Deep Learning based CNN Model for Classification and Detection of Individuals Wearing Face Mask

In response to the global COVID-19 pandemic, there has been a critical demand for protective measures, with face masks emerging as a primary safeguard. The approach involves a two-fold strategy: first, recognizing the presence of a face by detecting faces, and second, identifying masks on those faces. This project utilizes deep learning to create a model that can detect face masks in real-time streaming video as well as images. Face detection, a facet of object detection, finds applications in diverse fields such as security, biometrics, and law enforcement. Various detector systems worldwide have been developed and implemented, with convolutional neural networks chosen for their superior performance accuracy and speed in object detection. Experimental results attest to the model's excellent accuracy on test data. The primary focus of this research is to enhance security, particularly in sensitive areas. The research paper proposes a rapid image pre-processing method with masks centred on faces. Employing feature extraction and Convolutional Neural Network, the system classifies and detects individuals wearing masks. The research unfolds in three stages: image pre-processing, image cropping, and image classification, collectively contributing to the identification of masked faces. Continuous surveillance through webcams or CCTV cameras ensures constant monitoring, triggering a security alert if a person is detected without a mask.Comment: 8 Pages , 6 figures , 1 Tabl

Targeting medication non-adherence behavior in selected autoimmune diseases: a systematic approach to digital health program development

Background 29 autoimmune diseases, including Rheumatoid Arthritis, gout, Crohn’s Disease, and Systematic Lupus Erythematosus affect 7.6-9.4% of the population. While effective therapy is available, many patients do not follow treatment or use medications as directed. Digital health and Web 2.0 interventions have demonstrated much promise in increasing medication and treatment adherence, but to date many Internet tools have proven disappointing. In fact, most digital interventions continue to suffer from high attrition in patient populations, are burdensome for healthcare professionals, and have relatively short life spans. Objective Digital health tools have traditionally centered on the transformation of existing interventions (such as diaries, trackers, stage-based or cognitive behavioral therapy programs, coupons, or symptom checklists) to electronic format. Advanced digital interventions have also incorporated attributes of Web 2.0 such as social networking, text messaging, and the use of video. Despite these efforts, there has not been little measurable impact in non-adherence for illnesses that require medical interventions, and research must look to other strategies or development methodologies. As a first step in investigating the feasibility of developing such a tool, the objective of the current study is to systematically rate factors of non-adherence that have been reported in past research studies. Methods Grounded Theory, recognized as a rigorous method that facilitates the emergence of new themes through systematic analysis, data collection and coding, was used to analyze quantitative, qualitative and mixed method studies addressing the following autoimmune diseases: Rheumatoid Arthritis, gout, Crohn’s Disease, Systematic Lupus Erythematosus, and inflammatory bowel disease. Studies were only included if they contained primary data addressing the relationship with non-adherence. Results Out of the 27 studies, four non-modifiable and 11 modifiable risk factors were discovered. Over one third of articles identified the following risk factors as common contributors to medication non-adherence (percent of studies reporting): patients not understanding treatment (44%), side effects (41%), age (37%), dose regimen (33%), and perceived medication ineffectiveness (33%). An unanticipated finding that emerged was the need for risk stratification tools (81%) with patient-centric approaches (67%). Conclusions This study systematically identifies and categorizes medication non-adherence risk factors in select autoimmune diseases. Findings indicate that patients understanding of their disease and the role of medication are paramount. An unexpected finding was that the majority of research articles called for the creation of tailored, patient-centric interventions that dispel personal misconceptions about disease, pharmacotherapy, and how the body responds to treatment. To our knowledge, these interventions do not yet exist in digital format. Rather than adopting a systems level approach, digital health programs should focus on cohorts with heterogeneous needs, and develop tailored interventions based on individual non-adherence patterns

Evaluation of Radiant Power of the Light Curing Units Used in Clinics at Governmental and Privates Dental Faculties

Sami Abdulsalam Ali Hasan,1 Ibrahim Zaid Al-Shami,1 Mohsen Ali Al-Hamzi,1 Ghadeer Saleh Alwadai,2 Nada Ahmad Alamoudi,2 Saleh Ali Alqahtani,2 Arwa Daghrery,3 Wafa H Alaajam,4,5 Mansoor Shariff,6 Hussain Mohammed Kinani,7 Mohammed M Al Moaleem8 1Department of Conservative Dentistry, Faculty of Dentistry, Sana’a University, Sanaa, Yemen; 2Department: Restorative Dental Science, College of Dentistry, King Khalid University, Abha, Saudi Arabia; 3Department of Restorative Dental Sciences, College of Dentistry, Jazan University, Jazan, 45142, Saudi Arabia; 4Department of Restorative Dental Science, College of Dentistry, King Khalid University, Abha, Saudi Arabia; 5Department of Restorative Dentistry, Faculty of Dentistry, Sana’a University, Sana’a, Yemen; 6Prosthetic Department, College of Dentistry, King Khalid University, Abha, 62529, Saudi Arabia; 7Ministry of Health, Sabya General Hospital, Department of Dentistry, Jazan, Saudi Arabia; 8Department of Prosthetic Dental Science, College of Dentistry, Jazan University, Jazan, 45142, Saudi ArabiaCorrespondence: Mohammed M Al Moaleem, Department of Prosthetic Dental Science, College of Dentistry, Jazan University, Jazan, 45142, Saudi Arabia, Email [email protected]:  To evaluate the radiant power of the light cure units (LCUs) in relation to their type, radiant exitance, number of years in clinical use, and condition of LCUs tips in governmental and public clinics in Dental Faculties in Sana’a City.Materials and Methods: LCUs were collected from different colleges at Sanaa City, Yemen, then LCU data as type, clinical age ( 850 mW/cm², labeled as inadequate, marginal, and adequate radiant exitances, respectively. A Woodpecker radiometer was used with a mode lasting of 20 seconds was used with each LCU. Descriptive statistics of the different parameters were evaluated with SPSS version 25. One-way ANOVA and Mann–Whitney tests were performed to determine the mean difference between the groups with a significance value of ˂ 0.05 was considered.Results: Two hundred twenty-three LCUs were surveyed, and the majority were Light–emitting diode (LED). Forty-nine (21.9%), 117 (52.4%), 57 (25.6%) recorded lesser than, 400– 850, and more than 850 mW/cm², respectively. Radiant exitances of < year-old units were found to be higher than those of units used for ˃ 5 years with significant differences (p=0.001). The ANOVA test showed significant differences between the radiant exitance with clinical age and LCU tip conditions and a strong correlation p ˃ 0.050.Conclusion: LED curing lights were the most used in the tested Dental Faculties. More than half of the used LCU offered sufficient radiant exitance. Clinical age, the presence or absence of composite buildups, and damage to curing tips showed significantly affect radiant exitance values.Keywords: dental light cure, radiant exitance, contaminated tip, radiomete

Racism as a determinant of health: a systematic review and meta-analysis

Despite a growing body of epidemiological evidence in recent years documenting the health impacts of racism, the cumulative evidence base has yet to be synthesized in a comprehensive meta-analysis focused specifically on racism as a determinant of health. This meta-analysis reviewed the literature focusing on the relationship between reported racism and mental and physical health outcomes. Data from 293 studies reported in 333 articles published between 1983 and 2013, and conducted predominately in the U.S., were analysed using random effects models and mean weighted effect sizes. Racism was associated with poorer mental health (negative mental health: r = -.23, 95% CI [-.24,-.21], k = 227; positive mental health: r = -.13, 95% CI [-.16,-.10], k = 113), including depression, anxiety, psychological stress and various other outcomes. Racism was also associated with poorer general health (r = -.13 (95% CI [-.18,-.09], k = 30), and poorer physical health (r = -.09, 95% CI [-.12,-.06], k = 50). Moderation effects were found for some outcomes with regard to study and exposure characteristics. Effect sizes of racism on mental health were stronger in cross-sectional compared with longitudinal data and in non-representative samples compared with representative samples. Age, sex, birthplace and education level did not moderate the effects of racism on health. Ethnicity significantly moderated the effect of racism on negative mental health and physical health: the association between racism and negative mental health was significantly stronger for Asian American and Latino(a) American participants compared with African American participants, and the association between racism and physical health was significantly stronger for Latino(a) American participants compared with African American participants.<br /

A synthesis of evidence for policy from behavioural science during COVID-19

Scientific evidence regularly guides policy decisions, with behavioural science increasingly part of this process. In April 2020, an influential paper proposed 19 policy recommendations (‘claims’) detailing how evidence from behavioural science could contribute to efforts to reduce impacts and end the COVID-19 pandemic. Here we assess 747 pandemic-related research articles that empirically investigated those claims. We report the scale of evidence and whether evidence supports them to indicate applicability for policymaking. Two independent teams, involving 72 reviewers, found evidence for 18 of 19 claims, with both teams finding evidence supporting 16 (89%) of those 18 claims. The strongest evidence supported claims that anticipated culture, polarization and misinformation would be associated with policy effectiveness. Claims suggesting trusted leaders and positive social norms increased adherence to behavioural interventions also had strong empirical support, as did appealing to social consensus or bipartisan agreement. Targeted language in messaging yielded mixed effects and there were no effects for highlighting individual benefits or protecting others. No available evidence existed to assess any distinct differences in effects between using the terms ‘physical distancing’ and ‘social distancing’. Analysis of 463 papers containing data showed generally large samples; 418 involved human participants with a mean of 16,848 (median of 1,699). That statistical power underscored improved suitability of behavioural science research for informing policy decisions. Furthermore, by implementing a standardized approach to evidence selection and synthesis, we amplify broader implications for advancing scientific evidence in policy formulation and prioritization

Prostaglandin F2-alpha receptor (FPr) expression on porcine corpus luteum microvascular endothelial cells (pCL-MVECs)

<p>Abstract</p> <p>Background</p> <p>The corpus luteum (CL) is a transient endocrine gland and prostaglandin F2-alpha is considered to be the principal luteolysin in pigs. In this species, the in vivo administration of prostaglandin F2-alpha induces apoptosis in large vessels as early as 6 hours after administration. The presence of the prostaglandin F2-alpha receptor (FPr) on the microvascular endothelial cells (pCL-MVECs) of the porcine corpus luteum has not yet been defined. The aim of the study was to assess FPr expression in pCL-MVECs in the early and mid-luteal phases (EL-p, ML-p), and during pregnancy (P-p). Moreover, the effectiveness of prostaglandin F2-alpha treatment in inducing pCL-MVEC apoptosis was tested.</p> <p>Methods</p> <p>Porcine CLs were collected in the EL and ML phases and during P-p. All CLs from each animal were minced together and the homogenates underwent enzymatic digestion. The pCL-MVECs were then positively selected by an immunomagnetic separation protocol using Dynabeads coated with anti-CD31 monoclonal antibody and seeded in flasks in the presence of EGM 2-MV (Microvascular Endothelial Cell Medium-2). After 4 days of culture, the cells underwent additional immunomagnetic selection and were seeded in flasks until the confluent stage.</p> <p>PCR Real time, western blot and immunodetection assays were utilized to assess the presence of FPr on pCL-MVEC primary cultures. Furthermore, the influence of culture time (freshly isolated, cultured overnight and at confluence) and hormonal treatment (P4 and E2) on FPr expression in pCL-MVECs was also investigated. Apoptosis was detected by TUNEL assay of pCL-MVECs exposed to prostaglandin F2-alpha.</p> <p>Results</p> <p>We obtained primary cultures of pCL-MVECs from all animals. FPr mRNA and protein levels showed the highest value (ANOVA) in CL-MVECs derived from the early-luteal phase. Moreover, freshly isolated MVECs showed a higher FPr mRNA value than those cultured overnight and confluent cells (ANOVA). prostaglandin F2-alpha treatment failed to induce an apoptotic response in all the pCL-MVEC cultures.</p> <p>Conclusion</p> <p>Our data showing the presence of FPr on MVECs and the inability of prostaglandin F2-alpha to evoke an in vitro apoptotic response suggest that other molecules or mechanisms must be considered in order to explain the in vivo direct pro-apoptotic effect of prostaglandin F2-alpha at the endothelial level.</p

Burdens of type 2 diabetes and cardiovascular disease attributable to sugar-sweetened beverages in 184 countries

The consumption of sugar-sweetened beverages (SSBs) is associated with type 2 diabetes (T2D) and cardiovascular diseases (CVD). However, an updated and comprehensive assessment of the global burden attributable to SSBs remains scarce. Here we estimated SSB-attributable T2D and CVD burdens across 184 countries in 1990 and 2020 globally, regionally and nationally, incorporating data from the Global Dietary Database, jointly stratified by age, sex, educational attainment and urbanicity. In 2020, 2.2 million (95% uncertainty interval 2.0–2.3) new T2D cases and 1.2 million (95% uncertainty interval 1.1–1.3) new CVD cases were attributable to SSBs worldwide, representing 9.8% and 3.1%, respectively, of all incident cases. Globally, proportional SSB-attributable burdens were higher among men versus women, younger versus older adults, higher- versus lower-educated adults, and adults in urban versus rural areas. By world region, the highest SSB-attributable percentage burdens were in Latin America and the Caribbean (T2D: 24.4%; CVD: 11.3%) and sub-Saharan Africa (T2D: 21.5%; CVD: 10.5%). From 1990 to 2020, the largest proportional increases in SSB-attributable incident T2D and CVD cases were in sub-Saharan Africa (+8.8% and +4.4%, respectively). Our study highlights the countries and subpopulations most affected by cardiometabolic disease associated with SSB consumption, assisting in shaping effective policies and interventions to reduce these burdens globally

A healthy mistrust: how worldview relates to attitudes about breast cancer screening in a cross-sectional survey of low-income women

<p>Abstract</p> <p>Background</p> <p>Perceived racial discrimination is one factor which may discourage ethnic minorities from using healthcare. However, existing research only partially explains why some persons do accept health promotion messages and use preventive care, while others do not. This analysis explores 1) the psychosocial characteristics of those, within disadvantaged groups, who identify their previous experiences as racially discriminatory, 2) the extent to which perceived racism is associated with broader perspectives on societal racism and powerlessness, and 3) how these views relate to disadvantaged groups' expectation of mistreatment in healthcare, feelings of mistrust, and motivation to use care.</p> <p>Methods</p> <p>Using survey data from 576 African-American women, we explored the prevalence and predictors of beliefs and experiences related to social disengagement, racial discrimination, desired and actual racial concordance with medical providers, and fear of medical research. We then used both sociodemographic characteristics, and experiences and attitudes about disadvantage, to model respondents' scores on an index of personal motivation to receive breast cancer screening, measuring screening knowledge, rejection of fatalistic explanatory models of cancer, and belief in early detection, and in collaborative models of patient-provider responsibility.</p> <p>Results</p> <p>Age was associated with lower motivation to screen, as were depressive symptoms, anomie, and fear of medical research. Motivation was low among those more comfortable with African-American providers, regardless of current provider race. However, greater awareness of societal racism positively predicted motivation, as did talking to others when experiencing discrimination. Talking was most useful for women with depressive symptoms.</p> <p>Conclusion</p> <p>Supporting the Durkheimian concepts of both anomic and altruistic suicide, both disengagement (depression, anomie, vulnerability to victimization, and discomfort with non-Black physicians) as well as over-acceptance (low awareness of discrimination in society) predict poor health maintenance attitudes in disadvantaged women. Women who recognize their connection to other African-American women, and who talk about negative experiences, appear most motivated to protect their health.</p

Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950–2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021

Background: Estimates of demographic metrics are crucial to assess levels and trends of population health outcomes. The profound impact of the COVID-19 pandemic on populations worldwide has underscored the need for timely estimates to understand this unprecedented event within the context of long-term population health trends. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 provides new demographic estimates for 204 countries and territories and 811 additional subnational locations from 1950 to 2021, with a particular emphasis on changes in mortality and life expectancy that occurred during the 2020–21 COVID-19 pandemic period. Methods: 22 223 data sources from vital registration, sample registration, surveys, censuses, and other sources were used to estimate mortality, with a subset of these sources used exclusively to estimate excess mortality due to the COVID-19 pandemic. 2026 data sources were used for population estimation. Additional sources were used to estimate migration; the effects of the HIV epidemic; and demographic discontinuities due to conflicts, famines, natural disasters, and pandemics, which are used as inputs for estimating mortality and population. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate under-5 mortality rates, which synthesised 30 763 location-years of vital registration and sample registration data, 1365 surveys and censuses, and 80 other sources. ST-GPR was also used to estimate adult mortality (between ages 15 and 59 years) based on information from 31 642 location-years of vital registration and sample registration data, 355 surveys and censuses, and 24 other sources. Estimates of child and adult mortality rates were then used to generate life tables with a relational model life table system. For countries with large HIV epidemics, life tables were adjusted using independent estimates of HIV-specific mortality generated via an epidemiological analysis of HIV prevalence surveys, antenatal clinic serosurveillance, and other data sources. Excess mortality due to the COVID-19 pandemic in 2020 and 2021 was determined by subtracting observed all-cause mortality (adjusted for late registration and mortality anomalies) from the mortality expected in the absence of the pandemic. Expected mortality was calculated based on historical trends using an ensemble of models. In location-years where all-cause mortality data were unavailable, we estimated excess mortality rates using a regression model with covariates pertaining to the pandemic. Population size was computed using a Bayesian hierarchical cohort component model. Life expectancy was calculated using age-specific mortality rates and standard demographic methods. Uncertainty intervals (UIs) were calculated for every metric using the 25th and 975th ordered values from a 1000-draw posterior distribution. Findings: Global all-cause mortality followed two distinct patterns over the study period: age-standardised mortality rates declined between 1950 and 2019 (a 62·8% [95% UI 60·5–65·1] decline), and increased during the COVID-19 pandemic period (2020–21; 5·1% [0·9–9·6] increase). In contrast with the overall reverse in mortality trends during the pandemic period, child mortality continued to decline, with 4·66 million (3·98–5·50) global deaths in children younger than 5 years in 2021 compared with 5·21 million (4·50–6·01) in 2019. An estimated 131 million (126–137) people died globally from all causes in 2020 and 2021 combined, of which 15·9 million (14·7–17·2) were due to the COVID-19 pandemic (measured by excess mortality, which includes deaths directly due to SARS-CoV-2 infection and those indirectly due to other social, economic, or behavioural changes associated with the pandemic). Excess mortality rates exceeded 150 deaths per 100 000 population during at least one year of the pandemic in 80 countries and territories, whereas 20 nations had a negative excess mortality rate in 2020 or 2021, indicating that all-cause mortality in these countries was lower during the pandemic than expected based on historical trends. Between 1950 and 2021, global life expectancy at birth increased by 22·7 years (20·8–24·8), from 49·0 years (46·7–51·3) to 71·7 years (70·9–72·5). Global life expectancy at birth declined by 1·6 years (1·0–2·2) between 2019 and 2021, reversing historical trends. An increase in life expectancy was only observed in 32 (15·7%) of 204 countries and territories between 2019 and 2021. The global population reached 7·89 billion (7·67–8·13) people in 2021, by which time 56 of 204 countries and territories had peaked and subsequently populations have declined. The largest proportion of population growth between 2020 and 2021 was in sub-Saharan Africa (39·5% [28·4–52·7]) and south Asia (26·3% [9·0–44·7]). From 2000 to 2021, the ratio of the population aged 65 years and older to the population aged younger than 15 years increased in 188 (92·2%) of 204 nations. Interpretation: Global adult mortality rates markedly increased during the COVID-19 pandemic in 2020 and 2021, reversing past decreasing trends, while child mortality rates continued to decline, albeit more slowly than in earlier years. Although COVID-19 had a substantial impact on many demographic indicators during the first 2 years of the pandemic, overall global health progress over the 72 years evaluated has been profound, with considerable improvements in mortality and life expectancy. Additionally, we observed a deceleration of global population growth since 2017, despite steady or increasing growth in lower-income countries, combined with a continued global shift of population age structures towards older ages. These demographic changes will likely present future challenges to health systems, economies, and societies. The comprehensive demographic estimates reported here will enable researchers, policy makers, health practitioners, and other key stakeholders to better understand and address the profound changes that have occurred in the global health landscape following the first 2 years of the COVID-19 pandemic, and longer-term trends beyond the pandemic. Funding: Bill &amp; Melinda Gates Foundation

How prices and income influence global patterns in saturated fat intake by age, sex and world region: a cross-sectional analysis of 160 countries

Objective When considering proposals to improve diets, it is important to understand how factors like price and income can affect saturated fat (SF) intake and demand. In this study, we examine and estimate the influence of price and income on intake across 160 countries, by age and sex, and derive sensitivity measures (price elasticities) that vary by age, sex and world region. Design We econometrically estimate intake responsiveness to income and prices across countries, accounting for differences by world region, age and sex. Intake data by age, sex and country were obtained from the 2018 Global Dietary Database. These data were then linked to global price data for select food groups from the World Bank International Comparison Programme and income data from the World Development Indicators Databank (World Bank). Results Intake differences due to price were highly significant, with a 1% increase in price associated with a lower SF intake (% energy/d) of about 4.3 percentage points. We also find significant differences across regions. In high-income countries, median (age 40) intake reductions were 1.4, 0.8 and 0.2 percentage points, given a 1% increase in the price of meat, dairy, and oils and fats, respectively. Price elasticities varied with age but not sex. Intake differences due to income were insignificant when regional binary variables were included in the analysis. Conclusion The results of this study show heterogeneous associations among prices and intake within and across countries. Policymakers should consider these heterogeneous effects as they address global nutrition and health challenges. © 2024 BMJ Publishing Group. All rights reserved.The Global Dietary Database was supported by a grant from the Bill & Melina Gates Foundation: grant # OPP1176682