A comprehensive study on machine learning models combining with oversampling for bronchopulmonary dysplasia-associated pulmonary hypertension in very preterm infants.

BACKGROUND: Bronchopulmonary dysplasia-associated pulmonary hypertension (BPD-PH) remains a devastating clinical complication seriously affecting the therapeutic outcome of preterm infants. Hence, early prevention and timely diagnosis prior to pathological change is the key to reducing morbidity and improving prognosis. Our primary objective is to utilize machine learning techniques to build predictive models that could accurately identify BPD infants at risk of developing PH. METHODS: The data utilized in this study were collected from neonatology departments of four tertiary-level hospitals in China. To address the issue of imbalanced data, oversampling algorithms synthetic minority over-sampling technique (SMOTE) was applied to improve the model. RESULTS: Seven hundred sixty one clinical records were collected in our study. Following data pre-processing and feature selection, 5 of the 46 features were used to build models, including duration of invasive respiratory support (day), the severity of BPD, ventilator-associated pneumonia, pulmonary hemorrhage, and early-onset PH. Four machine learning models were applied to predictive learning, and after comprehensive selection a model was ultimately selected. The model achieved 93.8% sensitivity, 85.0% accuracy, and 0.933 AUC. A score of the logistic regression formula greater than 0 was identified as a warning sign of BPD-PH. CONCLUSIONS: We comprehensively compared different machine learning models and ultimately obtained a good prognosis model which was sufficient to support pediatric clinicians to make early diagnosis and formulate a better treatment plan for pediatric patients with BPD-PH.

Identification of Bioactive Compounds and Potential Mechanisms of Kuntai Capsule in the Treatment of Polycystic Ovary Syndrome by Integrating Network Pharmacology and Bioinformatics.

Objective: This study elucidates the potential therapeutic targets and molecular mechanisms of KTC in the treatment of PCOS. Materials and Methods: Using the Traditional Chinese Medicine System Pharmacology Database and Analysis Platform (TCMSP), the active ingredients and potential targets of KTC were obtained. The Gene Expression Omnibus (GEO) database was used to find differentially expressed genes (DEGs) related to PCOS. Search the CTD, DisGeNet, genecards, NCBI, OMIM, and PharmGKB databases for therapeutic targets related to PCOS. The intersection of potential targets, DEGs, and therapeutic targets was submitted to perform bioinformatics analysis by R language. Finally, the analyses' core targets and their corresponding active ingredients were molecularly docked. Results: 88 potential therapeutic targets of KTC for PCOS were discovered by intersecting the potential targets, DEGs, and therapeutic targets. According to bioinformatics analysis, the mechanisms of KTC treatment for PCOS could be linked to IL-17 signaling route, p53 signaling pathway, HIF-1 signaling pathway, etc. The minimal binding energies of the 5 core targets and their corresponding ingredients were all less than -6.5. Further research found that quercetin may replace KTC in the treatment of PCOS. Discussion and Conclusions. We explored the active ingredients and molecular mechanisms of KTC in the treatment of PCOS and found that quercetin may be the core ingredient of KTC in the treatment of PCOS.

In Silico Screening of Potential Spike Glycoprotein Inhibitors of SARS-CoV-2 with Drug Repurposing Strategy.

OBJECTIVE: To select potential molecules that can target viral spike proteins, which may potentially interrupt the interaction between the human angiotension-converting enzyme 2 (ACE2) receptor and viral spike protein by virtual screening. METHODS: The three-dimensional (3D)-coordinate file of the receptor-binding domain (RBD)-ACE2 complex for searching a suitable docking pocket was firstly downloaded and prepared. Secondly, approximately 15,000 molecular candidates were prepared, including US Food and Drug Administration (FDA)-approved drugs from DrugBank and natural compounds from Traditional Chinese Medicine Systems Pharmacology (TCMSP), for the docking process. Then, virtual screening was performed and the binding energy in Autodock Vina was calculated. Finally, the top 20 molecules with high binding energy and their Chinese medicine (CM) herb sources were listed in this paper. RESULTS: It was found that digitoxin, a cardiac glycoside in DrugBank and bisindigotin in TCMSP had the highest docking scores. Interestingly, two of the CM herbs containing the natural compounds that had relatively high binding scores, Forsythiae fructus and Isatidis radix, are components of Lianhua Qingwen (), a CM formula reportedly exerting activity against severe acute respiratory syndrome (SARS)-Cov-2. Moreover, raltegravir, an HIV integrase inhibitor, was found to have a relatively high binding score. CONCLUSIONS: A class of compounds, which are from FDA-approved drugs and CM natural compounds, that had high binding energy with RBD of the viral spike protein. Our work provides potential candidates for other researchers to identify inhibitors to prevent SARS-CoV-2 infection, and highlights the importance of CM and integrative application of CM and Western medicine on treating COVID-19.