Osteoporosis (OP) is characterized by diminished bone mass and deteriorating bone structure that increases the chance of fractures in the spine, hips, and wrists. In this paper, a novel data processing method of artificial intelligence (AI) is used for evaluating, predicting, and classifying OP risk factors in clinical data of men and women separately. Additionally, artificial intelligence was used to suggest the most appropriate sports programs for treatment. Data was obtained from dual-energy x-ray absorption scanning center of Ayatollah Kashani, Milad, and Khatam al-Anbia hospitals in Tehran, Iran. The subjects included 1224 men and women. Models were developed using decision tree, random forest (RF), k-nearest neighbor, support vector machine, gradient boosting (GB), Extra trees, Ada Boost (AB), and artificial neural network multilayer perceptron analysis to predict osteoporosis and to recommend sports programs. Data was divided into training (80%) and test dataset (20%). The results were obtained on a 20% test dataset. Area under receiver operating characteristic curve (AUROC) was used to compare the performance of the models. To predict healthy individuals, osteopenia and osteoporosis, the FR algorithm with AUROC 0.91 performed best in men and the GB algorithm with AUROC 0.95 performed best in women compared to other classification algorithms. Prediction of RF algorithm in women and men with AUROC 0.96 and 0.99, respectively, showed the highest performance in diagnosing the type of exercise for healthy individuals and those with osteopenia and OP. Eight AI algorithms were developed and compared to accurately predict osteoporosis risk factors and classify individuals into three categories: healthy, osteopenia, and OP. In addition, the AI algorithms were developed to recommend the most appropriate sports programs as part of treatment. Applying the AI algorithms in a clinical setting could help primary care providers classify patients with osteoporosis and improve treatment by recommending appropriate exercise programs.

Benzodiazepines (BZD) are widely used in neurological disorders. The use of classical benzodiazepines is limited due to side effects. In this study, based on the structure-activity relationship (SAR) of Benzodiazepine receptors, new derivatives of 4-amino-3,5-diphenyl-1,2,4-triazole as benzodiazepine agonists with selective effects were designed and synthesized. Docking studies showed that pharmacophore groups of the designed structures and zolpidem, a benzodiazepine receptor agonist, are properly matched and are located well in the GABA receptor. The triazole group of the compound 4j, N N-(3,5-diphenyl-4H-1,2,4-triazol-4-yl)-2-((4-fluorobenzyl)amino)acetamide, was near the nitrogen moiety of the imidazole ring of zolpidem providing the hydrogen bond acceptor in the suitable direction in the BDZ-binding site of GABAA receptor model (α1β2ϒ2). The compounds were synthesized with acceptable yield and in-vitro affinity for the BZD receptor was determined. Compound 4j had the best affinity for the BZD site of action on GABAA receptor complex (Ki = 2.56 nM and IC50 = 6.10 nM). In addition, the sedative-hypnotic effect, the locomotor activity, and evaluated memory of the novel compounds were assessed by pentobarbital-induced sleeping, open field, and passive avoidance tests respectively. Most of the novel compounds showed significant hypnotic activity with no impairment on learning and memory performance in the mouse. The pharmacological effects of the compounds were antagonized by flumazenil, a BZD antagonist, which confirms the involvement of BZD receptors in the biological effects.

: The COVID-19 pandemic has prompted researchers to find treatments and vaccines to control SARS-CoV-2. There are some hypotheses about the benefit of respiratory virus vaccines, like MMR, for COVID-19 pneumonia severity, morbidity, and mortality. The influenza vaccine is one of the most frequently used respiratory virus vaccines covered by one of the Iranian insurance institutes. We have a symmetrical group of participants that have received this vaccine that could be compared with each other. We compared 3,379 persons aged 20 - 75 years for the effect of the influenza vaccine on COVID-19 mortality. We ultimately found that it does not affect mortality caused by COVID-19 pneumonia, but it can decrease the hospitalization cost in people over 65 years with a history of chronic disease.

Background: Valproic acid (VPA), a branched short-chain fatty acid and histone deacetylase (HDAC) inhibitor, has diverse biological activities in human cells, including anti-cancer properties. Objectives: In the present study, we tested the cytotoxicity of VPA on the proliferation, cell cycle, and apoptosis of the human cervical cancer cell line, HeLa. Methods: HeLa cell line was cultured in Dulbecco’s modified eagle medium (DMEM) and the cytotoxicity effect of VPA (at 0 - 100 mM) on the HeLa cell was evaluated, using the 3‐(4,5‐dimethylthiazol‐2‐yl)‐2,5‐diphenyltetrazolium bromide (MTT) assay for 3 incubation times (24, 48, and 72 h). The effects of VPA on cell cycle arrest and apoptosis were evaluated, using flow cytometry. In addition, the alterations in the expression of Bax, Bcl-2, p53, and p21 were assessed with real‐time polymerase chain reaction (PCR). Results: Valproic acid reduced the viability of HeLa cells in a concentration- and time-dependent manner, and the IC50 values at 24, 48, and 72 h were 32.06, 21.29, and 14.51 mM, respectively. Further, VPA treatment remarkably increased the apoptosis of HeLa cells and arrested cells at the sub-G1 phase with a significant reduction in G2-M phase populations. The real-time PCR results demonstrated a significant increase in the expression of pro-apoptotic genes, including Bax, p53, and p21, as well as a reduction in the levels of the anti-apoptotic gene, Bcl-2. Conclusions: Valproic acid inhibits the proliferation of the HeLa cell line through the induction of the intrinsic pathway of apoptosis in a p35-dependent manner.