Matcha

Conclusion: Menon et al. model was able to predict well for our clinical data where it had the relative mean prediction error percentage ≤ 20%, relative median absolute prediction error ≤ 30% and relative root mean square error close to zero. Based on this final model, we proposed a dosing nomogram for various weight groups, which could potentially help to maintain the trough concentrations in the therapeutic range.

Conclusion: A generalizable PK model for gentamicin during TH in neonates was identified. Using the model, a dosing nomogram for gentamicin was designed.

Conclusion: Curcumin and Melissa officinalis had limited effects, resveratrol showed mixed outcomes with some side effects, and matcha green tea may improve cognition and sleep quality. Despite generally favorable results, the studies varied considerably in design and quality; nonetheless, herbal extracts represent a prominent category of natural interventions in AD and MCI, underscoring the need for further large-scale, high-quality clinical trials to confirm their therapeutic potential.

Conclusion: The PopPK models by Cohen-Wolkowiez et al. and Li et al. predicted our data well for piperacillin and tazobactam with the lower relative median absolute predictive error (rMAPE) of 8.61% and 16.48% and relative root mean square error (rRMSE) of 0.01 and 0.03, respectively. External evaluation of the published PopPK models of piperacillin and tazobactam resulted in enhancing their credibility to be implemented in clinical practice.