Ashish Sarraju

TL;DR: It is demonstrated that MBL indeed enhances infection of Ebola, Hendra, Nipah and West Nile viruses in low complement conditions, and support the concept of an innate immune haplotype that represents critical interactions between MBL and complement component C4 genes and that may modify susceptibility or resistance to certain glycosylated pathogens.

TL;DR: A classifier to identify potential FH patients using electronic health record (EHR) data at Stanford Health Care is effective in finding candidate patients for further FH screening and can lead to effective identification of the highest risk patients for enhanced management strategies.

TL;DR: The authors examined the appropriateness of artificial intelligence model responses to fundamental cardiovascular disease prevention questions and concluded that the model responses were appropriate for the task of predicting cardiovascular disease in a clinical setting.

TL;DR: Overall, ML models achieved comparable or improved performance compared to the PCE while allowing risk discrimination in a larger group of patients including PCE-ineligible patients, and may help bridge important gaps in ASCVD risk prediction.

TL;DR: Structurally defined LBTs are used as the starting point for re‐engineering the first coordination shell of the lanthanide ion to provide for high contrast through direct coordination of water to Gd3+ (resulting in the single LBT peptide, m‐sLBT), a first step in expanding the current base of specificity‐targeted agents available.