The process of matching pathway-targeted drugs to tumor mutational profile regardless of cancer type is critical in the development of targeted therapies. However, actionable mutations interact with distinct gene regulatory programs and signaling networks and can occur against different tumor-specific genetic backgrounds.
The aims of the project were to: (1) Evaluate the effect of pharmacist-led medication management service using patient-centered telemedicine on adverse drug events (ADE) for residents receiving high-risk drugs, and (2) Evaluate patient reported outcomes.
Genomics studies revealed numerous antibiotics-encoding genes across a wide range of bacterial and fungal species, including various species in the human microbiome. However, little is known about the hundreds of antibiotics produced by microorganisms in the human, soil and other host-oriented/environmental microbiomes. Deep exploration of this meta-antibiome critically depends on a transition from the current one-off process of antibiotics analysis to a high-throughput antibiotics sequencing.
This talk will provide a glimpse into the emerging science of human profiling from voice. The human voice is a powerful bio-parametric indicator. It carries information that can be linked to the current (referring to the time of recording of the voice) physical, physiological, demographic, medical, environmental and myriad other bio-relevant characteristics of the speaker.
Implementation of health information technology initiatives that directly and measurably impact patient care are a key strategic and operational goal for chief information officers of hospitals.
Identifying potential drug-drug interactions (DDIs) during the care process is important to ensure patient safety and mitigate risk. In the United States, Meaningful Use incentives have supported the widespread dissemination of potential DDI alerting. However, the majority of potential DDI alerts are ignored. While the majority of health systems use third-party commercial knowledge bases that are integrated into the EHR alerting framework, each institution has the ability to customize the alert types and thresholds in relation to the knowledge base.
Causal modeling is important in biomedicine because it describes a system’s behavior not only under observation but also under intervention. Logic-based causal discovery exploits this expressive power to identify causal models from data sets that may be obtained under different experimental conditions and measure different variables.
Pulmonary hypertension (PH) is a heterogenous collection of conditions characterized by an increase in blood pressure in the pulmonary vasculature. In the Chan lab, we are using both clinical and lab-based approaches to understanding the progression, mechanisms, and causes of PH.
Using NIH tools and expertise can improve odds of a successful application. In this workshop, we will discuss strategies to make the most of NIH staff and resources to plan and prepare a competitive application and manage the post-submission outcome. Bring any questions you have about the NIH and its grant application and review process.