Several machine learning classification algorithms were evaluated using a dataset focused on heart failure. Results obtained from logistic regression, random forest, decision tree, K-nearest neighbors, and multilayer perceptron (MLP) were compared to obtain the best model. The random forest method obtained specificity = 0.93, AUC = 0.97, and Matthews correlation coefficient (MCC) = 0.83. The accuracy was high; therefore, it was considered the best model. On the other hand, K-nearest neighbors and MLP (multi-layer perceptron) showed lower accuracy rates. These results confirm the effectiveness of the random forest method in identifying heart failure cases. This study underlines that the number of features, feature selection and quality, model type, and hyperparameter fit are also critical in these studies, as well as the importance of using machine learning techniques.

Background The rise of antibiotic-resistant bacteria presents a pressing need for exploring new natural compounds with innovative mechanisms to replace existing antibiotics. Bacteriocins offer promising alternatives for developing therapeutic and preventive strategies in livestock, aquaculture, and human health. Specifically, those produced by LAB are recognized as GRAS and QPS. This study aims to develop a deep learning model specifically designed to classify bacteriocins by their LAB origin, using interpretable k-mer features and embedding vectors to enable applications in antimicrobial discover. Methods We developed a deep learning neural network for binary classification of bacteriocin amino acid sequences (BacLAB vs. Non-BacLAB). Features were extracted using k-mers (k=3,5,7,15,20) and vector embeddings (EV). Ten feature combinations were tested (e.g., EV, EV+5-mers+7-mers). Sequences were filtered by length (50–2000 AA) to ensure uniformity, and class balance was maintained (24,964 BacLAB vs. 25,000 Non-BacLAB). The model was trained on Google Colab, demonstrating computational accessibility without specialized hardware. Results The ‘5-mers+7-mers+EV’ group achieved the best performance, with k-fold cross-validation (k=30) showing: 9.90% loss, 90.14% accuracy, 90.30% precision, 90.10% recall and F1 score. Folder 22 stood out with 8.50% loss, 91.47% accuracy, and 91.00% precision, recall, and F1 score. Five sets of 100 LAB-specific k-mers were identified, revealing conserved motifs. Despite high accuracy, sequence length variation (50–2000 AA) may bias k-mer representation, favoring longer sequences. Additionally, experimental validation is required to confirm the biological activity of predicted bacteriocins. These aspects highlight directions for future research. Conclusions The model developed in this study achieved consistent results with those seen in the reviewed literature. It outperformed some studies by 3-10%. Its implementation in resource-limited settings is feasible via cloud platforms like Google Colab. The identified k-mers could guide the design of synthetic antimicrobials, pending further in vitro validation.

Background The rise of antibiotic-resistant bacteria presents a pressing need for exploring new natural compounds with innovative mechanisms to replace existing antibiotics. Bacteriocins offer promising alternatives for developing therapeutic and preventive strategies in livestock, aquaculture, and human health. Specifically, those produced by LAB are recognized as GRAS and QPS. Methods In this study was used a deep learning neural network for binary classification of bacteriocin amino acid sequences, distinguishing those produced by LAB. The features were extracted using the k-mer method and vector embedding. Ten different groups were tested, combining embedding vectors and k-mers: EV, ‘EV+3-mers’, ‘EV+5-mers’, ‘EV+7-mers’, ‘EV+15-mers’, ‘EV+20-mers’, ‘EV+3-mers+5-mers’, ‘EV+3-mers+7-mers’, ‘EV+5-mers+7-mers’, and ‘EV+15-mers+20-mers’. Results Five sets of 100 characteristic k-mers unique to bacteriocins produced by LAB were obtained for values of k = 3, 5, 7, 15, and 20. Significant difference was observed between using only and concatenation. Specially, ‘5-mers+7-mers+EV ’ group showed superior accuracy and loss results. Employing k-fold cross-validation with k=30, the average results for loss, accuracy, precision, recall, and F1 score were 9.90%, 90.14%, 90.30%, 90.10%, and 90.10% respectively. Folder 22 stood out with 8.50% loss, 91.47% accuracy, and 91.00% precision, recall, and F1 score. Conclusions The model developed in this study achieved consistent results with those seen in the reviewed literature. It outperformed some studies by 3-10%. The lists of characteristic k-mers pave the way to identify new bacteriocins that could be valuable for therapeutic and preventive strategies within the livestock, aquaculture industries, and potentially in human health.

Background/Aim: This study analyzed clinical factors impacting the survival of COVID-19 patients with acute respiratory distress síndrome, or ARDS (CARDS) to ICU compared to non-COVID-19 ARDS patients. Methods: Clinical variables from 1,008 CARDS cases and 332 ARDS cases were computed using learning algorithms. The multivariable Cox proportional hazards regression models with the enter method evaluated risk factors and ICU mortality relationships. The survival analysis was completed with Kaplan-Meier and the log-rank tests. Results: A Random Forest model revealed that mechanical ventilation-related factors, oxygenation, blood pH, superinfection, shock, and ICU length of stay have the greatest effects on ICU survival. According to a multivariate Cox model, reintubation and a high-flow nasal cannula were essential for survival in CARDS patients during the ICU stay. The length of stay in the ICU diminishes in patients older than 45 years, regardless of the source of ARDS. Conclusion: This study gives recommendations for the respiratory care of ARDS in COVID-19 patients.