Traditional Chinese medicine promotes the control and treatment of dementia.

Dementia is a syndrome that impairs learning and memory. To date, there is no effective therapy for dementia. Current prescription drugs, such as cholinesterase inhibitors, fail to improve the condition of dementia and are often accompanied by severe adverse effects. In recent years, the number of studies into the use of traditional Chinese medicine (TCM) for dementia treatment has increased, revealing a formula that could significantly improve memory and cognitive dysfunctions in animal models. TCM showed fewer adverse effects, lower costs, and improved suitability for long-term use compared with currently prescribed drugs. Due to the complexity of ingredients and variations in bioactivity of herbal medicines, the multi-target nature of the traditional Chinese formula affected the outcome of dementia therapy. Innovations in TCM will create a platform for the development of new drugs for the prevention and treatment of dementia, further strengthening and enhancing the current influence of TCM.

Astaxanthin From Haematococcus pluvialis Prevents High-Fat Diet-Induced Hepatic Steatosis and Oxidative Stress in Mice by Gut-Liver Axis Modulating Properties.

Scope: Evidence is mounting that astaxanthin (ATX), a xanthophyll carotenoid, used as a nutritional supplement to prevent chronic metabolic diseases. The present study aims to identify the potential function of ATX supplementation in preventing steatohepatitis and hepatic oxidative stress in diet-induced obese mice. Methods and Results: In this study, ATX as dose of 0.25, 0.5, and 0.75% have orally administered to mice along with a high-fat diet (HFD) to investigate the role of ATX in regulating liver lipid metabolism and gut microbiota. The study showed that ATX dose-dependently reduces body weight, lipid droplet formation, hepatic triglycerides and ameliorated hepatic steatosis and oxidative stress. 0.75% ATX altered the levels of 34 lipid metabolites related to hepatic cholesterol and fatty acid metabolism which might be associated with downregulation of lipogenesis-related genes and upregulation of bile acid biosynthesis-related genes. The result also revealed that ATX alleviates HFD-induced gut microbiota dysbiosis by significantly inhibiting the growth of obesity-related Parabacteroides and Desulfovibrio while promoting the growth of Allobaculum and Akkermansia. Conclusion: The study results suggested that dietary ATX may prevent the development of hepatic steatosis and oxidative stress with the risk of metabolic disease by gut-liver axis modulating properties.

Selection of suitable candidate genes for mRNA expression normalization in bulbil development of Pinellia ternata.

Pinellia ternata (Thunb.) Breit. (Abbreviated as P. ternata). It is a commonly prescribed Chinese traditional medicinal herb for the treatment of phlegm, cough, and morning sick. Bulbil reproduction is one of the main reproductive methods of P. ternata. The accurate quantification of gene expression patterns associated with bulbil development might be helpful to explore the molecular mechanism involved in P. ternata reproduction. Quantitative real-time PCR was the most preferred method for expression profile and function analysis of mRNA. However, the reference genes in different tissues of P. ternata in different periods of bulbil development have not been studied in detail. In present study, the expression stability of eight candidate reference genes were determined with programs: geNorm, NormFinder, BestKeeper, and refFinder. Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) was identified as the top- rated reference gene in all samples of P. ternata, while different combinations of reference gene proved to be the most stable depending on development stage and tissue type. Furthermore, the reliability of GAPDH expression was verified by six P. ternata related genes in hormone and nutrient biosynthesis pathways, and the expression profiles of these genes were agreed with the results of RNA-seq digital gene expression analysis. These results can contribute to studies of gene expression patterns and functional analysis of P. ternata involved in bulbil development.

The value of clinical-ultrasonographic feature model to predict the severity of secondary hyperparathyroidism.

OBJECTIVES: To analyze conventional ultrasound (CUS) and contrast-enhanced ultrasound (CEUS) features in patients with secondary hyperparathyroidism (SHPT) and to evaluate the clinical-ultrasonographic feature based model for predicting the severity of SHPT. METHODS: From February 2016 to March 2021, a total of 59 patients (age 51.3 ± 11.7 years, seCr 797.8 ± 431.7 µmol/L, iPTH 1535.1 ± 1063.9 ng/L) with SHPT (including 181 parathyroid glands (PTGs)) without the history of intact parathyroid hormone (iPTH)-reducing drugs using were enrolled. The patients were divided into the mild SHPT group (mSHPT, iPTH <800 ng/L) and the severe SHPT group (sSHPT, iPTH ≥ 800 ng/L) according to the serum iPTH level. The clinical test data of patients were collected and CUS and CEUS examinations were performed for every patient. Multivariable logistic regression model according to clinical-ultrasonographic features was adopted to establish a nomogram. We performed K-fold cross-validation on this nomogram model and nomogram performance was determined by its discrimination, calibration, and clinical usefulness. RESULTS: There were 19 patients in the mSHPT group and 40 patients in the sSHPT group. Multivariable logistic regression indicated serum calcium, serum phosphorus and total volume of PTGs were independent predictors related with serum iPTH level. Even though CEUS score of wash-in and wash-out were showed related to severity of SHPT in univariate logistic regression analysis, they were not predictors of SHPT severity (p = 0.539, 0.474 respectively). The nomogram developed by clinical and ultrasonographic features showed good calibration and discrimination. The accuracy and the area under the curve (AUC), positive predictive value (PPV), negative predictive value (NPV) and accuracy of this model were 0.888, 92.5%, 63.2% and 83.1%, respectively. When applied to internal validation, the score revealed good discrimination with stratified fivefold cross-validation in the cohort (mean AUC = 0.833). CONCLUSIONS: The clinical-ultrasonographic features model has good performance for predicting the severity of SHPT.

Angiogenic Factor and Cytokine Analysis among Patients Treated with Adjuvant VEGFR TKIs in Resected Renal Cell Carcinoma.

PURPOSE: The use of VEGFR TKIs for the adjuvant treatment of renal cell carcinoma (RCC) remains controversial. We investigated the effects of adjuvant VEGFR TKIs on circulating cytokines in the ECOG-ACRIN 2805 (ASSURE) trial. EXPERIMENTAL DESIGN: Patients with resected high-risk RCC were randomized to sunitinib, sorafenib, or placebo. Plasma from 413 patients was analyzed from post-nephrectomy baseline, 4 weeks, and 6 weeks after treatment initiation. Mixed effects and Cox proportional hazards models were used to test for changes in circulating cytokines and associations between disease-free survival (DFS) and cytokine levels. RESULTS: VEGF and PlGF increased after 4 weeks on sunitinib or sorafenib (P < 0.0001 for both) and returned to baseline at 6 weeks on sunitinib (corresponding to the break in the sunitinib schedule) but not sorafenib (which was administered continuously). sFLT-1 decreased after 4 weeks on sunitinib and 6 weeks on sorafenib (P < 0.0001). sVEGFR-2 decreased after both 4 and 6 weeks of treatment on sunitinib or sorafenib (P < 0.0001). Patients receiving placebo had no significant changes in cytokine levels. CXCL10 was elevated at 4 and 6 weeks on sunitinib and sorafenib but not on placebo. Higher baseline CXCL10 was associated with worse DFS (HR 1.41 per log increase in CXCL10, Bonferroni-adjusted P = 0.003). This remained significant after adjustment for T-stage, Fuhrman grade, and ECOG performance status. CONCLUSIONS: Among patients treated with adjuvant VEGFR TKIs for RCC, drug-host interactions mediate changes in circulating cytokines. Elevated baseline CXCL10 was associated with worse DFS. Studies to understand functional consequences of these changes are under way.

High-fat diet induces dynamic metabolic alterations in multiple biological matrices of rats.

Obesity is a condition resulting from the interactions of individual biology and environmental factors causing multiple complications. To understand the system's metabolic changes associated with the obesity development and progression, we systematically analyzed the dynamic metabonomic changes induced by a high-fat diet (HFD) in multiple biological matrices of rats using NMR and GC-FID/MS techniques. Clinical chemistry and histopathological data were obtained as complementary information. We found that HFD intakes caused systematic metabolic changes in blood plasma, liver, and urine samples involving multiple metabolic pathways including glycolysis, TCA cycle, and gut microbiota functions together with the metabolisms of fatty acids, amino acids, choline, B-vitamins, purines, and pyrimidines. The HFD-induced metabolic variations were detectable in rat urine a week after HFD intake and showed clear dependence on the intake duration. B-vitamins and gut microbiota played important roles in the obesity development and progression together with changes in TCA cycle intermediates (citrate, α-ketoglutarate, succinate, and fumarate). 83-day HFD intakes caused significant metabolic alterations in rat liver highlighted with the enhancements in lipogenesis, lipid accumulation and lipid oxidation, suppression of glycolysis, up-regulation of gluconeogenesis and glycogenesis together with altered metabolisms of choline, amino acids and nucleotides. HFD intakes reduced the PUFA-to-MUFA ratio in both plasma and liver, indicating the HFD-induced oxidative stress. These findings provided essential biochemistry information about the dynamic metabolic responses to the development and progression of HFD-induced obesity. This study also demonstrated the combined metabonomic analysis of multiple biological matrices as a powerful approach for understanding the molecular basis of pathogenesis and disease progression.