Improving cardiovascular risk prediction with machine learning: a focus on perivascular adipose tissue characteristics.

BACKGROUND: Timely prevention of major adverse cardiovascular events (MACEs) is imperative for reducing cardiovascular diseases-related mortality. Perivascular adipose tissue (PVAT), the adipose tissue surrounding coronary arteries, has attracted increased amounts of attention. Developing a model for predicting the incidence of MACE utilizing machine learning (ML) integrating clinical and PVAT features may facilitate targeted preventive interventions and improve patient outcomes. METHODS: From January 2017 to December 2019, we analyzed a cohort of 1077 individuals who underwent coronary CT scanning at our facility. Clinical features were collected alongside imaging features, such as coronary artery calcium (CAC) scores and perivascular adipose tissue (PVAT) characteristics. Logistic regression (LR), Framingham Risk Score, and ML algorithms were employed for MACE prediction. RESULTS: We screened seven critical features to improve the practicability of the model. MACE patients tended to be older, smokers, and hypertensive. Imaging biomarkers such as CAC scores and PVAT characteristics differed significantly between patients with and without a 3-year MACE risk in a population that did not exhibit disparities in laboratory results. The ensemble model, which leverages multiple ML algorithms, demonstrated superior predictive performance compared with the other models. Finally, the ensemble model was used for risk stratification prediction to explore its clinical application value. CONCLUSIONS: The developed ensemble model effectively predicted MACE incidence based on clinical and imaging features, highlighting the potential of ML algorithms in cardiovascular risk prediction and personalized medicine. Early identification of high-risk patients may facilitate targeted preventive interventions and improve patient outcomes.

A nomogram based on radiomics intermuscular adipose analysis to indicate arteriosclerosis in patients with newly diagnosed type 2 diabetes.

Objective: Early identifying arteriosclerosis in newly diagnosed type 2 diabetes (T2D) patients could contribute to choosing proper subjects for early prevention. Here, we aimed to investigate whether radiomic intermuscular adipose tissue (IMAT) analysis could be used as a novel marker to indicate arteriosclerosis in newly diagnosed T2D patients. Methods: A total of 549 patients with newly diagnosed T2D were included in this study. The clinical information of the patients was recorded and the carotid plaque burden was used to indicate arteriosclerosis. Three models were constructed to evaluate the risk of arteriosclerosis: a clinical model, a radiomics model (a model based on IMAT analysis proceeded on chest CT images), and a clinical-radiomics combined model (a model that integrated clinical-radiological features). The performance of the three models were compared using the area under the curve (AUC) and DeLong test. Nomograms were constructed to indicate arteriosclerosis presence and severity. Calibration curves and decision curves were plotted to evaluate the clinical benefit of using the optimal model. Results: The AUC for indicating arteriosclerosis of the clinical-radiomics combined model was higher than that of the clinical model [0.934 (0.909, 0.959) vs. 0.687 (0.634, 0.730), P < 0.001 in the training set, 0.933 (0.898, 0.969) vs. 0.721 (0.642, 0.799), P < 0.001 in the validation set]. Similar indicative efficacies were found between the clinical-radiomics combined model and radiomics model (P = 0.5694). The AUC for indicating the severity of arteriosclerosis of the combined clinical-radiomics model was higher than that of both the clinical model and radiomics model [0.824 (0.765, 0.882) vs. 0.755 (0.683, 0.826) and 0.734 (0.663, 0.805), P < 0.001 in the training set, 0.717 (0.604, 0.830) vs. 0.620 (0.490, 0.750) and 0.698 (0.582, 0.814), P < 0.001 in the validation set, respectively]. The decision curve showed that the clinical-radiomics combined model and radiomics model indicated a better performance than the clinical model in indicating arteriosclerosis. However, in indicating severe arteriosclerosis, the clinical-radiomics combined model had higher efficacy than the other two models. Conclusion: Radiomics IMAT analysis could be a novel marker for indicating arteriosclerosis in patients with newly diagnosed T2D. The constructed nomograms provide a quantitative and intuitive way to assess the risk of arteriosclerosis, which may help clinicians comprehensively analyse radiomics characteristics and clinical risk factors more confidently.

[Dimo-thylidioctyl ammonium bromide-BCG polysaccharide nucleic acid adjuvant enhanced the immunogenicity of a Mycobacterium tuberculosis subunit vaccine].

OBJECTIVE: To investigate the activity of a novel adjuvant consisting of dimethyldioctyldecyl ammonium bromide (DDA) and BCG polysaccharide nucleic acid (BCG-PSN). METHODS: BCG-PSN was extracted by hot-phenol method, and combined with DDA and Mycobacterium tuberculosis fusion antigen AMM (Ag85B-MPT64(190-198)-Mtb8.4) to formulate the Mycobacterium tuberculosis subunit vaccine. Mice were immunized subcutaneously with a 2-week interval between the immunizations (0.2 ml/dose), and humoral and cell-mediated immunity were detected by ELISA and ELISPOT respectively. RESULTS: With the stimulation of Ag85B in vitro, the number of antigen specific IFN-gamma producing spleen lymphocytes were 222 +/- 79, 259 +/- 85, 230 +/- 64 per million respectively in the mice immunized with AMM + DDA + BCG-PSN, AMM + DDA, and BCG. Spleen lymphocytes in these 3 groups produced higher levels of IFN-gamma compared to the groups with the adjuvant of IFA or BCG-PSN alone or without adjuvant upon stimulation with Ag85B (t = 2.923-7.118, P < 0.05). Furthermore, the adjuvant consisting of DDA and BCG-PSN increased the ratio of Ig(2a)/IgG1 than DDA alone (0.125 vs. 0.025). Combined with AMM, the adjuvant DDA and the one consisting of DDA and BCG-PSN induced higher level of immunity than incomplete Freund's adjuvant (IFA), NaCl, and BCG-PSN alone. CONCLUSION: Mycobacterium tuberculosis subunit vaccine AMM + DDA + BCG-PSN induced a strong Th1-type immune response, and DDA + BCG-PSN, especially DDA promoted the immune response of the M. tuberculosis subunit vaccine in mice.

[Cloning and expression of Yersinia pestis V antigen in Escherichia coli BL21 (DE3)].

The structure gene of V antigen was amplified from the wild strain of Yersinia pestis by means of PCR, and was cloned into vector pET-42b (+). Then, V antigen was successfully expressed in E. coli BL21(DE3) with the induction of IPTG. The expressing amount of the aim protein is as high as 32.8 percent of the total bacterial protein.