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To assess the feasibility of quantitative analysis of dynamic computed tomography angiography (dCTA) for the detection of endoleaks in patients who underwent endovascular repair of abdominal aortic aneurysms (EVAR). Twenty patients scheduled for contrast-enhanced CT angiography (CTA) of the abdominal aorta post-EVAR were prospectively enrolled. All patients received a standard triphasic CTA protocol, followed by an additional dCTA. The dCTA acquisition enabled reconstruction of color-coded maps depicting blood perfusion and a dCTA dataset of the aneurysm sac. Observers assessed the dCTA and dynamic CT perfusion (dCTP) images for the detection of endoleaks, establishing diagnostic confidence based on a modified 5-point Likert scale. An index was calculated for the ratio between the endoleak and aneurysm sac using blood flow for dCTP and Hounsfield units (HU) for dCTA. The Wilcoxon test compared the endoleak index and the diagnostic confidence of the observers. In total, 19 patients (18 males, median age 74 years [70.5-75.7]) were included for analysis. Nine endoleaks were detected in 7 patients using triphasic CTA as the reference standard. There was complete agreement for endoleak detection between the two techniques on a per-patient basis. Both dCTA and dCTP identified an additional endoleak in one patient. The diagnostic confidence using dCTP for detection of endoleaks was not significantly superior to dCTA (5.0 [5-5] vs. 4.5 [4-5], respectively; p = 0.11); however, dCTP demonstrated superior diagnostic confidence for endoleak exclusion compared to dCTA (1.0 [1-1] vs 1.5 [1.5-1.5], respectively; p <0.01). Moreover, the dCTP endoleak index was significantly higher than the dCTA index (18.5 [10.8-20.5] vs. 3.5 [5-2.7], respectively; p = 0.02). Quantitative analysis of dCTP imaging can aid in the detection of endoleaks and demonstrates a higher endoleak detection rate than triphasic CTA, as well as a strong correlation with visual assessment of dCTA images.

Background: To investigate gender differences in epicardial adipose tissue (EAT) and plaque composition by coronary CT angiography (CCTA) and the association with cardiovascular outcome. Methods: Data of 352 patients (64.2 ± 10.3 years, 38% female) with suspected coronary artery disease (CAD) who underwent CCTA were retrospectively analyzed. EAT volume and plaque composition from CCTA were compared between men and women. Major adverse cardiovascular events (MACE) were recorded from follow-up. Results: Men were more likely to have obstructive CAD, higher Agatston scores, and a larger total and non-calcified plaque burden. In addition, men displayed more adverse plaque characteristics and EAT volume compared to women (all p < 0.05). After a median follow-up of 5.1 years, MACE occurred in 8 women (6%) and 22 men (10%). In multivariable analysis, Agatston calcium score (HR 1.0008, p = 0.014), EAT volume (HR 1.067, p = 0.049), and low-attenuation plaque (HR 3.82, p = 0.036) were independent predictors for MACE in men, whereas only low-attenuation plaque (HR 2.42, p = 0.041) showed predictive value for events in women. Conclusion: Women demonstrated less overall plaque burden, fewer adverse plaque characteristics, and a smaller EAT volume compared to men. However, low-attenuation plaque is a predictor for MACE in both genders. Thus, a differentiated plaque analysis is warranted to understand gender differences of atherosclerosis to guide medical therapy and prevention strategies.

ObjectivesWe sought to investigate the diagnostic performance of coronary CT angiography (cCTA)–derived plaque markers combined with deep machine learning–based fractional flow reserve (CT-FFR) to identify lesion-specific ischemia using invasive FFR as the reference standard.MethodsEighty-four patients (61 ± 10 years, 65% male) who had undergone cCTA followed by invasive FFR were included in this single-center retrospective, IRB-approved, HIPAA-compliant study. Various plaque markers were derived from cCTA using a semi-automatic software prototype and deep machine learning–based CT-FFR. The discriminatory value of plaque markers and CT-FFR to identify lesion-specific ischemia on a per-vessel basis was evaluated using invasive FFR as the reference standard.ResultsOne hundred three lesion-containing vessels were investigated. 32/103 lesions were hemodynamically significant by invasive FFR. In a multivariate analysis (adjusted for Framingham risk score), the following markers showed predictive value for lesion-specific ischemia (odds ratio [OR]): lesion length (OR 1.15, p = 0.037), non-calcified plaque volume (OR 1.02, p = 0.007), napkin-ring sign (OR 5.97, p = 0.014), and CT-FFR (OR 0.81, p < 0.0001). A receiver operating characteristics analysis showed the benefit of identifying plaque markers over cCTA stenosis grading alone, with AUCs increasing from 0.61 with ≥ 50% stenosis to 0.83 with addition of plaque markers to detect lesion-specific ischemia. Further incremental benefit was realized with the addition of CT-FFR (AUC 0.93).ConclusionCoronary CTA–derived plaque markers portend predictive value to identify lesion-specific ischemia when compared to cCTA stenosis grading alone. The addition of CT-FFR to plaque markers shows incremental discriminatory power.Key Points• Coronary CT angiography (cCTA)–derived quantitative plaque markers of atherosclerosis portend high discriminatory power to identify lesion-specific ischemia.• Coronary CT angiography–derived fractional flow reserve (CT-FFR) shows superior diagnostic performance over cCTA alone in detecting lesion-specific ischemia.• A combination of plaque markers with CT-FFR provides incremental discriminatory value for detecting flow-limiting stenosis.

Objectives To evaluate the long-term prognostic value of coronary CT angiography (cCTA)-derived plaque measures and clinical parameters on major adverse cardiac events (MACE) using machine learning (ML). Methods Datasets of 361 patients (61.9 ± 10.3 years, 65% male) with suspected coronary artery disease (CAD) who underwent cCTA were retrospectively analyzed. MACE was recorded. cCTA-derived adverse plaque features and conventional CT risk scores together with cardiovascular risk factors were provided to a ML model to predict MACE. A boosted ensemble algorithm (RUSBoost) utilizing decision trees as weak learners with repeated nested cross-validation to train and validate the model was used. Performance of the ML model was calculated using the area under the curve (AUC). Results MACE was observed in 31 patients (8.6%) after a median follow-up of 5.4 years. Discriminatory power was significantly higher for the ML model (AUC 0.96 [95%CI 0.93–0.98]) compared with conventional CT risk scores including Agatston calcium score (AUC 0.84 [95%CI 0.80–0.87]), segment involvement score (AUC 0.88 [95%CI 0.84–0.91]), and segment stenosis score (AUC 0.89 [95%CI 0.86–0.92], all p  < 0.05). Similar results were shown for adverse plaque measures (AUCs 0.72–0.82, all p  < 0.05) and clinical parameters including the Framingham risk score (AUCs 0.71–0.76, all p  < 0.05). The ML model yielded significantly higher diagnostic performance compared with logistic regression analysis (AUC 0.96 vs. 0.92, p  = 0.024). Conclusion Integration of a ML model improves the long-term prediction of MACE when compared with conventional CT risk scores, adverse plaque measures, and clinical information. ML algorithms may improve the integration of patient’s information to enhance risk stratification. Key Points • A machine learning (ML) model portends high discriminatory power to predict major adverse cardiac events (MACE). • ML-based risk stratification shows superior diagnostic performance for MACE prediction over coronary CT angiography (cCTA)-derived risk scores or clinical parameters alone. • A ML model outperforms conventional logistic regression analysis for the prediction of MACE.

This study investigated the impact of coronary CT angiography (cCTA)-derived plaque markers and machine-learning-based CT-derived fractional flow reserve (CT-FFR) to identify adverse cardiac outcome. Data of 82 patients (60 ± 11 years, 62% men) who underwent cCTA and invasive coronary angiography (ICA) were analyzed in this single-center retrospective, institutional review board-approved, HIPAA-compliant study. Follow-up was performed to record major adverse cardiac events (MACE). Plaque quantification of lesions responsible for MACE and control lesions was retrospectively performed semiautomatically from cCTA together with machine-learning based CT-FFR. The discriminatory value of plaque markers and CT-FFR to predict MACE was evaluated. After a median follow-up of 18.5 months (interquartile range 11.5 to 26.6 months), MACE was observed in 18 patients (21%). In a multivariate analysis the following markers were predictors of MACE (odds ratio [OR]): lesion length (OR 1.16, p = 0.018), low-attenuation plaque (<30 HU) (OR 4.59, p = 0.003), Napkin ring sign (OR 2.71, p = 0.034), stenosis ≥50% (OR 3.83, p 0.042), and CT-FFR ≤0.80 (OR 7.78, p = 0.001). Receiver operating characteristics analysis including stenosis ≥50%, plaque markers and CT-FFR ≤0.80 (Area under the curve 0.94) showed incremental discriminatory power over stenosis ≥50% alone (Area under the curve 0.60, p <0.0001) for the prediction of MACE. cCTA-derived plaque markers and machine-learning CT-FFR demonstrate predictive value to identify MACE. In conclusion, combining plaque markers with machine-learning CT-FFR shows incremental discriminatory power over cCTA stenosis grading alone.

PurposeTo evaluate the feasibility of fractional flow reserve (cFFR) derivation from coronary CT angiography (CCTA) in patients with myocardial bridging (MB), its relationship with MB anatomical features, and clinical relevance.MethodsThis retrospective study included 120 patients with MB of the left anterior descending artery (LAD) and 41 controls. MB location, length, depth, muscle index, instance, and stenosis rate were measured. cFFR values were compared between superficial MB (≤ 2 mm), deep MB (> 2 mm), and control groups. Factors associated with abnormal cFFR values (≤ 0.80) were analyzed.ResultsMB patients demonstrated lower cFFR values in MB and distal segments than controls (all p < 0.05). A significant cFFR difference was only found in the MB segment during systole between superficial (0.94, 0.90–0.96) and deep MB (0.91, 0.83–0.95) (p = 0.018). Abnormal cFFR values were found in 69 (57.5%) MB patients (29 [49.2%] superficial vs. 40 [65.6%] deep; p = 0.069). MB length (OR = 1.06, 95% CI 1.03–1.10; p = 0.001) and systolic stenosis (OR = 1.04, 95% CI 1.01–1.07; p = 0.021) were the main predictors for abnormal cFFR, with an area under the curve of 0.774 (95% CI 0.689–0.858; p < 0.001). MB patients with abnormal cFFR reported more typical angina (18.8% vs 3.9%, p = 0.023) than patients with normal values.ConclusionMB patients showed lower cFFR values than controls. Abnormal cFFR values have a positive association with symptoms of typical angina. MB length and systolic stenosis demonstrate moderate predictive value for an abnormal cFFR value.Key Points• MB patients showed lower cFFR values than controls.• Abnormal cFFR values have a positive association with typical angina symptoms.• MB length and systolic stenosis demonstrate moderate predictive value for an abnormal cFFR value.

ObjectivesTo investigate diagnostic accuracy and radiation dose of high-pitch CT coronary artery calcium scoring (CACS) with tin filtration (Sn100kVp) versus standard 120kVp high-pitch acquisition.Methods78 patients (58% male, 61.5±9.1 years) were prospectively enrolled. Subjects underwent clinical 120kVp high-pitch CACS using third-generation dual-source CT followed by additional high-pitch Sn100kVp acquisition. Agatston scores, calcium volume scores, Agatston score categories, percentile-based risk categorization and radiation metrics were compared.Results61/78 patients showed coronary calcifications. Median Agatston scores were 34.9 [0.7–197.1] and 41.7 [0.7–207.2] and calcium volume scores were 34.1 [0.7–218.0] for Sn100kVp and 35.7 [1.1–221.0] for 120kVp acquisitions, respectively (both p<0.0001). Bland-Altman analysis revealed underestimated Agatston scores and calcium volume scores with Sn100kVp versus 120kVp acquisitions (mean difference: 16.4 and 11.5). However, Agatston score categories and percentile-based risk categories showed excellent agreement (ĸ=0.98 and ĸ=0.99). Image noise was 25.8±4.4HU and 16.6±2.9HU in Sn100kVp and 120kVp scans, respectively (p<0.0001). Dose-length-product was 9.9±4.8mGy*cm and 40.9±14.4mGy*cm with Sn100kVp and 120kVp scans, respectively (p<0.0001). This resulted in significant effective radiation dose reduction (0.13±0.07mSv vs. 0.57±0.2mSv, p<0.0001) for Sn100kVp acquisitions.ConclusionCACS using high-pitch low-voltage tin-filtered acquisitions demonstrates excellent agreement in Agatston score and percentile-based cardiac risk categorization with standard 120kVp high-pitch acquisitions. Furthermore, radiation dose was significantly reduced by 78% while maintaining accurate risk prediction.Key points• Coronary artery calcium scoring with tin filtration reduces radiation dose by 78%.• There is excellent correlation between high-pitch Sn100kVp and standard 120kVp acquisitions.• Excellent agreement regarding Agatston score categories and percentile-based risk categorization was achieved.• No cardiac risk reclassifications were observed using Sn100kVp coronary artery calcium scoring.

Theory predicts that sexually dimorphic traits under strong sexual selection, particularly those involved with intersexual signaling, can accelerate speciation and produce bursts of diversification. Sexual dichromatism (sexual dimorphism in color) is widely used as a proxy for sexual selection and is associated with rapid diversification in several animal groups, yet studies using phylogenetic comparative methods to explicitly test for an association between sexual dichromatism and diversification have produced conflicting results. Sexual dichromatism is rare in frogs, but it is both striking and prevalent in African reed frogs, a major component of the diverse frog radiation termed Afrobatrachia. In contrast to most other vertebrates, reed frogs display female-biased dichromatism in which females undergo color transformation, often resulting in more ornate coloration in females than in males. We produce a robust phylogeny of Afrobatrachia to investigate the evolutionary origins of sexual dichromatism in this radiation and examine whether the presence of dichromatism is associated with increased rates of net diversification. We find that sexual dichromatism evolved once within hyperoliids and was followed by numerous independent reversals to monochromatism. We detect significant diversification rate heterogeneity in Afrobatrachia and find that sexually dichromatic lineages have double the average net diversification rate of monochromatic lineages. By conducting trait simulations on our empirical phylogeny, we demonstrate that our inference of trait-dependent diversification is robust. Although sexual dichromatism in hyperoliid frogs is linked to their rapid diversification and supports macroevolutionary predictions of speciation by sexual selection, the function of dichromatism in reed frogs remains unclear. We propose that reed frogs are a compelling system for studying the roles of natural and sexual selection on the evolution of sexual dichromatism across micro- and macroevolutionary timescales.

As human life expectancy has improved rapidly in industrialized societies, age-related cognitive impairment presents an increasing challenge. Targeting histopathological processes that correlate with age-related cognitive declines, such as neuroinflammation, low levels of neurogenesis, disrupted blood–brain barrier and altered neuronal activity, might lead to structural and functional rejuvenation of the aged brain. Here we show that a 6-week treatment of young (4 months) and old (20 months) rats with montelukast, a marketed anti-asthmatic drug antagonizing leukotriene receptors, reduces neuroinflammation, elevates hippocampal neurogenesis and improves learning and memory in old animals. By using gene knockdown and knockout approaches, we demonstrate that the effect is mediated through inhibition of the GPR17 receptor. This work illustrates that inhibition of leukotriene receptor signalling might represent a safe and druggable target to restore cognitive functions in old individuals and paves the way for future clinical translation of leukotriene receptor inhibition for the treatment of dementias. The leukotriene receptor antagonist montelukast is an anti-asthmatic drug. Here, the authors show that montelukast reduces neuroinflammation, promotes hippocampal neurogenesis and restores learning and memory in old rats suffering from ageing-associated cognitive dysfunction.

Background Over the course of the COVID-19 pandemic, colleges and universities have focused on creating policies, such as mask mandates, to minimize COVID-19 transmission both on their campuses and in the surrounding community. Adherence to and opinions about these policies remain largely unknown. Methods The Centers for Disease Control and Prevention (CDC) developed a cross-sectional study, the Mask Adherence and Surveillance at Colleges and Universities Project (MASCUP!), to objectively and inconspicuously measure rates of mask use at institutes of higher education via direct observation. From February 15 through April 11, 2021 the University of Colorado Boulder (CU, n = 2,808 observations) and Colorado State University Fort Collins (CSU, n = 3,225 observations) participated in MASCUP! along with 52 other institutes of higher education (n = 100,353 observations) spanning 21 states and the District of Columbia. Mask use was mandatory at both Colorado universities and student surveys were administered to assess student beliefs and attitudes. Results We found that 91.7%, 93.4%, and 90.8% of persons observed at indoor locations on campus wore a mask correctly at University of Colorado, Colorado State University, and across the 52 other schools, respectively. Student responses to questions about masking were in line with these observed rates of mask use where 92.9% of respondents at CU and 89.8% at CSU believe that wearing masks can protect the health of others. Both Colorado universities saw their largest surges in COVID-19 cases in the fall of 2020, with markedly lower case counts during the mask observation window in the spring of 2021. Conclusion High levels of mask use at Colorado’s two largest campuses aligned with rates observed at other institutes across the country. These high rates of use, coupled with positive student attitudes about mask use, demonstrate that masks were widely accepted and may have contributed to reduced COVID-19 case counts. This study supports an emerging body of literature substantiating masks as an effective, low-cost measure to reduce disease transmission and establishes masking (with proper education and promotion) as a viable tactic to reduce respiratory disease transmission on college campuses.