Manipal University

About: Manipal University is a education organization based out in Manipal, Karnataka, India. It is known for research contribution in the topics: Population & Health care. The organization has 9525 authors who have published 11207 publications receiving 110687 citations.

Effectiveness of an inactivated virus-based SARS-CoV-2 vaccine, BBV152, in India: a test-negative, case-control study

Abstract: BBV152 is a whole-virion inactivated SARS-CoV-2 vaccine that has been deployed in India. The results of the phase 3 trial have shown clinical efficacy of BBV152. We aimed to evaluate the effectiveness of BBV152 against symptomatic RT-PCR-confirmed SARS-CoV-2 infection.We conducted a test-negative, case-control study among employees of the All India Institute of Medical Sciences (a tertiary care hospital in New Delhi, India), who had symptoms suggestive of COVID-19 and had an RT-PCR test for SARS-CoV-2 during the peak of the second wave of the COVID-19 pandemic in India between April 15 and May 15, 2021. Cases (test-positives) and controls (test-negatives) were matched (1:1) on the basis of age and gender. The odds of vaccination with BBV152 were compared between cases and controls and adjusted for level of occupational exposure (to COVID-19), previous SARS-CoV-2 infection, and calendar time, using conditional logistic regression. The primary outcome was effectiveness of two doses of BBV152 (with the second dose received at least 14 days before testing) in reducing the odds of symptomatic RT-PCR-confirmed SARS-CoV-2 infection, expressed as (1 - odds ratio) × 100%.Between April 15 and May 15, 2021, 3732 individuals had an RT-PCR test. Of these, 2714 symptomatic employees had data on vaccination status, and 1068 matched case-control pairs were available for analysis. The adjusted effectiveness of BBV152 against symptomatic COVID-19 after two doses administered at least 14 days before testing was 50% (95% CI 33-62; p<0·0001). The adjusted effectiveness of two doses administered at least 28 days before testing was 46% (95% CI 22-62) and administered at least 42 days before testing was 57% (21-76). After excluding participants with previous SARS-CoV-2 infections, the adjusted effectiveness of two doses administered at least 14 days before testing was 47% (95% CI 29-61).This study shows the effectiveness of two doses of BBV152 against symptomatic COVID-19 in the context of a huge surge in cases, presumably dominated by the potentially immune-evasive delta (B.1.617.2) variant of SARS-CoV-2. Our findings support the ongoing roll-out of this vaccine to help control the spread of SARS-CoV-2, while continuing the emphasis on adherence to non-pharmacological measures.None.For the Hindi translation of the abstract see Supplementary Materials section.

Propensity of human embryonic stem cell lines during early stage of lineage specification controls their terminal differentiation into mature cell types.

Abstract: Human embryonic stem cells (hESCs) are able to stably maintain their characteristics for an unlimited period; nevertheless, substantial differences among cell lines in gene and protein expression not manifested during the undifferentiated state may appear when cells differentiate. It is widely accepted that developing an efficient protocol to control the differentiation of hESCs will enable us to produce adequate numbers of desired cell types with relative ease for diverse applications ranging from basic research to cell therapy and drug screening. Hence of late, there has been considerable interest in understanding whether and how hESC lines are equivalent or different to each other in their in vitro developmental tendencies. In this study, we compared the developmental competences of two hESC lines (HUES-9 and HUES-7) at molecular, cellular and functional levels, upon spontaneous differentiation without any added inducing agents. Both cell lines generated the three embryonic germ layers, extra-embryonic tissues and primordial germ cells during embryoid body (EB) formation. However HUES-9 showed a stronger propensity towards formation of neuroectodermal lineages, whereas HUES-7 differentiated preferentially into mesoderm and endoderm. Upon further differentiation, HUES-9 generated largely neural cells (neurons, oligodendrocytes, astrocytes and gangliosides) whereas HUES-7 formed mesendodermal derivatives, including cardiomyocytes, skeletal myocytes, endothelial cells, hepatocytes and pancreatic cells. Overall, our findings endorse the hypothesis that independently-derived hESCs biologically differ among themselves, thereby displaying varying differentiation propensity. These subtle differences not only highlight the importance of screening and deriving lines for lineage-specific differentiation but also indicate that individual lines may possess a repertoire of capabilities that is unique.

Detection of acute lymphoblastic leukemia using image segmentation and data mining algorithms

Abstract: Blood is composed of white blood cells, red blood cells, and platelets. Segmentation of the blood smear cells and extraction of features of the cells is essential in the field of medicine. Acute lymphoblastic leukemia is a form of blood cancer caused due to the abnormal increase in the production of immature white blood cells in the bone marrow. It mostly affects the children below 5 years and adults above 50 years of age. Due to the late diagnosis and cost of the devices used for the determination, the mortality rate has increased drastically. Flow cytometry technique that performs automated counting fails to identify the abnormal cells. Manual recount performed using hemocytometer are prone to errors and are imprecise. The proposed work aims to survey different computer-aided system techniques used to segment the blood smear image. The primary objective here is to derive knowledge from the different methodologies used for extracting features from white blood cells and develop a system that would accurately segment the blood smear image by overcoming the drawbacks of the previous works. The objective mentioned above is achieved in two ways. Firstly, a novel algorithm is developed to segment the nucleus and cytoplasm of white blood cell. Secondly, a model is built to extract the features and train the model. The different supervised classifiers are compared, and the one with the highest accuracy is used for the classification. Six hundred images are used in the experimentation. InfoGainAttributeEval and the Ranker Search method are used to achieve the feature selection which in turn helps in improvising the classifier performance. The result shows the classification of the acute lymphoblastic leukemia into its three respective categories namely: ALL-L1, ALL-L2, ALL-L3. The model can differentiate between a normal peripheral blood smear and an abnormal blood smear. The extracted feature values of a cancerous cell and a normal cell are also shown. The performance of the model is evaluated using the test images stained with various stains. The proposed algorithm achieved an overall accuracy of 98.6%. The promising results show that it can be used as a diagnostic tool by the pathologists.

Putting the pieces together: integration for forest landscape restoration implementation

Abstract: The concept of forest landscape restoration (FLR) is being widely adopted around the globe by governmental, non‐governmental agencies, and the private sector, all of whom see FLR as an approach that contributes to multiple global sustainability goals. Originally, FLR was designed with a clearly integrative dimension across sectors, stakeholders, space and time, and in particular across the natural and social sciences. Yet, in practice, this integration remains a challenge in many FLR efforts. Reflecting this lack of integration are the continued narrow sectoral and disciplinary approaches taken by forest restoration projects, often leading to marginalisation of the most vulnerable populations, including through land dispossessions. This article aims to assess what lessons can be learned from other associated fields of practice for FLR implementation. To do this, 35 scientists came together to review the key literature on these concepts to suggest relevant lessons and guidance for FLR. We explored the following large‐scale land use frameworks or approaches: land sparing/land sharing, the landscape approach, agroecology, and socio‐ecological systems. Also, to explore enabling conditions to promote integrated decision making, we reviewed the literature on understanding stakeholders and their motivations, tenure and property rights, polycentric governance, and integration of traditional and Western knowledge. We propose lessons and guidance for practitioners and policymakers on ways to improve integration in FLR planning and implementation. Our findings highlight the need for a change in decision‐making processes for FLR, better understanding of stakeholder motivations and objectives for FLR, and balancing planning with flexibility to enhance social–ecological resilience.