Cumulative effects of exercise training and consumption of propolis on managing diabetic dyslipidemia in adult women: a single-blind, randomized, controlled trial with pre-post-intervention assessments.

Dyslipidemia is an imbalance of various lipids, and propolis, as a natural resinous viscos mixture made by Apis mellifera L. could improve in this condition. In this single-blind, randomized trial, 60 women with type 2 diabetes and dyslipidemia were divided into four groups: (1) the patients who did not apply the combined training and 500 mg propolis capsules supplement (Control group); (2) subjects performed combined training, including aerobic and resistance training (EXR); (3) subjects received the 500 mg propolis supplement capsules (SUPP); (4) Subjects performed combined training along with receiving the 500 mg propolis supplement capsules (EXR + SUPP). We evaluated the concentration of CTRP12, SFRP5, interleukin-6 (IL6), superoxide dismutase (SOD), malondialdehyde (MDA), adiponectin, and total antioxidant capacity (TAC) before and after the intervention. MDA, TAC, IL6, CTRP12, SFRP5 IL6, adiponectin, and lipid profile levels ameliorated in the EXR + SUPP group. We found that 8 weeks of treatment by combined exercise training and propolis supplement decreased inflammation activity and increased antioxidant defense in women with diabetic dyslipidemia.Trial registration This study was registered in the Iranian Registry of Clinical Trials; IRCT code: IRCT20211229053561N1.

MiR-548k suppresses apoptosis in breast cancer cells by affecting PTEN/PI3K/AKT signaling pathway.

Breast cancer is the most aggressive and fatal form of cancer among women globally. Although the role of some miRNAs that are often dysregulated in breast cancer has been deciphered, the regulatory function of others still remains unknown. The current study was aimed at determining the biological role and underlying mechanism of miR-548k in breast cancer. In this study, the significant overexpression of miR-548k in breast cancer tissues compared to adjacent normal tissues was confirmed. Also, bioinformatics analysis indicated that PTEN, as a negative regulator of PI3K/AKT signaling pathway, was a potential target of miR-548k, and its expression was downregulated in breast cancer tissues rather than normal tissues. Furthermore, the ectopic increase of miR-548k decreased the expression of PTEN in breast cancer, suggesting that PTEN is one of the potential downstream targets of miR-548k. Besides, functional analysis was conducted to assess the capability of miR-548k to alter apoptosis along with the changed expression levels of miR-548k in breast cancer cells. Based on this investigation, forced increase of miR-548k disrupted programmed cell death in MCF-7 cells. Apart from this, in silico study of miR-548 family supported its association with the main components of PI3K/Akt signaling pathway, opening a prospective research area in cancer therapy. In brief, suppression of PTEN partly mediated by miR-548k diminished apoptosis and promoted cell proliferation through PI3K/Akt pathway in breast cancer, suggesting a novel therapeutic axis, miR-548k/PTEN/ PI3K/Akt, for treatment of breast cancer in the future.

Contourlet-based mammography mass classification using the SVM family.

This paper is concerned with the design and development of an automatic mass classification of mammograms. The proposed method consists of three stages. In the first stage, preprocessing is performed to remove the pectoral muscles and to segment regions of interest. In the next stage contourlet transform is employed as a feature extractor to obtain the contourlet coefficients. This stage is completed by feature selection based on the genetic algorithm, resulting in a more compact and discriminative texture feature set. This improves the accuracy and robustness of the subsequent classifiers. In the final stage, classification is performed based on successive enhancement learning (SEL) weighted SVM, support vector-based fuzzy neural network (SVFNN), and kernel SVM. The proposed approach is applied to the Mammograms Image Analysis Society dataset (MIAS) and classification accuracies of 96.6%, 91.5% and 82.1% are determined over an efficient computational time by SEL weighted SVM, SVFNN and kernel SVM, respectively. Experimental results illustrate that the contourlet-based feature extraction in conjunction with the state-of-art classifiers construct a powerful, efficient and practical approach for automatic mass classification of mammograms.