Overexpression of TSHR was found in a great majority of HCC tissues and associated with unfavorable prognosis. Cell-based experiments and gene mutation analysis suggested that TSHR in HCCs was functional.

Background Our study aimed to determine the association between homocysteine levels and cardiovascular disease (CVD) risk in middle-aged and elderly adults in a community in northern Taiwan. Methods Participants in our study included adults aged 50 to 85 years old during community health examinations in 2019. A total of 396 people were enrolled, the ethnicity of all participants is Chinese. We divided participants according to tertiles of ln[homocysteine] level (low, middle and high groups). The CVD risk was calculated by the Framingham cardiovascular risk score (FRS). An FRS ≥ 20% indicated high CVD risk. Pearson correlation coefficients were calculated between homocysteine level and other cardio-metabolic risk factors while adjusting for age. Multivariate logistic regression analysis was used to determine the association of high and middle ln[homocysteine] groups with high CVD risk after adjusting age, sex, uric acid, creatinine, and body mass index (BMI). The Youden index and receiver operating characteristic (ROC) curves were performed to determine the optimized cut-off value. Results There were 396 people enrolled for analysis; 41.4% of participants were male, and the average age was 64.79 (± 8.76). In our study, we showed a positive correlation of homocysteine with FRS. In the logistic regression models, higher ln[homocysteine] levels was associated with higher CVD risk with a odds ratio (OR) of 2.499 and 95% confidence interval (CI) of 1.214 to 5.142 in the high homocysteine level group compared with the low homocysteine group after adjusting for traditional CVD risk factors. The area under the ROC curve was 0.667, and a ln[homocysteine] cut-off value of 2.495 µmol/L was determined. Conclusions Middle-aged and elderly people with increased homocysteine levels were associated with higher FRSs in this Taiwan community. Furthermore, homocysteine was an independent risk factor for high CVD risk in this study.

Previous studies on CKD patients have mostly been retrospective, cross-sectional studies. Few studies have assessed the longitudinal assessment of patients over an extended period. In consideration of the heterogeneity of CKD progression. It’s critical to develop a longitudinal diagnosis and prognosis for CKD patients. We proposed an auto Machine Learning (ML) scheme in this study. It consists of four main parts: classification pipeline, cross-validation (CV), Taguchi method and improve strategies. This study includes datasets from 50,174 patients, data were collected from 32 chain clinics and three special physical examination centers, between 2015 and 2019. The proposed auto-ML scheme can auto-select the level of each strategy to associate with a classifier which finally shows an acceptable testing accuracy of 86.17%, balanced accuracy of 84.08%, sensitivity of 90.90% and specificity of 77.26%, precision of 88.27%, and F1 score of 89.57%. In addition, the experimental results showed that age, creatinine, high blood pressure, smoking are important risk factors, and has been proven in previous studies. Our auto-ML scheme light on the possibility of evaluation for the effectiveness of one or a combination of those risk factors. This methodology may provide essential information and longitudinal change for personalized treatment in the future.

Metabolic syndrome (MetS) has become the most important issue in family medicine and primary care because it is a cluster of metabolic abnormalities that are a burden on health care in many countries. Highly sensitive C-reactive protein (hsCRP), which is elevated in inflammatory situations, can be produced by monocyte-derived macrophages in adipose tissue. People with MetS tend to have more adipose tissue. Therefore, we aimed to investigate the association between hsCRP and MetS among elderly individuals aged 50 years and older in northern Taiwan. This study was a cross-sectional community-based study that included 400 middle-aged and elderly Taiwanese adults, and 400 participants were eligible for analysis. We divided the participants into a MetS group and a non-MetS group. Pearson’s correlations were calculated between hsCRP and other related risk factors. Furthermore, the relationship between hsCRP and MetS was analyzed with logistic regression. People in the MetS group were more likely to have higher hsCRP levels. The Pearson’s correlation analysis showed a positive correlation with hsCRP. In the logistic regression, hsCRP was significantly associated with MetS, even with the adjustment for BMI, uric acid, age, sex, smoking status, drinking status, hypertension, diabetes mellitus, and dyslipidemia. In summary, our research indicated that hsCRP could be an independent risk factor for MetS.