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H-type hypertension (HHT) is closely associated with cardiovascular and cerebrovascular complications, as well as peripheral vascular sclerosis. However, the mechanism underlying the relationship between HHT and cardiac remodeling is not completely clear. Therefore, this study aimed to investigate the structural differences in a cardiac ultrasound between patients with HHT and those with non-H-type hypertension (NHHT). This study was performed on 300 elderly patients ($\ge 60$ years) with essential hypertension and stratified into two groups based on their homocysteine (Hcy) levels: 150 with HHT and 150 with NHHT. The cardiac structure was assessed using color Doppler echocardiography. The key parameters were measured, including interventricular septal thickness (IVST), left ventricular posterior wall thickness (LVPWT), left ventricular mass index (LVMI), and others. The chi-square test was employed to examine the differences in cardiac ultrasound outcomes between HHT and NHHT groups. Left ventricular hypertrophy (LVH) was observed in 118 patients (78.7%) in the HHT group and 75 patients (50.0%) in the NHHT group. The HHT group demonstrated a significantly higher prevalence of LVH (${\chi }^2=5.183$, $p<0.0001$). Patients with HHT had significantly higher systolic blood pressure, Hcy levels, LVPWT, IVST, and LVMI compared with those with NHHT: systolic blood pressure ($165.2\pm 13.15$ mm Hg versus $148.6\pm 11.06$ mm Hg), Hcy ($15.36\pm 3.15\mu$mol/L versus $8.15\pm 3.12$  $\mu$mol/L), LVPWT ($12.5\pm 1.16$ mm versus $10.2\pm 1.22$ mm), IVST ($13.6\pm 1.25$ mm versus $11.2\pm 1.15$ mm), and LVMI ($121.3\pm 22.15$ g/m² versus $110.5\pm 23.36$ g/m²). The correlation analysis showed a notable positive relationship between Hcy levels and LVMI ($r=0.386$, $p<0.001$), and between systolic blood pressure and LVMI ($r=0.536$, $p<0.001$). The occurrence of LVH was notably greater in patients with HHT than in those with NHHT. Furthermore, Hcy and systolic blood pressure levels were positively correlated with LVMI.

To investigate the effects of the traditional Chinese medicines, Kuei-lu-erh-hsien-chiao and Chia-wei-hsiao-yao-san, on the cardiovascular systems of mimic menopausal rats, five groups were formed: group 1 (the control group) was given a sham operation and received distilled water, while groups 2, 3, 4 and 5 were ovariectomized and received distilled water, Kuei-lu-erh-hsien-chiao, Chia-wei-hsiao-yao-san and 17-β-estradiol, respectively, for 4 months. Our results demonstrated that the mean differences of the estrogen levels in groups 3 or 5 were significantly higher than those of group 2. These data suggest that there might be some estrogen-like substances in Kuei-lu-erh-hsien-chiao. However, the function of these estrogen-like substances was unknown. The mean differences of the triglyceride (TG) levels, high-density lipoprotein cholesterol (HDL-C) levels, low-density lipoprotein cholesterol (LDL-C) levels, and the ratios of TC/HDL-C and LDL-C/HDL-C in groups 1, 3, 4 and 5 were not significantly different from those in group 2. The mean differences of the total cholesterol (TC) levels in group 5 were significantly higher than those in group 2 (p<0.05), but no obvious difference of the TC levels was found between groups 2 and 4. Nevertheless, the mean differences of the homocysteine (Hcy) levels in groups 4 and 5 were statistically lower than those of group 2. Therefore, administration of Chia-wei-hsiao-yao-san declines the Hcy levels in OVX rats and does not affect the TC levels in these animals. In conclusion, our results indicate that Chia-wei-hsiao-yao-san shows a more profound effect than 17-β-estradiol in the prevention of atherosclerosis in these OVX rats.

Elevated plasma concentration of total homocysteine is a pathological condition that causes vascular endothelial injury and subsequently leads to the progression of endothelial apoptosis in atherosclerosis. Epigallocatechin gallate (EGCG), a well-known anti-oxidant in green tea, has been reported with benefits on metabolic and cardiovascular diseases. This study aimed to explore that EGCG ameliorates homocysteine-induced endothelial cell apoptosis through enhancing the sirtuin 1 (SIRT1)/AMP-activated protein kinase (AMPK) survival signaling pathway. Human umbilical endothelial cells were treated with homocysteine in the presence or absence of EGCG. We found that EGCG significantly increased the activities of SIRT1 and AMPK. EGCG diminished homocysteine-mediated nicotinamide adenine dinucleotide phosphate (NADPH) oxidase activation by inhibiting protein kinase C activation as well as reactive oxygen species (ROS) generation and recovered the activity of the endogenous antioxidant enzyme, superoxidase dismutase (SOD). Besides, EGCG also restores homocysteine-mediated dephosphorylation of Akt and decreases endothelial NO synthase (eNOS) expression. Furthermore, EGCG ameliorates homocysteine-activated pro-apoptotic events. The present study shows that EGCG prevents homocysteine-induced endothelial cell apoptosis via enhancing SIRT1/AMPK as well as Akt/eNOS signaling pathways. Results from this study indicated that EGCG might have some benefits for hyperhomocysteinemia.

Selective fluorescent and colorimetric sensing of cysteine in methanol-HEPES buffer (45 mM, pH $=$ 7.2, v/v $=$ 1/1) solution over various common amino acids and related thiol containing compounds has been achieved based on the cyclization reaction between the formyl group on 3-formylBODIPYs and cysteine/homocystein. Upon addition of cysteine/homocystein, 3-formylBODIPYs exhibited greatly enhanced fluorescence intensity as well as an abvious red-shift of the absorption peak (20–30 nm). The detection limits for cysteine were in the range of 1.18–2.73 $\times$10${}^{-6}$ M. The detection mechanism was studied by nuclear magnetic resonance and theoretical calculation.

With the maturation of metabolomics science and proliferation of biobanks, clinical metabolic profiling is an increasingly opportunistic frontier for advancing translational clinical research. Automated Machine Learning (AutoML) approaches provide exciting opportunity to guide feature selection in agnostic metabolic profiling endeavors, where potentially thousands of independent data points must be evaluated. In previous research, AutoML using high-dimensional data of varying types has been demonstrably robust, outperforming traditional approaches. However, considerations for application in clinical metabolic profiling remain to be evaluated. Particularly, regarding the robustness of AutoML to identify and adjust for common clinical confounders. In this study, we present a focused case study regarding AutoML considerations for using the Tree-Based Optimization Tool (TPOT) in metabolic profiling of exposure to metformin in a biobank cohort. First, we propose a tandem rank-accuracy measure to guide agnostic feature selection and corresponding threshold determination in clinical metabolic profiling endeavors. Second, while AutoML, using default parameters, demonstrated potential to lack sensitivity to low-effect confounding clinical covariates, we demonstrated residual training and adjustment of metabolite features as an easily applicable approach to ensure AutoML adjustment for potential confounding characteristics. Finally, we present increased homocysteine with long-term exposure to metformin as a potentially novel, non-replicated metabolite association suggested by TPOT; an association not identified in parallel clinical metabolic profiling endeavors. While warranting independent replication, our tandem rank-accuracy measure suggests homocysteine to be the metabolite feature with largest effect, and corresponding priority for further translational clinical research. Residual training and adjustment for a potential confounding effect by BMI only slightly modified the suggested association. Increased homocysteine is thought to be associated with vitamin B12 deficiency – evaluation for potential clinical relevance is suggested. While considerations for clinical metabolic profiling are recommended, including adjustment approaches for clinical confounders, AutoML presents an exciting tool to enhance clinical metabolic profiling and advance translational research endeavors.