Electrocatalytic Performance of 2D Monolayer WSeTe Janus Transition Metal Dichalcogenide for Highly Efficient H2 Evolution Reaction

Now-a-days, the development of clean and green energy sources is the prior interest of research due to increasing global energy demand and extensive usage of fossil fuels that create pollutants. Hydrogen has the highest energy density by weight among all chemical fuels. For the commercial-scale production of hydrogen, water electrolysis is the best method which in turn requires an efficient, cost-effective and earth-abundant electrocatalyst. Recent studies have shown that the 2D Janus TMDs are highly effective in the electrocatalytic activity for HER. Herein we report a 2D monolayer WSeTe Janus TMD electrocatalyst for HER. We studied the electronic properties of 2D monolayer WSeTe Janus TMD using periodic DFT calculations, and the direct electronic band gap was obtained to be 2.39 eV. After the calculations of electronic properties, we explored the HER intermediates including various transition state structures (Volmer TS, Heyrovsky TS, and Tafel TS) using a molecular cluster model of WSeTe noted as W10Se9Te12. The present calculations revealed that the 2D monolayer WSeTe Janus TMD is a potential electrocatalyst for HER. It has the lowest energy barriers for all the TSs among other TMDs, such as MoS2, Mn-MoS2, MoSSe, etc. The calculated Heyrovsky energy barrier (= 8.72 kcal.mol-1) for the Volmer-Heyrovsky mechanism is larger than the Tafel energy barrier (=3.27 kcal.mol-1) in the Volmer-Tafel mechanism. Hence our present study suggests that the formation of H2 is energetically more favorable via the Vomer-Tafel mechanism. This work helps shed light on the rational design of effective HER catalysts.Comment: 39 page

Socioeconomic inequalities in stillbirth and neonatal mortality rates: evidence on Particularly Vulnerable Tribal Groups in eastern India

BACKGROUND: Tribal peoples are among the most marginalised groups worldwide. Evidence on birth outcomes in these groups is scant. We describe inequalities in Stillbirth Rate (SBR), Neonatal Mortality Rate (NMR), and uptake of maternal and newborn health services between tribal and less disadvantaged groups in eastern India, and examine the contribution of poverty and education to these inequalities. METHODS: We used data from a demographic surveillance system covering a 1 million population in Jharkhand State (March 2017 - August 2019) to describe SBR, NMR, and service uptake. We used logistic regression analysis combined with Stata's adjrr-command to estimate absolute and relative inequalities by caste/tribe (comparing Particularly Vulnerable Tribal Groups (PVTG) and other Scheduled Tribes (ST) with the less marginalised Other Backward Class (OBC)/none, using the Indian government classification), and by maternal education and household wealth. RESULTS: PVTGs had a higher NMR (59/1000) than OBC/none (31/1000) (rate ratio (RR): 1.92, 95%CI: 1.55-2.38). This was partly explained by wealth and education, but inequalities remained large after adjustment (adjusted RR: 1.59, 95%CI: 1.28-1.98). NMR was also higher among other STs (44/1000), but disparities were smaller (RR: 1.47, 95%CI: 1.23-1.75). There was a systematic gradient in NMR by maternal education and household wealth. SBRs were only higher in poorer groups (RRpoorest vs. least poor:1.56, 95%CI: 1.14-2.13). Uptake of facility-based services was low among PVTGs (e.g. institutional birth: 25% vs. 69% in OBC/none) and among poorer and less educated women. However, 65% of PVTG women with an institutional birth used a maternity vehicle vs. 34% among OBC/none. Visits from frontline workers (Accredited Social Health Activists [ASHAs]) were similar across groups, and ASHA accompaniment of institutional births was similar across caste/tribe groups, and higher among poorer and less educated women. Attendance in participatory women's groups was similar across caste/tribe groups, and somewhat higher among richer and better educated women. CONCLUSIONS: PVTGs are highly disadvantaged in terms of birth outcomes. Targeted interventions that reduce geographical barriers to facility-based care and address root causes of high poverty and low education in PVTGs are a priority. For population-level impact, they are to be combined with broader policies to reduce socio-economic mortality inequalities. Community-based interventions reach disadvantaged groups and have potential to reduce the mortality gap

Global lockdown: An effective safeguard in responding to the threat of COVID-19

Rationale, aims, and objectives: The recent outbreak of coronavirus (COVID-19) has infected around 1,560,000 individuals till 10th April 2020, which has resulted in 95,000 deaths globally. While no vaccine or anti-viral drugs for COVID-19 are available, lockdown acts as a protective public health measures to reduce human interaction and lower transmission. The study aims to explore the impact of delayed planning or lack of planning for the lockdown and inadequate implementation of the lockdown, on the transmission rate of COVID-19. Method: Epidemiological data on the incidence and mortality of COVID-19 cases as reported by public health authorities were accessed from six countries based on total number of infected cases, viz., (United States of America (USA) and Italy (more than 100,000 cases); United Kingdom (UK), and France (50,000 to 100,000 cases), and India and Russia (6,000 to 10,000 cases).The Bayesian inferential technique was used to observe the changes (three points) in pattern of number of cases on different duration of exposure (in days)in these selected countries one month after WHO declaration about COVID-19 as a global pandemic. Results: On comparing the pattern of transmission rates observed in these six countries at posterior estimated change points, it is found that partial implementation of lockdown (in the USA), delayed planning in lockdown (Russia, UK and France), and inadequate implementation of the lockdown (in India and Italy) were responsible to the spread of infections. Conclusions: In order to control the spreading of COVID-19, like other national and international laws, lockdown must be implemented and enforced. It is suggested that on-time or adequate implementation of lockdown is a step towards social distancing and to control the spread of this pandemic

Economic evaluation of participatory women's groups scaled up by the public health system to improve birth outcomes in Jharkhand, eastern India

An estimated 2.4 million newborn infants died in 2020, 80% of them in sub-Saharan Africa and South Asia. To achieve the Sustainable Development Target for neonatal mortality reduction, countries with high mortality need to implement evidence-based, cost-effective interventions at scale. Our study aimed to estimate the cost, cost-effectiveness, and benefit-cost ratio of a participatory women's groups intervention scaled up by the public health system in Jharkhand, eastern India. The intervention was evaluated through a pragmatic cluster non-randomised controlled trial in six districts. We estimated the cost of the intervention at scale from a provider perspective, with a 42-month time horizon for 20 districts. We estimated costs using a combination of top-down and bottom-up approaches. All costs were adjusted for inflation, discounted at 3% per year, and converted to 2020 International Dollars (INT$). Incremental cost-effectiveness ratios (ICERs) were estimated using extrapolated effect sizes for the impact of the intervention in 20 districts, in terms of cost per neonatal deaths averted and cost per life year saved. We assessed the impact of uncertainty on results through one-way and probabilistic sensitivity analyses. We also estimated benefit-cost ratio using a benefit transfer approach. Total intervention costs for 20 districts were INT$ 15,017,396. The intervention covered an estimated 1.6 million livebirths across 20 districts, translating to INT$9.4 per livebirth covered. ICERs were estimated at INT$ 1,272 per neonatal death averted or INT$41 per life year saved. Net benefit estimates ranged from INT$ 1,046 million to INT$ 3,254 million, and benefit-cost ratios from 71 to 218. Our study suggests that participatory women's groups scaled up by the Indian public health system were highly cost-effective in improving neonatal survival and had a very favourable return on investment. The intervention can be scaled up in similar settings within India and other countries

Eff ect of participatory women’s groups facilitated by Accredited Social Health Activists on birth outcomes in rural eastern India: a cluster-randomised controlled trial

Background A quarter of the world’s neonatal deaths and 15% of maternal deaths happen in India. Few community-based strategies to improve maternal and newborn health have been tested through the country’s government-approved Accredited Social Health Activists (ASHAs). We aimed to test the eff ect of participatory women’s groups facilitated by ASHAs on birth outcomes, including neonatal mortality. Methods In this cluster-randomised controlled trial of a community intervention to improve maternal and newborn health, we randomly assigned (1:1) geographical clusters in rural Jharkhand and Odisha, eastern India to intervention (participatory women’s groups) or control (no women’s groups). Study participants were women of reproductive age (15–49 years) who gave birth between Sept 1, 2009, and Dec 31, 2012. In the intervention group, ASHAs supported women’s groups through a participatory learning and action meeting cycle. Groups discussed and prioritised maternal and newborn health problems, identifi ed strategies to address them, implemented the strategies, and assessed their progress. We identifi ed births, stillbirths, and neonatal deaths, and interviewed mothers 6 weeks after delivery. The primary outcome was neonatal mortality over a 2 year follow up. Analyses were by intention to treat. This trial is registered with ISRCTN, number ISRCTN31567106. Findings Between September, 2009, and December, 2012, we randomly assigned 30 clusters (estimated population 156 519) to intervention (15 clusters, estimated population n=82 702) or control (15 clusters, n=73 817). During the follow-up period (Jan 1, 2011, to Dec 31, 2012), we identifi ed 3700 births in the intervention group and 3519 in the control group. One intervention cluster was lost to follow up. The neonatal mortality rate during this period was 30 per 1000 livebirths in the intervention group and 44 per 1000 livebirths in the control group (odds ratio [OR] 0.69, 95% CI 0·53–0·89). Interpretation ASHAs can successfully reduce neonatal mortality through participatory meetings with women’s groups. This is a scalable community-based approach to improving neonatal survival in rural, underserved areas of India

Economic evaluation of participatory women's groups scaled up by the public health system to improve birth outcomes in Jharkhand, eastern India

An estimated 2.4 million newborn infants died in 2020, 80% of them in sub-Saharan Africa and South Asia. To achieve the Sustainable Development Target for neonatal mortality reduction, countries with high mortality need to implement evidence-based, cost-effective interventions at scale. Our study aimed to estimate the cost, cost-effectiveness, and benefit-cost ratio of a participatory women's groups intervention scaled up by the public health system in Jharkhand, eastern India. The intervention was evaluated through a pragmatic cluster non-randomised controlled trial in six districts. We estimated the cost of the intervention at scale from a provider perspective, with a 42-month time horizon for 20 districts. We estimated costs using a combination of top-down and bottom-up approaches. All costs were adjusted for inflation, discounted at 3% per year, and converted to 2020 International Dollars (INT$). Incremental cost-effectiveness ratios (ICERs) were estimated using extrapolated effect sizes for the impact of the intervention in 20 districts, in terms of cost per neonatal deaths averted and cost per life year saved. We assessed the impact of uncertainty on results through one-way and probabilistic sensitivity analyses. We also estimated benefit-cost ratio using a benefit transfer approach. Total intervention costs for 20 districts were INT$ 15,017,396. The intervention covered an estimated 1.6 million livebirths across 20 districts, translating to INT$9.4 per livebirth covered. ICERs were estimated at INT$ 1,272 per neonatal death averted or INT$41 per life year saved. Net benefit estimates ranged from INT$ 1,046 million to INT$ 3,254 million, and benefit-cost ratios from 71 to 218. Our study suggests that participatory women's groups scaled up by the Indian public health system were highly cost-effective in improving neonatal survival and had a very favourable return on investment. The intervention can be scaled up in similar settings within India and other countries.</p

Effect of participatory women's groups facilitated by Accredited Social Health Activists on birth outcomes in rural eastern India: A cluster-randomised controlled trial

Background: A quarter of the world's neonatal deaths and 15% of maternal deaths happen in India. Few community-based strategies to improve maternal and newborn health have been tested through the country's government-approved Accredited Social Health Activists (ASHAs). We aimed to test the effect of participatory women's groups facilitated by ASHAs on birth outcomes, including neonatal mortality. Methods: In this cluster-randomised controlled trial of a community interve

Community mobilisation with women's groups facilitated by Accredited Social Health Activists (ASHAs) to improve maternal and newborn health in underserved areas of Jharkhand and Orissa: study protocol for a cluster-randomised controlled trial

Background: Around a quarter of the world's neonatal and maternal deaths occur in India. Morbidity and mortality are highest in rural areas and among the poorest wealth quintiles. Few interventions to improve maternal and newborn health outcomes with government-mandated community health workers have been rigorously evaluated at scale in this setting.The study aims to assess the impact of a community mobilisation intervention with women's groups facilitated by ASHAs to improve maternal and newborn health outcomes among rural tribal communities of Jharkhand and Orissa.Methods/design: The study is a cluster-randomised controlled trial and will be implemented in five districts, three in Jharkhand and two in Orissa. The unit of randomisation is a rural cluster of approximately 5000 population. We identified villages within rural, tribal areas of five districts, approached them for participation in the study and enrolled them into 30 clusters, with approximately 10 ASHAs per cluster. Within each district, 6 clusters were randomly allocated to receive the community intervention or to the control group, resulting in 15 intervention and 15 control clusters. Randomisation was carried out in the presence of local stakeholders who selected the cluster numbers and allocated them to intervention or control using a pre-generated random number sequence. The intervention is a participatory learning and action cycle where ASHAs support community women's groups through a four-phase process in which they identify and prioritise local maternal and newborn health problems, implement strategies to address these and evaluate the result. The cycle is designed to fit with the ASHAs' mandate to mobilise communities for health and to complement their other tasks, including increasing institutional delivery rates and providing home visits to mothers and newborns. The trial's primary endpoint is neonatal mortality during 24 months of intervention. Additional endpoints include home care practices and health care-seeking in the antenatal, delivery and postnatal period. The impact of the intervention will be measured through a prospective surveillance system implemented by the project team, through which mothers will be interviewed around six weeks after delivery. Cost data and qualitative data are collected for cost-effectiveness and process evaluations

MONAI: An open-source framework for deep learning in healthcare

Artificial Intelligence (AI) is having a tremendous impact across most areas of science. Applications of AI in healthcare have the potential to improve our ability to detect, diagnose, prognose, and intervene on human disease. For AI models to be used clinically, they need to be made safe, reproducible and robust, and the underlying software framework must be aware of the particularities (e.g. geometry, physiology, physics) of medical data being processed. This work introduces MONAI, a freely available, community-supported, and consortium-led PyTorch-based framework for deep learning in healthcare. MONAI extends PyTorch to support medical data, with a particular focus on imaging, and provide purpose-specific AI model architectures, transformations and utilities that streamline the development and deployment of medical AI models. MONAI follows best practices for software-development, providing an easy-to-use, robust, well-documented, and well-tested software framework. MONAI preserves the simple, additive, and compositional approach of its underlying PyTorch libraries. MONAI is being used by and receiving contributions from research, clinical and industrial teams from around the world, who are pursuing applications spanning nearly every aspect of healthcare.Comment: www.monai.i