Methods: This case report describes a 10-month-old boy who presented with 2 months' history of gradually increasing weakness and pallor.
Results: The patient was diagnosed as a case of DBA based on peripheral blood finding, bone marrow aspiration with trephine biopsy reports, and genetic mutation analysis of the RPS19 gene. His father refused hematopoietic stem cell transplantation for financial constraints. Patient received prednisolone therapy with oral folic acid and iron supplements.
Conclusion: Hemoglobin raised from 6.7 to 9.8g/dL after 1 month of therapeutic intervention.
METHODS: To predict CD while prioritizing patient privacy, our study employed data anonymization involved adding Laplace noise to sensitive features like age and gender. The anonymized dataset underwent analysis using a differential privacy (DP) framework to preserve data privacy. DP ensured confidentiality while extracting insights. Compared with Logistic Regression (LR), Gaussian Naïve Bayes (GNB), and Random Forest (RF), the methodology integrated feature selection, statistical analysis, and SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) for interpretability. This approach facilitates transparent and interpretable AI decision-making, aligning with responsible AI development principles. Overall, it combines privacy preservation, interpretability, and ethical considerations for accurate CD predictions.
RESULTS: Our investigations from the DP framework with LR were promising, with an area under curve (AUC) of 0.848 ± 0.03, an accuracy of 0.797 ± 0.02, precision at 0.789 ± 0.02, recall at 0.797 ± 0.02, and an F1 score of 0.787 ± 0.02, with a comparable performance with the non-privacy framework. The SHAP and LIME based results support clinical findings, show a commitment to transparent and interpretable AI decision-making, and aligns with the principles of responsible AI development.
CONCLUSIONS: Our study endorses a novel approach in predicting CD, amalgamating data anonymization, privacy-preserving methods, interpretability tools SHAP, LIME, and ethical considerations. This responsible AI framework ensures accurate predictions, privacy preservation, and user trust, underscoring the significance of comprehensive and transparent ML models in healthcare. Therefore, this research empowers the ability to forecast CD, providing a vital lifeline to millions of CD patients globally and potentially preventing numerous fatalities.
OBJECTIVE: This study investigates the cardioprotective effects of arjunolic acid (AA) via MyD88-dependant TLR4 downstream signaling marker expression.
MATERIALS AND METHODS: The MTT viability assay was used to assess the cytotoxicity of AA. LPS induced in vitro cardiovascular disease model was developed in H9C2 and C2C12 myotubes. The treatment groups were designed such as control (untreated), LPS control, positive control (LPS + pyrrolidine dithiocarbamate (PDTC)-25 µM), and treatment groups were co-treated with LPS and three concentrations of AA (50, 75, and 100 µM) for 24 h. The changes in the expression of TLR4 downstream signaling markers were evaluated through High Content Screening (HCS) and Western Blot (WB) analysis.
RESULTS: After 24 h of co-treatment, the expression of TLR4, MyD88, MAPK, JNK, and NF-κB markers were upregulated significantly (2-6 times) in the LPS-treated groups compared to the untreated control in both HCS and WB experiments. Evidently, the HCS analysis revealed that MyD88, NF-κB, p38, and JNK were significantly downregulated in the H9C2 myotube in the AA treated groups. In HCS, the expression of NF-κB was downregulated in C2C12. Additionally, TLR4 expression was downregulated in both H9C2 and C2C12 myotubes in the WB experiment.
DISCUSSION AND CONCLUSIONS: TLR4 marker expression in H9C2 and C2C12 myotubes was subsequently decreased by AA treatment, suggesting possible cardioprotective effects of AA.