Statewide Evaluation of SARS-CoV-2 Diagnoses and Sexual Orientation and Gender Identity.

OBJECTIVE: Understanding and identifying disparities in COVID-19 testing outcomes can help allocate resources to where they are most needed. The objective of this study was to estimate the association between lesbian, gay, bisexual, transgender, and queer (LGBTQ+) identity and SARS-CoV-2 test positivity. METHODS: Data were from the Rhode Island SARS-CoV-2 surveillance database and included tests scheduled from June 8, 2020, through January 15, 2021. We used multivariable generalized estimating equations accounting for repeat testing to estimate the odds of receiving a positive test result for SARS-CoV-2 by LGBTQ+ identity and race/ethnicity, adjusting for sociodemographic and temporal confounders. RESULTS: In multivariable analysis of 232 025 tests, LGBTQ+ people had lower odds of receiving a positive test result than cisgender heterosexual people (5.4% vs 8.7%; adjusted odds ratio [aOR] = 0.63; 95% CI, 0.59-0.68). Compared with cisgender heterosexual White people, LGBTQ+ White people were significantly less likely (aOR = 0.67; 95% CI, 0.61-0.73) and cisgender heterosexual people of color were significantly more likely (aOR = 1.71; 95% CI, 1.64-1.78) to receive a positive test result. LGBTQ+ people of color had similar test positivity (aOR = 0.90; 95% CI, 0.79-1.02) as cisgender heterosexual White people. People in sexual minority groups were significantly less likely than heterosexual people to receive a positive test result, but we found no significant differences in test results among cisgender, transgender, and gender nonconforming people. CONCLUSIONS: LGBTQ+ people may be less likely than heterosexual people to receive a positive test result for SARS-CoV-2, potentially related to protective health practices and greater social isolation. Addressing racial and ethnic disparities among both LGBTQ+ people and cisgender heterosexual people should be a priority of the public health workforce.

Cost effectiveness of medical devices to diagnose pre-eclampsia in low-resource settings.

BACKGROUND: Maternal mortality remains a major health challenge facing developing countries, with pre-eclampsia accounting for up to 17 percent of maternal deaths. Diagnosis requires skilled health providers and devices that are appropriate for low-resource settings. This study presents the first cost-effectiveness analysis of multiple medical devices used to diagnose pre-eclampsia in low- and middle-income countries (LMICs). METHODS: Blood pressure and proteinuria measurement devices, identified from compendia for LMICs, were included. We developed a decision tree framework to assess the cost-effectiveness of each device using parameter values that reflect the general standard of care based on a survey of relevant literature and expert opinion. We examined the sensitivity of our results using one-way and second-order probabilistic multivariate analyses. RESULTS: Because the disability-adjusted life years (DALYs) averted for each device were very similar, the results were influenced by the per-use cost ranking. The most cost-effective device combination was a semi-automatic blood pressure measurement device and visually read urine strip test with the lowest combined per-use cost of $0.2004 and an incremental cost effectiveness ratio of $93.6 per DALY gained relative to a baseline with no access to diagnostic devices. When access to treatment is limited, it is more cost-effective to improve access to treatment than to increase testing rates or diagnostic device sensitivity. CONCLUSIONS: Our findings were not sensitive to changes in device sensitivity, however they were sensitive to changes in the testing rate and treatment rate. Furthermore, our results suggest that simple devices are more cost-effective than complex devices. The results underscore the desirability of two design features for LMICs: ease of use and accuracy without calibration. Our findings have important implications for policy makers, health economists, health care providers and engineers.

Static magnetic fields aggravate the effects of ionizing radiation on cell cycle progression in bone marrow stem cells.

In order to evaluate the influence of static magnetic fields (SMF) on the progression of cell cycle as a monitor of presumptive genotoxicity of these fields, the effects of a 15 mT SMF on cell cycle progression in rat bone marrow stem cells (BMSC) were examined. The cells were divided into two groups. One group encountered SMF alone for 5h continuously but the other group exposed with X ray before treatment with SMF. The population of cells did not show any significant difference in the first group but the second group that was exposed with acute radiation before encountering SMF showed a significant increase in the number of cells in G(2)/M phase. So SMF has intensified the effects of X ray, where SMF alone, did not had any detectable influence on cell cycle. These findings suggest that magnetic fields (MF) play their role by increasing the effects of genotoxic agents and because of the greater concentration of free radicals in the presence of radical pair producers, this effect is better detectable.

Effects of halogen substitution on Watson-Crick base pairing: a possible mechanism for radiosensitivity.

The halogen substituent effect on geometries and charge distributions of the A-T base pair derivatives was evaluated using density functional theory at B3LYP/6-31G* level. The results indicate that all of the substitutions affect geometries and charge distributions of the atoms contributing hydrogen bonds. These changes would be the reason of the radiosensitization of these compounds incorporating DNA.

gamma-Turn types prediction in proteins using the support vector machines.

Recently, two different models have been developed for predicting gamma-turns in proteins by Kaur and Raghava [2002. An evaluation of beta-turn prediction methods. Bioinformatics 18, 1508-1514; 2003. A neural-network based method for prediction of gamma-turns in proteins from multiple sequence alignment. Protein Sci. 12, 923-929]. However, the major limitation of previous methods is inability in predicting gamma-turns types. Thus, there is a need to predict gamma-turn types using an approach which will be useful in overall tertiary structure prediction. In this work, support vector machines (SVMs), a powerful model is proposed for predicting gamma-turn types in proteins. The high rates of prediction accuracy showed that the formation of gamma-turn types is evidently correlated with the sequence of tripeptides, and hence can be approximately predicted based on the sequence information of the tripeptides alone.