Abnormal lipid metabolism poses a risk for acute kidney injury (AKI), a prevalent complication among critically ill patients. Early detection and intervention of AKI are essential; however, reliable lipid-derived predictive biomarkers remain understudied. This study aimed to investigate the relationship between the novel lipid biomarker atherogenic index of plasma (AIP) and AKI in critically ill patients and to construct an AIP-integrated early warning model to identify high-risk patients. Patient data were derived from the MIMIC-IV database (training and internal validation sets) and a local hospital cohort (external validation set). Multivariable logistic regression was used to evaluate the association between AIP and AKI. Restricted cubic spline (RCS) regression was utilized to explore potential nonlinear relationships. 13 machine learning algorithms were applied to develop and validate prediction models. Additionally, the Shapley Additive Explanations (SHAP) method enhance model interpretability. We analyzed 6,062 ICU patients from the MIMIC-IV database and 833 patients from a Chinese hospital cohort. AIP was identified as an independent risk factor for AKI in multivariable logistic regression analyses. RCS regression revealed a nonlinear association between AIP and AKI. The XGBoost + AIP model achieved superior performance with AUCs of 0.8127 (internal) and 0.7228 (external), significantly outperforming the SOFA score (AUC 0.6968). Decision curve analysis (DCA) confirmed its clinical applicability. The SHAP method provides critical validation support for the reliability of the XGBoost model. AIP serves not only as a predictive biomarker but as a metabolic phenotype, potentially enabling AI-guided precision prevention strategies in future digital twin-driven AKI care pathways.

BackgroundHigh-altitude exposure precipitates coronary microvascular disease (CMVD), a pathology linked to hypoxia-induced ion channel dysfunction. However, prior observational evidence connecting serum potassium to CMVD resilience is limited by residual confounding, and the precise dose-response trajectory remains unmapped. This study utilized Propensity Score Matching (PSM) to rigorously isolate this association and characterize the dose-response trajectory across the observed physiological range.MethodsWe conducted a nested case-control analysis within a high-altitude training cohort. To minimize selection bias, 1:1 PSM was employed to strictly balance covariates—including age, blood pressure, and autonomic nervous regulation—between CMVD cases and controls. The final analytic sample comprised 462 participants (231 pairs). We applied conditional logistic regression and dose-response modeling to evaluate the association between pre-high-altitude serum potassium and CMVD risk.ResultsPost-matching analysis confirmed that higher baseline serum potassium was independently associated with a significantly reduced CMVD likelihood. The adjusted odds ratio was 0.18 (95% CI: 0.08–0.42, P <0 .001) per 1 mmol/L increase, translating to a clinically actionable 16% risk reduction for every 0.1 mmol/L increment.ConclusionHigher physiological serum potassium is robustly and linearly associated with reduced likelihood of CMVD under high-altitude hypoxic conditions. These association-based findings suggest the potential value of further investigating whether maintaining higher physiological potassium levels before high-altitude deployment may reduce CMVD risk, moving beyond merely screening for hypokalemia.

BACKGROUND:Septic patients with heart failure (HF) have higher mortality and poorer prognosis than patients with either disease alone. Currently, no tool exists for predicting survival rate in such patients. OBJECTIVE:This study aimed to develop an interpretable prediction model to predict survival rate for septic patients with HF. METHODS:Severe septic patients with HF were recruited from the MIMIC-IV database (as training and internal validation cohorts) as well as from the MIMIC-III database (as external validation cohorts). Four models including Deep Learning Survival (DeepSurv) were constructed and evaluated. Furthermore, Shapley Additive Explanations (SHAP) method was employed to explain the DeepSurv model. RESULTS:A total of 11,778 patients were included and 22 features were identified to construct the models. Among the 4 models, the DeepSurv model had the highest area under the curve (AUC) values with an AUC of 0.851 (internal) and 0.801 (external) and C-index of 0.8329 (internal) and 0.7816 (external). The mean cumulative/dynamic AUC values exceeded 0.85 in both internal and external validations. The Integrated Brier Score values were well below 0.25, at 0.068 and 0.093, respectively. Furthermore, the Decision Curve Analysis showed that the DeepSurv model achieved favorable net benefit. The SHAP method further confirmed the reliability of the DeepSurv model. CONCLUSION:Our DeepSurv model was the most comprehensive interpretable prediction model specifically developed and validated for septic critically ill patients with HF. It demonstrated good model performance in predicting the 28-day survival rate of such patients and will provide valuable decision support for clinicians.

OBJECTIVE:The readmission rate of critically ill Heart Failure (HF) patients remains high during the vulnerable phase. However, predictive models based on more comprehensive comorbidities and medication histories are lacking. This study aims to extract these factors to develop interpretable models for predicting readmission risk. METHODS:The authors recruited critically ill HF patients from the MIMIC-IV database (as training and internal validation cohorts) as well as from the MIMIC-III database (as external validation cohorts). Four models, including Neural Multitasking Logistic Regression (NMTLR) were constructed and evaluated. Furthermore, Shapley Additive Explanations (SHAP) interpreted feature importance, simplifying the optimal model based on variable importance. RESULTS:A total of 12,126 patients were included in this study. Among four predictive models, the NMTLR model demonstrated the best predictive performance with the Area Under the Curve (AUC) of 0.752 (internal) and 0.785 (external), with the C-index of 0.7408 (internal) and 0.7724 (external), and with the mean cumulative/dynamic Area Under the Curve (mean AUC) score of 0.747 (internal) and 0.763 (external). Moreover, the Integrated Brier Score (IBS) of the NMTLR model was 0.062 (internal) and 0.043 (external). The SHAP analysis showed over one-third of the top 20 features in the NMTLR model were comorbidities and medications, including a newly relevant drug named psychoanaleptics. Furthermore, the compact NMTLR model also performed well. CONCLUSION:Despite the limited representation of modern HF medications, both the full and compact NMTLR models are useful for predicting HF readmission. Additionally, psychoanaleptics might be a new predictor of readmission, warranting increased clinical attention.

A rapid and sensitive LC-MS/MS method was developed and validated to simultaneously determine famotidine (FAM) and metoprolol (MET) in rat plasma and applied to study the pharmacokinetic drug-drug interaction between these two drugs in rats. In this method, D4-famotine (D4-FAM) and D6-metoprolol (D6-MET) were used as the internal standard and methanol protein precipitation method was used for sample preparation. After extraction, the samples were carried on an Agilent Gemini-NX C18 column and subjected to a gradient elution process using a mixture of methanol and water containing 0.1% formic acid at a flow rate of 0.4 mL/min within 8 min. The monitored transitions were m/z 338.1 → 189.1 for FAM, 268.2 → 116.1 for MET, 342.1 → 190 for D4-FAM, and 274.2 → 122.1 for D6-MET. The analytes had good linearity in the range of 1-200 ng/mL for FAM and 1-400 ng/mL for MET, with the lower limit of quantitation of 1 ng/mL for both drugs. The validated method was verified to meet the determination requirements of biological samples. It was the first time to study the pharmacokinetics interaction between FAM and MET successfully, which would be necessary and beneficial to explore the clinical safety and efficacy of the combination of these two drugs in the treatment of HF.

INTRODUCTION:Previous studies have pointed out that persistent cough is a common complication after pulmonary resection and its occurrence is closely related to inflammatory response. However, there are no clinical studies to date that directly verify that the use of non-steroidal anti-inflammatory drugs (NSAIDs) can reduce the incidence of persistent cough after pulmonary resection (CAP). In view of this, this study aimed to explore and confirm whether exposure to NSAIDs can effectively reduce the incidence of CAP through a prospective cohort study. METHODS AND ANALYSIS:We will conduct a single-centre, prospective cohort comparative study to investigate the impact of NSAIDs use on persistent cough after video-assisted thoracoscopic (VATS) lung resection surgery. The study will include all patients without preoperative cough symptoms who are scheduled for VATS lung resection. These patients will be divided into exposed and non-exposed groups according to whether they used NSAIDs after surgery. The primary outcome measures of this study are the incidence of CAP and the association between NSAIDs exposure and the incidence of CAP in patients undergoing VATS lung resection, while the secondary outcome parameter was set as severity of cough. ETHICS AND DISSEMINATION:This research was approved by the Ethics Committee of the 920th Hospital of the Joint Logistics Support Force of the Chinese People's Liberation Army (2024-041-01). The study findings will be published in peer-reviewed journals and presented at professional conferences. TRIAL REGISTRATION NUMBER:NCT06476249.

Background and Objectives:High-altitude hypoxia is known to impair cardiac microvascular function, a pathophysiological state recognized as coronary microvascular disease (CMVD). This study aimed to investigate the independent association between serum potassium levels and the risk of developing CMVD, after controlling for traditional cardiovascular risk factors such as hypertension, dyslipidemia, and smoking. Methods:This case-control study enrolled 1,175 trainees with long-term exposure to high altitude (≥3,000 m), comprising 235 patients with CMVD (cases) and 940 healthy controls. We employed multivariable logistic regression analysis to systematically evaluate the associations of traditional risk factors and serum potassium levels with the risk of CMVD. Results:A key finding from our analysis was a significant inverse association between serum potassium levels and the odds of CMVD (OR = 0.26; 95% CI, 0.14-0.47). Specifically, higher potassium concentrations were correlated with a substantially lower disease risk. This inverse association was more prominent in individuals with a moderate body mass index and in smokers. Conclusion:This is the first study to demonstrate that a higher serum potassium level is independently associated with lower odds of CMVD in populations exposed to high altitudes. This finding provides a new direction for developing targeted health screening and preventive strategies in high-altitude regions, holding significant potential for protecting specific at-risk groups such as individuals with a particular BMI, and smokers.

BACKGROUND:Recent studies showed histamine H2 receptor antagonists (H2RAs) exposure was associated with reduced mortality in heart failure (HF) patients. However, specific HF patients who are sensitive to H2RAs exposure or not are yet to be further defined. AIM:This study aimed to identify HF patient characteristics that may benefit from H2RAs exposure. METHOD:Neural network-based variational autoencoders and Gaussian Mixture Model (GMM) clustering methods were employed to classify the critically ill patients with HF exposed to H2RAs based on Medical Information Mart for Intensive Care III and IV databases. Binary logistic and multivariable Cox regression analysis based on propensity score matching (PSM) were employed to estimate the association between H2RAs exposure of each cluster and all-cause mortality of included patients. RESULTS:A total of 9,585 H2RAs users among 23,855 included HF patients were identified into four clusters according to GMM clustering: cluster 1 (combined with acute kidney failure, septic shock, and pneumonia), cluster 2 (combined with atrial fibrillation), cluster 3 (combined with coronary artery disease (CAD) and/or had higher urine output), and cluster 4 (co-administered with calcium-antagonists). The cluster 3 patients were significantly associated with reduced all-cause mortality compared with non-H2RAs users after PSM, which were further validated in 14,280 HF patients from the large multi-center electronic intensive care unit Collaborative Research Database (eICU-CRD). CONCLUSION:Histamine H2 receptor antagonist exposure was more sensitive in HF patients who were combined with CAD. Furthermore, male HF patients or those with higher urine output were also sensitive to H2RAs exposure.

BackgroundSevere heart failure (HF) has a higher mortality during vulnerable period while targeted predictive tools, especially based on drug exposures, to accurately assess its prognoses remain largely unexplored. Therefore, this study aimed to utilize drug information as the main predictor to develop and validate survival models for severe HF patients during this period.MethodsWe extracted severe HF patients from the MIMIC-IV database (as training and internal validation cohorts) as well as from the MIMIC-III database and local hospital (as external validation cohorts). Three algorithms, including Cox proportional hazards model (CoxPH), random survival forest (RSF), and deep learning survival prediction (DeepSurv), were applied to incorporate the parameters (partial hospitalization information and exposure durations of drugs) for constructing survival prediction models. The model performance was assessed mainly using area under the receiver operator characteristic curve (AUC), brier score (BS), and decision curve analysis (DCA). The model interpretability was determined by the permutation importance and Shapley additive explanations values.ResultsA total of 11,590 patients were included in this study. Among the 3 models, the CoxPH model ultimately included 10 variables, while RSF and DeepSurv models incorporated 24 variables, respectively. All of the 3 models achieved respectable performance metrics while the DeepSurv model exhibited the highest AUC values and relatively lower BS among these models. The DCA also verified that the DeepSurv model had the best clinical practicality.ConclusionsThe survival prediction tools established in this study can be applied to severe HF patients during vulnerable period by mainly inputting drug treatment duration, thus contributing to optimal clinical decisions prospectively.

BACKGROUND:Anaplastic thyroid carcinoma (ATC) is a rare but the most aggressive type of thyroid carcinoma. Nevertheless, limited advances were made to reduce mortality and improve survival over the last decades. Therefore, identifying novel diagnostic biomarkers and therapeutic targets for ATC patients is still needed. MATERIALS AND METHODS:RNA sequencing data and corresponding clinical features were available from GEO and TCGA databases. We integrated WGCNA and PPI network analysis to identify hub genes associated with ATC development, and RT-qPCR was employed for data verification. Univariate and LASSO Cox regression analyses were used to generate prognostic signatures. RESULTS:Based on PPI and WGCNA, 6 hub genes were identified, namely KIF2C, PBK, TOP2A, CDK1, KIF20A, and ASPM, which play vital roles in ATC development. Subsequently, RT-qPCR experiments showed that most of these genes were significantly upregulated in CAL-62 cells compared to Nthy-ori 3-1 cells. Moreover, a prognostic signature featuring GPSM2, FGF5, ASXL3, CYP4B1, CLMP, and DUXAP9 was generated, which was also verified by RT-qPCR results and proved as an independent predictor of poorer prognosis of ATC. Additionally, a nomogram incorporating the risk score and clinicopathological parameters was further constructed for accurate prediction of 1-, 3- and 5-year survival probabilities of ATC. CONCLUSIONS:Our study identified 6 key genes critical to ATC development and constructed a prognostic signature. These findings provide reliable biomarkers and a relatively comprehensive tumorigenesis profile of ATC, which may inform future strategies for clinical diagnosis and pharmaceutical design.

Non-small cell lung cancer (NSCLC) is a prevalent and aggressive global malignancy. Conventional surgical treatments, radiotherapy, chemotherapy, and targeted therapies often fall short in halting disease progression due to inherent limitations, resulting in suboptimal prognosis. Despite the advent of immunotherapy drugs offering new hope for NSCLC treatment, current efficacy remains insufficient to meet all patient needs. Therefore, actively exploring novel immunotherapeutic approaches to further reduce mortality rates in NSCLC patients has become a crucial focus of NSCLC research. This article aims to systematically review the anti-tumor effects of interleukin-21 and follicular helper T cells in NSCLC immunotherapy by summarizing and analyzing relevant literatures from both domestic and international sources, as well as exploring the potential for enhancing NSCLC treatment prospects through immune checkpoint regulation via immunotherapeutic means.

This study was conducted as a cross-sectional survey using a questionnaire to analyze "demographic characteristics, nicotine dependence, and self-efficacy for smoking cessation" among healthy young adult male soldiers who are presently residing and training in high-altitude for a minimum period of 3 months. A total of 3307 questionnaires were distributed between January and August 2023 in the high-altitude areas (altitude approximately 1700m-3700m).The participants in the survey exhibited an average age of 24.5 ± 3.7 years. A notable 82% of the respondents migrated to the high-altitude from regions with an altitude below 1500m, while 12.5% of smokers had resided and worked in the high-altitude area for a duration of ≥6 months. A substantial 89.6% of participants shared living spaces with smoking roommates; 60.1% initiated smoking before reaching the age of 19; 75% of smokers maintained a smoking habit spanning 1-9 years; a predominant 94.2% manifested very low to moderate nicotine dependence, with the remaining 5.8% presenting high to very high dependence. A distinct correlation emerged between factors such as BMI, duration of residence and work at high altitudes, daily smoking quantity (in the last 6 months), maximum daily smoking amount, roommate smoking status, and smoking duration, and the heightened status of nicotine dependence. An impressive 82.1% of respondents conveyed a resolute intent to cease smoking, with pivotal determinants such as educational attainment, daily smoking quantity (in the last month), maximum daily smoking amount, and smoking duration significantly influencing the cessation intention. Nicotine dependence persists in young adult male soldier smokers post high-altitude exposure, with the majority expressing a strong desire to quit. Therefore, our focus should be on bolstering their willingness to quit, crafting a tailored cessation program, and elevating the success rate of smoking cessation.

Previous studies showed that long-term use of proton pump inhibitors (PPIs) was associated with cardiovascular events. However, the impact of short-term PPI exposure on intensive care unit (ICU) patients with myocardial infarction (MI) remains largely unknown. This study aims to determine the precise correlation between short-term PPI usage during hospitalization and prognostic outcomes of ICU-admitted MI patients using Medical Information Mart for Intensive Care IV database (MIMIC-IV). Propensity score matching (PSM) was applied to adjust confounding factors. The primary study outcome was rehospitalization with mortality and length of stay as secondary outcomes. Binary logistic, multivariable Cox, and linear regression analyses were employed to estimate the impact of short-term PPI exposure on ICU-admitted MI patients. A total of 7249 patients were included, involving 3628 PPI users and 3621 non-PPI users. After PSM, 2687 pairs of patients were matched. The results demonstrated a significant association between PPI exposure and increased risk of rehospitalization for MI in both univariate and multivariate [odds ratio (OR) = 1.157, 95

Previous studies found that histamine H2 receptor antagonists (H2RAs) had blood pressure lowering and cardioprotective effects, but the impact of H2RAs on the survival outcomes of critically ill patients with essential hypertension is still unclear. The aim of this study was to investigate the association of H2RAs exposure with all-cause mortality in patients with essential hypertension based on Medical Information Mart for Intensive Care III database. A total of 17,739 patients were included, involving 8482 H2RAs users and 9257 non-H2RAs users. Propensity score matching (PSM) was performed to improve balance between 2 groups that were exposed to H2RAs or not. Kaplan-Meier survival curves were used to compare the cumulative survival rates and multivariable Cox regression models were performed to evaluate the association between H2RAs exposure and all-cause mortality. After 1:1 PSM, 4416 pairs of patients were enrolled. The results revealed potentially significant association between H2RAs exposure and decreased 30-day, 90-day, and 1-year mortalities in multivariate analyses (HR = 0.783, 95% CI: 0.696-0.882 for 30-day; HR = 0.860, 95% CI: 0.778-0.950 for 90-day; and HR = 0.883, 95% CI: 0.811-0.961 for 1-year mortality, respectively). Covariate effect analyses showed that the use of H2RAs was more beneficial in essential hypertension patients with age ≥ 60, BMI ≥ 25 kg/m2, coronary arteriosclerosis, stroke, and acute kidney failure, respectively. In conclusion, H2RAs exposure was related to lower mortalities in critically ill patients with essential hypertension, which provided novel potential strategy for the use of H2RAs in essential hypertension patients.

To investigate the association between proton pump inhibitors (PPIs) administration during hospitalization and mortality and length of stay in critically ill pediatric patients. This is a retrospective observational cohort study on pediatric ICU patients (0 to 18 years). Propensity score matching (PSM), Kaplan–Meier curves, Cox proportional hazards model and Linear regression model was applied for assessing the effects of PPIs on mortality and other outcomes during hospitalization. A total of 2269 pediatric ICU patients were included, involving 1378 omeprazole (OME) users and 891 non-OME users. The results showed significant association between OME exposure and decreased ICU stay (β -0.042; 95

Objective To evaluate the correlation between IL-10 gene polymorphisms and genetic susceptibility to chronic leukemia （CL）.Methods Web of Science,Embase,Cochrane library,Pub Med,and CNKI databases were searched for retrieving case-control studies about the correlation between IL-10 gene polymorphisms and CL susceptibility.Meta-analysis was performed by Rev Man 5.4 software.Results A total of 8 studies were included,and the results showed that the IL-10 rs1800896 GG genotype was significantly associated with decreased CL susceptibility according to the homozygous codominant model and recessive model.However,no correlation was found between the other two loci and CL susceptibility.Conclusions The IL-10 rs1800896 polymorphism may provide new biomarkers and potential drug treatment targets for early clinical screening of CL,which help improve individualized prevention and treatment of clinical CL patients.

目的 评价TRA对HER2阳性BC患者肝毒性和胃肠道不良反应的风险.方法 检索PubMed、Cochrane Li-brary、Embase、Web of Science、CNKI及万方数据库,收集TRA治疗HER2阳性BC患者肝毒性和胃肠道不良反应的RCT研究.采用RevMan5.3软件进行Meta分析.结果 共纳入23项RCT,结果显示,ALT升高[RR=1.90,95％CI(1.52,2.38),P＜0.01)、AST升高(RR=2.18,95％CI(1.79,2.65),P＜0.01]发生率显著高于对照组,而恶心、呕吐发生率与对照组相比差异无统计学意义.结论 在TRA治疗HER2阳性BC患者后,肝毒性发生率明显升高,胃肠道不良反应发生率无显著差异.因此,在临床上需密切监测肝功能并注意胃肠道不良反应.

Background: Recently, increasing evidence has demonstrated that IL-10 single nucleotide polymorphisms (SNPs) are associated with the risk of acute leukemia (AL), but the findings of different articles remain controversial. Thus, we performed a meta-analysis to further investigate the exact roles of IL-10 SNPs in AL susceptibility. Methods: Six common Chinese and English databases were utilized to retrieve eligible studies. The strength of the association was assessed by calculating odds ratios and 95 % confidence intervals. All analyses were carried out using Review Manager (version 5.3) and STATA (version 15.1). The registered number of this research is CRD42022373362. Results: A total of 6391 participants were enrolled in this research. The results showed that the AG genotype of rs1800896 increased AL risk in the heterozygous codominant model (AG vs. AA, OR = 1.41, 95 % CI = 1.04-1.92, P = 0.03) and overdominant model (AG vs. AA + GG, OR = 1.32, 95 % CI = 1.04-1.70, P = 0.03). In the subgroup analysis, associations between the G allele, GG genotype, AG genotype, AG + GG genotype of rs1800896 and increased AL risk were also observed in the mixed population based on allelic, homozygote codominant, heterozygous codominant, dominant, and overdominant models. Furthermore, an association between the AC genotype of rs1800872 and increased AL risk was observed in the Caucasian population in the overdominant model. However, the rs1800871, rs3024489 and rs3024493 polymorphisms did not affect AL risk. Conclusion: IL-10 rs1800896 and rs1800872 affected the susceptibility of AL and therefore may be biomarkers for early screening and risk prediction of AL.

心力衰竭是心血管病主要死亡原因之一,随着心力衰竭患病率不断增加,探索寻找新的治疗靶点,进一步减少患者病死率,成为心力衰竭治疗的主要研究方向.主要通过对国内外发表的心力衰竭治疗靶点相关研究论文进行检索和整理,分类综述心力衰竭治疗的相关靶点,包括G蛋白偶联受体家族、Na+/H+交换体、去乙酰化酶家族、炎性细胞因子和趋化因子等,为心力衰竭治疗、新药研发提供新思路.

Background: Our previous study reported that histamine H2 receptor antagonists (H2RAs) exposure was associated with decreased mortality in critically ill patients with heart failure (HF) through the same pharmacological mechanism as β-blockers. However, population-based clinical study directly comparing the efficacy of H2RAs and β-blockers on mortality of HF patients are still lacking. This study aims to compare the association difference of H2RAs and β-blockers on mortality in critically ill patients with HF using the Medical Information Mart for Intensive Care III database (MIMIC-III).Methods: Study population was divided into 4 groups: β-blockers + H2RAs group, β-blockers group, H2RAs group, and Non-β-blockers + Non-H2RAs group. Kaplan–Meier curves and multivariable Cox regression models were employed to evaluate the differences of all-cause mortalities among the 4 groups. Propensity score matching (PSM) was used to increase comparability of four groups.Results: A total of 5593 patients were included. After PSM, multivariate analyses showed that patients in H2RAs group had close all-cause mortality with patients in β-blockers group. Furthermore, 30-day, 1-year, 5-year and 10-year all-mortality of patients in β-blockers + H2RAs group were significantly lower than those of patients in β-blockers group, respectively (HR: 0.64, 95%CI: 0.50–0.82 for 30-day; HR: 0.80, 95%CI: 0.69–0.93 for 1-year mortality; HR: 0.83, 95%CI: 0.74–0.93 for 5-year mortality; and HR: 0.85, 95%CI: 0.76–0.94 for 10-year mortality, respectively).Conclusion: H2RAs exposure exhibited comparable all-cause mortality-decreasing effect as β-blockers; and, furthermore, H2RAs and β-blockers had additive or synergistic interactions to improve survival in critically ill patients with HF.