The potential of predictive tools in cardiothoracic surgery

Author(s):  
Peter D. Drevets ◽  
Richard Lee
2020 ◽  
Vol 32 (4) ◽  
pp. 605
Author(s):  
Erle H. Austin ◽  
Todd K. Rosengart ◽  
Harvey I. Pass ◽  
Ravi Ghanta ◽  
Richard D. Weisel

2011 ◽  
Vol 14 (3) ◽  
pp. 142 ◽  
Author(s):  
Raja R. Gopaldas ◽  
Faisal G. Bakaeen ◽  
Danny Chu ◽  
Joseph S. Coselli ◽  
Denton A. Cooley

The future of cardiothoracic surgery faces a lofty challenge with the advancement of percutaneous technology and minimally invasive approaches. Coronary artery bypass grafting (CABG) surgery, once a lucrative operation and the driving force of our specialty, faces challenges with competitive stenting and poor reimbursements, contributing to a drop in applicants to our specialty that is further fueled by the negative information that members of other specialties impart to trainees. In the current era of explosive technological progress, the great diversity of our field should be viewed as a source of excitement, rather than confusion, for the upcoming generation. The ideal future cardiac surgeon must be a "surgeon-innovator," a reincarnation of the pioneering cardiac surgeons of the "golden age" of medicine. Equipped with the right skills, new graduates will land high-quality jobs that will help them to mature and excel. Mentorship is a key component at all stages of cardiothoracic training and career development. We review the main challenges facing our specialty�length of training, long hours, financial hardship, and uncertainty about the future, mentorship, and jobs�and we present individual perspectives from both residents and faculty members.


2020 ◽  
Vol 27 (35) ◽  
pp. 5856-5886 ◽  
Author(s):  
Chen Wang ◽  
Lukasz Kurgan

Therapeutic activity of a significant majority of drugs is determined by their interactions with proteins. Databases of drug-protein interactions (DPIs) primarily focus on the therapeutic protein targets while the knowledge of the off-targets is fragmented and partial. One way to bridge this knowledge gap is to employ computational methods to predict protein targets for a given drug molecule, or interacting drugs for given protein targets. We survey a comprehensive set of 35 methods that were published in high-impact venues and that predict DPIs based on similarity between drugs and similarity between protein targets. We analyze the internal databases of known PDIs that these methods utilize to compute similarities, and investigate how they are linked to the 12 publicly available source databases. We discuss contents, impact and relationships between these internal and source databases, and well as the timeline of their releases and publications. The 35 predictors exploit and often combine three types of similarities that consider drug structures, drug profiles, and target sequences. We review the predictive architectures of these methods, their impact, and we explain how their internal DPIs databases are linked to the source databases. We also include a detailed timeline of the development of these predictors and discuss the underlying limitations of the current resources and predictive tools. Finally, we provide several recommendations concerning the future development of the related databases and methods.


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