To Assess Non Alcoholic Fatty Liver Disease in Patients with Clinical and Subclinical Hypothyroidism.

INTRODUCTION: NAFLD is emerging as one of the leading causes of end stage liver disease worldwide. Metabolic syndrome is one of the central mechanism in the development of NAFLD. Hypothyroidism is one of the conditions associated with metabolic syndrome. In this study we assess the NAFLD induced by hypothyroidism. MATERIALS: This study was conducted in tertiary care hospitals attached to BMCRI. 44 patients including both clinical and subclinical hypothyroidism, who were non-diabetic and had no significant alcohol intake, were included in the study. The level of NAFLD was assessed by ultrasound and fibroscan and the same was compared between clinical and subclinical hypothyroid patients. RESULT: On comparing the Ultrasonography grading of hepatic steatosis between the clinical and subclinical hypothyroidism group it was found that a significantly higher grade (p < 0.05) of hepatic steatosis was seen in the clinical hypothyroidism group i.e 11 out of 29 subjects had grade III hepatic steatosis (37.9%), Out of the 29 subjects in the clinical hypothyroidism group, F2 F3 and F4 fibrosis grade was seen in 5,5 and 6 individuals respectively. While in the subclinical group 14 out of the 15 individuals had F0 to F1 grade of fibrosis. There was a significantly higher (p < 0.0.5) fibrosis score in Clinical hypothyroidism group when compared to subclinical group. CONCLUSION: In our study we observed that Clinical and Subclinical Hypothyroidism are independent risk factors for the development of NAFLD. There was significantly higher level of NAFLD in Clinical Hypothyroidism patients when compared with Subclinical Hypothyroidism subjects. References Younossi Z, Anstee QM, Marietti M, et al. Global burden of NAFLD and NASH: trends, predictions, risk factors and prevention. Nat Rev Gastroenterol Hepatol 2018;15(1):11-20. Kalra S, Vithalani M, Gulati G, et al. Study of prevalence of nonalcoholic fatty liver disease (NAFLD) in type 2 diabetes patients in India (SPRINT). J Assoc Physicians India 2013;61(7):448-53. Taylor PN, Albrecht D, Scholz A, et al. Global epidemiology of hyperthyroidism and hypothyroidism. Nat Rev Endocrinol 2018;14(5):301-316. Kim YA, Park Y. Prevalence and risk factors of subclinical thyroid disease. Endocrinol Metab (Seoul) 2014;29(1):20-29.

Impact of COVID-19 on heart failure hospitalization and outcome in India - A cardiological society of India study (CSI-HF in COVID 19 times study - "The COVID C-HF study").

OBJECTIVES: The presentation and outcomes of acute decompensated heart failure (ADHF) during COVID times (June 2020 to Dec 2020) were compared with the historical control during the same period in 2019. METHODS: Data of 4806 consecutive patients of acute HF admitted in 22 centres in the country were collected during this period. The admission patterns, aetiology, outcomes, prescription of guideline-directed medical therapy (GDMT) and interventions were analysed in this retrospective study. RESULTS: Admissions for acute heart failure during the pandemic period in 2020 decreased by 20% compared to the corresponding six-month period in 2019, with numbers dropping from 2675 to 2131. However, no difference in the epidemiology was seen. The mean age of presentation in 2019 was 61.75 (±13.7) years, and 59.97 (±14.6) years in 2020. There was a significant decrease in the mean age of presentation (p = 0.001). Also. the proportion of male patients decreased significantly from 68.67% to 65.84% (p = 0.037). The in-hospital mortality for acute heart failure did not differ significantly between 2019 and 2020 (4.19% and 4.,97%) respectively (p = 0.19). The proportion of patients with HFrEF did not change in 2020 compared to 2019 (76.82% vs 75.74%, respectively). The average duration of hospital stay was 6.5 days. CONCLUSION: The outcomes of ADHF patients admitted during the Covid pandemic did not differ significantly. The length of hospital stay remained the same. The study highlighted the sub-optimal use of GDMT, though slightly improving over the last few years.

Automated detection of muscle fatigue conditions from cyclostationary based geometric features of surface electromyography signals.

In this study, an attempt has been made to develop an automated muscle fatigue detection system using cyclostationary based geometric features of surface electromyography (sEMG) signals. For this purpose, signals are acquired from fifty-eight healthy volunteers under dynamic muscle fatiguing contractions. The sEMG signals are preprocessed and the epochs of signals under nonfatigue and fatigue conditions are considered for the analysis. A computationally effective Fast Fourier transform based accumulation algorithm is adapted to compute the spectral correlation density coefficients. The boundary of spectral density coefficients in the complex plane is obtained using alpha shape method. The geometric features, namely, perimeter, area, circularity, bending energy, eccentricity and inertia are extracted from the shape and the machine learning models based on multilayer perceptron (MLP) and extreme learning machine (ELM) are developed using these biomarkers. The results show that the cyclostationarity increases in fatigue condition. All the extracted features are found to have significant difference in the two conditions. It is found that the ELM model based on prominent features classifies the sEMG signals with a maximum accuracy of 94.09% and F-score of 93.75%. Therefore, the proposed approach appears to be useful for analysing the fatiguing contractions in neuromuscular conditions.

Analysis of Isometric Muscle Contractions using Analytic Bump Continuous Wavelet Transform.

In this study, an attempt has been made to distinguish between nonfatigue and fatigue conditions in surface Electromyography (sEMG) signal using the time frequency distribution obtained from analytic Bump Continuous Wavelet Transform. For the analysis, sEMG signals from biceps brachii muscle of 22 healthy subjects are acquired during isometric contraction protocol. The signals acquired is preprocessed and partitioned into ten equal segments followed by the decomposition of selected segments using analytic Bump wavelets. Further, Singular Value Decomposition is applied to the time frequency distribution matrix and the maximum singular value and entropy feature for each segment are obtained. The usefulness of both the features is estimated using the Wilcoxon sign rank test that gives higher significance with a p < .00001. It is observed that the proposed method is capable of analyzing the fatigue regions in sEMG signals.