Childhood dyslexia risk elevated by heavy metal mixtures from e-waste: A machine learning-driven mixture modeling study.

ALB suppresses epithelial-mesenchymal transition and glycolysis via SPARC regulation in metastatic clear cell renal cell carcinoma.

Quantification of immunity is a challenge in clinical practice due to the complexity and heterogeneity of immune cells. This study aimed to establish comprehensive reference ranges for immune indicators and characterize immune heterogeneity in healthy adults. A total of 115 healthy adults aged 18-65 years were enrolled. Sixty immune indicators encompassing natural immunity (NK cells, monocytes, dendritic cells, myeloid-derived suppressor cells), cellular immunity (T cells, regulatory T cells, T follicular helper cells, T helper cells), and humoral immunity (B cells), along with nutritional and metabolic indicators, were simultaneously detected. Flow cytometry was used to measure the number, phenotype, and functional subsets of immune cells. Unsupervised k-means clustering was performed to identify immune subtypes. RNA-sequencing was conducted on representative individuals from each cluster for transcriptomic validation. The reference ranges for 60 immune indicators were established, with over half (38/60) exhibiting coefficient of variation > 30%, indicating substantial heterogeneity. Gender differences were minimal, whereas age-related changes were pronounced in adaptive immune cells. Specifically, human leukocyte antigen DR-positive (HLA-DR+) T cells (%) increased from 20.76% ± 7.75% (18-30 years) to 30.06% ± 10.82% (51-65 years, P = 0.001), while CD45RA+ regulatory T (Treg) cells (%) and naive CD8+ T cells (%) decreased progressively with age (P < 0.001). Correlation analysis between immune cells and routine laboratory indicators revealed that nutritional indicators like albumin (ALB) were positively correlated with the number of immune cells such as CD8+ T cells, while lipid metabolism indicators like low-density lipoprotein (LDL) were negatively correlated with T helper cell differentiation (P < 0.01). Clustering analysis identified three distinct immune subtypes: "potential type" (26.1%, n = 30) characterized by high naive T cells (44.91% ± 9.88% CD4+ T cells, 33.86% ± 13.82% CD8+ T cells) and CD1c-positive myeloid dendritic cells (CD1c+ mDCs) (45.17% ± 11.58%); "effector NK type" (34.8%, n = 45) with elevated NK cell count (704.22 ± 280.79 cells/μL) and cytotoxic function (93.16% ± 2.38% perforin+ NK cells); and "effector T type" (39.1%, n = 40) distinguished by increased HLA-DR+ T cells (19.48% ± 7.1% CD4+ T cells, 45.11% ± 10.92% CD8+ T cells) and effector memory (EM) CD4+ T cells (37.85% ± 11.01%). A further RNA-sequencing analysis confirmed the transcriptomic characteristics of different immune subtypes, which was in accordance with phenotype analysis. Specifically, adults in the potential type had strong adaptive immunity; those in the effector NK type showed upregulated NK cell-mediated cytotoxicity; those in the effector T type exhibited enhanced T-helper 1 immune responses. This study provides a systematic framework for immunity quantification by establishing reference ranges and classifying healthy adults into three immune subtypes with distinct metabolic and transcriptomic features. These findings could enhance understanding of immune heterogeneity in healthy individuals and guide personalized immune monitoring and intervention strategies in clinical practice.

The predictive value of Lactate dehydrogenase (LDH)/ Albumin (ALB) ratio (LAR) in patients with severe burns has not been explored. The aim of the study was to investigate the correlation between LAR at admission and short-term mortality in severe burned patients. Patients with a primary diagnosis of severe burns, defined as 30% Total Body Surface Area (TBSA) or more, admitted to the burn center of Changhai Hospital were screened, and 324 patients were ultimately enrolled in this study. Binary logistic regression, univariate and multivariate analyses, Least Absolute Shrinkage and Selection Operator (LASSO) regression, Receiver Operating Characteristic (ROC) analysis, Kaplan-Meier curve and nomogram were used to analyze and present the relationship between admission LAR and short-term mortality. Patients with high admission LAR were more likely to die than low LAR. Age, LAR, TBSA, tracheostomy and heart disease were used to establish the nomogram with LAR having the highest area under the curve (AUC) value. We utilized nomograms to visually express data analysis results. This nomogram incorporates the lymphocyte-to-albumin ratio (LAR), a robust and readily accessible prognostic marker, to aid in the identification of patients with severe burns who are at high risk for short-term mortality. Therefore, it is well-suited for early risk stratification, from initial patient assessment after admission to the early inpatient phase, particularly in mass-casualty incidents like forest fires and explosions.

BackgroundGustave Roussy Immune Score (GRIm-Score), a new prognostic index based on nutritional and inflammatory status, acts as an adverse prognostic factor in patients diagnosed with esophageal cancer (EC). However, the clinical prognostic significance of the GRIm-Score in these patients after receiving neoadjuvant chemoradiotherapy (nCRT) remains unclear. The aim of the study was to evaluate the prognostic value of GRIm-Score in patients with EC following nCRT.MethodsA retrospective study was conducted involving 432 patients with EC who had undergone surgical resection. The GRIm-Score of each enrolled patient was calculated on the basis of three key parameters: lactate dehydrogenase (LDH), neutrophil-lymphocyte ratio (NLR), and albumin (ALB). Overall survival (OS) and disease-free survival (DFS) were set as the primary study endpoints, which were analyzed utilizing Cox proportional hazards regression analysis, the Kaplan-Meier method, and propensity score matching (PSM).ResultsThe study cohort comprised 359 male patients (83.1%) and 73 female patients (16.9%), with a mean age of 62.1±7.7 years and an age range of 39 to 80 years. Following the implementation of PSM, the matched research cohort was divided into a high GRIm-Score group and a low GRIm-Score group, with 55 patients in each group respectively. Patients with a high GRIm-Score exhibited inferior OS (cohort: P<0.001; PSM: P=0.009) and DFS (cohort: P<0.001; PSM: P=0.01). Before PSM, the GRIm-Score was confirmed as an independent prognostic factor for OS (P=0.02) in multivariate regression analyses, while none of the individual indicators of NLR, LDH, and ALB exhibited such prognostic significance. However, after PSM, the GRIm-Score acted as a powerful independent prognostic factor for both OS (P=0.03) and DFS (P=0.04) in these multivariate analyses. Further subgroup analyses demonstrated that the GRIm-Score could effectively identify pT3–4 stage EC patients with inferior OS or DFS, which suggests that the GRIm-Score plays a complementary role in the clinical decision-making for adjuvant therapy in EC patients.ConclusionsIn patients with EC who underwent nCRT followed by surgical resection, the GRIm-Score was verified as an independent prognostic factor. Additionally, this study constitutes the first investigation to elucidate the prognostic significance of the GRIm-Score in EC patients after receiving nCRT.

Lactate dehydrogenase (LDH) is widely used as a nonspecific marker of tissue damage and cellular turnover and has been associated with metabolic and inflammatory processes, but its relationship with automated monitoring data and blood biochemical indicators in early-lactation dairy cows is still not well described. The aim of this study was to evaluate associations between LDH activity, blood biochemical parameters, and automated monitoring indicators in early-lactation Holstein cows. A total of 91 clinically healthy cows were classified into two groups according to LDH activity: Group 1 (LDH &lt; 1364 U/L; n = 53) and Group 2 (LDH ≥ 1364 U/L; n = 38). Blood samples were collected once per cow during early lactation, whereas automated monitoring parameters were continuously recorded and daily averages corresponding to the sampling day were used for analysis. Cows with higher LDH activity had significantly higher aspartate aminotransferase (AST) activity and moderate increases in albumin (ALB), creatinine (CREA), gamma-glutamyl transferase (GGT), calcium (Ca), phosphorus (PHOS), and iron (Fe). Correlation analysis showed a strong positive association between LDH and AST (r = 0.799, p &lt; 0.001), while moderate positive correlations were observed with ALB, alanine aminotransferase (ALT), CREA, Ca, GGT, Fe, and PHOS. Receiver operating characteristic (ROC) analysis showed the best discrimination ability for AST, while CREA, ALB, Fe, PHOS, Ca, and GGT showed moderate classification performance. Automated monitoring parameters did not differ significantly between groups; however, cows with higher LDH activity tended to show lower rumination time together with higher milk electrical conductivity, higher milk yield, higher fat-to-protein ratio (FPR), and higher somatic cell count (SCC). Overall, the results indicate that LDH is more closely related to systemic biochemical variation than to immediate changes in production or behavioral indicators, and support the use of biochemical markers together with automated monitoring data when evaluating physiological adaptation during early lactation.

ObjectiveThis study evaluated the prognostic value of nutritional status indexes [albumin (ALB), hemoglobin (Hb), Geriatric Nutritional Risk Index (GNRI)] in elderly chronic heart failure (CHF) patients.MethodsA total of 190 elderly CHF patients were categorized into good (n = 142) and poor (n = 48) prognosis groups based on 1-year outcomes (rehospitalization or all-cause death). Clinical data, including cardiac function [New York Heart Association (NYHA) class], inflammatory markers, and nutritional indexes (ALB, Hb, GNRI), were analyzed. Spearman correlation was used to assess the relationship between nutritional markers and NYHA class. Patients were stratified by median ALB, Hb, and GNRI levels to compare poor prognosis incidence. Kaplan–Meier survival and Cox regression analyses identified prognostic factors, while Receiver Operating Characteristic (ROC) curves evaluated predictive performance.ResultsThe poor prognosis group exhibited significantly lower ALB, Hb, and GNRI levels (P < 0.001). These markers declined with worsening NYHA class (P < 0.001) and correlated negatively with cardiac function. Low ALB, Hb, and GNRI groups had higher poor prognosis rates (P < 0.001), confirmed by Kaplan–Meier analysis. Cox regression identified left ventricular ejection fraction (LVEF), N-terminal pro-B-type natriuretic peptide (NT-proBNP), NYHA class, ALB, Hb, and GNRI as independent prognostic factors. ROC analysis showed ALB [area under the curve (AUC) = 0.845], Hb (AUC = 0.884), and GNRI (AUC = 0.896) as strong predictors with high sensitivity/specificity.ConclusionReduced ALB, Hb, and GNRI levels are associated with poor CHF prognosis in elderly patients. These nutritional indexes offer reliable predictive value for clinical prognosis assessment.

ObjectiveThis study aims to investigate the efficacy of enteral nutrition support combined with prone position mechanical ventilation in patients with severe pneumonia.MethodsThis retrospective cohort study included 55 patients with severe pneumonia, who were allocated to a control group (n = 35) receiving conventional mechanical ventilation combined with early enteral nutrition support, and an observation group (n = 20) receiving prone position mechanical ventilation combined with early enteral nutrition support. The intervention lasted for 1 week. Changes in blood gas indicators were compared before and after the intervention. Improvement in nutritional status and inflammatory indicators, including serum prealbumin (PAB), albumin (ALB), haemoglobin (HGB) and C-reactive protein (CRP), and procalcitonin (PCT), were assessed. The incidence of adverse events during the intervention was compared between groups. This study was approved by the Ethics Review Committee of our hospital, and written informed consent was obtained from all participants.ResultsAfter the intervention, both groups showed increased PaO2, SpO2, and PaO2/FiO2 levels and decreased PaCO2 levels, with more pronounced improvement observed in the observation group. Nutritional indicators (PAB, ALB, and HGB) improved in the observation group. CRP and PCT levels were reduced in both groups, with the observation group demonstrating lower levels. The observation group showed a lower incidence of adverse events than the control group (15.00% vs. 42.86%).ConclusionEnteral nutrition support combined with prone position mechanical ventilation reduces the incidence of adverse events, improves respiratory function and nutritional status, and alleviates inflammatory response in patients with severe pneumonia.

Malnutrition, frequently indicated by hypoalbuminemia, is prevalent post-stroke and associated with adverse functional outcomes. However, the independent role of serum albumin (ALB) in multidomain functional recovery-encompassing motor, balance, cognitive, and daily living domains-remains underexplored in Chinese populations. This multicenter study aimed to quantify the independent association between serum ALB levels and functional impairment in Chinese post-stroke patients. In this cross-sectional study, 1,741 patients from rehabilitation centers across China were enrolled. ALB levels were categorized into quartiles (Q1: <37.7 g/L; Q2: 37.7-40.0 g/L; Q3: 40.0-42.8 g/L; Q4: ≥42.8 g/L). Outcomes included motor function (Fugl-Meyer Assessment), activities of daily living (Modified Barthel Index), balance (Berg Balance Scale), and cognition (Montreal Cognitive Assessment). Multivariable linear regression models adjusted for demographics, comorbidities, lesion characteristics, and illness duration. Subgroup analyses tested interactions by age, sex, BMI, and lesion topography. Each 1-g/L ALB increase independently predicted functional gains: FMA (β = 1.35, 95% CI: 0.99-1.72), ADL (β = 1.77, 1.44-2.10), BBS (β = 1.02, 0.78-1.26), MoCA (β = 0.30, 0.21-0.40) (all p < 0.001). Dose-dependent improvements were observed across quartiles (Q4 vs. Q1: FMA Δβ = 15.11 [11.09-19.12]; ADL Δβ = 19.35[15.76-22.93]; P trend < 0.001). Sex significantly modified ALB-FMA associations (P interaction = 0.017), with females showing stronger effects (β = 1.81 [1.12-2.51]) than males (β = 1.15 [0.72-1.58]). Cerebellar lesions demonstrated non-significant trend toward amplified associations (FMA: β = 2.16 [0.72-3.59]). ALB levels are independently and dose-dependently associated with motor, ADL, balance, and cognitive function in post-stroke patients. Compared to lower quartiles, patients with ALB ≥42.8 g/L (highest quartile) exhibit superior functional outcomes. A sex-specific pattern is observed solely in motor function, where the correlation is more pronounced in females. ALB may serve as a biological indicator for risk stratification during stroke rehabilitation.

This study aimed to explore genetic variants associated with serum albumin (ALB) levels in Chinese winter sports athletes using genome-wide association analysis (GWAS) and to investigate potential regulatory mechanisms using bioinformatics annotation. A total of 382 Chinese winter sports athletes were recruited. ALB levels were compared between elite and non-elite athletes. GWAS was conducted using PLINK v1.9, with ALB as the phenotype and sex, age, and principal components as covariates. Associated SNPs were annotated using GTEx and SNPnexus. No significant differences were observed in ALB levels between elite and non-elite male or female athletes, and ALB levels in all groups followed a normal distribution. We identified 113 SNPs reaching a suggestive significance threshold (p < 1 × 10-5), with per-variant variance explained estimates (7.11-11.76%) reflecting model fit within this cohort. A stepwise regression model highlighted nine candidate SNPs that together explained 51.1% of ALB variance in the study sample. Functional annotation suggested that several variants show eQTL or sQTL signals in tissues relevant to ALB biology (e.g., liver and kidney), and pathway enrichment analyses implicated amino acid and hormone metabolism. Overall, these findings are hypothesis-generating; independent replication in additional and ancestry-matched cohorts (and follow-up functional studies) is required to confirm the robustness of the associations and clarify causal mechanisms.

In this study, we investigated the effects of high temperature on plasma biochemical indicators and hematological parameters in blunt snout bream ( Megalobrama amblycephala ) following high-temperature stress. The fish (17.72 ±0.05 g) were exposed to two temperature conditions: a control group at ambient temperature (25°C, measured temperature 25.25 ± 0.34°C) and a high-temperature treatment group (34°C, measured temperature 33.07 ± 0.26°C). 6 fish were randomly sampled from each group at 0, 3, 6, 12, 24, and 48 hours. The results showed that high temperature significantly affects both plasma and hematological parameters. In the high-temperature group, both alanine aminotransferase (ALT) and aspartate aminotransferase (AST) activities initially increased and then decreased with prolonged stress duration. Alkaline phosphatase (ALP) activity reached its lowest point at 48 hours in this group(P &lt; 0.05). Total protein (TP) and albumin (ALB) levels in the high-temperature group decreased significantly after 3 hours (P &lt; 0.05). Glucose (Glu) levels in the high-temperature group increased significantly at 24 hours (P &lt; 0.05). As stress duration increased, white blood cell count (WBC), red blood cell count (RBC), hemoglobin (HGB) levels, and hematocrit (HCT) in the high-temperature group all showed an upward trend. The results indicate that high-temperature stress induces pronounced stress responses, liver dysfunction, and adaptive alterations in blood oxygen-carrying capacity in blunt snout bream.

Escherichia coli is the predominant uropathogen in urinary tract infections (UTIs), but culture-based identification is time-consuming. This study aimed to develop an explainable, culture-independent model to distinguish E. coli from other uropathogens using routinely collected clinical data. We retrospectively analyzed 308 hospitalized patients with culture-confirmed UTIs at Fuding Hospital, Fujian University of Traditional Chinese Medicine (January-December 2023), classified as E. coli (n = 158) or non-E. coli (n = 150). Species identification was performed using an automated microbiology system. Nineteen predictors (sex, urinary leukocyte grade, and 17 routine laboratory variables) were used. Associations with E. coli UTI were examined using univariate and multivariable logistic regression. A Random Forest (RF) classifier was developed with SHapley Additive exPlanations (SHAP) for interpretability. Data were split using a stratified 70/30 train-test split; 5-fold stratified cross-validation within the training set was used for hyperparameter tuning, and final performance (discrimination and calibration) was reported on the held-out test set. RF was additionally benchmarked against regularized logistic regression, calibrated linear SVM, and gradient boosting using the same protocol. E. coli accounted for 51.3% of isolates, followed by Enterococcus spp. (18.5%) and Klebsiella spp. (7.8%). Compared with non-E. coli cases, E. coli infections were more common in females and showed higher lymphocyte counts (LYM), alanine aminotransferase (ALT), and albumin (ALB) (all P < 0.05). Multivariable logistic regression identified sex, LYM, and urinary leukocyte grade as independent predictors. On the held-out test set, RF achieved moderate discrimination (ROC-AUC = 0.66; average precision = 0.66) with calibration assessed by Brier score and calibration slope. SHAP highlighted Sex, LYM, and ALT as the most influential predictors and revealed patient-level heterogeneity in feature effects. E. coli remains the predominant pathogen among hospitalized UTIs. An explainable RF model using routine laboratory variables provided moderate, reproducible discrimination of E. coli vs non-E. coli UTIs and may support earlier decision-making while awaiting culture results.

BackgroundColorectal cancer (CRC) is one of the most common and highly prevalent cancers worldwide, posing a serious threat to public health. Current CRC screening and diagnosis primarily depend on colonoscopy, an invasive procedure that often misses early-stage tumors, contributing to delayed diagnoses. The aim of this study is to develop a simpler, more accessible screening method to assist clinicians in the early identification and diagnosis of CRC and its precancerous lesions.MethodsUsing the patient’s hospitalization number as the unique identifier, invalid age records were excluded, non-numerical laboratory test results were removed, and only the first diagnostic test result for each parameter per patient (i.e., the initial test value at first diagnosis) was retained. The study distinguished between the CRC experimental group and the control group. The study collected laboratory test data from each participant, including tumor markers, biochemical parameters, immunological indicators, complete blood count, coagulation tests, and routine urinalysis. We selected light gradient boosting machine (LightGBM), logistic regression (LR), random forest (RF), and extreme gradient boosting (XGBoost) to construct the models. Finally, the SHapley Additive explanations (SHAP) algorithm was employed to interpret the models.ResultsAfter analyzing the four selected models, the intersection of the top-ranked features across all models was identified, ultimately screening eight laboratory parameters to construct the diagnostic colorectal laboratory digital biomarker (ColoLDB) model: specific gravity (SG), carbohydrate antigen 19-9 (CA19-9), carcinoembryonic antigen (CEA), age, albumin (ALB), cytokeratin 19 fragment (CYFRA21-1), high-density lipoprotein cholesterol (HDL-C) and carbohydrate antigen 72-4 (CA72-4). In the test set, the RF machine learning model demonstrated optimal performance in identifying CRC, achieving an area under the curve (AUC) of 0.863 (95% confidence interval: 0.792–0.922), an accuracy of 0.900, a sensitivity of 0.225, a specificity of 0.997, a positive predictive value (PPV) of 0.917, and a negative predictive value (NPV) of 0.900. When the specificity was set at 0.903, the ColoLDB model’s sensitivity reached 0.694. In comparison, a diagnostic model combining CEA and CA19-9 yielded an AUC of 0.688, a sensitivity of 0.429 and a specificity of 0.947. The RF diagnostic ColoLDB model exhibited superior diagnostic efficacy compared to the combined CEA and CA19-9 diagnosis model.ConclusionsOur research findings indicate that eight laboratory test indicators may be related the risk of developing CRC. Our RF diagnostic ColoLDB model is an innovative and practical tool that effectively predicts the occurrence of CRC, enhancing the diagnostic efficiency for this disease. This method holds promise as a valuable tool for diagnosing CRC.

To develop a machine learning (ML) model for predicting prolonged healing (>8 weeks) in diabetic wounds, focusing on dynamic C-reactive protein (CRP) trajectories. This was a retrospective single-center cohort study. We included 465 patients with type 2 diabetes, standardized wound sizes (5-8 cm2), and debridement alone (2021-2024: training set, n = 325; 2025: temporal validation set, n = 140). Serial CRP was measured at admission (CRP), post-antibiotic preoperatively (CRP_2nd), and postoperatively at discharge (CRP_3rd). Therapeutic response variables (therapeutic_response_1/2/all) were calculated as percentage changes in serial CRP levels across treatment phases, reflecting anti-inflammatory/antimicrobial efficacy. LASSO regression selected features, 12 ML models were constructed, and performance was evaluated via AUC, sensitivity, and specificity. SHAP analysis interpreted predictions. The GradientBoosting model exhibited superior performance (validation set: accuracy = 0.9357, sensitivity = 0.8689, specificity = 0.9873). LASSO regression identified 15 key variables [including CRP_2nd, CRP_3rd, albumin (ALB)]. SHAP analysis revealed CRP_2nd as the most influential predictor (mean absolute SHAP value = 0.460), with elevated CRP_2nd/CRP_3rd associated with prolonged healing and higher ALB/favorable therapeutic responses as protective factors. Dynamic CRP trajectories, particularly CRP_2nd, are critical for predicting prolonged diabetic wound healing. The GradientBoosting model provides a clinically actionable tool for risk stratification.

This study evaluated the effects of supplementing methionine to a low-protein diet on nutrient digestibility, nitrogen (N) metabolism, growth performance, serum biochemical parameters, fur quality, and intestinal microbiota composition in blue foxes (Vulpes lagopus) during the fur-growing period. Fifty 17-week-old blue foxes were randomly allocated to five experimental groups (n = 10 per group). The control group received a diet containing 28% crude protein (CP), while the experimental groups were fed a 22% CP diet supplemented with 0%, 0.35%, 0.55%, or 0.75% methionine on a dry matter (DM) basis, designated as M0, M1, M2, and M3, respectively. Results demonstrated that the final body weight (FW), total weight gain (TW), and average daily gain (ADG) of the M3 group were comparable to the control group (p > 0.05). Methionine supplementation significantly enhanced fur quality and stimulated hair follicle development (p < 0.05). Although the reduction in dietary protein level led to decreased N intake and fecal N excretion, the M2 and M3 groups exhibited significantly higher N retention compared to the control, M0, and M1 groups (p < 0.05). Regarding nutrient digestibility, the M2 and M3 groups showed higher DM digestibility (p < 0.05), while the M3 group maintained organic matter (OM) digestibility comparable to the control group (p > 0.05). The highest CP digestibility was observed in the M3 group (p < 0.05). Additionally, ether extract (EE) digestibility was significantly improved in the methionine-supplemented groups (M1-M3) relative to the control (p < 0.05). Serum analysis revealed dose-dependent increases in total protein (TP), albumin (ALB), and high-density lipoprotein (HDL) concentrations in the M2 and M3 groups. Conversely, low-density lipoprotein (LDL) levels were elevated in these groups compared to the control and M0 groups (p < 0.05). Liver function parameters were also significantly improved in the M2 and M3 groups (p < 0.05). Furthermore, methionine supplementation enhanced the diversity and richness of the intestinal microbiota and altered its composition at the phylum and genus levels. In conclusion, supplementing low-protein diets with methionine can maintain growth performance, improve fur quality, enhance nutrient utilization efficiency, and support intestinal microbiota homeostasis in blue foxes. The optimal supplementation level is 0.75%, resulting in a total dietary methionine concentration of 1.1% on a DM basis.

This study aimed to investigate the association between the C-reactive protein to albumin ratio (CAR) and unplanned readmissions in patients with ulcerative colitis (UC) and to evaluate its potential value as a predictive indicator. This study included 412 patients with UC who were hospitalized at the Affiliated Hospital of Shandong University of Traditional Chinese Medicine between June 2017 and June 2024. Cox proportional hazards models were used to evaluate the relationship between CAR, C-reactive protein (CRP), albumin (ALB), and unplanned readmission in patients with UC. Kaplan-Meier survival curves were plotted to analyze the differences in unplanned readmission rates across different value ranges. Restricted Cubic Splines (RCS) were employed to explore the dose-response relationship between these three variables and unplanned readmissions. Additionally, a subgroup analysis was conducted to evaluate the applicability of the model across different populations. The predictive performance of CAR was assessed using Receiver Operating Characteristic analysis. During the 1-year follow-up, the unplanned readmission rate among patients with UC was 27.43%. After adjusting for potential confounders, each 1-unit increase in CAR was associated with a 126.9% higher risk of unplanned readmission. Kaplan-Meier survival curves demonstrated significant differences in unplanned readmission rates among UC patients stratified by CAR, CRP, and ALB quartiles (log-rank test, P < 0.001). The RCS curves revealed a positive correlation (P for overall < 0.001) and a non-linear relationship (P for non-linear < 0.001) between CAR and unplanned readmission rates in patients with UC. Threshold effect analysis identified an inflection point for unplanned readmissions in the regression model (W = 0.654). Subgroup analysis suggested a potential interaction between hypertension and CAR in relation to unplanned readmission in patients with UC. Finally, the CAR demonstrated good predictive performance at the 1-month, 3-month, 6-month, and 1-year follow-up periods, with the area under the receiver operating characteristic curve values of 0.813, 0.779, 0.778, and 0.799, respectively. Elevated CAR levels were significantly correlated with increased rates of unplanned readmissions, suggesting its potential as an independent prognostic indicator for patients with UC.

Early detection of malnutrition is critical for timely intervention in lung cancer patients undergoing thoracoscopic resection. Existing black-box prediction models lack clinical interpretability, limiting trust and application. The present study was conducted to predict malnutrition risk by establishing an explainable machine learning (ML) model and evaluate the model performance across several sites, so as to develop a web-based application to aid clinical decision-making. A retrospective analysis was conducted on 1, 134 lung cancer patients who underwent thoracoscopic resection at Dongguan People's Hospital between October 2021 and October 2024, consisting of a training set (n = 795) and a testing set (n = 339). Meanwhile, an external validation cohort (n=273) was prospectively enrolled at the Affiliated Hospital of Guangdong Medical University from March to June of 2025. Furthermore, univariate and multivariate analyses were employed to determine the individual risk variables for post-operative malnutrition. This study constructed eight ML models using Gradient Boosting Machine (GBM), Neural Network, Logistic Regression, Extreme Gradient Boosting (XGBoost), Random Forest, K-Nearest Neighbors (KNN), Adaptive Boosting (AdaBoost), and Support Vector Machine (SVM). The performance of the established models was assessed by decision curve analysis (DCA) and receiver operating characteristic (ROC) curves. Meanwhile, feature contributions and visualize model outputs were quantified using the SHapley Additive exPlanations (SHAP) method to enhance clinical interpretability. Consequently, a web-based risk calculator was created to assist in personalized forecasting. Among 1, 407 total patients, post-operative malnutrition incidence was 11.3% (159/1, 407). Multivariate analysis identified seven independent risk factors: albumin (ALB), Nutritional Risk Screening 2002 score, age, intraoperative blood loss, total drainage volume, Basic Activities of Daily Living (BADL) score, and serum potassium (K). The XGBoost model outperformed others, with AUC 0.845 (95% CI: 0.771-0.919) in the testing set and 0.886 (95% CI: 0.841-0.932) in external validation. SHAP analysis clarified the relative importance of risk factors, improving interpretability. The XGBoost-based explainable ML model effectively predicts malnutrition risk in lung cancer patients after thoracoscopic resection. Integrating high predictive performance with interpretability, it supports clinical risk stratification and personalized nutritional interventions to improve post-operative outcomes. A publicly available web-based calculator facilitates easy clinical application.

The difference between hematocrit (HCT) and plasma albumin (ALB), denoted as HCT-ALB, has been proposed as a new predictor of sepsis and infectious diseases, among others. The role of this index in predicting all-cause mortality in patients with acute pancreatitis (AP), however, has not been evaluated. The aim of this study was therefore to elucidate the correlation between HCT-ALB and 90-day all-cause mortality in non-elderly patients with AP. This retrospective cohort study utilized data from the MIMIC-IV (v1.0) database and included non-elderly adult patients diagnosed with AP who were admitted to the intensive care unit during the study. The primary outcome was the ability of HCT-ALB to predict mortality within 90 days of admission. A total of 254 patients met the inclusion criteria, and the mortality rate at 90 days after admission was 11.8%. A significant difference in HCT-ALB was observed between survivor and nonsurvivor groups, and the area under the receiver operating characteristic curve for predicting 90-day mortality was 0.692, with an optimal threshold value of 9.05 as calculated by the Youden Index. When modeled for prediction using HCT-ALB, age, and blood urea nitrogen, the predictive model showed good predictive efficacy. In conclusion, HCT-ALB serves as an independent predictor of all-cause mortality within 90 days of admission in non-elderly patients with AP. A predictive model that integrates HCT-ALB with age and serum urea nitrogen shows strong predictive efficacy.

Cisplatin (CIS) is a highly effective chemotherapeutic agent widely used to treat solid tumors. However, its clinical use is significantly limited by dose-dependent hepatotoxicity, characterized by hepatocellular injury and apoptosis. Despite extensive research efforts, an effective pharmacological strategy to reduce CIS-induced liver dysfunction remains elusive. Sacubitril/valsartan (VS), an angiotensin receptor-neprilysin inhibitor, has shown cytoprotective and anti-apoptotic effects in various models of organ toxicity. However, its ability to protect against CIS-induced liver damage has not been thoroughly studied. This research aimed to assess the hepatoprotective potential of VS in rat models of cisplatin-induced liver toxicity, focusing on oxidative markers including reactive oxygen species (ROS) and malondialdehyde (MDA), as well as the roles of caspase-3 inhibition and modulation of retinoid X receptor-alpha (RXR-α) in its mechanism. In this study, adult male Wistar rats were randomly assigned to four groups: control, VS-treated, cisplatin-treated, and CIS + VS co-treated. Hepatotoxicity was induced by administering cisplatin at 8mg/kg via intraperitoneal injection, repeated over three cycles. Meanwhile, VS was given orally at 60mg/kg daily for 10 days. Liver biochemical markers, including ROS, MDA, alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), total protein (TP), albumin (ALB), total bilirubin (TBIL), and lactate dehydrogenase (LDH), were measured using ELISA. Liver tissue was examined histologically with hematoxylin and eosin staining, and the expression of caspase-3 and RXR-α was evaluated through immunofluorescence. Cisplatin administration significantly increased ROS, MDA, ALT, AST, ALP, TBIL, and LDH levels, while decreasing TP and ALB, indicating severe liver dysfunction. Histopathology showed extensive hepatocellular degeneration, necrosis, and inflammation. Co-treatment with VS significantly normalized liver function tests, improved protein levels, and maintained normal liver histology. Additionally, VS markedly reduced caspase-3 immunoreactivity while increasing RXR-α expression compared to CIS alone. Sacubitril/valsartan appears to protect the liver from cisplatin toxicity, primarily by inhibiting oxidative stress and apoptosis through caspase-3 suppression, and modulating RXR-α signaling. These results provide new insights into the mechanisms involved and suggest that VS may be a promising adjunct therapy to lessen cisplatin-induced hepatotoxicity during chemotherapy.

This study aims to investigate the differences in clinical characteristics and prognosis between patients with HBV-ACLF (Hepatitis B Virus-related Acute-on-Chronic Liver Failure) and those with ALD-ACLF (Alcohol-Associated Acute-on-Chronic Liver Failure), and to identify risk factors associated with 90-day mortality in both cohorts. This study enrolled 56 patients with HBV-ACLF and 83 patients with ALD-ACLF to compare their clinical characteristics and conduct analyses of risk factors associated with 90-day prognosis. Compared with the HBV-ACLF cohort, the ALD-ACLF group exhibited a higher proportion of male patients and a greater prevalence of ascites. Additionally, significant differences were observed in laboratory parameters, with ALD-ACLF patients showing higher levels of WBC (white blood cells), N (neutrophils), and M (monocytes) but lower levels of ALT (alanine aminotransferase), AST (aspartate aminotransferase), ALB (albumin), K (potassium), and Na (sodium) compared to HBV-ACLF patients (p < 0.05). In terms of prognostic factors, TBIL (total bilirubin) and PT (prothrombin time) were identified as independent risk factors for 90-day mortality in HBV-ACLF patients, while WBC, TBIL, and PT were associated with 90-day mortality in ALD-ACLF patients. Patients with ALD-ACLF typically present with a higher prevalence of comorbidities, such as ascites and infections, compared to those with HBV-ACLF. However, no significant differences in prognosis were observed between the two cohorts. For HBV-ACLF patients, elevated TBIL and prolonged PT were identified as independent risk factors for 90-day mortality. In contrast, in addition to TBIL and PT, elevated white WBC was also associated with 90-day mortality in ALD-ACLF patients. These findings warrant further validation through multicenter studies with larger sample sizes.