Meta-analysis exploring the effectiveness and safety of different doses of estrogen in the treatment of osteoporosis in perimenopausal women in China.

OBJECTIVE: This study aims to systematically evaluate the effectiveness and safety of using different doses of estrogen in the treatment of perimenopausal osteoporosis in China. MATERIALS AND METHODS: Computer searches of the Cochrane Library, PubMed, Embase, CBM, CNKI, WanFang Date, and VIP databases were conducted. Randomized Controlled Trials (RCTs) on different doses of estrogen for the treatment of osteoporosis in Chinese perimenopausal women were searched for the period 01/01/2000-06/09/2022. Document screening and data extraction were completed independently by 2 researchers and assessed using the Cochrane recommended risk bias assessment tool for RCTs. The software used for analysis in this study was Stata, version 16.0. RESULTS: A total of 10 RCTs with a cumulative total of 804 patients were included. Meta-analysis results showed that low doses of estrogen were more effective in improving patient outcomes [OR=0.521, 95% CI (0.300-0.907), z = 2.31, p ≤ 0.05] and bone mineral density [SMD = -0.218, 95% CI (-0.42,-0.016), z = 2.11, p ≤ 0.05] was not superior. For bone metabolism and sex hormone indicators, the standard dose group had a slight advantage, but the difference was small (p > 0.05) and not statistically significant. With regard to safety, the incidence of adverse reactions was higher with the standard dose of estrogen. CONCLUSIONS: In China, standard doses of estrogen are used for clinical effectiveness. However, vigilance must be maintained for potential safety concerns that may arise during treatment.

Docosahexaenoic Acid Reverses Epithelial-Mesenchymal Transition and Drug Resistance by Impairing the PI3K/AKT/ Nrf2/GPX4 Signalling Pathway in Docetaxel-Resistant PC3 Prostate Cancer Cells.

Drug resistance is a serious problem in cancer therapy. Growing evidence has shown that docosahexaenoic acid has anti-inflammatory and chemopreventive abilities. Studies have shown that autophagy inhibition and ferroptosis are promising therapeutic strategies for overcoming multidrug resistance. This study was aimed to examine whether docosahexaenoic acid (DHA) could reverse docetaxel resistance in prostate cancer cells. Cell survival was examined by MTT and colony formation. Protein expression was determined by Western blot. Reactive oxygen species (ROS) production was measured by flow cytometry. DHA displayed anti-cancer effects on proliferation, colony formation, migration, apoptosis, autophagy and epithelial mesenchymal transition. Glutathione-S-transferase π is an enzyme that plays an important role in drug resistance. DHA inhibited GSTπ protein expression and induced cytoprotective autophagy by regulating the PI3K/AKT signalling pathway in PC3R cells. DHA combined with PI3K inhibitor (LY294002) enhanced apoptosis by alleviating the expression of LC3B, (pro-) caspase- 3 and (uncleaved) PARP. DHA induced ferroptosis by attenuating the expression of glutathione peroxidase 4 (GPX4) and nuclear erythroid 2-related factor 2 (Nrf2). DHA-treated PC3R cells produced ROS. The ROS and cytotoxicity were reversed by treatment with ferrostatin-1. DHA combined with docetaxel inhibited EMT by regulating the expression of E-cadhein and N-cadherin. In summary, DHA reversed drug resistance and induced cytoprotective autophagy and ferroptosis by regulating the PI3K/AKT/Nrf2/GPX4 signalling pathway in PC3R cells. We propose that DHA could be developed as a chemosensitizer and that the PI3K/AKT /Nrf2/GPX4 signalling pathway might be a promising therapeutic target for overcoming cancer drug resistance.

Prediction of protein homo-oligomer types by pseudo amino acid composition: Approached with an improved feature extraction and Naive Bayes Feature Fusion.

The interaction of non-covalently bound monomeric protein subunits forms oligomers. The oligomeric proteins are superior to the monomers within the scope of functional evolution of biomacromolecules. Such complexes are involved in various biological processes, and play an important role. It is highly desirable to predict oligomer types automatically from their sequence. Here, based on the concept of pseudo amino acid composition, an improved feature extraction method of weighted auto-correlation function of amino acid residue index and Naive Bayes multi-feature fusion algorithm is proposed and applied to predict protein homo-oligomer types. We used the support vector machine (SVM) as base classifiers, in order to obtain better results. For example, the total accuracies of A, B, C, D and E sets based on this improved feature extraction method are 77.63, 77.16, 76.46, 76.70 and 75.06% respectively in the jackknife test, which are 6.39, 5.92, 5.22, 5.46 and 3.82% higher than that of G set based on conventional amino acid composition method with the same SVM. Comparing with Chou's feature extraction method of incorporating quasi-sequence-order effect, our method can increase the total accuracy at a level of 3.51 to 1.01%. The total accuracy improves from 79.66 to 80.83% by using the Naive Bayes Feature Fusion algorithm. These results show: 1) The improved feature extraction method is effective and feasible, and the feature vectors based on this method may contain more protein quaternary structure information and appear to capture essential information about the composition and hydrophobicity of residues in the surface patches that buried in the interfaces of associated subunits; 2) Naive Bayes Feature Fusion algorithm and SVM can be referred as a powerful computational tool for predicting protein homo-oligomer types.