P
Parvathi Mudigonda
Researcher at Wake Forest University
Publications - 4
Citations - 82
Parvathi Mudigonda is an academic researcher from Wake Forest University. The author has contributed to research in topics: Internal medicine & Heart failure. The author has an hindex of 2, co-authored 2 publications receiving 71 citations.
Papers
More filters
Journal ArticleDOI
Interleukin-23 and interleukin-17: Importance in pathogenesis and therapy of psoriasis
Parvathi Mudigonda,Tejaswi Mudigonda,Ashley Feneran,Habibollah S Alamdari,Laura F. Sandoval,Steven R. Feldman +5 more
TL;DR: Anti-p40 antibodies, briakinumab and ustekinumab, were tolerated in clinical trials and substantially improved psoriasis and further trials of anti IL-17 therapies are needed to assess their clinical use and potential for infection and other adverse events.
Journal ArticleDOI
Palliative cancer care ethics: Principles and challenges in the Indian setting
TL;DR: As the infrastructure of comprehensive cancer centers develop, paralleled with an increase in training of palliative care professionals, significant improvements need to be made in order to elevate the status of palledative cancer care in India.
Journal ArticleDOI
Right heart failure after left ventricular assist device: From mechanisms to treatments
Claudio Bravo,Andrew G. Navarro,Karanpreet K. Dhaliwal,Maziar Khorsandi,Jeffrey E. Keenan,Parvathi Mudigonda,Kevin D. O'Brien,Claudius Mahr +7 more
TL;DR: The unique right ventricular physiology and changes elicited by LVADs that might cause both early- and late-onset RHF is described and the currently available treatments for RHF are analyzed, including mechanical circulatory support options and medical therapies.
Journal ArticleDOI
Long-Term Outcomes and Risk Stratification of Patients With Heart Failure With Recovered Ejection Fraction.
Andrew Perry,Parvathi Mudigonda,Gary Huang,Binish G. Qureshi,Richard Cheng,Wayne C. Levy,Song Li +6 more
TL;DR: In this paper , the long-term outcomes of patients with heart failure with recovered ejection fraction, identify predictors of adverse events, and develop a risk stratification model using Cox regression analysis.