Search

Background: Hepatitis B-associated cirrhosis (HBC) is associated with severe complications and adverse clinical outcomes. This study aimed to develop and validate a predictive model for the occurrence of multiple complications (three or more) in patients with HBC and to explore the effects of multiple complications on HBC prognosis. Methods: In this retrospective cohort study, data from 121 HBC patients treated at Nanjing Second Hospital from February 2009 to November 2019 were analysed. The maximum follow-up period was 10.75 years, with a median of 5.75 years. Eight machine learning techniques were employed to construct predictive models, including C5.0, linear discriminant analysis (LDA), least absolute shrinkage and selection operator (LASSO), k-nearest neighbour (KNN), gradient boosting decision tree (GBDT), support vector machine (SVM), generalised linear model (GLM) and naive Bayes (NB), utilising variables such as medical history, demographics, clinical signs, and laboratory test results. Model performance was evaluated via receiver operating characteristic (ROC) curve analysis, residual analysis, calibration curve analysis, and decision curve analysis (DCA). The influence of multiple complications on HBC survival time was assessed via Kaplan‒Meier curve analysis. Furthermore, LASSO and univariable and multivariable Cox regression analyses were conducted to identify independent prognostic factors for overall survival (OS) in patients with HBC, followed by ROC, C-index, calibration curve, and DCA curve analyses of the constructed prognostic nomogram model. This study utilized bootstrap resampling for internal validation and employed the Medical Information Mart for Intensive Care IV (MIMIC-IV) database for external validation. Results: The GBDT model exhibited the highest area under the curve (AUC) and emerged as the optimal model for predicting the occurrence of multiple complications. The key predictive factors included posthospitalisation fever (PHF), body mass index (BMI), retinol binding protein (RBP), total bilirubin (TB) levels, and eosinophils (EOS). Kaplan–Meier analysis revealed that patients with multiple complications had significantly worse OS than those with fewer complications. Additionally, multivariable Cox regression analysis, informed by least absolute shrinkage and LASSO selection, identified hepatocellular carcinoma (HCC), multiple complications, and lactate dehydrogenase (LDH) levels as independent prognostic factors for OS. The prognostic model demonstrated 1-year, 3-year, and 5-year OS ROC AUCs of 0.802, 0.793, and 0.817, respectively. For the internal validation cohort, the corresponding AUC values were 0.797, 0.832, and 0.835. In contrast, the external validation cohort yielded a 1-year ROC AUC of 0.707. Calibration curves indicated good consistency of the model, and DCA demonstrated the model's clinical utility, showing high net benefits within certain threshold ranges. Compared with the univariable models, the multivariable ROC curves indicated higher AUC values for this prognostic model, and the model also possessed the best c-index. Conclusion: The GBDT prediction model provides a reliable tool for the early identification of high-risk HBC patients prone to developing multiple complications. The concurrent occurrence of multiple complications is an independent prognostic factor for OS in patients with HBC. The constructed prognostic model demonstrated remarkable predictive performance and clinical applicability, indicating its crucial role in enhancing patient outcomes through timely and targeted interventions. [ABSTRACT FROM AUTHOR]

Background: Lysine glutarylation (Kglu) is one of the most important Post-translational modifications (PTMs), which plays significant roles in various cellular functions, including metabolism, mitochondrial processes, and translation. Therefore, accurate identification of the Kglu site is important for elucidating protein molecular function. Due to the time-consuming and expensive limitations of traditional biological experiments, computational-based Kglu site prediction research is gaining more and more attention. Results: In this paper, we proposed GBDT_KgluSite, a novel Kglu site prediction model based on GBDT and appropriate feature combinations, which achieved satisfactory performance. Specifically, seven features including sequence-based features, physicochemical property-based features, structural-based features, and evolutionary-derived features were used to characterize proteins. NearMiss-3 and Elastic Net were applied to address data imbalance and feature redundancy issues, respectively. The experimental results show that GBDT_KgluSite has good robustness and generalization ability, with accuracy and AUC values of 93.73%, and 98.14% on five-fold cross-validation as well as 90.11%, and 96.75% on the independent test dataset, respectively. Conclusion: GBDT_KgluSite is an effective computational method for identifying Kglu sites in protein sequences. It has good stability and generalization ability and could be useful for the identification of new Kglu sites in the future. The relevant code and dataset are available at https://github.com/flyinsky6/GBDT_KgluSite. [ABSTRACT FROM AUTHOR]

Introduction: The length of hospital stay (LOHS) caused by COVID-19 has imposed a financial burden, and cost on the healthcare service system and a high psychological burden on patients and health workers. The purpose of this study is to adopt the Bayesian model averaging (BMA) based on linear regression models and to determine the predictors of the LOHS of COVID-19. Methods: In this historical cohort study, from 5100 COVID-19 patients who had registered in the hospital database, 4996 patients were eligible to enter the study. The data included demographic, clinical, biomarkers, and LOHS. Factors affecting the LOHS were fitted in six models, including the stepwise method, AIC, BIC in classical linear regression models, two BMA using Occam's Window and Markov Chain Monte Carlo (MCMC) methods, and GBDT algorithm, a new method of machine learning. Results: The average length of hospitalization was 6.7 ± 5.7 days. In fitting classical linear models, both stepwise and AIC methods (R2 = 0.168 and adjusted R2 = 0.165) performed better than BIC (R2 = 0.160 and adjusted = 0.158). In fitting the BMA, Occam's Window model has performed better than MCMC with R2 = 0.174. The GBDT method with the value of R2 = 0.64, has performed worse than the BMA in the testing dataset but not in the training dataset. Based on the six fitted models, hospitalized in ICU, respiratory distress, age, diabetes, CRP, PO2, WBC, AST, BUN, and NLR were associated significantly with predicting LOHS of COVID-19. Conclusion: The BMA with Occam's Window method has a better fit and better performance in predicting affecting factors on the LOHS in the testing dataset than other models. [ABSTRACT FROM AUTHOR]