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Background: Dairying in India has witnessed a radical transformation from a largely unorganised activity into a thriving organised industry. However, there are only a limited number of earlier attempts that specifically evaluated the milk production pattern of India. Moreover, most of these earlier studies are quite dated and have employed autoregressive integrated moving average (ARIMA) models, which suffer from the problem of local optima. To overcome this lacuna, we have utilised a genetic algorithm (GA) in the ARIMA framework. Its suitability to forecast the annual milk production in India has also been assessed comparatively with respect to the traditional ARIMA methodology. Methods: For the current study, data on annual milk production (in million tonnes) in India for the period from 1980 to 2019 have been utilised. For both the approaches under investigation, the whole data series is first divided into two sets, namely the training set and the testing set. The production data of 1980-2016 have been utilised for the model building purpose while retaining the last 3 years' data for the post-sample evaluation. Result: Outcomes emanated from the post-sample assessment clearly suggest that the ARIMA-GA approach has outperformed the traditional ARIMA methodology in terms of root mean square error (RMSE) and mean absolute percentage error (MAPE) values. It is also evident that GA has substantially minimised the error related to the parameter estimation. [ABSTRACT FROM AUTHOR]

Background: Cardiovascular diseases (CVD) such as ischemic heart disease (IHD) and cerebrovascular accidents such as stroke lead to 17.7 million deaths and are the leading cause of death worldwide. Global Burden of Disease study found that age-standardized CVD death rate in India (272/100,000 population) is much higher than the global average (235/100,000 population). Aims and Objectives: This study aimed to compare among different coronary risk factors like dyslipidemia, diabetes mellitus hypertension (HTN), and obesity which may lead to coronary artery disease (CAD) and early detection of ischemic heart disease by the stress treadmill test (TMT) among the patients having coronary risk factors. Materials and Methods: This is a prospective and observational study among 170 patients of age group 18-60 years having one or more coronary risk factors such as dyslipidemia, HTN, diabetes mellitus, and obesity. Patients with diagnosed IHD and other chronic debilitating diseases were excluded from the study. Results: Out of 170 patients, 153 patients were males and 17 patients were females. Among all these patients, 42 patients were suffering from dyslipidemia, 44 patients were suffering from Type 2 diabetes, 45 patients were suffering from HTN, and 39 patients were having more than one risk factor. All 170 patients with coronary risk factors performed TMT. Out of those patients, 53 were found to be TMTpositive. Binary regression analysis showed among all the selected risk factors, dyslipidemia (P<0.05; odds ratio [OR]-2.104) and HTN (P<0.05; OR-2.159) were found to be significantly associated with the positive outcome of TMT. All 53 TMT-positive patients were selected for CAG. Out of those patients, 37 were found to be suffering from CAD. Binary regression analysis showed among all the selected risk factors, dyslipidemia (P<0.05; OR-1.964) and HTN (P<0.05; OR-2.125) were found to be significantly associated with the positive outcome of CAG. Conclusion: Dyslipidemia and HTN were found to be significantly associated with the TMT-positivity as well as CAD. Diabetes mellitus and obesity were not statistically significant, though OR was more than 1 for both these risk factors. [ABSTRACT FROM AUTHOR]

For the past several decades, dengue fever has been emerging in epidemic proportions in several regions of the world. During August–September 2019, an increasing number of fever cases were being reported from some areas of North 24 Parganas district of West Bengal, India. Accordingly, outbreak investigation of fever cases from these affected areas of Bongoan, Barasat, and Habra was carried out. To characterize clinical and biochemical features of fever cases as well as to investigate the utility of CRP as a Dengue severity marker in resource‐limited settings. We systematically enrolled 108 patients from the affected region of North 24 Parganas. Standard diagnostic assays along with routine serological and biochemical parameters were performed. Of the 108 patients, 77 (71%) were confirmed with Dengue infection followed by 22 (20%) DENV seronegative and 9 (8%) coinfected DENV cases. Among the 77 confirmed Dengue patients, 53 (69%) had primary infection while 24 (31%) had secondary infection. Among the DENV clinical symptoms, fever (r = 0.50; p = 0.004), headache (r = 0.40; p = 0.03) and abdominal pain (r = −0.40; p = 0.02) were found to bear significant correlation with DENV viral load. The predominant circulating serotype was found to be DENV2. CRP Dengue severity cut‐off level of 10.15 mg/L (AUC: 0.85; 86% sensitivity, 77% specificity) was obtained. CRP had correlation with viral load (r = 0.4, p = 0.05) within febrile phase of infection. The performance of biomarkers can be influenced by local epidemiology, geography, and several patient factors, therefore, CRP Dengue severity cut‐off value may be region‐specific. This study for the first time attempts to estimate CRP Dengue severity cut‐off value based on routine immunoturbidometric evaluation from Dengue Hyperendemic zones of North 24 Parganas, West Bengal, Eastern India. [ABSTRACT FROM AUTHOR]

Background: Pediatric brain tumours are the most common solid tumours in children and comprise approximately 20-25% of all pediatric malignancies worldwide. Aims: There is no data about the epidemiological profile of primary brain tumours in pediatric age group from the North-East India. The main aim of the study was to assess the epidemiological profile of pediatric brain tumours in children of the North-Eastern region of India and their patterns of care. Materials and Methods: In this analysis, data regarding age, sex, community, site of the tumour, clinical features, histology, geographical distribution and treatment delivered were collected from hospital cancer registry. Results: A total of 115 cases of pediatric primary brain tumours from age 0-18 years from 2012-2017 were identified and included in this study. According to this study, males (61.7%) slightly outnumbered females (38.3%) with a male: female ratio of 1.61:1 and incidence was most common in the 6-12 years age group. When divided according to anatomical site, primary brain tumours were found to be most common in cerebellum (44.35%) followed by cerebrum (36.26%). Infratentorial tumours (53%) were more than supratentorial tumours (47%). Histologically, the most common variety was found to be Medulloblastoma (34.8%) followed by Astocytomas (26.95%). Patients were mostly seen to belong to Middle Assam region (38.2%). Among the communities, Hindus (61.74%) were most commonly found to have primary pediatric brain tumours. Around 96% of the patients were offered to take radiotherapy out of which only 67% took radiotherapy, 2.6% defaulted treatment(drop outs) and 27% didn't turn up for treatment. 4.35% of the patients took other modalities of treatment. Conclusion: Multimodality management including surgery, radiotherapy and chemotherapy remains the cornerstone in the management of pediatric brain tumours. [ABSTRACT FROM AUTHOR]

This chapter examines the long-run and short-run spillover effects of US quantitative easing (QE) on the money market in India. The study adopts the autoregressive distributed lag bounds testing co-integration approach for monthly data from September 2008 to May 2019 to investigate the spillover impact. The results reveal that a 10% rise in US QE led to a 25 bps softening of the weighted average call rate and a 2-5 bps hardening of the Treasury Bill. This impact was beyond the active participation by the Reserve Bank of India on the policy rate and liquidity in the system during the QE episodes. It suggests that the Indian money market is susceptible to US QE. [ABSTRACT FROM AUTHOR]