Deep Learning for Echo Analysis, Tracking, and Evaluation of Mitral Regurgitation (DELINEATE-MR).

BACKGROUND: Artificial intelligence, particularly deep learning (DL), has immense potential to improve the interpretation of transthoracic echocardiography (TTE). Mitral regurgitation (MR) is the most common valvular heart disease and presents unique challenges for DL, including the integration of multiple video-level assessments into a final study-level classification. METHODS: A novel DL system was developed to intake complete TTEs, identify color MR Doppler videos, and determine MR severity on a 4-step ordinal scale (none/trace, mild, moderate, and severe) using the reading cardiologist as a reference standard. This DL system was tested in internal and external test sets with performance assessed by agreement with the reading cardiologist, weighted κ, and area under the receiver-operating characteristic curve for binary classification of both moderate or greater and severe MR. In addition to the primary 4-step model, a 6-step MR assessment model was studied with the addition of the intermediate MR classes of mild-moderate and moderate-severe with performance assessed by both exact agreement and ±1 step agreement with the clinical MR interpretation. RESULTS: A total of 61 689 TTEs were split into train (n=43 811), validation (n=8891), and internal test (n=8987) sets with an additional external test set of 8208 TTEs. The model had high performance in MR classification in internal (exact accuracy, 82%; κ=0.84; area under the receiver-operating characteristic curve, 0.98 for moderate/severe MR) and external test sets (exact accuracy, 79%; κ=0.80; area under the receiver-operating characteristic curve, 0.98 for moderate or greater MR). Most (63% internal and 66% external) misclassification disagreements were between none/trace and mild MR. MR classification accuracy was slightly higher using multiple TTE views (accuracy, 82%) than with only apical 4-chamber views (accuracy, 80%). In subset analyses, the model was accurate in the classification of both primary and secondary MR with slightly lower performance in cases of eccentric MR. In the analysis of the 6-step classification system, the exact accuracy was 80% and 76% with a ±1 step agreement of 99% and 98% in the internal and external test set, respectively. CONCLUSIONS: This end-to-end DL system can intake entire echocardiogram studies to accurately classify MR severity and may be useful in helping clinicians refine MR assessments.

A snapshot of SARS-CoV-2 viral RNA throughout wastewater treatment plants in Arkansas.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can spread the viral RNA in wastewater by the feces of those experience COVID-19 symptoms. While wastewater monitoring of SARS-CoV-2 in the raw sewage has been confirmed as an effective tool to predict COVID-19 infection, the goal of this study is to assess the presence of SARS-CoV-2 viral RNA throughout various wastewater treatment processes. Wastewater samples were collected from wastewater treatment plants (WWTPs) in the state of Arkansas from August 2020 to June 2021 and measured for the relative concentration of SARS-CoV-2 viral RNA using RT-qPCR. The gene concentrations in the raw wastewater measured in this study were similar to other published studies, targeting the N1 and N2 genes of the virus. The viral RNA concentration was measured after each wastewater treatment step within WWTPs, including primary sedimentation, activated sludge, filtration and disinfection. Results show the most viral RNA removal occurred in the secondary treatment (activated sludge). The viral RNA was only occasionally detected after disinfection (chlorination or UV disinfection). Overall, WWTPs can remove the SARS-CoV-2 viral RNA at an average of 98.7%, while complete removal was achieved on 82% of the sampling days. Further investigation is required to ensure complete viral RNA removal from wastewater such as improving existing treatment process or supplementing with additional treatment steps. PRACTITIONER POINTS: The viral RNA of SARS-CoV-2 was detected in Arkansas wastewater treatment plants. SARS-CoV-2 was rarely detected in treated effluent from wastewater treatment plants. Activated sludge was effective removing SARS-CoV-2 viral RNA from wastewater. This study was limited by the direct RNA extraction from wastewater, which lowered the sensitivity of detection.

Discovery of an embryonically derived bipotent population of endothelial-macrophage progenitor cells in postnatal aorta.

Converging evidence indicates that extra-embryonic yolk sac is the source of both macrophages and endothelial cells in adult mouse tissues. Prevailing views are that these embryonically derived cells are maintained after birth by proliferative self-renewal in their differentiated states. Here we identify clonogenic endothelial-macrophage (EndoMac) progenitor cells in the adventitia of embryonic and postnatal mouse aorta, that are independent of Flt3-mediated bone marrow hematopoiesis and derive from an early embryonic CX3CR1+ and CSF1R+ source. These bipotent progenitors are proliferative and vasculogenic, contributing to adventitial neovascularization and formation of perfused blood vessels after transfer into ischemic tissue. We establish a regulatory role for angiotensin II, which enhances their clonogenic and differentiation properties and rapidly stimulates their proliferative expansion in vivo. Our findings demonstrate that embryonically derived EndoMac progenitors participate in local vasculogenic responses in the aortic wall by contributing to the expansion of endothelial cells and macrophages postnatally.

Risk of ADHD, Autism Spectrum Disorder, and Executive Function Impairment in Metopic Craniosynostosis.

INTRODUCTION: Favorable behavioral interactions are critical for academic and interpersonal success. An association between metopic synostosis and behavioral impairments has not been fully elucidated. Behavioral dysfunction in school age children with surgically corrected metopic synostosis was evaluated using targeted testing to detect the most common behavioral abnormalities in this population. METHODS: Parents of children 6-18 years old with metopic synostosis completed the Conners Short 3 rd edition (Conners-3: ADHD), Social Responsiveness Scale 2 nd edition (SRS-2: autism spectrum disorder), Behavior Rating Inventory of Executive Function 2 nd edition (BRIEF-2: executive functioning), and Child's Behavioral Checklist (CBCL: behavioral/emotional functioning). Children also completed neurocognitive testing. Multivariable regression was used to determine predictors of clinically significant behavioral impairments. RESULTS: 60 children were enrolled. Average age at surgery was 9.2 ± 7.9 months, with an average age at assessment of 10.3 ± 3.5 years. Nearly half of patients demonstrated symptoms associated with ADHD, demonstrated by reaching or exceeding borderline clinical levels for inattention and hyperactivity subscales of the Conners-3. Greater age at surgery was associated with worse executive function, measured by reaching or exceeding clinically significant levels of the executive function subscale of the Conners-3 (p=0.04) and subscales of the BRIEF-2 (behavioral regulator index [p=0.05], cognitive regulatory index [p=0.03], and global executive composite [p=0.04]). CONCLUSIONS: Nearly half of patients with surgically corrected metopic synostosis reached borderline clinical scores for inattention and hyperactivity. Greater age at surgery was associated with worse executive function. Prompt surgical correction of metopic synostosis may portend improved long-term emotional and behavioral function.