The Expression of miR-34c-5p Induces G0/G1 Cell Cycle Arrest and Apoptosis in SW480 Colon Cancer Cell.

Background: Expression of the miR-34 family, including miR-34a/b/c, has been reported to inhibit the progression of several cancer types by inhibiting cell proliferation and inducing apoptosis. Objectives: We attempted to investigate the effect of SW480 cell transfection with miR-34c-5p mimics on cell proliferation. Methods: To do this, SW480 colon cancer cell line was transfected with miR-34c-5p mimics, scramble sequence, and the vehicle in PBS mock, and then cell proliferation was assessed by MTT assay. The population of cells in cell cycle phases, ROS generation, and apoptosis rate were evaluated by flow cytometry. Additionally, we determined the relative expression of apoptotic genes through real-time PCR technique. Results: We observed a reduced proliferation rate in cells transfected with miR-34c-5p compared to the control group (P <0.05). We also found that miR-34c-5p caused a significant increase in apoptosis rate (P < 0.001) and cell cycle arrest in the G0 and G1 phases (P < 0.05). Moreover, a significant increase was reported in the expression of pro-apoptotic genes, including BAK (P < 0.001), BAX and BAD (P < 0.0001), and Caspase 7/9 (P < 0.0001). Conclusions: However, no remarkable difference was seen in the expression of MCL1, BCL2, and CASPASE 3 genes. Our conclusion is that overexpression of miR-34c-5p could be considered a promising approach for colorectal cancer treatment.

Cell Therapy and Investigation of the Angiogenesis of Fibroblasts with Collagen Hydrogel on the Healing of Diabetic Wounds.

Objectives: A diabetic ulcer is a common disease in patients with diabetes. Because of antibiotic resistance, new therapeutic alternatives are being considered in diabetic foot patients to reduce complications and mortality. This study aimed to evaluate the effect of collagen hydrogel on the wound-healing process in diabetic rats. Materials and Methods: Diabetic wounds were induced with streptozotocin in all 42 male Wistar rats. The rats were divided into four groups: (a) treated with fibroblast cells, (b) collagen hydrogel, (c) collagen cultured with fibroblast cells, and (d) a control group. Microscopic and histological (hematoxylin and eosin staining and Mason trichrome staining), measurement of wound surface with image J, skin density and thickness by the ultrasound probe, and skin elasticity with cytometer tool were used to evaluate wound healing at days 14 and 21 after the treatment. Results: The results showed that treating diabetic wounds with fibroblasts cultured in collagen hydrogel greatly reduces inflammatory responses in the skin tissue and significantly accelerates the healing process. In addition, 21 days after the start of treatment, skin elasticity, thickness, and density were higher in the collagen + fibroblast group than in the control group. Conclusion: In addition, the results of the present study show that diabetic wound dressing can significantly reduce the inflammatory phase in the wound healing process by increasing the speed of collagen synthesis, skin density and elasticity, and angiogenesis.

Prediction of acute organophosphate poisoning severity using machine learning techniques.

Poisoning with organophosphate compounds is a significant public health risk, especially in developing countries. Considering the importance of early and accurate prediction of organophosphate poisoning prognosis, the aim of this study was to develop a machine learning-based prediction model to predict the severity of organophosphate poisoning. The data of patients with organophosphate poisoning were retrospectively extracted and split into training and test sets in a ratio of 70:30. The feature selection was done by least absolute shrinkage and selection operator method. Selected features were fed into five machine learning techniques, including Histogram Boosting Gradient, eXtreme Gradient Boosting, K-Nearest Neighborhood, Support Vector Machine (SVM) (kernel = linear), and Random Forest. The Scikit-learn library in Python programming language was used to implement the models. Finally, the performance of developed models was measured using ten-fold cross-validation methods and some evaluation criteria with 95 % confidence intervals. A total of 1237 patients were used to train and test the machine learning models. According to the criteria determining severe organophosphate poisoning, 732 patients were assigned to group 1 (patients with mild to moderate poisoning) and 505 patients were assigned to group 2 (patients with severe poisoning). With an AUC value of 0.907 (95 % CI 0.89-0.92), the model developed using XGBoost outperformed other models. Feature importance evaluation found that venous blood gas-pH, white blood cells, and plasma cholinesterase activity were the top three variables that contribute the most to the prediction performance of the prognosis in patients with organophosphate poisoning. XGBoost model yield an accuracy of 90.1 % (95 % CI 0.891-0.918), specificity of 91.4 % (95 % CI 0.90-0.92), a sensitivity of 89.5 % (95 % CI 0.87-0.91), F-measure of 91.2 % (95 % CI 0.90-0.921), and Kappa statistic of 91.2 % (95 % CI 0.90-0.92). The machine learning-based prediction models can accurately predict the severity of organophosphate poisoning. Based on feature selection techniques, the most important predictors of organophosphate poisoning were VBG-pH, white blood cell count, plasma cholinesterase activity, VBG-BE, and age. The best algorithm with the highest predictive performance was the XGBoost classifier.

A Meta-analysis of ADIPOQ rs2241766 polymorphism association with type 2 diabetes.

Objective: There is extensive research on the association between polymorphisms in the adiponectin gene (ADIPOQ) and type 2 diabetes (T2D). The present meta-analytic study explored the association between ADIPOQ rs2241766 polymorphisms and T2D. Metolds: Articles were collected by searching Google Scholar, Scopus, and PubMed electronic databases until 2021. They were searched using a systematic search of original and sensitive English keywords and their equivalent keywords based on the keywords "type 2 diabetes", "ADIPOQ", and "rs2241766". The article selection criteria were based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flow diagram. Results: The results revealed that there was no bias in this study. Some studies, such as Joshaghani et al. (odds ratio [OR] = 2.18), Hussain et al. (OR = 2.12), Momin (OR = 4.45), and Amal et al. (OR = 1.84), showed an increasing effect of ADIPOQ rs266729 polymorphism on T2D with 95% CI (P ˂ 0.01), while some have shown no significant association between them. Conclusion: The results of this meta-analytic study showed the relationship between ADIPOQ and rs2241766. Also, it was found that Rs2241766 polymorphism and allele increase the risk, and rs2241766 increases the risk of developing T2D (OR = 1.29).