Publications by authors named "J Jacob Kurian"
Mathematical neuroscience is the branch of interdisciplinarity between mathematical modeling and neuroscience through computational techniques to study the structure, function, and dynamics of the brain. The objective of this paper is to undertake a comprehensive review of research trends in mathematical neuroscience and important developments in the period from 1973 to 2024. From this source of bibliographic data, Scopus alone returns 727 retrieved documents, consisting of journals, book chapters, and conference papers.
View Article and Find Full Text PDFDefined as the application of mathematical models and methods for the study of disease spread and control, Mathematical epidemiology has now emerged as a very important area for understanding public health dynamics. The paper presents an overall bibliometric analysis of research in mathematical epidemiology using the Scopus database. This overview comprises 1,787 documents: journal articles, book chapters, and conference papers from 819 sources.
View Article and Find Full Text PDFClassically, aromaticity portrays the unique stability and peculiar reactivities of cyclic planar conjugated systems with (4n+2) π electrons. Understanding the electronic environments in new chemical frameworks through experimental and theoretical validation is central to this ever-expanding theme in chemical science. Such investigations in curved π-surfaces have special significance as they can unravel the variations when the planarity requirement is slightly lifted.
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- Cardiac pressure overload can lead to problems for patients with congenital heart defects, and using stem cell-derived heart cells might help improve heart function alongside surgery.
- Researchers successfully implanted human stem cell-derived heart cells into the hearts of rhesus macaques facing induced pressure overload, achieving good integration with the existing heart tissue.
- Although some monkeys experienced episodes of ventricular tachycardia after the cell transplant, these events generally resolved within a few weeks, indicating a need for monitoring but also promising potential for this treatment approach.
Stochastic models play a pivotal role in disease prediction by accounting for randomness and uncertainty in biological systems. This study offers a visualization of trends in the application of stochastic models for disease prediction from 1990 to 2024, based on a bibliometric analysis of Scopus data. Key findings reveal a significant growth in research post-2014, largely driven by global health challenges like COVID-19.
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