Deciphering Mechanochemical Influences of Emergent Actomyosin Crosstalk using QCM-D.

Cytoskeletal protein ensembles exhibit emergent mechanics where behavior exhibited in teams is not necessarily the sum of the components' single molecule properties. In addition, filaments may act as force sensors that distribute feedback and influence motor protein behavior. To understand the design principles of such emergent mechanics, we developed an approach utilizing QCM-D to measure how actomyosin bundles respond mechanically to environmental variables that alter constituent myosin II motor behavior. We demonstrate that QCM-D can detect changes in actomyosin viscoelasticity due to molecular-level alterations, such as motor concentration and nucleotide state, thus providing evidence for actin's role as a mechanical force-feedback sensor and a new approach for deciphering the fundamental mechanisms of emergent cytoskeletal ensemble crosstalk. Justification: Cytoskeletal ensembles exhibit mechanics that are not necessarily the sum of the components' single molecule properties, and this emergent behavior is not well understood. Cytoskeletal filaments may also act as force sensors that influence constituent motor protein behavior. To understand the elusive design principles of such emergent mechanics, we innovated an approach using QCM-D to measure how actomyosin bundles sense and respond mechanically to environmental variables. We demonstrate for the first time that QCM-D can detect changes in actomyosin viscoelasticity due to molecular-level alterations, thus providing evidence for actin's role as a mechanical force-feedback sensor and a new approach for deciphering the fundamentals of emergent cytoskeletal ensemble crosstalk.

Self-Supervised Learning for Improved Optical Coherence Tomography Detection of Macular Telangiectasia Type 2.

Importance: Deep learning image analysis often depends on large, labeled datasets, which are difficult to obtain for rare diseases. Objective: To develop a self-supervised approach for automated classification of macular telangiectasia type 2 (MacTel) on optical coherence tomography (OCT) with limited labeled data. Design, Setting, and Participants: This was a retrospective comparative study. OCT images from May 2014 to May 2019 were collected by the Lowy Medical Research Institute, La Jolla, California, and the University of Washington, Seattle, from January 2016 to October 2022. Clinical diagnoses of patients with and without MacTel were confirmed by retina specialists. Data were analyzed from January to September 2023. Exposures: Two convolutional neural networks were pretrained using the Bootstrap Your Own Latent algorithm on unlabeled training data and fine-tuned with labeled training data to predict MacTel (self-supervised method). ResNet18 and ResNet50 models were also trained using all labeled data (supervised method). Main Outcomes and Measures: The ground truth yes vs no MacTel diagnosis is determined by retinal specialists based on spectral-domain OCT. The models' predictions were compared against human graders using accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), area under precision recall curve (AUPRC), and area under the receiver operating characteristic curve (AUROC). Uniform manifold approximation and projection was performed for dimension reduction and GradCAM visualizations for supervised and self-supervised methods. Results: A total of 2636 OCT scans from 780 patients with MacTel and 131 patients without MacTel were included from the MacTel Project (mean [SD] age, 60.8 [11.7] years; 63.8% female), and another 2564 from 1769 patients without MacTel from the University of Washington (mean [SD] age, 61.2 [18.1] years; 53.4% female). The self-supervised approach fine-tuned on 100% of the labeled training data with ResNet50 as the feature extractor performed the best, achieving an AUPRC of 0.971 (95% CI, 0.969-0.972), an AUROC of 0.970 (95% CI, 0.970-0.973), accuracy of 0.898%, sensitivity of 0.898, specificity of 0.949, PPV of 0.935, and NPV of 0.919. With only 419 OCT volumes (185 MacTel patients in 10% of labeled training dataset), the ResNet18 self-supervised model achieved comparable performance, with an AUPRC of 0.958 (95% CI, 0.957-0.960), an AUROC of 0.966 (95% CI, 0.964-0.967), and accuracy, sensitivity, specificity, PPV, and NPV of 90.2%, 0.884, 0.916, 0.896, and 0.906, respectively. The self-supervised models showed better agreement with the more experienced human expert graders. Conclusions and Relevance: The findings suggest that self-supervised learning may improve the accuracy of automated MacTel vs non-MacTel binary classification on OCT with limited labeled training data, and these approaches may be applicable to other rare diseases, although further research is warranted.

Uses of Social Determinants of Health Data to Address Cardiovascular Disease and Health Equity: A Scoping Review.

Background Cardiovascular disease is the leading cause of morbidity and mortality worldwide. Prior research suggests that social determinants of health have a compounding effect on health and are associated with cardiovascular disease. This scoping review explores what and how social determinants of health data are being used to address cardiovascular disease and improve health equity. Methods and Results After removing duplicate citations, the initial search yielded 4110 articles for screening, and 50 studies were identified for data extraction. Most studies relied on similar data sources for social determinants of health, including geocoded electronic health record data, national survey responses, and census data, and largely focused on health care access and quality, and the neighborhood and built environment. Most focused on developing interventions to improve health care access and quality or characterizing neighborhood risk and individual risk. Conclusions Given that few interventions addressed economic stability, education access and quality, or community context and social risk, the potential for harnessing social determinants of health data to reduce the burden of cardiovascular disease remains unrealized.

Rural-urban disparities in health outcomes, clinical care, health behaviors, and social determinants of health and an action-oriented, dynamic tool for visualizing them.

While rural-urban disparities in health and health outcomes have been demonstrated, because of their impact on (and intervenability to improve) health and health outcomes, we sought to examine cross-sectional and longitudinal inequities in health, clinical care, health behaviors, and social determinants of health (SDOH) between rural and non-rural counties in the pre-pandemic era (2015 to 2019), and to present a Health Equity Dashboard that can be used by policymakers and researchers to facilitate examining such disparities. Therefore, using data obtained from 2015-2022 County Health Rankings datasets, we used analysis of variance to examine differences in 33 county level attributes between rural and non-rural counties, calculated the change in values for each measure between 2015 and 2019, determined whether rural-urban disparities had widened, and used those data to create a Health Equity Dashboard that displays county-level individual measures or compilations of them. We followed STROBE guidelines in writing the manuscript. We found that rural counties overwhelmingly had worse measures of SDOH at the county level. With few exceptions, the measures we examined were getting worse between 2015 and 2019 in all counties, relatively more so in rural counties, resulting in the widening of rural-urban disparities in these measures. When rural-urban gaps narrowed, it tended to be in measures wherein rural counties were outperforming urban ones in the earlier period. In conclusion, our findings highlight the need for policymakers to prioritize rural settings for interventions designed to improve health outcomes, likely through improving health behaviors, clinical care, social and environmental factors, and physical environment attributes. Visualization tools can help guide policymakers and researchers with grounded information, communicate necessary data to engage relevant stakeholders, and track SDOH changes and health outcomes over time.

An observational, sequential analysis of the relationship between local economic distress and inequities in health outcomes, clinical care, health behaviors, and social determinants of health.

BACKGROUND: Socioeconomic status has long been associated with population health and health outcomes. While ameliorating social determinants of health may improve health, identifying and targeting areas where feasible interventions are most needed would help improve health equity. We sought to identify inequities in health and social determinants of health (SDOH) associated with local economic distress at the county-level. METHODS: For 3,131 counties in the 50 US states and Washington, DC (wherein approximately 325,711,203 people lived in 2019), we conducted a retrospective analysis of county-level data collected from County Health Rankings in two periods (centering around 2015 and 2019). We used ANOVA to compare thirty-three measures across five health and SDOH domains (Health Outcomes, Clinical Care, Health Behaviors, Physical Environment, and Social and Economic Factors) that were available in both periods, changes in measures between periods, and ratios of measures for the least to most prosperous counties across county-level prosperity quintiles, based on the Economic Innovation Group's 2015-2019 Distressed Community Index Scores. RESULTS: With seven exceptions, in both periods, we found a worsening of values with each progression from more to less prosperous counties, with least prosperous counties having the worst values (ANOVA p < 0.001 for all measures). Between 2015 and 2019, all except six measures progressively worsened when comparing higher to lower prosperity quintiles, and gaps between the least and most prosperous counties generally widened. CONCLUSIONS: In the late 2010s, the least prosperous US counties overwhelmingly had worse values in measures of Health Outcomes, Clinical Care, Health Behaviors, the Physical Environment, and Social and Economic Factors than more prosperous counties. Between 2015 and 2019, for most measures, inequities between the least and most prosperous counties widened. Our findings suggest that local economic prosperity may serve as a proxy for health and SDOH status of the community. Policymakers and leaders in public and private sectors might use long-term, targeted economic stimuli in low prosperity counties to generate local, community health benefits for vulnerable populations. Doing so could sustainably improve health; not doing so will continue to generate poor health outcomes and ever-widening economic disparities.

Modeling to explore and challenge inherent assumptions when cultural norms have changed: a case study on left-handedness and life expectancy.

BACKGROUND: In 1991, Halpern and Coren claimed that left-handed people die nine years younger than right-handed people. Most subsequent studies did not find support for the difference in age of death or its magnitude, primarily because of the realization that there have been historical changes in reported rates of left-handedness. METHODS: We created a model that allowed us to determine whether the historical change in left-handedness explains the original finding of a nine-year difference in life expectancy. We calculated all deaths in the United States by birth year, gender, and handedness for 1989 (the Halpern and Coren study was based on data from that year) and contrasted those findings with the modeled age of death by reported and counterfactual estimated handedness for each birth year, 1900-1989. RESULTS: In 1989, 2,019,512 individuals died, of which 6.4% were reportedly left-handed based on concurrent annual handedness reporting. However, it is widely believed that cultural pressures may have caused an underestimation of the true rate of left-handedness. Using a simulation that assumed no age of death difference between left-handed and right-handed individuals in this cohort and adjusting for the reported rates of left-handedness, we found that left-handed individuals were expected to die 9.3 years earlier than their right-handed counterparts due to changes in the rate of left-handedness over time. This difference of 9.3 years was not found to be statistically significant compared to the 8.97 years reported by Halpern and Coren. When we assumed no change in the rate of left-handedness over time, the survival advantage for right-handed individuals was reduced to 0.02 years, solely driven by not controlling for gender. When we considered the estimated age of death for each birth cohort, we found a mean difference of 0.43 years between left-handed and right-handed individuals, also driven by handedness difference by gender. CONCLUSION: We found that the changing rate of left-handedness reporting over the years entirely explains the originally reported observation of nine-year difference in life expectancy. In epidemiology, new information on past reporting biases could warrant re-exploration of initial findings. The simulation modeling approach that we use here might facilitate such analyses.

Evaluating the generalizability of deep learning image classification algorithms to detect middle ear disease using otoscopy.

To evaluate the generalizability of artificial intelligence (AI) algorithms that use deep learning methods to identify middle ear disease from otoscopic images, between internal to external performance. 1842 otoscopic images were collected from three independent sources: (a) Van, Turkey, (b) Santiago, Chile, and (c) Ohio, USA. Diagnostic categories consisted of (i) normal or (ii) abnormal. Deep learning methods were used to develop models to evaluate internal and external performance, using area under the curve (AUC) estimates. A pooled assessment was performed by combining all cohorts together with fivefold cross validation. AI-otoscopy algorithms achieved high internal performance (mean AUC: 0.95, 95%CI: 0.80-1.00). However, performance was reduced when tested on external otoscopic images not used for training (mean AUC: 0.76, 95%CI: 0.61-0.91). Overall, external performance was significantly lower than internal performance (mean difference in AUC: -0.19, p ≤ 0.04). Combining cohorts achieved a substantial pooled performance (AUC: 0.96, standard error: 0.01). Internally applied algorithms for otoscopy performed well to identify middle ear disease from otoscopy images. However, external performance was reduced when applied to new test cohorts. Further efforts are required to explore data augmentation and pre-processing techniques that might improve external performance and develop a robust, generalizable algorithm for real-world clinical applications.

Identifying long-term effects of SARS-CoV-2 and their association with social determinants of health in a cohort of over one million COVID-19 survivors.

BACKGROUND: Despite an abundance of information on the risk factors of SARS-CoV-2, there have been few US-wide studies of long-term effects. In this paper we analyzed a large medical claims database of US based individuals to identify common long-term effects as well as their associations with various social and medical risk factors. METHODS: The medical claims database was obtained from a prominent US based claims data processing company, namely Change Healthcare. In addition to the claims data, the dataset also consisted of various social determinants of health such as race, income, education level and veteran status of the individuals. A self-controlled cohort design (SCCD) observational study was performed to identify ICD-10 codes whose proportion was significantly increased in the outcome period compared to the control period to identify significant long-term effects. A logistic regression-based association analysis was then performed between identified long-term effects and social determinants of health. RESULTS: Among the over 1.37 million COVID patients in our datasets we found 36 out of 1724 3-digit ICD-10 codes to be statistically significantly increased in the post-COVID period (p-value < 0.05). We also found one combination of ICD-10 codes, corresponding to 'other anemias' and 'hypertension', that was statistically significantly increased in the post-COVID period (p-value < 0.05). Our logistic regression-based association analysis with social determinants of health variables, after adjusting for comorbidities and prior conditions, showed that age and gender were significantly associated with the multiple long-term effects. Race was only associated with 'other sepsis', income was only associated with 'Alopecia areata' (autoimmune disease causing hair loss), while education level was only associated with 'Maternal infectious and parasitic diseases' (p-value < 0.05). CONCLUSION: We identified several long-term effects of SARS-CoV-2 through a self-controlled study on a cohort of over one million patients. Furthermore, we found that while age and gender are commonly associated with the long-term effects, other social determinants of health such as race, income and education levels have rare or no significant associations.

The Risk of Hospitalization and Mortality After Breakthrough SARS-CoV-2 Infection by Vaccine Type: Observational Study of Medical Claims Data.

BACKGROUND: Several risk factors have been identified for severe COVID-19 disease by the scientific community. In this paper, we focus on understanding the risks for severe COVID-19 infections after vaccination (ie, in breakthrough SARS-CoV-2 infections). Studying these risks by vaccine type, age, sex, comorbidities, and any prior SARS-CoV-2 infection is important to policy makers planning further vaccination efforts. OBJECTIVE: We performed a comparative study of the risks of hospitalization (n=1140) and mortality (n=159) in a SARS-CoV-2 positive cohort of 19,815 patients who were all fully vaccinated with the Pfizer, Moderna, or Janssen vaccines. METHODS: We performed Cox regression analysis to calculate the risk factors for developing a severe breakthrough SARS-CoV-2 infection in the study cohort by controlling for vaccine type, age, sex, comorbidities, and a prior SARS-CoV-2 infection. RESULTS: We found lower hazard ratios for those receiving the Moderna vaccine (P<.001) and Pfizer vaccine (P<.001), with the lowest hazard rates being for Moderna, as compared to those who received the Janssen vaccine, independent of age, sex, comorbidities, vaccine type, and prior SARS-CoV-2 infection. Further, individuals who had a SARS-CoV-2 infection prior to vaccination had some increased protection over and above the protection already provided by the vaccines, from hospitalization (P=.001) and death (P=.04), independent of age, sex, comorbidities, and vaccine type. We found that the top statistically significant risk factors for severe breakthrough SARS-CoV-2 infections were age of >50, male gender, moderate and severe renal failure, severe liver disease, leukemia, chronic lung disease, coagulopathy, and alcohol abuse. CONCLUSIONS: Among individuals who were fully vaccinated, the risk of severe breakthrough SARS-CoV-2 infection was lower for recipients of the Moderna or Pfizer vaccines and higher for recipients of the Janssen vaccine. These results from our analysis at a population level will be helpful to public health policy makers. Our result on the influence of a previous SARS-CoV-2 infection necessitates further research into the impact of multiple exposures on the risk of developing severe COVID-19.

Label-Free Detection of ß-Sheet Polymorphism.

The ability to detect and characterize multiple secondary structures or polymorphs within peptide and protein aggregates is crucial to treatment and prevention of amyloidogenic diseases, production of novel biomaterials, and many other applications. Here we report a label-free method to distinguish multiple ß-sheet configurations within a single peptide aggregate using two-dimensional infrared spectroscopy. By calculating the transition dipole strength (TDS) spectrum from the ratio of linear and two-dimensional signals, we can extract maximum TDS values which provide higher sensitivity to vibrational coupling, and thus specifics of protein structure, than vibrational frequency alone. TDS spectra of AcKFE8 aggregates reveal two distinct ß-sheet structures within fibers that appear homogeneous by other techniques. Furthermore, TDS spectra taken during early stages of aggregation show additional peaks that may indicate the presence of more weakly coupled ß-sheet structures. These results demonstrate a unique and powerful spectroscopic method capable of distinguishing multiple oligomeric and polymorphic motifs throughout the aggregation using only native vibrational modes.

Investigating the effects of N-terminal acetylation on KFE8 self-assembly with 2D IR spectroscopy.

Peptide self-assembly is an exciting and robust approach to create novel nanoscale materials for biomedical applications. However, the complex interplay between intra- and intermolecular interactions in peptide aggregation means that minor changes in peptide sequence can yield dramatic changes in supramolecular structure. Here, we use two-dimensional infrared spectroscopy to study a model amphiphilic peptide, KFE8, and its N-terminal acetylated counterpart, AcKFE8. Two-dimensional infrared spectra of isotope-labeled peptides reveal that AcKFE8 aggregates comprise two distinct ß-sheet structures although KFE8 aggregates comprise only one of these structures. Using an excitonic Hamiltonian to simulate the vibrational spectra of model ß-sheets, we determine that the spectra are consistent with antiparallel ß-sheets with different strand alignments, specifically a two-residue shift in the register of the ß-strands. These findings bring forth new insights into how N-terminal acetylation may subtly impact secondary structure, leading to larger effects on overall aggregate morphology. In addition, these results highlight the importance of understanding the residue-level structural differences that result from changes in peptide sequence to facilitate the rational design of peptide materials.

Trends in Characteristics of Adults Enrolled in Traditional Fee-for-Service Medicare and Medicare Advantage, 2011-2019.

BACKGROUND: While overall Medicare Part C (Medicare Advantage) enrollment has grown more rapidly than fee-for-service Medicare enrollment, changes in the growth and characteristics of different enrollee populations have not been examined. OBJECTIVES: For 2011-2019, to compare changes in the growth and characteristics of younger (age younger than 65) and older (age 65 and older) Medicare beneficiaries enrolled in Medicare Part A only, Medicare Parts A & B, and Medicare Part C. RESEARCH DESIGN: This was a retrospective, observational study. SUBJECTS: Medicare beneficiaries who were alive and enrolled in Medicare Part A only, Medicare Parts A & B, or Medicare Part C on June 30 of each year and in no other plan that year. MEASURES: For each plan type and age group the numbers and mean ages of enrollees and the proportion of enrollees who were: black, female, concurrently enrolled in Medicaid, and (for older enrollees), whose initial reason for eligibility was old age and survivors' benefits. RESULTS: Between 2011 and 2019, Medicare Part C experienced rapid expansions of 85.0% among older and 109.5% among younger enrollees. Part C enrollees were increasingly likely to be dually enrolled in Medicaid, Black and, among younger enrollees, female. CONCLUSIONS: Trends in demographic characteristics and changes in policy and growth in employer group plan offerings will likely continue to impact health care service utilization and costs in the Medicare population. Particularly as Medicare expansion to younger age groups is considered, future research should explore disparities in risk scores and care equity, quality, and costs across different Medicare enrollment options.