Ayesha Sultana's research while affiliated with St. George's University and other places

Glycated hemoglobin (HbA1c) and glycated albumin (GA) are vital markers for assessing glucose control in diabetes. This cross-sectional study involving 901 diagnosed type 2 diabetics aimed to compare calculated HbA1c, using the formula HbA1c = 2.6 + 0.03 × FBS (mg/dL), with directly measured HbA1c. Simultaneously, the study assessed the agreement between the two methods through regression analysis and explored correlations with various measures of glycemic control. The non-parametric Kolmogorov-Smirnov test indicated a non-normal data distribution, prompting appropriate statistical tests. Spearman’s correlation coefficient revealed a strong correlation of calculated HbA1c, calculated GA, and estimated average glucose with measured parameters. Wilcoxon rank sum test indicated a significant difference between directly measured and calculated HbA1c (Z -9.487033, p < 0.0001). Passing Bablok regression analysis showed a significant deviation from linearity. Despite the potential cost benefits in resource-poor settings, caution is advised regarding interchangeable use of calculated and directly measured HbA1c in clinical decision-making. Data shows the importance of robust analytical methods in glycemic control assessment, offering insights for managing type 2 diabetes mellitus.

Artificial intelligence (AI) has become a revolutionary influence in the field of ophthalmology, providing unparalleled capabilities in data analysis and pattern recognition. This narrative review delves into the crucial role that AI plays, particularly in the context of anterior segment diseases with a genetic basis. Corneal dystrophies (CDs) exhibit significant genetic diversity, manifested by irregular substance deposition in the cornea. AI-driven diagnostic tools exhibit promising accuracy in the identification and classification of corneal diseases. Importantly, chat generative pre-trained transformer (ChatGPT)-4.0 shows significant advancement over its predecessor, ChatGPT-3.5. In the realm of glaucoma, AI significantly contributes to precise diagnostics through inventive algorithms and machine learning models, surpassing conventional methods. The incorporation of AI in predicting glaucoma progression and its role in augmenting diagnostic efficiency is readily apparent. Additionally, AI-powered models prove beneficial for early identification and risk assessment in cases of congenital cataracts, characterized by diverse inheritance patterns. Machine learning models achieving exceptional discrimination in identifying congenital cataracts underscore AI's remarkable potential. The review concludes by emphasizing the promising implications of AI in managing anterior segment diseases, spanning from early detection to the tailoring of personalized treatment strategies. These advancements signal a paradigm shift in ophthalmic care, offering optimism for enhanced patient outcomes and more streamlined healthcare delivery.

Preeclampsia, a complex and potentially life-threatening condition during pregnancy, poses significant challenges to maternal and fetal health. This comprehensive narrative review explores the transformative role of Artificial Intelligence (AI) in the detection, prediction, and management of preeclampsia. Predictive models have been developed by leveraging diverse structured and unstructured data to ascertain effective techniques for preeclampsia prediction. The methodologies most prominently employed include the Random Forest, Support Vector Machine, and Artificial Neural Network (ANN). The additional algorithms include the following: Decision Tree, Naive Bayes, K-Nearest Neighbor and XG Boost. Furthermore, we explore the potential opportunities and obstacles within the realm of preeclampsia prediction, thereby promoting further advancements in artificial intelligence systems research.

Artificial intelligence (AI) techniques have the potential to revolutionize drug release modeling, optimize therapy for personalized medicine, and minimize side effects. By applying AI algorithms, researchers can predict drug release profiles, incorporate patient-specific factors, and optimize dosage regimens to achieve tailored and effective therapies. This AI-based approach has the potential to improve treatment outcomes, enhance patient satisfaction, and advance the field of pharmaceutical sciences. International collaborations and professional organizations play vital roles in establishing guidelines and best practices for data collection and sharing. Open data initiatives can enhance transparency and scientific progress, facilitating algorithm validation. Categories: Healthcare Technology Keywords:  drug discovery research, future of healthcare, nano technology, drug design, artificial intelligence in healthcare