Highlights The COVID-19 outbreak has had a significant impact on caregiver strain compared to perceived strain before the pandemic. Prevalence of depressive symptoms is high among caregivers of children with special needs. Negative perception of homecare therapy is associated with higher perceived strain and poor mental health. Not using tele-rehabilitation and perception of it being a poor medium for rehabilitation pose greater mental health risks.

This study suggests an association between HLA-B*1502 and carbamazepine-induced SJS in Indian patients.

The fresh and dried rhizome of Zingiber officinale Roscoe (commonly known as ginger) is widely used in traditional medicine. We have studied the effect of the juice of Z. officinale (4 mL kg(-1), p.o. daily) for 6 weeks on streptozotocin (STZ)-induced type I diabetic rats with particular reference to the involvement of serotonin (5-hydroxytryptamine; 5-HT) receptors in glycaemic control. In normoglycaemic rats, 5-HT (1mg kg(-1), i.p.) produced hyperglycaemia and hypoinsulinaemia, which was significantly prevented by the juice of Z. officinale. STZ-diabetes produced a significant increase in fasting glucose levels that was associated with a significant decrease in serum insulin levels. Treatment with Z. officinale produced a significant increase in insulin levels and a decrease in fasting glucose levels in diabetic rats. In an oral glucose tolerance test, treatment with Z. officinale was found to decrease significantly the area under the curve of glucose and to increase the area under the curve of insulin in STZ-diabetic rats. Treatment with Z. officinale also caused a decrease in serum cholesterol, serum triglyceride and blood pressure in diabetic rats. Our data suggest a potential antidiabetic activity of the juice of Z. officinale in type I diabetic rats, possibly involving 5-HT receptors.

Computer technology has been advanced tremendously and the interest has been increased for the potential use of 'Artificial Intelligence (AI)' in medicine and biological research. One of the most interesting and extensively studied branches of AI is the 'Artificial Neural Networks (ANNs)'. Basically, ANNs are the mathematical algorithms, generated by computers. ANNs learn from standard data and capture the knowledge contained in the data. Trained ANNs approach the functionality of small biological neural cluster in a very fundamental manner. They are the digitized model of biological brain and can detect complex nonlinear relationships between dependent as well as independent variables in a data where human brain may fail to detect. Nowadays, ANNs are widely used for medical applications in various disciplines of medicine especially in cardiology. ANNs have been extensively applied in diagnosis, electronic signal analysis, medical image analysis and radiology. ANNs have been used by many authors for modeling in medicine and clinical research. Applications of ANNs are increasing in pharmacoepidemiology and medical data mining. In this paper, authors have summarized various applications of ANNs in medical science.

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