Naturally segregating genetic variants contribute to thermal tolerance in a Drosophila melanogaster model system.

Thermal tolerance is a fundamental physiological complex trait for survival in many species. For example, everyday tasks such as foraging, finding a mate, and avoiding predation are highly dependent on how well an organism can tolerate extreme temperatures. Understanding the general architecture of the natural variants within the genes that control this trait is of high importance if we want to better comprehend thermal physiology. Here, we take a multipronged approach to further dissect the genetic architecture that controls thermal tolerance in natural populations using the Drosophila Synthetic Population Resource as a model system. First, we used quantitative genetics and Quantitative Trait Loci mapping to identify major effect regions within the genome that influences thermal tolerance, then integrated RNA-sequencing to identify differences in gene expression, and lastly, we used the RNAi system to (1) alter tissue-specific gene expression and (2) functionally validate our findings. This powerful integration of approaches not only allows for the identification of the genetic basis of thermal tolerance but also the physiology of thermal tolerance in a natural population, which ultimately elucidates thermal tolerance through a fitness-associated lens.

Risk Factors for Nondiagnostic Imaging in a Real-World Deployment of Artificial Intelligence Diabetic Retinal Examinations in an Integrated Healthcare System: Maximizing Workflow Efficiency Through Predictive Dilation.

OBJECTIVE: In the pivotal clinical trial that led to Food and Drug Administration De Novo "approval" of the first fully autonomous artificial intelligence (AI) diabetic retinal disease diagnostic system, a reflexive dilation protocol was used. Using real-world deployment data before implementation of reflexive dilation, we identified factors associated with nondiagnostic results. These factors allow a novel predictive dilation workflow, where patients most likely to benefit from pharmacologic dilation are dilated a priori to maximize efficiency and patient satisfaction. METHODS: Retrospective review of patients who were assessed with autonomous AI at Johns Hopkins Medicine (8/2020 to 5/2021). We constructed a multivariable logistic regression model for nondiagnostic results to compare characteristics of patients with and without diagnostic results, using adjusted odds ratio (aOR). P < .05 was considered statistically significant. RESULTS: Of 241 patients (59% female; median age = 59), 123 (51%) had nondiagnostic results. In multivariable analysis, type 1 diabetes (T1D, aOR = 5.82, 95% confidence interval [CI]: 1.45-23.40, P = .01), smoking (aOR = 2.86, 95% CI: 1.36-5.99, P = .005), and age (every 10-year increase, aOR = 2.12, 95% CI: 1.62-2.77, P < .001) were associated with nondiagnostic results. Following feature elimination, a predictive model was created using T1D, smoking, age, race, sex, and hypertension as inputs. The model showed an area under the receiver-operator characteristics curve of 0.76 in five-fold cross-validation. CONCLUSIONS: We used factors associated with nondiagnostic results to design a novel, predictive dilation workflow, where patients most likely to benefit from pharmacologic dilation are dilated a priori. This new workflow has the potential to be more efficient than reflexive dilation, thus maximizing the number of at-risk patients receiving their diabetic retinal examinations.

The Contribution of Socioeconomic Factors to HIV RNA Suppression in Persons With HIV Engaged in Care in the NA-ACCORD.

INTRODUCTION: Socioeconomic status (SES) influences well-being among people living with HIV (people with HIV [PWH]); when individual-level SES information is not available, area-level SES indicators may be a suitable alternative. We hypothesized that (1) select ZIP code-level SES indicators would be associated with viral suppression and (2) accounting for ZIP code-level SES would attenuate racial disparities in viral suppression among PWH. SETTING: The NA-ACCORD, a collaboration of clinical and interval cohorts of PWH, was used. METHODS: Participants with ≥1 viral load measurement and ≥1 US residential 5-digit ZIP code(s) between 2010 and 2018 were included. In this serial cross-sectional analysis, multivariable logistic regression models were used to quantify the annual association of race and ethnicity with viral suppression, in the presence of SES indicators and sex, hepatitis C status, and age. RESULTS: We observed a dose-response relationship between SES factors and viral suppression. Lower income and education were associated with 0.5-0.7-fold annual decreases in odds of viral suppression. We observed racial disparities of approximately 40% decreased odds of viral suppression among non-Hispanic Black compared with non-Hispanic White participants. The disparity persisted but narrowed by 3%-4% when including SES in the models. CONCLUSIONS: ZIP code-based SES was associated with viral suppression, and accounting for SES narrowed racial disparities in viral suppression among PWH in the NA-ACCORD. Inclusion of ZIP code-level indicators of SES as surrogates for individual-level SES should be considered to improve our understanding of the impact of social determinants of health and racial disparities on key outcomes among PWH in North America.

Naturally segregating genetic variants contribute to thermal tolerance in a D. melanogaster model system.

Thermal tolerance is a fundamental physiological complex trait for survival in many species. For example, everyday tasks such as foraging, finding a mate, and avoiding predation, are highly dependent on how well an organism can tolerate extreme temperatures. Understanding the general architecture of the natural variants of the genes that control this trait is of high importance if we want to better comprehend how this trait evolves in natural populations. Here, we take a multipronged approach to further dissect the genetic architecture that controls thermal tolerance in natural populations using the Drosophila Synthetic Population Resource (DSPR) as a model system. First, we used quantitative genetics and Quantitative Trait Loci (QTL) mapping to identify major effect regions within the genome that influences thermal tolerance, then integrated RNA-sequencing to identify differences in gene expression, and lastly, we used the RNAi system to 1) alter tissue-specific gene expression and 2) functionally validate our findings. This powerful integration of approaches not only allows for the identification of the genetic basis of thermal tolerance but also the physiology of thermal tolerance in a natural population, which ultimately elucidates thermal tolerance through a fitness-associated lens.