Background: The COVID-19 pandemic has led to the risk of common mental illnesses. Consultation liaison psychiatry has been one of the most requested services in the face of this pandemic. We aimed to assess (a) the prevalence of psychiatric illness, (b) different types of psychiatric diagnoses, (c) presenting complaints, (d) reasons for psychiatric referrals, and (e) psychiatric intervention done on COVID-19 positive inpatients referred to consultation liaison psychiatry at tertiary care hospital. Method: This was a retrospective study of data collected from April 1, 2020, to September 15, 2020. Total 300 patients were referred and diagnosed with clinical interview and Diagnostic and Statistical Manual for Mental Disorder Fifth Edition criteria. Analysis was done using chi-square test, Kruskal–Wallis test, and fisher exact test. Results: Out of 300 patients, 26.7% had no psychiatric illness. Adjustment disorder was the commonest psychiatric diagnosis (43%), followed by delirium (10%). Statistically significant differences were found for parameters like Indian Council of Medical Research Category 4 of the patient, (hospitalized severe acute respiratory infection) (P value < 0.001), medical comorbidity (P value = 0.023), and past history of psychiatric consultation (Fisher exact test statistic value <0.001). Behavioral problem (27.6%) was the commonest reason for psychiatric referral. Worrying thoughts (23.3%) was the most frequent complaint. A total of 192 (64.3%) patients were offered pharmacotherapy. Conclusions: Psychiatric morbidity was quite high (73.3%) among them and adjustment disorder was the commonest (43%) psychiatric diagnosis followed by delirium (10%). Pharmacotherapy was prescribed to 64.3% patients and psychosocial management was offered to most of the referred patients.

Background: Caregivers of children with thalassemia major experience higher caregiver burden and psychiatric morbidity. Aims: The aims of this study were as follows: (1) to assess the caregiver burden and psychiatric morbidity among caregivers of children with thalassemia major and (2) correlation between caregiver burden and psychiatric morbidity. Settings and Design: This was an observational, cross-sectional study carried out at the tertiary care hospital setup among 245 caregivers of children with thalassemia major. Subjects and Methods: Sociodemographic details of children and their caregivers and clinical variables of children were obtained. The Caregiver Burden Scale and General Health Questionnaire (GHQ)-12 were applied. Psychiatric diagnosis was made after clinical interview as per the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition. Statistical Analysis Used: Chi-square test, t-test, analysis of variance, and Pearson coefficient correlation were used for statistical analysis. Results: 33.06% were having severe burden, 30.61% moderate-to-severe burden, 27.34% mild-to-moderate burden, and 8.97% were having little or no burden. 13.46% of caregivers had psychiatric illness, out of which 8.57% had major depressive disorder, 4.08% generalized anxiety disorder, and 0.8% alcohol use disorder. “Weakly positive correlation” (P = 0.0284) was found between GHQ score and burden score. Conclusions: Higher caregiver burden and presence of psychiatric morbidity among caregivers of thalassemia children suggest that caregivers should be screened at regular intervals for early detection and management of psychiatric morbidity. Social and professional networks for psychosocial support and self-help groups should be planned for caregiver burden.

Cardiovascular diseases have a high morbidity rate and per year it leads to 17 million deaths worldwide. Coronary Atherosclerosis is one of the chief causes of stroke and progressive heart disease marked by lipids and fibrous elements accumulation in the arteries. Artificial intelligence (AI) has established remarkable progress in recent times in clinical practice helping patients and healthcare professionals in the accurate and faster diagnosis of diseases. Prediction models in the identification of Atherosclerosis have set foot in the academic literature to assist in making medical decisions during urgent circumstances. This paper aimed to systematically review automatic atherosclerotic plaque detection algorithms. The advantages of these latest techniques in automatic recognition of atherosclerotic plaque, its composition, classification strategy and future predictions in terms of severity are elucidated in this review with limitations and research gaps. The findings suggest that deep learning models are the future of diagnosis and ensemble learning algorithms are best in non-invasive accurate detection of cardiovascular diseases.

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