Health Dimensions of COVID-19 in India and Beyond

This open access book addresses the multiple health dimensions posed by the COVID-19 pandemic in India and other countries including nine in Asia, five in Sub-Saharan Africa, and New Zealand. It explores the impact of the pandemic on mental health, sexual and reproductive health and rights, health financing, self-care, and vaccine development and distribution. The contributing authors discuss its impact on vulnerable populations, including interstate migrants and female sex workers. The significant role of media and communications, rapid dissemination of information in social media, and its impact during the COVID-19 pandemic era are discussed. It closes with lessons learned from the experiences of countries that have contained the pandemic. With contributions from experts from around the world, this book presents solutions of problems that relate to COVID-19. It is a valuable resource appealing to a wide readership across the social sciences and the humanities. Readers include governments, academicians, researchers, policy-makers, program implementers, as well as lay persons

Health Dimensions of COVID-19 in India and Beyond

This open access book addresses the multiple health dimensions posed by the COVID-19 pandemic in India and other countries including nine in Asia, five in Sub-Saharan Africa, and New Zealand. It explores the impact of the pandemic on mental health, sexual and reproductive health and rights, health financing, self-care, and vaccine development and distribution. The contributing authors discuss its impact on vulnerable populations, including interstate migrants and female sex workers. The significant role of media and communications, rapid dissemination of information in social media, and its impact during the COVID-19 pandemic era are discussed. It closes with lessons learned from the experiences of countries that have contained the pandemic. With contributions from experts from around the world, this book presents solutions of problems that relate to COVID-19. It is a valuable resource appealing to a wide readership across the social sciences and the humanities. Readers include governments, academicians, researchers, policy-makers, program implementers, as well as lay persons

ABSTRACT BOOK 50th World Conference on Lung Health of the International Union Against Tuberculosis and Lung Disease (The Union)

The International Journal of Tuberculosis and Lung Disease is an official journal of The Union. The Journal’s main aim is the continuing education of physicians and other health personnel, and the dissemination of the most up-to-date infor mation in the field of tuberculosis and lung health. It publishes original articles and commissioned reviews not only on the clinical and biological and epidemiological aspects, but also—and more importantly—on community aspects: fundamental research and the elaboration, implementation and assessment of field projects and action programmes for tuberculosis control and the promo tion of lung health. The Journal welcomes articles submitted on all aspects of lung health, including public health-related issues such as training programmes, cost-benefit analysis, legislation, epidemiology, intervention studies and health systems research

The smart food triple bottom line – starting with diversifying staples

The Smart Food initiative engages in finding foodsystem solutions that, in unison, are good for consumers (nutritious and healthy), the planet (environmentally sustainable) and the producers, especially smallholder famers. This is the Smart Food triple bottom line. A key objective of Smart Food is to diversify staples. By focussing on staples across Africa and Asia, which typically comprise 70 percent of the plate and are often eaten three times a day, we can make a big impact

Development of a nanobody-based amperometric immunocapturing assay for sensitive and specific detection of Toxocara canis excretory-secretory antigen

Introduction Human Toxocariasis (HT) is a zoonosis that, despite of its wide distribution around the world, remains poorly diagnosed. The identification of specific IgG immunoglobulins against the Toxocara canis Excretory-Secretory antigen (TES), a mix of glycoproteins that the parasite releases during its migration to the target organs in infected patients, is currently the only laboratory tool to detect the disease. The main drawbacks of this test are the inability to distinguish past and active infections together with lack of specificity. These factors seriously hamper the diagnosis, follow-up and control of the disease. Aim To develop an amperometric immunocapturing diagnostic assay based on single domain immunoglobulins from camelids (nanobodies) for specific and sensitive detection of TES. Methods After immunization of an alpaca (Vicugna pacos) with TES, RNA from peripheral blood lymphocytes was used as template for cDNA amplification with oligo dT primers and library construction. Isolation and screening of TES-specific nanobodies were carried out by biopanning and the resulting nanobodies were expressed in Escherichia coli. Two-epitopes amperometric immunocapturing assay was designed using paramagnetic beads coated with streptavidin and bivalent nanobodies. Detection of the system was carried out with nanobodies chemically coupled to horseradish peroxidase. The reaction was measured by amperometry and the limit of detection (LOD) was compared to conventional sandwich ELISA. Results We obtained three nanobodies that specifically recognize TES with no-cross reactivity to antigens of Ascaris lumbricoides and A. suum. The LOD of the assay using PBST20 0.05% as diluent was 100 pg/ml, 10 times more sensitive than sandwich ELISA. Conclusion Sensitive and specific detection of TES for discrimination of active and past infections is one of the most difficult challenges of T. canis diagnosis. The main advantage of our system is the use of two different nanobodies that specifically recognize two different epitopes in TES with a highly sensitive and straightforward readout. Considering that the amounts of TES available for detection in clinical samples are in the range of picograms or a few nanograms maximum, the LOD found in our experiments suggests that the test is potentially useful for the detection of clinically relevant cases of HT

ESICM LIVES 2017 : 30th ESICM Annual Congress. September 23-27, 2017.

INTRODUCTION. Unplanned readmission to intensive care is highly undesirable in that it contributes to increased variance in care, disruption, difficulty in resource allocation and may increase length of stay and mortality particularly if subject to delays. Unlike the ICU admission from the ward, readmission prediction has received relatively little attention, perhaps in part because at the point of ICU discharge, full physiological information is systematically available to the clinician and so it is expected that readmission should be largely due to unpredictable factors. However it may be that there are multidimensional trends that are difficult for the clinician to perceive that may nevertheless be predictive of readmission. OBJECTIVES. We investigated whether machine learning (ML) techniques could be used to improve on the simple published SWIFT score [1] for the prediction of unplanned readmission to ICU within 48 hours. METHODS. We extracted systolic BP, pulse pressure, heart and respiration rate, temperature, SpO2, bilirubin, creatinine, INR, lactate, white cell count, platelet count, pH, FiO2, and total Glasgow Coma Score from ICU stays of over 2000 adult patients from our hospital electronic patient record system. We trained our own custom multidimensional / time-sensitive algorithmic ML system to predict failed discharges defined as either readmission or unexpected death within 48 hours of discharge. We used 10-fold cross validation to assess performance. We also assessed the effect of augmenting our system by transfer learning (TL) with 44,000 additional cases from the MIMIC III database. RESULTS. The SWIFT score performed relatively poorly with an AUROC of around 0.6 which our ML system trained on local data was also able to match. However when augmented with an additional dataset by TL, the AUROC for the ML system improved statistically and clinically significantly to over 0.7. CONCLUSIONS. Machine learning is able to improve on predictors based on simple multiple logistic regression. Thus there is likely to be information in the trends and in combinations of variables. A disadvantage with this technique is that ML approaches require large amounts of data for training. However, ML approaches can be improved by TL. Basing prediction models on locally derived data augmented by TL is a potentially novel approach to generating tools that customised to the institution yet can exploit the potential power of ML algorithms. REFERENCES [1] Gajic O, Malinchoc M, Comfere TB, et al. The Stability and Workload Index for Transfer score predicts unplanned intensive care unit patient readmission: initial development and validation. Crit Care Med. 2008;36(3):676–82. Grant Acknowledgement This work was internally funded

XVI Agricultural Science Congress 2023: Transformation of Agri-Food Systems for Achieving Sustainable Development Goals

The XVI Agricultural Science Congress being jointly organized by the National Academy of Agricultural Sciences (NAAS) and the Indian Council of Agricultural Research (ICAR) during 10-13 October 2023, at hotel Le Meridien, Kochi, is a mega event echoing the theme “Transformation of Agri-Food Systems for achieving Sustainable Development Goals”. ICAR-Central Marine Fisheries Research Institute takes great pride in hosting the XVI ASC, which will be the perfect point of convergence of academicians, researchers, students, farmers, fishers, traders, entrepreneurs, and other stakeholders involved in agri-production systems that ensure food and nutritional security for a burgeoning population. With impeding challenges like growing urbanization, increasing unemployment, growing population, increasing food demands, degradation of natural resources through human interference, climate change impacts and natural calamities, the challenges ahead for India to achieve the Sustainable Development Goals (SDGs) set out by the United Nations are many. The XVI ASC will provide an interface for dissemination of useful information across all sectors of stakeholders invested in developing India’s agri-food systems, not only to meet the SDGs, but also to ensure a stable structure on par with agri-food systems around the world. It is an honour to present this Book of Abstracts which is a compilation of a total of 668 abstracts that convey the results of R&D programs being done in India. The abstracts have been categorized under 10 major Themes – 1. Ensuring Food & Nutritional Security: Production, Consumption and Value addition; 2. Climate Action for Sustainable Agri-Food Systems; 3. Frontier Science and emerging Genetic Technologies: Genome, Breeding, Gene Editing; 4. Livestock-based Transformation of Food Systems; 5. Horticulture-based Transformation of Food Systems; 6. Aquaculture & Fisheries-based Transformation of Food Systems; 7. Nature-based Solutions for Sustainable AgriFood Systems; 8. Next Generation Technologies: Digital Agriculture, Precision Farming and AI-based Systems; 9. Policies and Institutions for Transforming Agri-Food Systems; 10. International Partnership for Research, Education and Development. This Book of Abstracts sets the stage for the mega event itself, which will see a flow of knowledge emanating from a zeal to transform and push India’s Agri-Food Systems to perform par excellence and achieve not only the SDGs of the UN but also to rise as a world leader in the sector. I thank and congratulate all the participants who have submitted abstracts for this mega event, and I also applaud the team that has strived hard to publish this Book of Abstracts ahead of the event. I wish all the delegates and participants a very vibrant and memorable time at the XVI ASC