Prevalence and association of mycoplasma infection in the development of coronary artery disease

Coronary heart disease (CHD) has been associated with significant morbidity and mortality worldwide. Although remain controversial, several studies have demonstrated the association of M. pneumoniae infections with atherosclerosis. We evaluated the possible association of mycoplasma infections in patients diagnosed with atherosclerosis by ELISA and PCR methods. Atherosclerotic tissue samples and blood samples were collected for the detection of mycoplasma antibodies (IgA) by ELISA from the 97 patients with coronary artery disease (CAD). M. pneumoniae specific IgA, IgG and IgM were measured by using the Anti-M. pneumoniae IgA/IgG/IgM ELISA. Detection of M. pneumoniae targeting the P1 adhesion gene was performed by PCR Acute infection of M. pneumoniae was diagnosed in 43.3% (42) of patients by PCR. The M. pneumoniae specific antibodies were detected in 36.1% (35) of patients. Twenty-five (25.8%) cases had IgG antibodies, 15 (15.5%) cases had IgM antibodies, 3 (3.1%) cases had IgA antibodies, 10 (10.3%) cases had both IgM + IgG antibodies and 1 (1%) case of each had IgM + IgA and IgG + IgA antibodies. None of the cases was positive for all three antibodies. A Pearson correlation coefficient analysis revealed an excellent correlation between the PCR and the serological results (r=0.921, p<0.001). A majority (17, 40.5%) of the M. pneumoniae positive patients are within the 41-50 years of age group, followed by 10 (23.8%) patients in the age group of 61-70 years and 2 (4.8%) patients were >70 years of age. Our study reported an unusually higher prevalence of M. pneumoniae by serological tests (36.1%) and PCR (43.3%). Although the hypothesis of the association of M. pneumoniae and CAD is yet to be proven, the unusually high prevalence of M. pneumoniae in CAD patients indicates an association, if not, in the development of atherosclerosis.

Bridging the Gap between Structured and Free-form Radiology Reporting: A Case-study on Coronary CT Angiography

Free-form radiology reports associated with coronary computed tomography angiography (CCTA) include nuanced and complicated linguistics to report cardiovascular disease. Standardization and interpretation of such reports is crucial for clinical use of CCTA. Coronary Artery Disease Reporting and Data System (CAD-RADS) has been proposed to achieve such standardization by implementing a strict template-based report writing and assignment of a score between 0 and 5 indicating the severity of coronary artery lesions. Even after its introduction, free-form unstructured report writing remains popular among radiologists. In this work, we present our attempts at bridging the gap between structured and unstructured reporting by natural language processing. We present machine learning models that while being trained only on structured reports, can predict CAD-RADS scores by analysis of free-text of unstructured radiology reports. The best model achieves 98% accuracy on structured reports and 92% 1-margin accuracy (difference of \le 1 in the predicted and the actual scores) for free-form unstructured reports. Our model also performs well under very difficult circumstances including nuanced and widely varying terminology used for reporting cardiovascular functions and diseases, scarcity of labeled data for training our model, and uneven class label distribution.

Kernel Conversion for Robust Quantitative Measurements of Archived Chest Computed Tomography Using Deep Learning-Based Image-to-Image Translation

Chest computed tomography (CT) is used to screen for lung cancer and evaluate pulmonary and extra-pulmonary abnormalities such as emphysema and coronary artery calcification, particularly in smokers. In real-world practice, lung abnormalities are visually assessed using high-contrast thin-slice images which are generated from raw scan data using sharp reconstruction kernels with the sacrifice of increased image noise. In contrast, accurate CT quantification requires low-contrast thin-slice images with low noise, which are generated using soft reconstruction kernels. However, only sharp-kernel thin-slice images are archived in many medical facilities due to limited data storage space. This study aimed to establish deep neural network (DNN) models to convert sharp-kernel images to soft-kernel-like images with a final goal to reuse historical chest CT images for robust quantitative measurements, particularly in completed previous longitudinal studies. By using pairs of sharp-kernel (input) and soft-kernel (ground-truth) images from 30 patients with chronic obstructive pulmonary disease (COPD), DNN models were trained. Then, the accuracy of kernel conversion based on the established DNN models was evaluated using CT from independent 30 smokers with and without COPD. Consequently, differences in CT values between new images converted from sharp-kernel images using the established DNN models and ground-truth soft-kernel images were comparable with the inter-scans variability derived from repeated phantom scans (6 times), showing that the conversion error was the same level as the measurement error of the CT device. Moreover, the Dice coefficients to quantify the similarity between low attenuation voxels on given images and the ground-truth soft-kernel images were significantly higher on the DNN-converted images than the Gaussian-filtered, median-filtered, and sharp-kernel images (p &lt; 0.001). There were good agreements in quantitative measurements of emphysema, intramuscular adipose tissue, and coronary artery calcification between the converted and the ground-truth soft-kernel images. These findings demonstrate the validity of the new DNN model for kernel conversion and the clinical applicability of soft-kernel-like images converted from archived sharp-kernel images in previous clinical studies. The presented method to evaluate the validity of the established DNN model using repeated scans of phantom could be applied to various deep learning-based image conversions for robust quantitative evaluation.

Comparison of Myocardial Layer-Specific Strain and Global Myocardial Work Efficiency During Treadmill Exercise Stress in Detecting Significant Coronary Artery Disease

BackgroundMyocardial layer-specific strain can identify myocardial ischemia. Global myocardial work efficiency (GWE) based on non-invasive left ventricular (LV) pressure-strain loops is a novel parameter to determine LV function considering afterload. The study aimed to compare the diagnostic value of GWE and myocardial layer-specific strain during treadmill exercise stress testing to detect significant coronary artery disease (CAD) with normal baseline wall motion.MethodsEighty-nine patients who referred for coronary angiography due to suspected of CAD were included. Forty patients with severe coronary artery stenosis were diagnosed with significant CAD, and 49 were defined as non-significant CAD. Stress echocardiography was performed 24 h before angiography. Layer-specific longitudinal strains were assessed from the endocardium, mid-myocardium, and epicardium by 2D speckle-tracking echocardiography. Binary logistic regression analyses were performed to evaluate the association between significant CAD and echocardiographic parameters. A receiver operating characteristic curve was used to assess the capability of layer-specific strain and GWE to diagnose significant CAD.ResultsPatients with significant CAD had the worse function in all three myocardial layers at peak exercise compared with those with non-significant CAD when assessed with global longitudinal strain (GLS). At the peak exercise and recovery periods, GWE was lower in patients with significant CAD than in patients with non-significant CAD. In multivariable binary logistic regression analysis, peak endocardial GLS (OR: 1.35, p = 0.006) and peak GWE (OR: 0.76, p = 0.001) were associated with significant CAD. Receiver operating characteristic curves showed peak GWE to be superior to mid-myocardial, epicardial, and endocardial GLS in identifying significant CAD. Further, adding peak GWE to endocardial GLS could improve diagnostic capabilities.ConclusionsBoth GWE and endocardial GLS contribute to improving the diagnostic performance of exercise stress echocardiography. Furthermore, adding peak GWE to peak endocardial GLS provides incremental diagnostic value during a non-invasive screening of significant CAD before radioactive or invasive examinations.

Is postoperative cognitive decline after cardiac surgery associated with plasma beta amyloid 1–42 levels?

Background Postoperative cognitive decline following cardiac surgery is one of the frequently reported complications affecting postoperative outcome, characterized by impairment of memory or concentration. The aetiology is considered multifactorial and the research conducted so far has presented contradictory results. The proposed mechanisms to explain the cognitive decline associated with cardiac surgery include the neurotoxic accumulation of β-amyloid (Aβ) proteins similar to Alzheimer's disease. The comparison of coronary artery bypass grafting procedures concerning postoperative cognitive decline and plasmatic Aβ1-42 concentrations has not yet been conducted. Methods The research was designed as a controlled clinical study of patients with coronary artery disease undergoing surgical myocardial revascularization with or without the use of a cardiopulmonary bypass machine. All patients completed a battery of neuropsychological tests and plasmatic Aβ1-42 concentrations were collected. Results The neuropsychological test results postoperatively were significantly worse in the cardiopulmonary bypass group and the patients had larger shifts in the Aβ1-42 preoperative and postoperative values than the group in which off-pump coronary artery bypass was performed. Conclusions The conducted research confirmed the earlier suspected association of plasmatic Aβ1-42 concentration to postoperative cognitive decline and the results further showed that there were less changes and lower concentrations in the off-pump coronary artery bypass group, which correlated to less neurocognitive decline. There is a lot of clinical contribution acquired by this research, not only in everyday decision making and using amyloid proteins as biomarkers, but also in the development and application of non-pharmacological and pharmacological neuroprotective strategies.