BEGIN:VCALENDAR
VERSION:2.0
PRODID:icalendar-ruby
CALSCALE:GREGORIAN
X-WR-CALNAME:InspireME Seminar - Machine Learning-Calibrated Multiscale Mod
 el Predicts Long-Term Outcomes of Pharmacological and Surgical Treatments 
 for Mitral Valve Regurgitation - Dr. Johane Bracamonte
X-WR-TIMEZONE:Mountain Time (US & Canada)
BEGIN:VEVENT
DTSTAMP:20260907T060317Z
UID:tag:localist.com\,2008:EventInstance_51082256639521
DTSTART:20251114T190000Z
DTEND:20251114T200000Z
DESCRIPTION:Primary mitral valve regurgitation (MR) is a common heart disea
 se that induces volume\noverload (VO) and cardiac remodeling. The most eff
 ective treatment for MR is mitral valve\nrepair (MVr). However\, clinical 
 outcomes of MVr are often suboptimal\, even in asymptomatic\npatients with
  functional hearts\, 20% of whom may experience systolic dysfunction withi
 n 12\nmonths. Recent research has highlighted various clinical markers tha
 t correlate with poor MVr\noutcomes but cannot reliably predict long-term 
 response for individual patients. Understanding how neurohormonal factors\
 , mechanics\, and remodeling determine MVr outcomes may require more compl
 ex\, integrative approaches than currently exist. So\, I propose enhancing
  multiscale computational modeling with machine learning to 1) predict cha
 nges in LV size and function following MVr and 2) identify pathways associ
 ated with remodeling and deterioration of cardiac function in the context 
 of MVr.  As a first step\, we have developed a multiscale model of cardiac
  function and remodeling encompassing ventricular mechanics\, circulation 
 hemodynamics\, and a network model of cardiomyocyte molecular signaling pa
 thways. Then\, we used a Markov Chain Monte Carlo algorithm to integrate d
 ata from 76 experimental studies of volume overload. The resulting calibra
 ted model can generate accurate probabilistic predictions on the effect of
  drug and hormonal interventions within and outside the context of experim
 ental volume overload and can predict the reversal of cardiac hypertrophy 
 following mitral valve replacement in dogs. Furthermore\, this model ident
 ifies a molecular pathway that could link the loss of ventricular contract
 ile function to exacerbated ventricular hypertrophy and impaired recovery 
 following mitral valve repair.
GEO:43.600767;-116.199525
LOCATION:Micron Center for Materials Research (MCMR)\, 105
SUMMARY:InspireME Seminar - Machine Learning-Calibrated Multiscale Model Pr
 edicts Long-Term Outcomes of Pharmacological and Surgical Treatments for M
 itral Valve Regurgitation - Dr. Johane Bracamonte
URL;VALUE=URI:https://events.boisestate.edu/event/inspireme-seminar-machine
 -learning-calibrated-multiscale-model-predicts-long-term-outcomes-of-pharm
 acological-and-surgical-treatments-for-mitral-valve-regurgitation-dr-johan
 e-bracamonte
END:VEVENT
END:VCALENDAR