To explore the changes in gene expression profiles and their toxicological mechanisms in the midbrain tissue of mice with acute diquat poisoning analyzed by referenced transcriptome sequencing.

Eight C57BL/6 mice were divided into an experimental group and a control group, with four mice in each group. Mice in the experimental group were given 300 mg/kg diquat by oral gavage, while those in the control group were given an equal volume of isotonic NaCl solution. At 24 hours after exposure, the midbrain tissue of mice was isolated and RNA was extracted for referenced transcriptome sequencing. Differentially expressed genes were screened through the DESeq2 software package, and the Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway and protein-protein interaction (PPI) network were used to provide functional annotations for potential candidate genes.

Upregulated genes were enriched in the apoptosis pathway, tumor necrosis factor (TNF) signaling pathway, and hypoxia inducible factor-1 signaling pathway. Down-regulated genes were enriched in neural signal regulatory pathways and various synthetic metabolic pathways, involving neural active ligand-receptor interactions, γ-aminobutyric acidergic synapses, cell adhesion molecules, as well as lipid and glycan biosynthesis processes. PPI network analysis showed that TNF, intercellular adhesion molecule 1 (ICAM1), C-C motif chemokine ligand 2 (CCL2), C-X-C motif chemokine ligand 10 (CXCL10), nucleoporin 214 (NUP214), CCL3, vascular endothelial growth factor A (VEGFA), tumor necrosis factor receptor super family member 1A (TNFRSF1A), platelet endothelial cell adhesion molecule 1 (PECAM1), and exportin 1 (XPO1) were key hub genes in the network.

In mice with acute diquat poisoning, the midbrain tissue shows a transcriptome imbalance characterized by extensive inhibition of anabolism and coordinated activation of inflammatory/apoptotic pathways. Ten key hub genes are identified, providing a molecular basis for clarifying the networked mechanism of its central nervous system toxicity and screening intervention targets.

To evaluate multiple machine learning algorithms for predicting in-hospital mortality among patients with acute left heart failure (ALHF), and to construct and externally validate a risk prediction model based on the optimal algorithm.

A total of 15 983 patients from the Medical Information Mart for Intensive Care-Ⅳ 3.1 database (training cohort) and 14 428 patients from the eICU database (external validation cohort) diagnosed with ALHF were included in this study. The outcome variable was in-hospital mortality among patients with ALHF. Machine learning analyses were performed using logistic regression, neural networks, stochastic gradient descent, scorecard modeling, adaptive boosting, k-nearest neighbors, classification and regression tree, and random forest algorithms. Univariate and multivariate logistic regression analyses were conducted in the training cohort to identify influencing factors associated with in-hospital mortality in patients with ALHF. Independent risk factors with an odds ratio (OR) ≥ 1.2 identified from the multivariate logistic regression were incorporated into a nomogram. The nomogram model was further validated using the external validation cohort. The predictive performance of the model was assessed by receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA).

Logistic regression demonstrated superior overall performance among all models, achieving the highest area under the curve (AUC) of 0.749, F1 score of 0.322, and Matthews correlation coefficient of 0.260. Multivariate logistic regression in the training cohort identified vasopressin, dopamine, malignant tumor, liver failure, endotracheal intubation, norepinephrine, epinephrine, mechanical ventilation, and acute myocardial infarction as independent risk factors with the OR ≥ 1.2. The nomogram constructed from these factors exhibited strong predictive performance with an AUC of 0.768 in the training cohort and 0.770 in the external validation cohort. Predicted probabilities closely matched observed outcomes in both cohorts, with calibration curves approaching the ideal reference line, indicating a good fit. DCA showed that within a high-risk threshold range of 0.13-0.92 in the training cohort, the model's net benefits significantly exceeded those of the "intervene-all" and "intervene-none" strategies, demonstrating robust clinical utility. Similar findings were observed in the external validation cohort within a high-risk threshold range of 0.13-0.93.

Among multiple machine learning algorithms, logistic regression provides the optimal prediction of in-hospital mortality risk for ALHF patients. The constructed nomogram incorporating key risk factors demonstrates favorable predictive accuracy and external validity, offering strong support for clinical identification of high-risk patients and optimization of therapeutic decision-making.

To develop and validate a nomogram for predicting the 28-day survival rate of patients undergoing emergency surgery.

Clinical data were collected from 1 956 adult patients who underwent emergency surgery at the Sir Run Run Shaw Hospital, Zhejiang University School of Medicine between January 2020 and June 2024. The cohort was randomly divided into a training set (n = 1 369) and a validation set (n = 587). According to the 28-day postoperative survival status, patients in the training set were classified into a survival group (n = 1 188) and a non-survival group (n = 181). The least absolute shrinkage and selection operator (LASSO) regression was applied for variable selection, followed by multivariable logistic regression to establish a prediction model, which was subsequently visualized as a nomogram. Model discrimination was evaluated using the area under the receiver operating characteristic curve (AUC). Calibration was assessed by bootstrap resampling with 1 000 iterations, and decision curve analysis (DCA) was performed to evaluate the clinical utility of the model.

Compared with the survival group, patients in the non-survival group had significantly higher Charlson comorbidity index (CCI), white blood cell count, neutrophil count, lymphocyte count, serum creatinine, blood urea nitrogen (BUN), uric acid, intraoperative blood loss, and incidence of postoperative complications (all P < 0.05). In contrast, the proportion of male patients, diastolic blood pressure, fibrinogen (FIB), C-reactive protein, and serum potassium were significantly lower in the non-survival group than in the survival group (all P < 0.05). Multivariable logistic regression identified seven independent predictors of 28-day postoperative mortality: male, CCI, neutrophil count, lymphocyte count, FIB, BUN, and uric acid (all P < 0.05). A nomogram integrating these seven predictors was constructed to estimate the risk of 28-day postoperative mortality in patients undergoing emergency surgery. Receiver operating characteristic curve analysis demonstrated that the nomogram achieved AUCs of 0.759 [95% confidence interval (CI) (0.721, 0.798), P < 0.001] in the training set and 0.725 [95%CI (0.701, 0.820), P < 0.001] in the validation set, indicating its good discriminatory performance. Calibration curves showed good agreement between the predicted probabilities and the observed outcomes. DCA further demonstrated that the nomogram provided substantial net clinical benefit.

The nomogram developed in this study can effectively predict the 28-day survival rate of patients undergoing emergency surgery.

To explore the trajectory of lactate dehydrogenase (LDH) changes and the 28-day mortality risk in patients with sepsis based on the group-based trajectory modeling (GBTM).

Data of 857 adult patients with sepsis in the Medical Information Mart for Intensive Care-Ⅳ were collected. The change trajectories of LDH were constructed and grouped based on GBTM, and the baseline characteristics of each group were compared. The Kaplan-Meier survival curve was used to compare the 28-day cumulative survival rate of each trajectory group, and multivariate Cox proportional hazards regression analysis was applied to evaluate the effects of different LDH trajectories on the 28-day mortality rate of patients with sepsis.

Four LDH trajectories were identified through GBTM: a low-level stable group (trajectory 1, 166 cases), a high-level rapid decline group (trajectory 2, 145 cases), a low-level slow decline group (trajectory 3, 286 cases), and a medium-level slow decline group (trajectory 4, 260 cases). Patients in each group were evaluated in terms of heart rate, respiratory rate, diastolic blood pressure, simplified acute physiology score Ⅱ, sequential organ failure assessment score, red blood cells, red blood cell distribution width, hematocrit, hemoglobin, white blood cells, creatinine, urea nitrogen, alanine aminotransferase, aspartate aminotransferase, bilirubin, lactic acid, international normalized ratio, prothrombin time, activated partial thromboplastin time, continues renal replacement treatment, heart failure, acute kidney injury, hypertension, respiratory failure, 28-day survival time, and 28-day mortality rate; all above indicators showed statistically significant differences among the four groups (all P < 0.05). The Kaplan-Meier survival curve showed that there was a statistically significant difference in the 28-day cumulative survival rate among the four groups (χ² = 15.595, P = 0.001), and the cumulative survival rate of trajectory 2 was the lowest. Multivariate Cox proportional hazards regression analysis showed that after adjusting confounding variables, the mortality risk in the trajectory 2 [hazard ratio (HR) = 2.000, 95% confidence interval (CI) (1.205, 3.320), P = 0.017] and trajectory 4 [HR = 1.978, 95%CI (1.251, 3.128), P = 0.011] was much higher than that in the trajectory 1.

Specific LDH trajectories (rapid decline at high levels and slow decline at medium levels) are independent predictive factors for 28-day mortality in patients with sepsis (using a low-level stable LDH trajectory as a reference). Longitudinal LDH trajectories are helpful for identifying high-risk patients with sepsis and provide references for prognosis assessment and clinical management.

To analyze the dynamic lactate trajectory characteristics within 72 hours after ICU admission in patients with acute kidney injury (AKI) following cardiac surgery, and to explore their association with clinical outcomes.

A retrospective cohort study was conducted using the Medical Information Mart for Intensive Care-Ⅳ database (2008-2022), enrolling 1 297 postoperative AKI patients. Group-based trajectory modeling (GBTM) was employed to identify lactate trajectory patterns. Multivariable Cox proportional hazards regression and logistic regression models were used to analyze the association between trajectory groups and clinical outcomes including in-hospital mortality and major adverse kidney events at 30 days (MAKE30). Predictive performance was evaluated using receiver operating characteristic (ROC) curves.

Three distinct lactate trajectories were identified: a low-level stable group (n = 743), a partial clearance group (n = 452), and a rebound hyperlactate group (n = 102). Compared with the low-level stable group, both the partial clearance group and the rebound hyperlactate group showed significantly higher rates of in-hospital mortality, MAKE30 incidence, mechanical ventilation lasting ≥ 2 days, and ICU stay of ≥ 7 days (all P < 0.05). Multivariate Cox regression analysis showed that the risk of in-hospital mortality in the rebound hyperlactate group was 12.40 times of the low-level stable group [adjusted hazard ratio = 12.40, 95% confidence interval (CI) (6.04, 25.46), P < 0.001]. Multivariate logistic regression analysis indicated that the risk of MAKE30 in the rebound hyperlactate group was 4.01 times of the low-level stable group [adjusted odds ratio = 4.01, 95%CI (2.19, 7.35), P < 0.001]. The lactate trajectory model demonstrated an area under the curve (AUC) of 0.869 for predicting in-hospital mortality, which was superior to that of a single lactate measurement (AUC = 0.835, P = 0.017).

Early dynamic lactate trajectories represent strong independent predictors of in-hospital mortality and other adverse outcomes in AKI patients with post-cardiac surgery. Early monitoring of lactate changes holds significant clinical value for identifying their adverse outcomes.

To systematically evaluate the predictive value of aspiration risk models in neurological intensive care unit patients.

A computerized search was conducted in databases such as CNKI, VIP, Wanfang, CBM, Web of Science, the Cochrane Library, PubMed, and Embase for studies on prediction models for aspiration risk in neurological intensive care unit patient from the inception of each database to December 30, 2025. Two researchers independently conducted data retrieval and material extraction. The prediction model risk of bias assessment tool (PROBAST) was used to evaluate the quality of the included literature. A meta-analysis was conducted using RevMan 5.4 and MedCalc software.

A total of 21 articles were included. The total sample size ranged from 103 to 3 408 patients, and the number of aspiration patients with severe neuropathy ranged from 24 to 448. All the 21 studies were rated as having a high risk of bias. The meta-analysis showed that the area under the receiver operating characteristic curve of the model was 0.879 [95% confidence interval (CI) (0.849, 0.909), Z = 57.892, P < 0.001]. A Glasgow coma scale (GCS) score ≥ 8 [odds ratio (OR) = 2.43, 95%CI (1.29, 4.57), Z = 2.75, P = 0.006], a high National Institutes of Health stroke scale (NIHSS) score [OR = 1.96, 95%CI (1.49, 2.60), Z = 4.74, P < 0.001], history of aspiration [OR = 3.26, 95%CI (1.40, 7.61), Z = 2.74, P = 0.006], age ≥ 60 years [OR = 1.35, 95%CI (1.19, 1.52), Z = 4.79, P < 0.001], Kubota water swallowing test ≥ level 3 [OR = 4.74, 95%CI (2.50, 9.01), Z = 4.76, P < 0.001], gastric residual volume ≥ 150 mL [OR = 2.31, 95%CI (1.69, 3.16), Z = 5.26, P < 0.001], brainstem infarction [OR = 2.85, 95%CI (2.14, 3.80), Z = 7.14, P < 0.001], and mechanical ventilation [OR = 5.31, 95%CI (1.76, 16.05), Z = 2.96, P = 0.003] were all risk factors for aspiration in neurological intensive care unit patients.

The aspiration risk prediction model for neurological intensive care unit patients demonstrates good performance but carries a high risk of overall bias.