Study on Relation of Metabolic Syndrome with Menopause

This study is carried out to determine the prevalence of metabolic syndrome (MS) in 148 women between 36 to 65 years using the International Diabetes Federation criteria in the North-Eastern part of India. The prevalence of MS and all its individual components were found to be significantly higher among postmenopausal as compared to premenopausal and perimenopausal women. Various components of MS except waist circumference shows a significant increase and homeostasis model assessment index for insulin resistance also showed significant differences between the three groups. All the MS diagnostic markers (except serum high density lipoprotein) showed a strong positive correlation with MS score among the groups. Further MS score correlated with indicators of insulin resistance evaluated. This study concluded that MS is highly prevalent among North-East Indian postmenopausal women seeking primary health care and its determinant factors related to age and sedentary habits. Thus recognizing and treating MS early with proper intervention can minimize complication.     

A low-cost 3D printed microfluidic bioreactor and imaging chamber for live-organoid imaging

Organoids are biological systems grown in vitro and are observed to self-organize into 3D cellular tissues of specific organs. Brain organoids have emerged as valuable models for the study of human brain development in health and disease. Researchers are now in need of improved culturing and imaging tools to capture the in vitro dynamics of development processes in the brain. Here, we describe the design of a microfluidic chip and bioreactor, to enable in situ tracking and imaging of brain organoids on-chip. The low-cost 3D printed microfluidic bioreactor supports organoid growth and provides an optimal imaging chamber for live-organoid imaging, with drug delivery support. This fully isolated design of a live-cell imaging and culturing platform enables long-term live-imaging of the intact live brain organoids as it grows. We can thus analyze their self-organization in a controlled environment with high temporal and spatial resolution.