD14+ monocytes and Megakaryocyte progenitors. ZBP1, XAF1, IFI44L, SOCS1, and PARP14 were relatively higher in cells with high PANscore. We developed a machine learning based Boruta algorithm for profiling PANoptosis related subgroups with in predicting survival and clinical features in the sepsis.

This study aims to identify the influential parameters and heavy metals in water and assess the water quality classification at the Alpine glacial lakes and rivers in three districts of Pakistan. For this purpose, nine water quality parameters (Cd, Cr, Pb, Ni, Fe, As, and TDS) in mg/L, pH, Ec µS/Cm are used to compute the Water Quality Index (WQI). The Boruta approach was utilized for the identification of influential parameters associated with the water quality classes. Moreover, we employed supervised machine learning models, including a decision tree, the k-nearest neighbor method, a neural network model (multi-layer perception), a support vector machine, and a random forest, to predict and validate the water quality class. The performance of all algorithms is assessed by an accuracy metric. The accuracy rates for the validation set were observed to be 83% for the decision tree model, 75% for the K-nearest neighbor method, 83% for the neural network, 88% for the support vector machine, and 88% for the random forest model. Water quality assessments for observed locations specify significant insights, revealing that 49% of the locations exhibit low water quality. According to the current study, the government should address problems with water quality in Pakistan’s impacted areas by implementing suitable measures designed water monitoring systems and innovative technologies.

Accurate construction cost calculation is crucial for assessing project viability and selecting design programs. This paper enhances calculation accuracy by first employing the Boruta algorithm to identify vital cost-influencing factors, which serve as the basis for an improved construction cost model. We introduce an enhanced Artificial Neural Network (ANN) model that integrates the AdaBoost algorithm and cost-sensitive methods to refine construction cost estimations. The efficacy of this model is demonstrated through its overall engineering cost error rate of 3.92%, with specific errors in single-side cost, labor, materials, and machinery usage at 3.51%, 7.09%, 3.36%, and 7.93%, respectively. These results meet established accuracy standards, showcasing the model’s potential to significantly improve construction cost management and control.