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Antithrombotic therapy is a central component of treatment after acute coronary syndromes. The focus is on antiplatelet therapy, but balancing the benefit with the bleeding risk is still controversial. Clinical research and recommended approaches to management are reviewed.

Diverse race and ethnicity representation remains lacking in science and technology (S&T) careers in the United States (US). Due to systematic barriers across S&T training stages, there may be sequential loss of diverse representation leading to low representation, often conceptualized as a leaky pipeline. We aimed to quantify the contemporary leaky pipeline of S&T training in the US. We analyzed US S&T degree data, stratified by sex and then by race or ethnicity, obtained from survey data the National Science Foundation and the National Center for Science and Engineering Statistics. We assessed changes in race and ethnicity representation in 2019 at two major S&T transition points: bachelor to doctorate degrees (2003-2019) and doctorate degrees to postdoctoral positions (2010-2019). We quantified representation changes at each point as the ratio of representation in the later stage to earlier stage (representation ratio [RR]). We assessed secular trends in the representation ratio through univariate linear regression. For 2019, the survey data included for bachelor degrees, 12,714,921 men and 10.612,879 women; for doctorate degrees 14,259 men and 12,860 women; and for postdoctoral data, 11,361 men and 8.672 women. In 2019, we observed that Black, Asian, and Hispanic women had comparable loss of representation among women in the bachelor to doctorate transition (RR 0.86, 95% confidence interval [CI] 0.81-0.92; RR 0.85, 95% CI 0.81-0.89; and RR 0.82, 95% CI 0.77-0.87, respectively), while among men, Black and Asian men had the greatest loss of representation (Black men RR 0.72, 95% CI 0.66-0.78; Asian men RR 0.73, 95% CI 0.70-0.77)]. We observed that Black men (RR 0.60, 95% CI 0.51-0.69) and Black women (RR 0.56, 95% CI 0.49-0.63) experienced the greatest loss of representation among men and women, respectively, in the doctorate to postdoctoral transition. Black women had a statistically significant decrease in their representation ratio in the doctorate to postdoctoral transition from 2010 to 2019 (p-trend = 0.02). We quantified diverse race and ethnicity representation in contemporary US S&T training and found that Black men and women experienced the most consistent loss in representation across the S&T training pipeline. Findings should spur efforts to mitigate the structural racism and systemic barriers underpinning such disparities.

Clinical laboratory tests are a critical component of the continuum of care. We evaluate the genetic basis of 35 blood and urine laboratory measurements in the UK Biobank ( n  = 363,228 individuals). We identify 5,794 independent loci associated with at least one trait ( p  < 5 × 10 −9 ), containing 3,374 fine-mapped associations and additional sets of large-effect (>0.1 s.d.) protein-altering, human leukocyte antigen (HLA) and copy number variant (CNV) associations. Through Mendelian randomization (MR) analysis, we discover 51 causal relationships, including previously known agonistic effects of urate on gout and cystatin C on stroke. Finally, we develop polygenic risk scores (PRSs) for each biomarker and build ‘multi-PRS’ models for diseases using 35 PRSs simultaneously, which improved chronic kidney disease, type 2 diabetes, gout and alcoholic cirrhosis genetic risk stratification in an independent dataset (FinnGen; n  = 135,500) relative to single-disease PRSs. Together, our results delineate the genetic basis of biomarkers and their causal influences on diseases and improve genetic risk stratification for common diseases. Genetic analysis of 35 blood and urine laboratory measurements from 363,228 individuals identifies 1,857 loci associated with at least one laboratory value.

Early-career researchers from Spanish-speaking Latin American countries and small U.S. universities are underrepresented in international scientific databases. They have geographical importance, infectious disease endemicity, and exceptional researchers in microbiology, but these factors do not translate to representation in high-impact scientific publications. Many reasons could be involved, including financial burdens such as the inability to pay article processing charges. Additional teaching, institutional, and service responsibilities also highly influence their research productivity. Despite this, they are expected to publish high-impact articles, and their career development highly depends on it. There is an opportunity for global peer collaboration to tackle this inequity and uplift underrepresented scientists, which will ultimately provide benefits and sustainability to the global microbial sciences.

Hispanic populations generally experience more adverse socioeconomic conditions yet demonstrate lower mortality compared with Non-Hispanic White (NHW) populations in the US. This finding of a mortality advantage is well-described as the “Hispanic paradox.” The Coronavirus Disease 2019 (COVID-19) pandemic has disproportionately affected Hispanic populations. To quantify these effects, we evaluated US national and county-level trends in Hispanic versus NHW mortality from 2011 through 2020. We found that a previously steady Hispanic mortality advantage significantly decreased in 2020, potentially driven by COVID-19-attributable Hispanic mortality. Nearly 16% of US counties experienced a reversal of their pre-pandemic Hispanic mortality advantage such that their Hispanic mortality exceeded NHW mortality in 2020. An additional 50% experienced a decrease in a pre-pandemic Hispanic mortality advantage. Our work provides a quantitative understanding of the disproportionate burden of the pandemic on Hispanic health and the Hispanic paradox and provides a renewed impetus to tackle the factors driving these concerning disparities.

Lipoprotein(a) (Lp(a)) is a highly proatherogenic lipid fraction that is a clinically significant risk modifier. Patients wanting to learn more about Lp(a) are likely to use online patient educational materials (OPEMs). However, the readability of OPEMs may exceed the health literacy of the public. This study aims to assess the readability of OPEMs related to Lp(a). We hypothesized that the readability of these online materials would exceed the sixth grade level recommended by the American Medical Association. Using an online search engine, we queried the top 20 search results from 10 commonly used Lp(a)-related search terms to identify a total of 200 websites. We excluded duplicate websites, advertised results, research journal articles, or non-patient-directed materials, such as those intended only for health professionals or researchers. Grade level readability was calculated using 5 standard readability metrics (automated readability index, SMOG index, Coleman-Liau index, Gunning Fog score, Flesch-Kincaid score) to produce robust point (mean) and interval (CI) estimates of readability. Generalized estimating equations were used to model grade level readability by each search term, with the 5 readability scores nested within each OPEM. A total of 27 unique websites were identified for analysis. The average readability score for the aggregated results was a 12.2 (95% CI 10.9798-13.3978) grade level. OPEMs were grouped into 6 categories by primary source: industry, lay press, research foundation and nonprofit organizations, university or government, clinic, and other. The most readable category was OPEMs published by universities or government agencies (9.0, 95% CI 6.8-11.3). The least readable OPEMs on average were the ones published by the lay press (13.0, 95% CI 11.2-14.8). All categories exceeded the sixth grade reading level recommended by the American Medical Association. Lack of access to readable OPEMs may disproportionately affect patients with low health literacy. Ensuring that online content is understandable by broad audiences is a necessary component of increasing the impact of novel therapeutics and recommendations regarding Lp(a).

Infection with SARS-CoV-2 is typically compared with influenza to contextualize its health risks. SARS-CoV-2 has been linked with coagulation disturbances including arterial thrombosis, leading to considerable interest in antithrombotic therapy for Coronavirus Disease 2019 (COVID-19). However, the independent thromboembolic risk of SARS-CoV-2 infection compared with influenza remains incompletely understood. We evaluated the adjusted risks of thromboembolic events after a diagnosis of COVID-19 compared with influenza in a large retrospective cohort. We used a US-based electronic health record (EHR) dataset linked with insurance claims to identify adults diagnosed with COVID-19 between April 1, 2020 and October 31, 2020. We identified influenza patients diagnosed between October 1, 2018 and April 31, 2019. Primary outcomes [venous composite of pulmonary embolism (PE) and acute deep vein thrombosis (DVT); arterial composite of ischemic stroke and myocardial infarction (MI)] and secondary outcomes were assessed 90 days post-diagnosis. Propensity scores (PS) were calculated using demographic, clinical, and medication variables. PS-adjusted hazard ratios (HRs) were calculated using Cox proportional hazards regression. There were 417,975 COVID-19 patients (median age 57y, 61% women), and 345,934 influenza patients (median age 47y, 66% women). Compared with influenza, patients with COVID-19 had higher venous thromboembolic risk (HR 1.53, 95% CI 1.38-1.70), but not arterial thromboembolic risk (HR 1.02, 95% CI 0.95-1.10). Secondary analyses demonstrated similar risk for ischemic stroke (HR 1.11, 95% CI 0.98-1.25) and MI (HR 0.93, 95% CI 0.85-1.03) and higher risk for DVT (HR 1.36, 95% CI 1.19-1.56) and PE (HR 1.82, 95% CI 1.57-2.10) in patients with COVID-19. In a large retrospective US cohort, COVID-19 was independently associated with higher 90-day risk for venous thrombosis, but not arterial thrombosis, as compared with influenza. These findings may inform crucial knowledge gaps regarding the specific thromboembolic risks of COVID-19.

Current risk scores using clinical risk factors for predicting ischemic heart disease (IHD) events—the leading cause of global mortality—have known limitations and may be improved by imaging biomarkers. While body composition (BC) imaging biomarkers derived from abdominopelvic computed tomography (CT) correlate with IHD risk, they are impractical to measure manually. Here, in a retrospective cohort of 8139 contrast-enhanced abdominopelvic CT examinations undergoing up to 5 years of follow-up, we developed multimodal opportunistic risk assessment models for IHD by automatically extracting BC features from abdominal CT images and integrating these with features from each patient’s electronic medical record (EMR). Our predictive methods match and, in some cases, outperform clinical risk scores currently used in IHD risk assessment. We provide clinical interpretability of our model using a new method of determining tissue-level contributions from CT along with weightings of EMR features contributing to IHD risk. We conclude that such a multimodal approach, which automatically integrates BC biomarkers and EMR data, can enhance IHD risk assessment and aid primary prevention efforts for IHD. To further promote research, we release the Opportunistic L3 Ischemic heart disease (OL3I) dataset, the first public multimodal dataset for opportunistic CT prediction of IHD.

Background The COVID-19 pandemic has further exposed inequities in our society, demonstrated by disproportionate COVID-19 infection rate and mortality in communities of color and low-income communities. One key area of inequity that has yet to be explored is disparities based on preferred language. Methods We conducted a retrospective cohort study of 164,368 adults tested for COVID-19 in a large healthcare system across Washington, Oregon, and California from March – July 2020. Using electronic health records, we constructed multi-level models that estimated the odds of testing positive for COVID-19 by preferred language, adjusting for age, race/ethnicity, and social factors. We further investigated interaction between preferred language and both race/ethnicity and state. Analysis was performed from October–December 2020. Results Those whose preferred language was not English had higher odds of having a COVID-19 positive test (OR 3.07, p  < 0.001); this association remained significant after adjusting for age, race/ethnicity, and social factors. We found significant interaction between language and race/ethnicity and language and state, but the odds of COVID-19 test positivity remained greater for those whose preferred language was not English compared to those whose preferred language was English within each race/ethnicity and state. Conclusions People whose preferred language is not English are at greater risk of testing positive for COVID-19 regardless of age, race/ethnicity, geography, or social factors – demonstrating a significant inequity. Research demonstrates that our public health and healthcare systems are centered on English speakers, creating structural and systemic barriers to health. Addressing these barriers are long overdue and urgent for COVID-19 prevention.