RESUMO
Sepsis-associated acute kidney injury (SA-AKI) is a serious complication in critically ill patients, resulting in higher mortality, morbidity, and cost. The intricate pathophysiology of SA-AKI requires vigilant clinical monitoring and appropriate, prompt intervention. While traditional statistical analyses have identified severe risk factors for SA-AKI, the results have been inconsistent across studies. This has led to growing interest in leveraging artificial intelligence (AI) and machine learning (ML) to predict SA-AKI better. ML can uncover complex patterns beyond human discernment by analyzing vast datasets. Supervised learning models like XGBoost and RNN-LSTM have proven remarkably accurate at predicting SA-AKI onset and subsequent mortality, often surpassing traditional risk scores. Meanwhile, unsupervised learning reveals clinically relevant sub-phenotypes among diverse SA-AKI patients, enabling more tailored care. In addition, it potentially optimizes sepsis treatment to prevent SA-AKI through continual refinement based on patient outcomes. However, utilizing AI/ML presents ethical and practical challenges regarding data privacy, algorithmic biases, and regulatory compliance. AI/ML allows early risk detection, personalized management, optimal treatment strategies, and collaborative learning for SA-AKI management. Future directions include real-time patient monitoring, simulated data generation, and predictive algorithms for timely interventions. However, a smooth transition to clinical practice demands continuous model enhancements and rigorous regulatory oversight. In this article, we outlined the conventional methods used to address SA-AKI and explore how AI and ML can be applied to diagnose and manage SA-AKI, highlighting their potential to revolutionize SA-AKI care.
RESUMO
Nowadays, the effect of medicinal plants on the reduction of the prevalence of cardiovascular diseases and atherosclerosis has been confirmed. Liver phosphatidate phosphohydrolase [PAP] is a key regulatory enzyme in the glycerolipid metabolism. The aim of this study was to assess the effects of sesame oil on liver PAP activity, liver triglyceride, liver cholesterol and serum lipoprotein levels in hypercholesterolemic rabbits. In this experimental study 27 New Zealand rabbits were randomly assigned to 3 groups [n=9]. Group1 [control] was fed with standard diet. Group II [hypercholesterolemic group] animals received hypercholesterolemic diet [1%] without treatment. Group III was fed with hypercholesterolemic diet [1%] plus sesame oil [5%]. After two months, liver PAP activity, liver triglyceride and cholesterol content, serum lipoproteins and malondialdehyde, and antioxidant capacity were measured. One way ANOVA was used for analysis of the mean values of the variables and for pair-wise comparison of the groups we used Tukey's test. Group III had a significant decrease [P< 0.05] in the liver PAP activity compared to group II. In group II, consumption of the enriched cholesterol diet led to a significant elevation [P< 0.05] in serum lipoproteins compared to group I [control]. Also, sesame oil in group III decreased the serum lipoproteins, liver triglyceride, and liver cholesterol in comparison to group II [p<0.05]. However, a significant elevation [P< 0.05] in serum antioxidant capacity and a significant reduction in malondialdehyde level occurred in group III compared to group II [P<0.05]. Sesame oil can be effective in reducing risk factors of cardiovascular diseases by decreasing serum lipids through making desirable alterations in serum lipoproteins. Also addition of sesame oil to hypercholesterolemic diets can reduce the liver PAP activity resulting in reduced liver triglyceride synthesis, which can decrease the risk of development of fatty liver in hypercholesterolemic diets