All India Institute of Medical Sciences Rishikesh

INTRODUCTION: Strengthening The Reporting Of Cohort Studies in Surgery (STROCSS) guidelines were developed in 2017 in order to improve the reporting quality of observational studies in surgery and updated in 2019. In order to maintain relevance and continue upholding good reporting quality among observational studies in surgery, we aimed to update STROCSS 2019 guidelines. METHODS: A STROCSS 2021 steering group was formed to come up with proposals to update STROCSS 2019 guidelines. An expert panel of researchers assessed these proposals and judged whether they should become part of STROCSS 2021 guidelines or not, through a Delphi consensus exercise. RESULTS: 42 people (89%) completed the DELPHI survey and hence participated in the development of STROCSS 2021 guidelines. All items received a score between 7 and 9 by greater than 70% of the participants, indicating a high level of agreement among the DELPHI group members with the proposed changes to all the items. CONCLUSION: We present updated STROCSS 2021 guidelines to ensure ongoing good reporting quality among observational studies in surgery.

BACKGROUND: Artificial intelligence (AI) is the term used to describe the use of computers and technology to simulate intelligent behavior and critical thinking comparable to a human being. John McCarthy first described the term AI in 1956 as the science and engineering of making intelligent machines. OBJECTIVE: This descriptive article gives a broad overview of AI in medicine, dealing with the terms and concepts as well as the current and future applications of AI. It aims to develop knowledge and familiarity of AI among primary care physicians. MATERIALS AND METHODS: PubMed and Google searches were performed using the key words 'artificial intelligence'. Further references were obtained by cross-referencing the key articles. RESULTS: Recent advances in AI technology and its current applications in the field of medicine have been discussed in detail. CONCLUSIONS: AI promises to change the practice of medicine in hitherto unknown ways, but many of its practical applications are still in their infancy and need to be explored and developed better. Medical professionals also need to understand and acclimatize themselves with these advances for better healthcare delivery to the masses.

BACKGROUND: During the current ongoing COVID-19 pandemic, psychological problems like anxiety, depression, irritability, mood swings, inattention and sleep disturbance are fairly common among quarantined children in several studies. A systematic review of these publications to provide an accurate burden of these psychiatric/behavioral problems is needed for planning mitigating measures by the health authorities. METHODS: Different electronic databases (MEDLINE, EMBASE, Web of Science, CENTRAL, medRxiv and bioRxiv) were searched for articles describing psychological/behavioral complications in children/adolescents with/without pre-existing behavioral abnormalities and their caregivers related to the COVID-19 pandemic. Only original articles with/without comparator arms and a minimum sample size of 50 were included in the analysis. The pooled estimate of various psychological/behavioral problems was calculated using a random-effect meta-analysis. RESULTS: Fifteen studies describing 22 996 children/adolescents fulfilled the eligibility criteria from a total of 219 records. Overall, 34.5%, 41.7%, 42.3% and 30.8% of children were found to be suffering from anxiety, depression, irritability and inattention. Although the behavior/psychological state of a total of 79.4% of children was affected negatively by the pandemic and quarantine, at least 22.5% of children had a significant fear of COVID-19, and 35.2% and 21.3% of children had boredom and sleep disturbance. Similarly, 52.3% and 27.4% of caregivers developed anxiety and depression, respectively, while being in isolation with children. CONCLUSION: Anxiety, depression, irritability, boredom, inattention and fear of COVID-19 are predominant new-onset psychological problems in children during the COVID-19 pandemic. Children with pre-existing behavioral problems like autism and attention deficit hyperactivity disorder have a high probability of worsening of their behavioral symptoms.

The correct prediction of heart disease can prevent life threats, and incorrect prediction can prove to be fatal at the same time. In this paper different machine learning algorithms and deep learning are applied to compare the results and analysis of the UCI Machine Learning Heart Disease dataset. The dataset consists of 14 main attributes used for performing the analysis. Various promising results are achieved and are validated using accuracy and confusion matrix. The dataset consists of some irrelevant features which are handled using Isolation Forest, and data are also normalized for getting better results. And how this study can be combined with some multimedia technology like mobile devices is also discussed. Using deep learning approach, 94.2% accuracy was obtained.

Research within the anatomical sciences often relies on human cadaveric tissues. Without the good will of these donors who allow us to use their bodies to push forward our anatomical knowledge, most human anatomical research would come to a standstill. However, many research papers omit an acknowledgement to the donor cadavers or, as no current standardized versions exist, use language that is extremely varied. To remedy this problem, 20 editors-in-chiefs from 17 anatomical journals joined together to put together official recommendations that can be used by authors when acknowledging the donor cadavers used in their studies. The goal of these recommendations is to standardize the writing approach by which donors are acknowledged in anatomical studies that use human cadaveric tissues. Such sections in anatomical papers will not only rightfully thank those who made the donation but might also encourage, motivate, and inspire future individuals to make such gifts for the betterment of the anatomical sciences and patient care.

Coronavirus belongs to the family of Coronaviridae, comprising single-stranded, positive-sense RNA genome (+ ssRNA) of around 26 to 32 kilobases, and has been known to cause infection to a myriad of mammalian hosts, such as humans, cats, bats, civets, dogs, and camels with varied consequences in terms of death and debilitation. Strikingly, novel coronavirus (2019-nCoV), later renamed as severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), and found to be the causative agent of coronavirus disease-19 (COVID-19), shows 88% of sequence identity with bat-SL-CoVZC45 and bat-SL-CoVZXC21, 79% with SARS-CoV and 50% with MERS-CoV, respectively. Despite key amino acid residual variability, there is an incredible structural similarity between the receptor binding domain (RBD) of spike protein (S) of SARS-CoV-2 and SARS-CoV. During infection, spike protein of SARS-CoV-2 compared to SARS-CoV displays 10-20 times greater affinity for its cognate host cell receptor, angiotensin-converting enzyme 2 (ACE2), leading proteolytic cleavage of S protein by transmembrane protease serine 2 (TMPRSS2). Following cellular entry, the ORF-1a and ORF-1ab, located downstream to 5' end of + ssRNA genome, undergo translation, thereby forming two large polyproteins, pp1a and pp1ab. These polyproteins, following protease-induced cleavage and molecular assembly, form functional viral RNA polymerase, also referred to as replicase. Thereafter, uninterrupted orchestrated replication-transcription molecular events lead to the synthesis of multiple nested sets of subgenomic mRNAs (sgRNAs), which are finally translated to several structural and accessory proteins participating in structure formation and various molecular functions of virus, respectively. These multiple structural proteins assemble and encapsulate genomic RNA (gRNA), resulting in numerous viral progenies, which eventually exit the host cell, and spread infection to rest of the body. In this review, we primarily focus on genomic organization, structural and non-structural protein components, and potential prospective molecular targets for development of therapeutic drugs, convalescent plasm therapy, and a myriad of potential vaccines to tackle SARS-CoV-2 infection.

BACKGROUND: The COVID-19 pandemic has led to a complete shut-down of the entire world and almost all the countries are presently in a "lockdown" mode. While the lockdown strategy is an essential step to curb the exponential rise of COVID-19 cases, the impact of the same on mental health is not well known. AIM: This study aimed to evaluate the psychological impact of lockdown due to COVID-19 pandemic on the general public with an objective to assess the prevalence of depression, anxiety, perceived stress, well-being, and other psychological issues. MATERIALS AND METHODS: It was an online survey conducted under the aegis of the Indian Psychiatry Society. Using the Survey Monkey platform, a survey link was circulated using the Whatsapp. The survey questionnaire included perceived stress scale, Patient Health Questionnaire-9, Generalized Anxiety Disorder-7, Warwick-Edinburgh Mental Well-being Scale to assess perceived stress, anxiety, depression, and mental well-being, respectively. The survey link was circulated starting from April 6, 2020 and was closed on April 24, 2020. RESULTS: During the survey, a total of 1871 responses were collected, of which 1685 (90.05%) responses were analyzed. About two-fifth (38.2%) had anxiety and 10.5% of the participants had depression. Overall, 40.5% of the participants had either anxiety or depression. Moderate level of stress was reported by about three-fourth (74.1%) of the participants and 71.7% reported poor well-being. CONCLUSIONS: The present survey suggests that more than two-fifths of the people are experiencing common mental disorders, due to lockdown and the prevailing COVID-19 pandemic. This finding suggests that there is a need for expanding mental health services to everyone in the society during this pandemic situation.

INTRODUCTION: To mitigate the spread of the pandemic coronavirus infection (COVID-19), governments across the world have adopted "lockdowns" which have confined many individuals to their homes. This disrupts normal life routines, elements of which are important circadian cues. The pandemic is also associated with new stressors, altered roles, and uncertainties about health and economic security, which are also likely to affect sleep. The current study is an online survey of sleep experience, routines, physical activity, and symptoms of anxiety and depression, to study the alterations associated with the lockdown. MATERIALS AND METHODS: The survey was conducted in early May 2020 using a questionnaire circulated through social media platforms. Questions related to demographic characteristics, current and previous sleep schedules, routine, and working patterns. Insomnia (Insomnia Severity Index - 4), Stress (Perceived Stress Scale - 4), anxiety and depressive symptoms (Patient Health Questionnaire - 4) and physical activity (International Physical Activities Questionnaire) were assessed using standardized instruments. RESULTS: A total of 958 valid responses were received. Compared to the prelockdown period, there was a shift to a later bedtime and waking time, with a reduction in night-time sleep and an increase in day-time napping. These effects were visible across occupational groups, but mostly affected working individuals except health professionals. Sleep quality deteriorated across groups. Reductions in sleep duration were associated with depressive symptoms. CONCLUSIONS: The COVID-19 lockdown is associated with changes in sleep schedule and in the quantity and quality of night-time sleep. Although these changes are associated with elevated rates of emotional symptoms, it is unclear from these cross-sectional results, whether sleep deterioration produces psychological distress, or vice versa.

BACKGROUND: For more than three decades, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) has provided a framework to quantify health loss due to diseases, injuries, and associated risk factors. This paper presents GBD 2023 findings on disease and injury burden and risk-attributable health loss, offering a global audit of the state of world health to inform public health priorities. This work captures the evolving landscape of health metrics across age groups, sexes, and locations, while reflecting on the remaining post-COVID-19 challenges to achieving our collective global health ambitions. METHODS: The GBD 2023 combined analysis estimated years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life-years (DALYs) for 375 diseases and injuries, and risk-attributable burden associated with 88 modifiable risk factors. Of the more than 310 000 total data sources used for all GBD 2023 (about 30% of which were new to this estimation round), more than 120 000 sources were used for estimation of disease and injury burden and 59 000 for risk factor estimation, and included vital registration systems, surveys, disease registries, and published scientific literature. Data were analysed using previously established modelling approaches, such as disease modelling meta-regression version 2.1 (DisMod-MR 2.1) and comparative risk assessment methods. Diseases and injuries were categorised into four levels on the basis of the established GBD cause hierarchy, as were risk factors using the GBD risk hierarchy. Estimates stratified by age, sex, location, and year from 1990 to 2023 were focused on disease-specific time trends over the 2010-23 period and presented as counts (to three significant figures) and age-standardised rates per 100 000 person-years (to one decimal place). For each measure, 95% uncertainty intervals [UIs] were calculated with the 2·5th and 97·5th percentile ordered values from a 250-draw distribution. FINDINGS: Total numbers of global DALYs grew 6·1% (95% UI 4·0-8·1), from 2·64 billion (2·46-2·86) in 2010 to 2·80 billion (2·57-3·08) in 2023, but age-standardised DALY rates, which account for population growth and ageing, decreased by 12·6% (11·0-14·1), revealing large long-term health improvements. Non-communicable diseases (NCDs) contributed 1·45 billion (1·31-1·61) global DALYs in 2010, increasing to 1·80 billion (1·63-2·03) in 2023, alongside a concurrent 4·1% (1·9-6·3) reduction in age-standardised rates. Based on DALY counts, the leading level 3 NCDs in 2023 were ischaemic heart disease (193 million [176-209] DALYs), stroke (157 million [141-172]), and diabetes (90·2 million [75·2-107]), with the largest increases in age-standardised rates since 2010 occurring for anxiety disorders (62·8% [34·0-107·5]), depressive disorders (26·3% [11·6-42·9]), and diabetes (14·9% [7·5-25·6]). Remarkable health gains were made for communicable, maternal, neonatal, and nutritional (CMNN) diseases, with DALYs falling from 874 million (837-917) in 2010 to 681 million (642-736) in 2023, and a 25·8% (22·6-28·7) reduction in age-standardised DALY rates. During the COVID-19 pandemic, DALYs due to CMNN diseases rose but returned to pre-pandemic levels by 2023. From 2010 to 2023, decreases in age-standardised rates for CMNN diseases were led by rate decreases of 49·1% (32·7-61·0) for diarrhoeal diseases, 42·9% (38·0-48·0) for HIV/AIDS, and 42·2% (23·6-56·6) for tuberculosis. Neonatal disorders and lower respiratory infections remained the leading level 3 CMNN causes globally in 2023, although both showed notable rate decreases from 2010, declining by 16·5% (10·6-22·0) and 24·8% (7·4-36·7), respectively. Injury-related age-standardised DALY rates decreased by 15·6% (10·7-19·8) over the same period. Differences in burden due to NCDs, CMNN diseases, and injuries persisted across age, sex, time, and location. Based on our risk analysis, nearly 50% (1·27 billion [1·18-1·38]) of the roughly 2·80 billion total global DALYs in 2023 were attributable to the 88 risk factors analysed in GBD. Globally, the five level 3 risk factors contributing the highest proportion of risk-attributable DALYs were high systolic blood pressure (SBP), particulate matter pollution, high fasting plasma glucose (FPG), smoking, and low birthweight and short gestation-with high SBP accounting for 8·4% (6·9-10·0) of total DALYs. Of the three overarching level 1 GBD risk factor categories-behavioural, metabolic, and environmental and occupational-risk-attributable DALYs rose between 2010 and 2023 only for metabolic risks, increasing by 30·7% (24·8-37·3); however, age-standardised DALY rates attributable to metabolic risks decreased by 6·7% (2·0-11·0) over the same period. For all but three of the 25 leading level 3 risk factors, age-standardised rates dropped between 2010 and 2023-eg, declining by 54·4% (38·7-65·3) for unsafe sanitation, 50·5% (33·3-63·1) for unsafe water source, and 45·2% (25·6-72·0) for no access to handwashing facility, and by 44·9% (37·3-53·5) for child growth failure. The three leading level 3 risk factors for which age-standardised attributable DALY rates rose were high BMI (10·5% [0·1 to 20·9]), drug use (8·4% [2·6 to 15·3]), and high FPG (6·2% [-2·7 to 15·6]; non-significant). INTERPRETATION: Our findings underscore the complex and dynamic nature of global health challenges. Since 2010, there have been large decreases in burden due to CMNN diseases and many environmental and behavioural risk factors, juxtaposed with sizeable increases in DALYs attributable to metabolic risk factors and NCDs in growing and ageing populations. This long-observed consequence of the global epidemiological transition was only temporarily interrupted by the COVID-19 pandemic. The substantially decreasing CMNN disease burden, despite the 2008 global financial crisis and pandemic-related disruptions, is one of the greatest collective public health successes known. However, these achievements are at risk of being reversed due to major cuts to development assistance for health globally, the effects of which will hit low-income countries with high burden the hardest. Without sustained investment in evidence-based interventions and policies, progress could stall or reverse, leading to widespread human costs and geopolitical instability. Moreover, the rising NCD burden necessitates intensified efforts to mitigate exposure to leading risk factors-eg, air pollution, smoking, and metabolic risks, such as high SBP, BMI, and FPG-including policies that promote food security, healthier diets, physical activity, and equitable and expanded access to potential treatments, such as GLP-1 receptor agonists. Decisive, coordinated action is needed to address long-standing yet growing health challenges, including depressive and anxiety disorders. Yet this can be only part of the solution. Our response to the NCD syndemic-the complex interaction of multiple health risks, social determinants, and systemic challenges-will define the future landscape of global health. To ensure human wellbeing, economic stability, and social equity, global action to sustain and advance health gains must prioritise reducing disparities by addressing socioeconomic and demographic determinants, ensuring equitable health-care access, tackling malnutrition, strengthening health systems, and improving vaccination coverage. We live in times of great opportunity. FUNDING: Gates Foundation and Bloomberg Philanthropies.

The discovery and advances of medicines may be considered as the ultimate relevant translational science effort that adds to human invulnerability and happiness. But advancing a fresh medication is a quite convoluted, costly, and protracted operation, normally costing USD ~2.6 billion and consuming a mean time span of 12 years. Methods to cut back expenditure and hasten new drug discovery have prompted an arduous and compelling brainstorming exercise in the pharmaceutical industry. The engagement of Artificial Intelligence (AI), including the deep-learning (DL) component in particular, has been facilitated by the employment of classified big data, in concert with strikingly reinforced computing prowess and cloud storage, across all fields. AI has energized computer-facilitated drug discovery. An unrestricted espousing of machine learning (ML), especially DL, in many scientific specialties, and the technological refinements in computing hardware and software, in concert with various aspects of the problem, sustain this progress. ML algorithms have been extensively engaged for computer-facilitated drug discovery. DL methods, such as artificial neural networks (ANNs) comprising multiple buried processing layers, have of late seen a resurgence due to their capability to power automatic attribute elicitations from the input data, coupled with their ability to obtain nonlinear input-output pertinencies. Such features of DL methods augment classical ML techniques which bank on human-contrived molecular descriptors. A major part of the early reluctance concerning utility of AI in pharmaceutical discovery has begun to melt, thereby advancing medicinal chemistry. AI, along with modern experimental technical knowledge, is anticipated to invigorate the quest for new and improved pharmaceuticals in an expeditious, economical, and increasingly compelling manner. DL-facilitated methods have just initiated kickstarting for some integral issues in drug discovery. Many technological advances, such as "message-passing paradigms", "spatial-symmetry-preserving networks", "hybrid de novo design", and other ingenious ML exemplars, will definitely come to be pervasively widespread and help dissect many of the biggest, and most intriguing inquiries. Open data allocation and model augmentation will exert a decisive hold during the progress of drug discovery employing AI. This review will address the impending utilizations of AI to refine and bolster the drug discovery operation.

As the demand for quality healthcare increases, healthcare systems worldwide are grappling with time constraints and excessive workloads, which can compromise the quality of patient care. Artificial intelligence (AI) has emerged as a powerful tool in clinical medicine, revolutionizing various aspects of patient care and medical research. The integration of AI in clinical medicine has not only improved diagnostic accuracy and treatment outcomes, but also contributed to more efficient healthcare delivery, reduced costs, and facilitated better patient experiences. This review article provides an extensive overview of AI applications in history taking, clinical examination, imaging, therapeutics, prognosis and research. Furthermore, it highlights the critical role AI has played in transforming healthcare in developing nations.

BACKGROUND: Timely and comprehensive analyses of causes of death stratified by age, sex, and location are essential for shaping effective health policies aimed at reducing global mortality. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2023 provides cause-specific mortality estimates measured in counts, rates, and years of life lost (YLLs). GBD 2023 aimed to enhance our understanding of the relationship between age and cause of death by quantifying the probability of dying before age 70 years (70q0) and the mean age at death by cause and sex. This study enables comparisons of the impact of causes of death over time, offering a deeper understanding of how these causes affect global populations. METHODS: GBD 2023 produced estimates for 292 causes of death disaggregated by age-sex-location-year in 204 countries and territories and 660 subnational locations for each year from 1990 until 2023. We used a modelling tool developed for GBD, the Cause of Death Ensemble model (CODEm), to estimate cause-specific death rates for most causes. We computed YLLs as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. Probability of death was calculated as the chance of dying from a given cause in a specific age period, for a specific population. Mean age at death was calculated by first assigning the midpoint age of each age group for every death, followed by computing the mean of all midpoint ages across all deaths attributed to a given cause. We used GBD death estimates to calculate the observed mean age at death and to model the expected mean age across causes, sexes, years, and locations. The expected mean age reflects the expected mean age at death for individuals within a population, based on global mortality rates and the population's age structure. Comparatively, the observed mean age represents the actual mean age at death, influenced by all factors unique to a location-specific population, including its age structure. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 250-draw distribution for each metric. Findings are reported as counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2023 include a correction for the misclassification of deaths due to COVID-19, updates to the method used to estimate COVID-19, and updates to the CODEm modelling framework. This analysis used 55 761 data sources, including vital registration and verbal autopsy data as well as data from surveys, censuses, surveillance systems, and cancer registries, among others. For GBD 2023, there were 312 new country-years of vital registration cause-of-death data, 3 country-years of surveillance data, 51 country-years of verbal autopsy data, and 144 country-years of other data types that were added to those used in previous GBD rounds. FINDINGS: The initial years of the COVID-19 pandemic caused shifts in long-standing rankings of the leading causes of global deaths: it ranked as the number one age-standardised cause of death at Level 3 of the GBD cause classification hierarchy in 2021. By 2023, COVID-19 dropped to the 20th place among the leading global causes, returning the rankings of the leading two causes to those typical across the time series (ie, ischaemic heart disease and stroke). While ischaemic heart disease and stroke persist as leading causes of death, there has been progress in reducing their age-standardised mortality rates globally. Four other leading causes have also shown large declines in global age-standardised mortality rates across the study period: diarrhoeal diseases, tuberculosis, stomach cancer, and measles. Other causes of death showed disparate patterns between sexes, notably for deaths from conflict and terrorism in some locations. A large reduction in age-standardised rates of YLLs occurred for neonatal disorders. Despite this, neonatal disorders remained the leading cause of global YLLs over the period studied, except in 2021, when COVID-19 was temporarily the leading cause. Compared to 1990, there has been a considerable reduction in total YLLs in many vaccine-preventable diseases, most notably diphtheria, pertussis, tetanus, and measles. In addition, this study quantified the mean age at death for all-cause mortality and cause-specific mortality and found noticeable variation by sex and location. The global all-cause mean age at death increased from 46·8 years (95% UI 46·6-47·0) in 1990 to 63·4 years (63·1-63·7) in 2023. For males, mean age increased from 45·4 years (45·1-45·7) to 61·2 years (60·7-61·6), and for females it increased from 48·5 years (48·1-48·8) to 65·9 years (65·5-66·3), from 1990 to 2023. The highest all-cause mean age at death in 2023 was found in the high-income super-region, where the mean age for females reached 80·9 years (80·9-81·0) and for males 74·8 years (74·8-74·9). By comparison, the lowest all-cause mean age at death occurred in sub-Saharan Africa, where it was 38·0 years (37·5-38·4) for females and 35·6 years (35·2-35·9) for males in 2023. Lastly, our study found that all-cause 70q0 decreased across each GBD super-region and region from 2000 to 2023, although with large variability between them. For females, we found that 70q0 notably increased from drug use disorders and conflict and terrorism. Leading causes that increased 70q0 for males also included drug use disorders, as well as diabetes. In sub-Saharan Africa, there was an increase in 70q0 for many non-communicable diseases (NCDs). Additionally, the mean age at death from NCDs was lower than the expected mean age at death for this super-region. By comparison, there was an increase in 70q0 for drug use disorders in the high-income super-region, which also had an observed mean age at death lower than the expected value. INTERPRETATION: We examined global mortality patterns over the past three decades, highlighting-with enhanced estimation methods-the impacts of major events such as the COVID-19 pandemic, in addition to broader trends such as increasing NCDs in low-income regions that reflect ongoing shifts in the global epidemiological transition. This study also delves into premature mortality patterns, exploring the interplay between age and causes of death and deepening our understanding of where targeted resources could be applied to further reduce preventable sources of mortality. We provide essential insights into global and regional health disparities, identifying locations in need of targeted interventions to address both communicable and non-communicable diseases. There is an ever-present need for strengthened health-care systems that are resilient to future pandemics and the shifting burden of disease, particularly among ageing populations in regions with high mortality rates. Robust estimates of causes of death are increasingly essential to inform health priorities and guide efforts toward achieving global health equity. The need for global collaboration to reduce preventable mortality is more important than ever, as shifting burdens of disease are affecting all nations, albeit at different paces and scales. FUNDING: Gates Foundation.

INTRODUCTION: Prior estimates of dementia prevalence in India were based on samples from selected communities, inadequately representing the national and state populations. METHODS: From the Longitudinal Aging Study in India (LASI) we recruited a sample of adults ages 60+ and administered a rich battery of neuropsychological tests and an informant interview in 2018 through 2020. We obtained a clinical consensus rating of dementia status for a subsample (N = 2528), fitted a logistic model for dementia status on this subsample, and then imputed dementia status for all other LASI respondents aged 60+ (N = 28,949). RESULTS: The estimated dementia prevalence for adults ages 60+ in India is 7.4%, with significant age and education gradients, sex and urban/rural differences, and cross-state variation. DISCUSSION: An estimated 8.8 million Indians older than 60 years have dementia. The burden of dementia cases is unevenly distributed across states and subpopulations and may therefore require different levels of local planning and support. HIGHLIGHTS: The estimated dementia prevalence for adults ages 60+ in India is 7.4%. About 8.8 million Indians older than 60 years live with dementia. Dementia is more prevalent among females than males and in rural than urban areas. Significant cross-state variation exists in dementia prevalence.

Putrescine, spermine, and spermidine are the important polyamines (PAs), found in all living organisms. PAs are formed by the decarboxylation of amino acids, and they facilitate cell growth and development via different cellular responses. PAs are the integrated part of the cellular and genetic metabolism and help in transcription, translation, signaling, and post-translational modifications. At the cellular level, PA concentration may influence the condition of various diseases in the body. For instance, a high PA level is detrimental to patients suffering from aging, cognitive impairment, and cancer. The levels of PAs decline with age in humans, which is associated with different health disorders. On the other hand, PAs reduce the risk of many cardiovascular diseases and increase longevity, when taken in an optimum quantity. Therefore, a controlled diet is an easy way to maintain the level of PAs in the body. Based on the nutritional intake of PAs, healthy cell functioning can be maintained. Moreover, several diseases can also be controlled to a higher extend via maintaining the metabolism of PAs. The present review discusses the types, important functions, and metabolism of PAs in humans. It also highlights the nutritional role of PAs in the prevention of various diseases.

Much has been written about the well-being and quality of patients in recent years but little attention has been focused on well-being of healthcare workers (HCWs) who provide comprehensive healthcare to patients. It has been found that the HCWs are more stressed because of less staffs, increasing work load, longer working hours, high clientele expectation and peculiar problems and hazards of work place. There is increased morbidity in HCWs in comparison to general population. Though they are aware, measures of well being, engaging the HCWs in promotion of their workplace and making changes to enhance its realization needs to be done to improve their health by themselves, at administrative and institutional level.

BACKGROUND: In 2016, of the estimated 257 million people living with chronic hepatitis B virus (HBV) infection worldwide, only a small proportion was diagnosed and treated. The insufficiency of information on the proportion of people infected with HBV who are eligible for treatment limits the interpretation of global treatment coverage. We aimed to estimate the proportion of people with chronic HBV infection who were eligible for antiviral treatment worldwide, based on the WHO 2015 guidelines. METHODS: In this systematic review and meta-analysis, we searched Medline, EMBASE, and the Cochrane databases from Jan 1, 2007, to Jan 31, 2018, for studies describing HBsAg-positive people in the population or health-care facilities. We extracted information from published studies using a standardised form to estimate the frequency of cirrhosis, abnormal alanine aminotransferase (ALT), HBV DNA exceeding 2000 IU/mL or 20 000 IU/mL, presence of HBeAg, and eligibility for treatment as per WHO and other guidelines as reported in the studies. We pooled proportions through meta-analysis with random effects. The study was registered with PROSPERO, CRD42020132345. FINDINGS: Of the 13 497 studies, 162 were eligible and included in our analysis. These studies included 145 789 participants. The pooled estimate of the proportion of cirrhosis was 9% (95% CI 8-10), ranging from 6% (4-8) in community settings to 10% (9-11) in clinic settings. Examining the proportion of participants who had characteristics used to determine eligibility in the WHO guidelines, 1750 (10·1%) of 17 394 had HBV DNA exceeding 20 000 IU/mL, and 20 425 (30·8%) of 66 235 had ALT above the upper limit of normal. 32 studies reported eligibility for treatment according to WHO or any other guidelines, with a pooled estimate of eligibility at 19% (95% CI 18-20), ranging from 12% (6-18) for studies in community settings to 25% (19-30) in clinic settings. INTERPRETATION: Many studies described people with HBV infection, but few reported information in a way that allowed assessment of eligibility for treatment. Although about one in ten of the 257 million people with HBV infection (26 million) might be in urgent need of treatment because of cirrhosis, a larger proportion (12-25%) is eligible for treatment in accordance with different guidelines. Future studies describing people with HBV infection should report on treatment eligibility, according to broadly agreed definitions. FUNDING: WHO and US Centers for Disease Control and Prevention.

BACKGROUND: Knowledge about neurological complications of COVID-19 in children is limited due to the paucity of data in the existing literature. Some systematic reviews are available describing overall clinical features of COVID-19 in children and neurological complications of COVID-19 in adults. But to the best of our knowledge, no systematic review has been performed to determine neurological manifestations of COVID-19. METHODS: Six different electronic databases (MEDLINE, EMBASE, Web of Science, CENTRAL, medRxiv and bioRxiv) were searched for articles related to COVID-19 and neurological complications in children. Studies/case series reporting neurological manifestations of COVID-19 in patients aged ≤18 years, as well as case reports, as neurological complications appear to be rare. The pooled estimate of various non-specific and specific neurological manifestations was performed using a random effect meta-analysis. RESULTS: Twenty-one studies/case series and five case reports (3707 patients) fulfilled the eligibility criteria and were included in this systematic review, from a total of 460 records. Headache, myalgia and fatigue were predominant non-specific neurological manifestations, presenting altogether in 16.7% cases. Total of 42 children (1%) were found to have been reported with definite neurological complications, more in those suffering from a severe illness (encephalopathy-25, seizure-12, meningeal signs-17). Rare neurological complications were intracranial hemorrhage, cranial nerve palsy, Guillain-Barré syndrome and vision problems. All children with acute symptomatic seizures survived suggesting a favorable short-term prognosis. CONCLUSION: Neurological complications are rare in children suffering from COVID-19. Still, these children are at risk of developing seizures and encephalopathy, more in those suffering from severe illness.

Acne vulgaris, a common and chronic disorder of the pilosebaceous unit, affects up to 85% of adolescent and young adults. While a lot is already known about acne and its treatment, still the gaps in our understanding of acne remains. This article will review the emerging evidence in the complex pathogenesis of acne and provide an overview of the potential future therapy in management of acne vulgaris.Key pointsWhat is known? Propionibacterium acnes targeted therapy has been the mainstay in the management of acne till now.What is new? Sebocyte activity is controlled via a range of cellular pathways and hormones in addition to androgens. This has opened an array of therapeutic options to be available for treating acne in the near future.

Neurosciences and Neuro-technology are continuously advancing and so individuals, society and healthcare professionals have to up date themselves with advancement. Brain computer Interface (BCI) is one such emerging technology in Neurosciences. In a nutshell, BCI technology provides a direct communication between brain and external device bypassing the normal neuromuscular pathways. BCI not only serves medical field & health care but also has role in various other arenas of human life like entertainment, gaming, education, self-control, marketing and so on. Associated with its advantages, BCI takes along with its pitfalls too which may fall into various categories like technological, neurological and ethical. In this review paper, authors discuss about the basic concept of BCI, brain signals and components. We also reviewed the applications of BCI in different fields and practical issues related to usability of BCI. Given the fact that it has a multidisciplinary realm, i.e. neurosciences, physicians of all specialties, nurses, engineers, hospital manager and administration, this review on the subject is written in common language.

Modified BG Prasad socioeconomic scale has been in use for determining the socio-economic status of study subjects in community-based health studies in India since 1961.It is an income-based scale and, therefore, constant update is required to take inflation and depreciation of rupee into account. For industrial workers (IW), the consumer price index (CPI) is used to calculate updated income categories at any given point of time, viz Jan 2019. These details of the calculations involved will help many researchers to calculate specific income categories for their ongoing and prospective research work in current calendar year. On the Department of Labour website (www.labourbureaunew.gov.in), state-specific CPI values are also available and should be used to determine more accurate income categories.