Predictive modeling for acute kidney injury after percutaneous coronary intervention in patients with acute coronary syndrome: a machine learning approach.

BACKGROUND: Acute kidney injury (AKI) is one of the preventable complications of percutaneous coronary intervention (PCI). This study aimed to develop machine learning (ML) models to predict AKI after PCI in patients with acute coronary syndrome (ACS). METHODS: This study was conducted at Tehran Heart Center from 2015 to 2020. Several variables were used to design five ML models: Naïve Bayes (NB), Logistic Regression (LR), CatBoost (CB), Multi-layer Perception (MLP), and Random Forest (RF). Feature importance was evaluated with the RF model, CB model, and LR coefficients while SHAP beeswarm plots based on the CB model were also used for deriving the importance of variables in the population using pre-procedural variables and all variables. Sensitivity, specificity, and the area under the receiver operating characteristics curve (ROC-AUC) were used as the evaluation measures. RESULTS: A total of 4592 patients were included, and 646 (14.1%) experienced AKI. The train data consisted of 3672 and the test data included 920 cases. The patient population had a mean age of 65.6 ± 11.2 years and 73.1% male predominance. Notably, left ventricular ejection fraction (LVEF) and fasting plasma glucose (FPG) had the highest feature importance when training the RF model on only pre-procedural features. SHAP plots for all features demonstrated LVEF and age as the top features. With pre-procedural variables only, CB had the highest AUC for the prediction of AKI (AUC 0.755, 95% CI 0.713 to 0.797), while RF had the highest sensitivity (75.9%) and MLP had the highest specificity (64.35%). However, when considering pre-procedural, procedural, and post-procedural features, RF outperformed other models (AUC: 0.775). In this analysis, CB achieved the highest sensitivity (82.95%) and NB had the highest specificity (82.93%). CONCLUSION: Our analyses showed that ML models can predict AKI with acceptable performance. This has potential clinical utility for assessing the individualized risk of AKI in ACS patients undergoing PCI. Additionally, the identified features in the models may aid in mitigating these risk factors.

Growth Inhibitory Effect of Lactocare on Vibrio cholerae.

BACKGROUND AND OBJECTIVE: Acute microbial diarrheal diseases are the major public health problems in the developing countries. People affected by diarrheal diseases have the lowest financial resources and poorest hygienic facilities. Children under five, primarily in Asian and African countries, are mostly the subjects affected by microbial diseases transmitted through water.The current study aimed at investigating the comparative inhibitory effect of Lactocare (commercial probiotic) on clinical samples and standard strains of Vibrio cholerae. METHODS: A total of 20 clinical samples and a standard strain (ATCC 14035) were provided by Health Reference Laboratory and Biotechnology Institute, respective ly. In order to confirm the samples, biochemical analysis and the polymerase chain reaction (PCR) were performed on intergenic space. Afterward, agar well diffusion method was performed in order to measure the minimum inhibitory concentration to monitor the antimicrobial activity of Lactocare. RESULTS: Colony count of V. cholerae for the standard strain in 30% and mean for clinical samples in 50% concentration of Lactocare treatment revealed that it would propel to death phase. Since the number of colonies decreased to 100, it was considered that higher concentrations of Lactocare would completely inhibit the growth of V. cholera. CONCLUSION: Probiotics are employed to develop new pharmaceutical preparations and functional foods in order to promote the public health.