Assessment of the Expression of Smile Intensity with or without Wearing Mask

Introduction: Smile is the most important means of communication of our feelings and emotions. The usage of a mask for prevention of respiratory illness can hamper the basic communication to some extent. This study was conducted to assess the smile intensity with or without wearing mask. Method: Photographs of 6 participants were taken in no smile, mild smile, moderate smile and full smile intensity were taken and mask was added to them using adobe photoshop. 95 participants (32 Interns, 32 UG and 31PG) rated these masked and unmasked photographs on a scale of 0-3 based on the smile intensity perceived by them. Comparisons of rating of the same photographs were done for masked and unmasked group.  Results: Ratings for the unmasked photographs were more accurate compared to their masked version. The actual rating and the ratings given by the participants were similar in case of unmasked photos. Masked photographs ratings were dissimilar from the actual ratings. Conclusion: Mask usage causes difficulty in accurately assessing the smile intensity and limits the recognition of facial expressions

Dentigerous cyst involving mandibular second permanent molar: A case report

Dentigerous cyst is the most common developmental odontogenic cyst, frequently noted as an incidental finding on radiographs. It commonly affects the permanent mandibular third molars and maxillary canines. We present a case of a dentigerous cyst involving an impacted mandibular second molar in a 21-year-old female

Clinico-epidemiological profile of malaria: Analysis from a primary health centre in Karnataka, Southern India

Malaria continues to be a major public health problem in India and worldwide. The present study was based on records from a primary health centre in Karnataka. Morbidity patterns and important features of malaria transmission specific to Udupi district were investigated. The incidence of malaria and various morbidity patterns during 2010 and 2011 were compared and analyzed. Factors such as rapid urbanization, increased construction activities and influx of migratory workers were highlighted as the leading causes for the advent of malaria in the area. Recommendations have been provided for implementation in the near future

Do bond attributes affect green bond yield? Evidence from Indian green bonds

Over the years, green finance tools have gained considerable attention with the increased concern to achieve sustainability in the economy. Green bonds are one such new innovative green finance tool embodied with bonds and green attributes. However, research on the Indian green bond is relatively modest. Thus, this study aims to analyze the impact of bond attributes on green bond yield. The study retrieves green bond data from the Bloomberg and Climate Bonds Initiative databases from 2015 to 2022. To test the framed hypotheses, the study employs a panel regression technique with a random effect model. The findings of the study show a significant positive effect of bond ratings (β = 2.80926, p < 0.05) on green bond yield based on the argument that good-rated bonds serve as collateral in the security market. On the contrary, the result also reveals a significant negative effect of bond maturity (β = –0.327296, p < 0.05) and bond label (β = –3.16480, p < 0.05) on green bond yield. The results based on the observation suggest that when the certified bond is issued, this signals the greenness of the bond in the market and attracts high demand, whereas the long maturity ensures the green project construction for a longer period, resulting in a lower bond value. Thus, empirical findings reveal that bond attributes are the major factors in influencing bond yield. The obtained results serve as a prerequisite for potential issuers, investors, and policymakers to further popularize the green bond in the country

Upregulation of ASCL1 and inhibition of Notch signaling pathway characterize progressive astrocytoma

Astrocytoma is the most common type of brain cancer constituting more than half of all brain tumors. With an aim to identify markers describing astrocytoma progression, we have carried out microarray analysis of astrocytoma samples of different grades using cDNA microarray containing 1152 cancer-specific genes. Data analysis identified several differentially regulated genes between normal brain tissue and astrocytoma as well as between grades II/III astrocytoma and glioblastoma multiforme (GBM; grade IV). We found several genes known to be involved in malignancy including Achaete-scute complex-like 1 (Drosophila) (ASCL1; Hash 1). As ASCL has been implicated in neuroendocrine, medullary thyroid and small-cell lung cancers, we chose to examine the role of ASCL1 in the astrocytoma development. Our data revealed that ASCL1 is overexpressed in progressive astrocytoma as evidenced by increased levels of ASCL1 transcripts in 85.71% (6/7) of grade II diffuse astrocytoma (DA), 90% (9/10) of grade III anaplastic astrocytoma (AA) and 87.5% (7/8) of secondary GBMs, while the majority of primary de novo GBMs expressed similar to or less than normal brain levels (66.67%; 8/12). ASCL1 upregulation in progressive astrocytoma is accompanied by inhibition of Notch signaling as seen by uninduced levels of HES1, a transcriptional target of Notch1, increased levels of HES6, a dominant-negative inhibitor of HES1-mediated repression of ASCL1, and increased levels of Notch ligand Delta1, which is capable of inhibiting Notch signaling by forming intracellular Notch ligand autonomous complexes. Our results imply that inhibition of Notch signaling may be an important early event in the development of grade II DA and subsequent progression to grade III AA and secondary GBM. Furthermore, ASCL1 appears to be a putative marker to distinguish primary GBM from secondary GBM

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation

Economic Essays on Adult Students

Adult students are an important component of the current U.S education landscape. They account for over 40% of the degree-seeking fresh enrollees in the U.S. colleges and according to the U.S. Department of Education, their growth will soon outpace that of traditional students. Adult students have also received considerable attention in higher education policy circles as an important resource to meet the future skills demand in U.S. industries. The focus on adult students is integral to sustaining the health of the U.S economy. Chapters in this dissertation aim to understand and quantify issues surrounding adult students. Chapter 1 of this dissertation analyzes the characteristics and factors that help or inhibit the decisions to return to school of adult students. Using an endogenous switching model and data from the Survey of Income Program and Participation (SIPP) 2008, I examine the determinants of the return decision. The results show positive selection bias from observed earnings of those who return, and the probability of returning to school hinges significantly on family size, family income, and the presence of children under 18. Chapter 2 analyzes the pecuniary returns to returning adults using the National Longitudinal Youth Survey of 1979 (NLSY79). I find 10-20% returns to returning adults across different education degrees. I also find that the post-return experience premium is higher for returners relative to non-returners. Chapter 3 analyzes the degree of persistence or state dependence in enrollment behavior of adult students using NLSY data from 1989-1994 and dynamic panel estimation methods. The results suggest that state dependence effects exist with respect to the previous enrollment incidence for men and women. For men I find that about 20% of the observed persistence in the enrollment probability is accounted for by state-dependence, as compared with roughly 36% for women