Using optical coherence tomography and intravascular ultrasound imaging to quantify coronary plaque cap stress/strain and progression: A follow-up study using 3D thin-layer models

Accurate plaque cap thickness quantification and cap stress/strain calculations are of fundamental importance for vulnerable plaque research. To overcome uncertainties due to intravascular ultrasound (IVUS) resolution limitation, IVUS and optical coherence tomography (OCT) coronary plaque image data were combined together to obtain accurate and reliable cap thickness data, stress/strain calculations, and reliable plaque progression predictions. IVUS, OCT, and angiography baseline and follow-up data were collected from nine patients (mean age: 69; m: 5) at Cardiovascular Research Foundation with informed consent obtained. IVUS and OCT slices were coregistered and merged to form IVUS + OCT (IO) slices. A total of 114 matched slices (IVUS and OCT, baseline and follow-up) were obtained, and 3D thin-layer models were constructed to obtain stress and strain values. A generalized linear mixed model (GLMM) and least squares support vector machine (LSSVM) method were used to predict cap thickness change using nine morphological and mechanical risk factors. Prediction accuracies by all combinations (511) of those predictors with both IVUS and IO data were compared to identify optimal predictor(s) with their best accuracies. For the nine patients, the average of minimum cap thickness from IVUS was 0.17 mm, which was 26.08% lower than that from IO data (average = 0.23 mm). Patient variations of the individual errors ranged from ‒58.11 to 20.37%. For maximum cap stress between IO and IVUS, patient variations of the individual errors ranged from ‒30.40 to 46.17%. Patient variations of the individual errors of maximum cap strain values ranged from ‒19.90 to 17.65%. For the GLMM method, the optimal combination predictor using IO data had AUC (area under the ROC curve) = 0.926 and highest accuracy = 90.8%, vs. AUC = 0.783 and accuracy = 74.6% using IVUS data. For the LSSVM method, the best combination predictor using IO data had AUC = 0.838 and accuracy = 75.7%, vs. AUC = 0.780 and accuracy = 69.6% using IVUS data. This preliminary study demonstrated improved plaque cap progression prediction accuracy using accurate cap thickness data from IO slices and the differences in cap thickness, stress/strain values, and prediction results between IVUS and IO data. Large-scale studies are needed to verify our findings

Lactobacillus gasseri RW2014 Ameliorates Hyperlipidemia by Modulating Bile Acid Metabolism and Gut Microbiota Composition in Rats

Hyperlipidemia is a leading risk of cardiovascular and cerebrovascular disease. Dietary supplementation with probiotics has been suggested as an alternative intervention to lower cholesterol. In the current study, we isolated a strain of Lactobacillus gasseri RW2014 (LGA) from the feces of a healthy infant fed with breast milk, and it displayed bile salt hydrolase (BSH) activity. Using this strain we determined its cholesterol-lowering and fatty liver-improving functions. SD rats were randomly divided into four groups. The control rats were fed a commercial chow diet and the other three groups were fed a high-fat diet (HFD) for a 7-week experiment period. After two weeks of feeding, the rats in PBS, simvastin, and LGA group were daily administered through oral gavage with 2 mL PBS, simvastin (1 mg/mL), and 2 × 109 CFU/mouse live LGA in PBS, respectively. After five weeks of such treatment, the rats were euthanized and tissue samples were collected. Blood lipid and inflammatory factors were measured by ELISA, gut microbiota was determined by 16S rRNA sequencing, and bile acids profiles were detected by metabolomics. We found that LGA group had lower levels of blood cholesterol and liver steatosis compared to the simvastin group. LGA also significantly reducedthe levels of inflammatory factors in the serum, including TNFα, IL-1β, MCP-1, IL-6, and exotoxin (ET), and increased the levels of short-chain fatty acids in feces, including isobutyric acid, butyric acid, isovaleric acid, valeric acid, and hexanoic acid. In addition, LGA altered the compositions of gut microbiota as manifested by the increased ratio of Firmicutes/Bacteroides and the relative abundance of Blautia genus. Targeted metabolomics results showed that bile acids, especially free bile acids and secondary bile acids in feces, were increased in LGA rats compared with the control rats. Accordingly, the rats administrated with LGA also had a higher abundance of serum bile acids, including 23-norcholic acid, 7-ketolithocholic acid, β-muricholic acid, cholic acid, and deoxycholic acid. Together, this study suggests that LGA may exert a cholesterol-lowering effect by modulating the metabolism of bile acids and the composition of gut microbiota