Revision of Administrative Law as Shortcut to Constitutional Revision

East Asian Languages and Civilization

Deep learning-enabled coronary CT angiography for plaque and stenosis quantification and cardiac risk prediction: an international multicentre study

BACKGROUND: Atherosclerotic plaque quantification from coronary CT angiography (CCTA) enables accurate assessment of coronary artery disease burden and prognosis. We sought to develop and validate a deep learning system for CCTA-derived measures of plaque volume and stenosis severity. METHODS: This international, multicentre study included nine cohorts of patients undergoing CCTA at 11 sites, who were assigned into training and test sets. Data were retrospectively collected on patients with a wide range of clinical presentations of coronary artery disease who underwent CCTA between Nov 18, 2010, and Jan 25, 2019. A novel deep learning convolutional neural network was trained to segment coronary plaque in 921 patients (5045 lesions). The deep learning network was then applied to an independent test set, which included an external validation cohort of 175 patients (1081 lesions) and 50 patients (84 lesions) assessed by intravascular ultrasound within 1 month of CCTA. We evaluated the prognostic value of deep learning-based plaque measurements for fatal or non-fatal myocardial infarction (our primary outcome) in 1611 patients from the prospective SCOT-HEART trial, assessed as dichotomous variables using multivariable Cox regression analysis, with adjustment for the ASSIGN clinical risk score. FINDINGS: In the overall test set, there was excellent or good agreement, respectively, between deep learning and expert reader measurements of total plaque volume (intraclass correlation coefficient [ICC] 0·964) and percent diameter stenosis (ICC 0·879; both p<0·0001). When compared with intravascular ultrasound, there was excellent agreement for deep learning total plaque volume (ICC 0·949) and minimal luminal area (ICC 0·904). The mean per-patient deep learning plaque analysis time was 5·65 s (SD 1·87) versus 25·66 min (6·79) taken by experts. Over a median follow-up of 4·7 years (IQR 4·0–5·7), myocardial infarction occurred in 41 (2·5%) of 1611 patients from the SCOT-HEART trial. A deep learning-based total plaque volume of 238·5 mm(3) or higher was associated with an increased risk of myocardial infarction (hazard ratio [HR] 5·36, 95% CI 1·70–16·86; p=0·0042) after adjustment for the presence of deep learning-based obstructive stenosis (HR 2·49, 1·07–5·50; p=0·0089) and the ASSIGN clinical risk score (HR 1·01, 0·99–1·04; p=0·35). INTERPRETATION: Our novel, externally validated deep learning system provides rapid measurements of plaque volume and stenosis severity from CCTA that agree closely with expert readers and intravascular ultrasound, and could have prognostic value for future myocardial infarction

Smoking reduces surfactant protein D and phospholipids in patients with and without chronic obstructive pulmonary disease

<p>Abstract</p> <p>Background</p> <p>Pulmonary surfactant D (SP-D) has important regulatory functions for innate immunity and has been implicated as a biomarker for chronic obstructive pulmonary disease (COPD). We hypothesized that COPD patients would have reduced bronchoalveolar lavage (BAL) fluid SP-D levels compared to healthy smoking and non-smoking controls.</p> <p>Methods</p> <p>BAL SP-D and phospholipids were quantified and corrected for dilution in 110 subjects (65 healthy never smokers, 23 smokers with normal spirometry, and 22 smokers with COPD).</p> <p>Results</p> <p>BAL SP-D was highest in never smokers (mean 51.9 μg/mL ± 7.1 μg/mL standard error) compared to both smokers with normal spirometry (16.0 μg/mL ± 11.8 μg/mL) and subjects with COPD (19.1 μg/mL ± 12.9 μg/mL; P < 0.0001). Among smokers with COPD, BAL SP-D correlated significantly with FEV₁% predicted (R = 0.43; P < 0.05); however, the strongest predictor of BAL SP-D was smoking status. BAL SP-D levels were lowest in current smokers (12.8 μg/mL ± 11.0 μg/mL), intermediate in former smokers (25.2 μg/mL ± 14.2 μg/mL; P < 0.008), and highest in never smokers. BAL phospholipids were also lowest in current smokers (6.5 nmol ± 1.5 nmol), intermediate in former smokers (13.1 nmol ± 2.1 nmol), and highest in never smokers (14.8 nmol ± 1.1 nmol; P < 0.0001).</p> <p>Conclusions</p> <p>These data suggest that smokers, and especially current smokers, exhibit significantly reduced BAL SP-D and phospholipids compared to nonsmokers. Our findings may help better explain the mechanism that leads to the rapid progression of disease and increased incidence of infection in smokers.</p

Morphometric Characterization of Rat and Human Alveolar Macrophage Cell Models and their Response to Amiodarone using High Content Image Analysis

© The Author(s) 2017. This article is an open access publication. Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.Purpose. Progress to the clinic may be delayed or prevented when vacuolated or “foamy” alveolar macrophages are observed during non-clinical inhalation toxicology assessment. The first step in developing methods to study this response in vitro is to characterize macrophage cell lines and their response to drug exposures.Methods. Human (U937) and rat (NR8383) cell lines and primary rat alveolar macrophages obtained by bronchoalveolar lavage were characterized using high content fluorescence imaging analysis quantification of cell viability, morphometry, and phospholipid and neutral lipid accumulation. Results. Cell health, morphology and lipid content were comparable (p<0.05) for both cell lines and the primary macrophages in terms of vacuole number, size and lipid content. Responses to amiodarone, a known inducer of phospholipidosis, required analysis of shifts in cell population profiles (the proportion of cells with elevated vacuolation or lipid content) rather than average population data which was insensitive to the changes observed.Conclusions. A high content image analysis assay was developed and used to provide detailed morphological characterization of rat and human alveolar-like macrophages and their response to a phospholipidosis-inducing agent. This provides a basis for development of assays to predict or understand macrophage vacuolation following inhaled drug exposure.Peer reviewedFinal Published versio