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Oil content in sorghum [Sorghum bicolor (L.) Moench] grains is an important component in determining sorghum as a functional food. Sorghum has unique advantage in terms of its high tolerance to drought and high temperatures. With its ability to yield high with low inputs, it can be used as an alternative oil source with clinical advantages. Breeding efforts towards identifying end use of specific cultivars are required for increased profitability to the farmers. In the present study, oil content was estimated in 19 genotypes over three locations (Gulberga, Solapur and Hyderabad) which were sown in randomized block design with three replications during post rainy season of 2016-17. Oil content ranged from 3.14–4.76% with the highest value in IS 30466. Germplasm lines, IS 31681, IS 1212, IS 30536, IS 30507, IS 603 and IS 30466 were found better adapted to all test environments with stable mean oil content more than population mean. Significant effects of both genotype and environment were observed for oil content, while genotype × environment interaction was not significant indicating that the genotypes do not show differential response to the environment. Oil content had significant correlation with germ size (r = 0.484), while the associations with 100-seed weight and grain hardness were non-significant. The information generated and the genotypes identified will help in breeding sorghum with high oil content thus enhancing the demand for sorghum as an industrial crop.

Abstract Background Kerala, the southern state of India, has experienced sudden rise in the prevalence estimates of diabetes. A cohort study on the incidence of type 2 diabetes mellitus (T2DM) in Kerala state thus aptly bridges the lacuna of incidence estimate of T2DM from a population at risk. Methods A 10-year prospective cohort study was carried out in two urban wards of central Kerala. The individuals who participated in the baseline survey in 2007 were again invited for a follow-up study in 2017. The data was analyzed using IBM SPSS Statistics for windows (version 21.0). Logistic regression analysis was used to estimate odds ratios and 95% confidence intervals. Findings are based on the 10-year follow-up data from 869 participants from the cohort. Results The overall follow-up and response rate of the study was 68.9 and 86.9% respectively. During the follow-up period, 190 people (21.9%) developed T2DM. The incidence rate of T2DM and impaired fasting glucose (IFG) were 24.5 per 1000 person years and 45.01 per 1000 person years respectively. Nearly 60% of participants with baseline IFG were converted to T2DM group in the follow-up period. Age > 45 years, family history of T2DM, BMI ≥ 25 kg/m2 and presence of central obesity emerged as important risk factors for incident T2DM. Conclusion High incidence of prediabetes over diabetes observed in this study shows an epidemic trend of T2DM in Kerala, India. It requires an immediate public health action.

The well-being of any country depends on the health of its citizens. As India is the most populated country and an emerging economy, a solution for India might also be a model for the rest of the world. Over the last eight years, India has taken significant steps to incorporate Digital Health (DH) at the core of its healthcare delivery system. Challenges, importance, fallout, specific applications, and growth of DH in the Indian setting are discussed in this report. [ABSTRACT FROM AUTHOR]

Byline: Dayananda. Shamurailatpam, A. Manikandan, K. Ganapathy, M. Noufal, Kartikeshwar. Patro, T. Rajesh, R. Jalali Purpose: The purpose of this study is to evaluate the performance characteristic of volumetric image-guided [...]

Abstract This observational study investigated the potential of radiomics as a non-invasive adjunct to CT in distinguishing COVID-19 lung nodules from other benign and malignant lung nodules. Lesion segmentation, feature extraction, and machine learning algorithms, including decision tree, support vector machine, random forest, feed-forward neural network, and discriminant analysis, were employed in the radiomics workflow. Key features such as Idmn, skewness, and long-run low grey level emphasis were identified as crucial in differentiation. The model demonstrated an accuracy of 83% in distinguishing COVID-19 from other benign nodules and 88% from malignant nodules. This study concludes that radiomics, through machine learning, serves as a valuable tool for non-invasive discrimination between COVID-19 and other benign and malignant lung nodules. The findings suggest the potential complementary role of radiomics in patients with COVID-19 pneumonia exhibiting lung nodules and suspicion of concurrent lung pathologies. The clinical relevance lies in the utilization of radiomics analysis for feature extraction and classification, contributing to the enhanced differentiation of lung nodules, particularly in the context of COVID-19.

Abstract Effective training of deep image segmentation models is challenging due to the need for abundant, high-quality annotations. To facilitate image annotation, we introduce Physics Informed Contour Selection (PICS)—an interpretable, physics-informed algorithm for rapid image segmentation without relying on labeled data. PICS draws inspiration from physics-informed neural networks (PINNs) and an active contour model called snake. It is fast and computationally lightweight because it employs cubic splines instead of a deep neural network as a basis function. Its training parameters are physically interpretable because they directly represent control knots of the segmentation curve. Traditional snakes involve minimization of the edge-based loss functionals by deriving the Euler–Lagrange equation followed by its numerical solution. However, PICS directly minimizes the loss functional, bypassing the Euler Lagrange equations. It is the first snake variant to minimize a region-based loss function instead of traditional edge-based loss functions. PICS uniquely models the three-dimensional (3D) segmentation process with an unsteady partial differential equation (PDE), which allows accelerated segmentation via transfer learning. To demonstrate its effectiveness, we apply PICS for 3D segmentation of the left ventricle on a publicly available cardiac dataset. We also demonstrate PICS’s capacity to encode the prior shape information as a loss term by proposing a new convexity-preserving loss term for left ventricle. Overall, PICS presents several novelties in network architecture, transfer learning, and physics-inspired losses for image segmentation, thereby showing promising outcomes and potential for further refinement.

Pediatric neuromuscular diseases are a genetically and clinically heterogenous group of disorders of which 30-60% remain molecularly undiagnosed even after evaluation with exome or genome sequencing. Estimates for added diagnostic benefit of transcriptomic sequencing range between 7-32% across different disease groups. Recently, open-source computational tools designed to systematically survey transcriptomic sequencing data for aberrant events have become available. In this study we assess the sensitivity of DROP (OUTRIDER, FRASER and MAE), MINTIE, LeafcutterMD, and ANEVA-H to detect known outliers in pediatric neuromuscular cohorts in order to identify the optimal set of tools for diagnostic use. Each tool selected utilizes a different statistical strategy and experimental design geared for rare disease. Tools were run on three transcriptome cohorts from the NINDS Neuromuscular and Neurogenetic Diseases of Childhood Section: muscle tissue (n=71), cycloheximide treated fibroblasts (n=79) and untreated fibroblasts (n=37). For splicing tools, MINTIE is the best performing tool (recall rate = 9/21, 42%), followed by FRASER (recall rate = 16/41, 36%), then LeafcutterMD (recall rate = 0/41, 0%). For expression outliers, OUTRIDER detects 6% of true positives (1/15), while the addition of healthy GTEx controls does not improve detection. For monoallelic expression, MAE and ANEVA-H will be compared. Additional evaluations to identify the optimal RNA library preparation methodology for these tools is also in progress. Thus far, OUTRIDER yielded one new candidate diagnosis of TRIP4 related disease. Analysis for new candidate variants is ongoing. Based on these results, and those of ongoing experiments, we will provide recommendations for the use of these tools in diagnostic pipelines for rare pediatric neuromuscular disease. [ABSTRACT FROM AUTHOR]

Byline: K. Ganapathy Until six decades ago, death was considered to be a specific point in time, referring to the moment at which life ends. With the availability of ventilators, [...]

Background: Based on the theme of World Tuberculosis (TB) Day 2018—“Wanted Leaders for TB free world,” various studies and country-level project have witnessed how training leaders on TB has helped enhance TB awareness in the community. This study was conducted to evaluate the effectiveness of Advocacy Communication and Social Mobilization (ACSM) intervention strategy through key community leaders, using Reach, Effectiveness, Adoption, Implementation, Maintenance (RE-AIM) framework. Materials and Methods: This study was undertaken by the Department of Community Medicine, Sri Manakula Vinayagar Medical College and Hospital (SMVMCH), Puducherry, in the field practice area of Primary Health Centre (PHC), Thirubuvanai. The STOP TB, ENGAGE-TB, and National TB Elimination Program (NTEP) guidelines were used for interventions. Key community leaders (KCL) list (N = 117) was obtained from Mannadipet commune panchayat and PHC. The study was conducted in four phases: phase 1 (baseline): quantitative data: proportion of presumptive TB cases from notification register; phase 2 (intervention): training program for staff nurses, auxiliary nurse midwife (ANM), Accredited Social Health Activist (ASHA), and Anganwadi workers (group activity), participatory rural appraisal (PRA) activity for self-help group (SHG) leaders, and health education session for religious leaders and ex-panchayat leaders; phase 3: strengthening of ongoing activity: health education session and active case finding for TB; and phase 4: end line: outcome of the intervention, evaluated using the RE-AIM framework. Results: As an outcome of the intervention, there was a marginal increase (21 cases) in the proportion of presumptive TB cases when comparing before (2018) and after (2019) intervention. The proportion of presumptive TB cases identified and referred by KCL was 312. A total of 77 presumptive TB cases were identified through community-based active case finding (ACF). Two sputum samples (on the spot early morning) were collected and sent for Cartridge-based nucleic acid amplification testing (CBNAAT), of which three cases were positive. Conclusion: This study was community led and involved leaders from diverse backgrounds, optimizing the chance of success. For sustainability, we have initiated TB support group.