Acute Kidney Injury Prediction Model Using Cystatin-C, Beta-2 Microglobulin, and Neutrophil Gelatinase-Associated Lipocalin Biomarker in Sepsis Patients.

Introduction: AKI is a frequent complication in sepsis patients and is estimated to occur in almost half of patients with severe sepsis. However, there is currently no effective therapy for AKI in sepsis. Therefore, the therapeutic approach is focused on prevention. Based on this, there is an opportunity to examine a panel of biomarker models for predicting AKI. Material and Methods: This prospective cohort study analysed the differences in Cystatin C, Beta-2 Microglobulin, and NGAL levels in sepsis patients with AKI and sepsis patients without AKI. The biomarker modelling of AKI prediction was done using machine learning, namely Orange Data Mining. In this study, 130 samples were analysed by machine learning. The parameters used to obtain the biomarker panel were 23 laboratory examination parameters. Results: This study used SVM and the Naïve Bayes model of machine learning. The SVM model's sensitivity, specificity, NPV, and PPV were 50%, 94.4%, 71.4%, and 87.5%, respectively. For the Naïve Bayes model, the sensitivity, specificity, NPV, and PPV were 83.3%, 77.8%, 87.5%, and 71.4%, respectively. Discussion: This study's SVM machine learning model has higher AUC and specificity but lower sensitivity. The Naïve Bayes model had better sensitivity; it can be used to predict AKI in sepsis patients. Conclusion: The Naïve Bayes machine learning model in this study is useful for predicting AKI in sepsis patients.

Application of Topical Sucralfate and Topical Platelet-Rich Plasma Improves Wound Healing in Diabetic Ulcer Rats Wound Model.

PURPOSE: One of the most serious and devastating complications of diabetes mellitus is diabetic ulcers. They are difficult to treat and often result in limb loss. Topical sucralfate and platelet-rich plasma have the potential to improve the healing outcomes of chronic ulcers, including diabetic ulcers. This research aims to determine the effectiveness of sucralfate and platelet-rich plasma therapy for the improvement of diabetic ulcer wound healing. PATIENTS AND METHODS: Ninety Wistar rats were used in this study and were classified into five groups. Four of the five groups were diabetic induced and were treated with topical sucralfate only, platelet-rich plasma only, combination of topical sucralfate and platelet-rich plasma, and diabetic control group which received standard therapy only. The non-diabetic control group did not receive any therapy. We observed macrophage amount, platelet-derived growth factor, vascular endothelial growth factor, and hypoxia-inducible factor as a biomarker. Rats were terminated after 7th and 14th days and were subjected to immunohistochemistry staining and examination. RESULTS: We found that topical sucralfate and platelet-rich plasma increase macrophage levels, vascular endothelial growth factor expression and platelet-derived growth factor expression in diabetic wound cells. We also found a reduction in hypoxia inducible factor-1α expression. Combination of topical sucralfate and platelet-rich plasma for 14 days gave the most significant improvement in terms of wound healing compared to topical sucralfate or platelet-rich plasma alone. CONCLUSION: The combination of topical sucralfate and platelet-rich plasma therapy results in the best improvement in diabetic ulcer wound healing compared to sucralfate or platelet-rich plasma monotherapy or conventional wound healing therapy.