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The RNA extracted on day 01 and day 03 must be pooled together to be used in the RT-PCR. Second, we propose the inclusion of the control marker genes specific to nasal goblet cell, type-II pneumocyte and absorptive enterocytes to ensure the specificity of the RNA source. Overall, these additional steps in the proposed method would increase the chances of SARS-CoV-2 detection in the infected population and would limit the false-negative diagnosis of COVID-19 and hence the spread of this disease.•RT-PCR based COVID-19 diagnosis is vulnerable to the false-negative results due to inaccurate sample isolation or RNA extraction.•RNA pool of multiple samples from an individual improves the chances of detection of SARS-CoV-2 by RT-PCR.•Inclusion of specific marker genes would ensure the right RNA source from the desired cell.

Trauma is a major global health problem and majority of the deaths occur in low- and middle-income countries (LMICs), at even higher rates in the rural areas. The three-delay model assesses three different delays in accessing healthcare and can be applied to improve surgical and trauma healthcare delivery. CAY10683 Prior to implementing change, the capacities of the rural India healthcare system need to be identified.

The object of this study was to estimate surgical and trauma care capacities of government health facilities in rural Nanakpur, Haryana, India using the Personnel, Infrastructure, Procedures, Equipment and Supplies (PIPES) and International Assessment of Capacity for Trauma (INTACT) tools.

The PIPES and INTACT tools were administered at eight government health facilities serving the population of Nanakpur in June 2015. Data analysis was performed per tool subsection, and an overall score was calculated. Higher PIPES or INTACT indices correspond to greater surgical or trauma care capacity, respectiv India.

Estimates region-related prevalence of hypertension and attempts to identify its related factors at the district levels are required for prevention and management of hypertension.

The aim of this study was to investigate the epidemic features and related factors of hypertension and its awareness, treatment, and control rates among the northern Iranian population.

It was a community based cross-sectional study based on data from PERSIAN Guilan Cohort Study (PGCS). In total, 10,520 participants (aged 35-70 years) from the Guilan Province in northern Iran included in this study, between October 8, 2014, and January 20, 2017. Hypertension was defined as systolic blood pressure ≥140 mmHg or diastolic blood pressure ≥90 mmHg or a prior diagnosis of hypertension or being on antihypertensive medication. Potential correlates of hypertension and its awareness, treatment and control were analyzed by multivariate logistic regression adjusted for demographic factors, anthropometric characteristics, lifestyle variabluntreated. Modifying risk factors (such as weight lose and increase physical activity) and increasing hypertension awareness (by screening) is essential for primary and secondary prevention of high blood pressure in this population, especially in urban areas and among males, younger ages, and less educated.

Hypertension is highly prevalent in the northern Iranian population. About half of affected persons are unaware of their disease and untreated. Modifying risk factors (such as weight lose and increase physical activity) and increasing hypertension awareness (by screening) is essential for primary and secondary prevention of high blood pressure in this population, especially in urban areas and among males, younger ages, and less educated.[This retracts the article DOI 10.1016/j.crwh.2020.e00253.].Recently, in December 2019 the Coronavirus disease surprisingly influenced the lives of millions of people in the world with its swift spread. To support medical experts/doctors with the overpowering challenge of prediction of total cases in India, a machine-learning algorithm was developed. In this research article, the author describes the possibility of predicting the COVID-19 total, active cases, death and cured cases in India up to 25th June 2020 by applying linear regression and support vector machine. It is extremely tricky to manage the occurrence of corona virus since it is expanding exponentially day to day and is difficult to handle with a limited number of doctors and beds to treat the infected individuals with limited time. Hence, it is essential to develop a machine learning based computerized predicting model. The development effort in this article is based on publicly available data that is downloaded from KAGGLE to estimate the spread of the disease within a short period. We have calculated the RMSE, R2, MAE of LR and SVR models and concluded that the RMSE of linear regression is less than the SVR. Therefore, the LR will help doctors to forecast for the next few days.The current researchers all over the world are striving to facilitate the answer to COVID-19 in a unique commitment of scientific collaborations, and with Cognitive technologies, highly flexible learning processes are needed to maintain the transmission of knowledge, prototype and code, by integrating application areas to specific culture and cross-border cooperation. Experts in artificial intelligence and machine learning technology were tracked and predicted with real-time data that was created worldwide by this pandemic situation and distributed COVID-19 patient information timely. Considered physiological features followed by clinical tests of patients with COVID-19 with very simple access to subsequent data transformation was relevant, but complicated. This paper works on in-depth Exploratory Data Analysis (EDA) prediction analysis over global database of COVID-19 medical data from around the world will be available benefiting future artificial predictive, analytical and biomedical research additional COVID-19 approaches including associated pandemics.The COVID-19 coronavirus pandemic is an unparalleled threat intoday's quickly developing climate, and we face it as a global community. Like climate change, it is challenging our resilience from environmental health, social security, and government, to knowledge exchange and economic policy in all sectors of the economy and all fields of growth. So much as climate change, everybody's coming together would require the initiative. Throughout Europe and America, several organizations have mobilized to ensure that the neediest are not left behind, encouraging emergencies and disruptions avoidance and preparedness. The coronavirus outbreak has highlighted the growing community's strengths and vulnerabilities that it has influenced, and has provided us with the ability to benefit from each other's accomplishments and shortcomings. The comparison graph has also been shown in this paper displaying European and American scenarios. The globe might feel smaller amid disaster states and global travel bans, but it is a period when teamwork and looking outward were never more relevant.