RESUMEN
BACKGROUND: The aim of this study was using bioinformatic tools to identify hub genes in the relationship between septic cardiomyopathy (SCM) and cuproptosis and predict potential Chinese herbal drug candidates. METHODS: SCM datasets were downloaded from the gene expression omnibus. Cuproptosis related genes were collected from a research published on Science in March, 2022. The expression profiles of genes related to cuproptosis in SCM were extracted. Differentially expressed genes (DEGs) were analyzed using R package limma. A single-sample gene set enrichment analysis was conducted to measure the correlation between DEGs and immune cell infiltration. Hub genes were screened out by random forest model. Finally, HERB database and COREMINE database were used to predict Chinese herbal drugs for hub genes and carry out molecular docking. RESULTS: A total of 9 DEGs were identified. Cuproptosis differential genes PDHB, DLAT, DLD, FDX1, GCSH, LIAS were significantly correlated with one or more cells and their functions in immune infiltration. The random forest model screened pyruvate dehydrogenase E1 beta subunit (PDHB) as the hub gene. PDHB was negatively correlated with Plasmacytoid dendritic cell infiltration. Pyruvic acid, rhodioloside and adenosine were predicted with PDHB as the target, and all three components are able to bind to PDHB. CONCLUSIONS: Cuproptosis related gene PDHB is associated with the occurrence and immune infiltration of septic cardiomyopathy. Rhodioloside and other Chinese herbal drugs may play a role in the treatment of SCM by regulating the expression of PDHB.
RESUMEN
BACKGROUND: Feeding intolerance in patients with sepsis is associated with a lower enteral nutrition (EN) intake and worse clinical outcomes. The aim of this study was to develop and validate a predictive model for enteral feeding intolerance in the intensive care unit patients with sepsis. METHODS: In this dual-center, retrospective, case-control study, a total of 195 intensive care unit patients with sepsis were enrolled from June 2018 to June 2020. Data of 124 patients for 27 clinical indicators from one hospital were used to train the model, and data from 71 patients from another hospital were used to assess the external predictive performance. The predictive models included logistic regression, naive Bayesian, random forest, gradient boosting tree, and deep learning (multilayer artificial neural network) models. RESULTS: Eighty-six (44.1%) patients were diagnosed with enteral feeding intolerance. The deep learning model achieved the best performance, with areas under the receiver operating characteristic curve of 0.82 (95% confidence interval = 0.74-0.90) and 0.79 (95% confidence interval = 0.68-0.89) in the training and external sets, respectively. The deep learning model showed good calibration; based on the decision curve analysis, the model's clinical benefit was considered useful. Lower respiratory tract infection was the most important contributing factor, followed by peptide EN and shock. CONCLUSIONS: The new prediction model based on deep learning can effectively predict enteral feeding intolerance in intensive care unit patients with sepsis. Simple clinical information such as infection site, nutrient type, and septic shock can be useful in stratifying a septic patient's risk of EN intolerance.
