Bone marrow (BM) mesenchymal stem/stromal cells (MSCs) are vital in hematopoiesis. Whether BM-MSCs alter their characteristics in Myelodysplastic Syndromes (MDS) is still controversial. We characterized MSCs of de novo MDS patients in Sri Lanka who have not been reported previously in the literature. We also analyzed MSCs derived from different MDS subtypes. MSCs were culture-expanded, characterized by flow cytometry, and induced towards osteogenic and adipogenic differentiation. Growth properties were determined using growth curves and population doubling times. Karyotyping and FISH were performed on MSCs. Cell morphology, differentiation potential, and CD marker expression of MDS-MSCs of all subtypes were comparable to those of control-MSCs. No significant growth differences were observed between control MSCs and MDS-MSCs of all subtypes (p > 0.05). 31% of MDS-MSCs had chromosomal aberrations (der(3),del(6q),del(7p), loss of chromosomes) whose BM karyotypes were normal. Highest percentage of karyotypic abnormalities was observed in RCMD-MSCs. Patients with abnormal BM karyotypes had no aberrant MSC clones. Results show that in spite of presence of genetically abnormal clones in MDS-MSC populations, in vitro phenotypic and growth characteristics of MSCs in MDS remain unchanged. Further, the occurrence of genetic abnormalities in BM-MSCs in MDS could be considered as an autonomous event from that of their hematopoietic counterparts.

The COVID-19 pandemic, within a short time span, has had a significant impact on every aspect of life in almost every country on the planet. As it evolved from a local epidemic isolated to certain regions of China, to the deadliest pandemic since the influenza outbreak of 1918, scientists all over the world have only amplified their efforts to combat it. In that battle, Artificial Intelligence, or AI, with its wide ranging capabilities and versatility, has played a vital role and thus has had a sizable impact. In this review, we present a comprehensive analysis of the use of AI techniques for spatio-temporal modeling and forecasting and impact modeling on diverse populations as it relates to COVID-19. Furthermore, we catalogue the articles in these areas based on spatio-temporal modeling, intrinsic parameters, extrinsic parameters, dynamic parameters and multivariate inputs (to ascertain the penetration of AI usage in each sub area). The manner in which AI is used and the associated techniques utilized vary for each body of work. Majority of articles use deep learning models, compartment models, stochastic methods and numerous statistical methods. We conclude by listing potential paths of research for which AI based techniques can be used for greater impact in tackling the pandemic.

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