Bone protective effects of the polysaccharides from Grifola frondosa on ovariectomy-induced osteoporosis in mice via inhibiting PINK1/Parkin signaling, oxidative stress and inflammation.

BACKGROUND: Polysaccharides from Grifola frondosa(GFP) have gained worldwide attention owing to their promising biological activities and potential health benefits. PURPOSE: This study aimed to investigate the effects of GFP on alleviation of osteoporosis in ovariectomized (OVX) mice and examine the underlying mechanism. METHOD: A mouse model of postmenopausal osteoporosis was established by OVX method, Forty eight C57BL/6 female mice were randomly divided into Normal group, OVX alone (Model group, n = 8), OVX + 10 mg/kg GFP (GFP-L group, n = 8), OVX + 20 mg/kg GFP (GFP-M group, n = 8), OVX + 40 mg/kg GFP (GFP-H group, n = 8), OVX + 10 mg/kg Estradiol valerate (Positive group, n = 8). RESULTS: The results showed that compared with Model group, the concentrations of interleukin (IL)-1ß, interleukin (IL)-6 and Tumor necrosis factor-α (TNF-α) were significantly reduced, the activity of superoxide dismutase (SOD) and glutathione (GSH) were significantly increased, the content of myeloperoxidase (MPO) and malondialdehyde (MDA) were significantly reduced, and the proteins levels of PINK1, Parkin, Beclin-1 and LC3-II were significantly decreased in the GFP groups. CONCLUSION: This study demonstrates that GFP alleviates ovariectomy-induced osteoporosis via reduced secretion of inflammatory cytokines, improvement in the oxidative stress status in the body, and inhibition of the PINK1/Parkin signaling pathway.

Self-assembled C-Ag hybrid nanoparticle on nanoporous GaN enabled ultra-high enhancement factor SERS sensor for sensitive thiram detection.

Considering pesticide residues cause significant harm to public health and the environment, developing a simple, sensitive, and reliable approach to pesticide residue detection to address this issue is necessary. In this study, an ultrasensitive and reliable surface-enhanced Raman scattering (SERS) sensor was developed using cetylpyridinium chloride as a protecting and reducing agent for the in situ synthesis and self-assembly of C-Ag nanoparticles on nanoporous GaN for the quantitative detection of thiram. A systematic investigation of the performance of the SERS sensor revealed that the SERS sensor delivered a limit of detection (LOD) of 10-14 M and an enhancement factor of up to 1.80 × 1011 with reasonable uniformity and reproducibility, with the stability of the SERS sensor demonstrated via long-term storage for up to 22 weeks in air. The enhancement mechanism of the SERS sensor was verified using a finite-difference time-domain simulation. The SERS sensor successfully detected thiram in real samples with an LOD of 10-10 M. Hence, this study provides an effective platform for monitoring food safety and the environment.

Applying Machine Learning Analysis Based on Proximal Femur of Abdominal Computed Tomography to Screen for Abnormal Bone Mass in Femur.

RATIONALE AND OBJECTIVES: To evaluate the performance of machine learning analysis based on proximal femur of abdominal computed tomography (CT) scans in screening for abnormal bone mass in femur. MATERIALS AND METHODS: 222 patients aged 50 years or older who underwent abdominal CT and dual-energy X-ray absorptiometry scans within 14 days were retrospectively enrolled. The patients were randomly assigned to a training cohort (n = 155) and a testing cohort (n = 67) in a ratio of 7:3. A total of 2288 candidate radiomic features were extracted from the volume region of interest - the left proximal femur of the abdominal CT scans. The most valuable radiomic features were selected using minimum-Redundancy Maximum-Relevancy and the least absolute shrinkage and selection operator to construct the radiomics model. The predictive performance was assessed with receiver operating characteristic curve. RESULTS: 13 features were chosen to establish the radiomics model. The radiomics model using logistic regression displayed excellent prediction performance in distinguishing normal bone mass and abnormal bone mass, with the area under the curve (AUC), accuracy, sensitivity and specificity of 0.917 (95% CI, 0.867-0.967), 0.826, 0.935 and 0.780 in the training cohort. The testing cohort indicated a better performance with AUC, accuracy, sensitivity and specificity of 0.963 (95% CI, 0.919-0.999), 0.851, 0.923 and 0.889. CONCLUSION: The radiomics model based on proximal femur of abdominal CT scans had a high predictive performance to identify abnormal bone mass in femur, which can be used as a tool for opportunistic osteoporosis screening.

Serum metabolomics probes the molecular mechanism of action of acupuncture on metabolic pathways related to glucose metabolism in patients with polycystic ovary syndrome-related obesity.

Polycystic ovary syndrome (PCOS) is a common endocrine syndrome, and obesity is the most common clinical manifestation. Acupuncture is effective in treating PCOS, but the differences in the biological mechanisms of acupuncture therapy and Western medicine treatment have not been determined. Thus, the purpose of this study was to find glucose metabolism-related pathways in acupuncture treatment and differentiate them from Western medical treatment. Sixty patients with PCOS-related obesity were randomly distributed into three groups: patients receiving (1) acupuncture treatment alone, (2) conventional Western medicine treatment, and (3) acupuncture combined with Western medicine treatment. A targeted metabolomics approach was used to identify small molecules and metabolites related to glucose metabolism in the serum of each group, and ultra-high-performance liquid chromatography-tandem mass spectrometry was used to analyze different metabolic fractions. The results showed acupuncture treatment modulates the activity of citric and succinic acids in the tricarboxylic acid cycle, regulates glycolytic and gluconeogenesis pathways, and improves the levels of sex hormones and energy metabolism. The intervention effects on the metabolic pathways were different between patients receiving combination therapy and patients receiving acupuncture therapy alone, suggesting that the dominant modulatory effect of Western drugs may largely conceal the efficacy of acupuncture intervention.

Traditional Chinese Medicine Treatment for Androgenetic Alopecia Based on Animal Experiments: A Systematic Review and Meta-Analysis.

Background: In the present study, we systematically evaluated the effects of Traditional Chinese Medicine (TCM) on androgenetic alopecia (AGA) in rodent models (rats and mice) to provide potential evidence for the treatment of AGA by TCM. Methods: Previous research papers focusing on the treatment of AGA by TCM were retrieved from various electronic databases (PubMed, Embase, The Cochrane Library, CNKI, Vip, Wanfang data, and CBM) up to October 30, 2021. Screening of the literature was performed independently, and data were extracted and assessed. A meta-analysis was performed using RevMan 5.3 software. Results: When compared with the model groups, a group of C57BL/6 mice treated with TCM showed an increase in the total number of hair follicles (mean difference [MD] = 11.99, 95% confidence interval [CI] [5.94,18.03], P=0.0001), as well as a decrease in serum testosterone (T) level (MD = -1.10, 95% CI [-1.43, -0.78], P < 0.00001), skin discoloration time (MD = -2.93, 95% CI [-4.03, -1.84], P < 0.00001), and skin hair growth time (MD = -3.16, 95% CI [-4.16, -2.16], P < 0.00001). Terminal hair/vellus hair also increased in TCM-treated AGA animals (MD = 3.02, 95% CI [2.05, 3.98], P < 0.00001). No significant difference was found in serum estradiol (E2) level, skin tissue E2 level, or skin tissue T level between the TCM-treated group and the model group. Conclusion: TCM can increase the total number of hair follicles and terminal hair/vellus hair ratio, and reduce skin discoloration time and skin hair growth time in AGA animal models. These effects may be related to the reduction of the serum T level in AGA animals. These conclusions need to be verified by high-quality studies as the current analysis may be affected by the number and quality of the studies identified.