Design of Experiments-Driven Optimization of Spray Drying for Amorphous Clotrimazole Nanosuspension.

This study employed a Quality by Design (QbD) approach to spray dry amorphousclotrimazole nanosuspension (CLT-NS) consisting of Soluplus® and microcrystallinecellulose. Using the Box-Behnken Design, a systematic evaluation was conducted toanalyze the impact of inlet temperature, % aspiration, and feed rate on the criticalquality attributes (CQAs) of the clotrimazole spray-dried nanosuspension (CLT-SDNS). In this study, regression analysis and ANOVA were employed to detect significantfactors and interactions, enabling the development of a predictive model for the spraydrying process. Following optimization, the CLT-SD-NS underwent analysis using Xraypowder diffraction (XRPD), Fourier transform infrared spectroscopy (FTIR), Dynamic Scanning Calorimetry (DSC), and in vitro dissolution studies. The resultsshowed significant variables, including inlet temperature, feed rate, and aspiration rate,affecting yield, redispersibility index (RDI), and moisture content of the final product. The models created for critical quality attributes (CQAs) showed statistical significanceat a p-value of 0.05. XRPD and DSC confirmed the amorphous state of CLT in theCLT-SD-NS, and FTIR indicated no interactions between CLT and excipients. In vitrodissolution studies showed improved dissolution rates for the CLT-SD-NS (3.12-foldincrease in DI water and 5.88-fold increase at pH 7.2 dissolution media), attributed torapidly redispersing nanosized amorphous CLT particles. The well-designed studyutilizing the Design of Experiments (DoE) methodology.

Ethical Considerations in the Use of Artificial Intelligence and Machine Learning in Health Care: A Comprehensive Review.

Artificial intelligence (AI) and machine learning (ML) technologies are revolutionizing health care by offering unprecedented opportunities to enhance patient care, optimize clinical workflows, and advance medical research. However, the integration of AI and ML into healthcare systems raises significant ethical considerations that must be carefully addressed to ensure responsible and equitable deployment. This comprehensive review explored the multifaceted ethical considerations surrounding the use of AI and ML in health care, including privacy and data security, algorithmic bias, transparency, clinical validation, and professional responsibility. By critically examining these ethical dimensions, stakeholders can navigate the ethical complexities of AI and ML integration in health care, while safeguarding patient welfare and upholding ethical principles. By embracing ethical best practices and fostering collaboration across interdisciplinary teams, the healthcare community can harness the full potential of AI and ML technologies to usher in a new era of personalized data-driven health care that prioritizes patient well-being and equity.