14-weeks combined exercise epigenetically modulated 118 genes of menopausal women with prediabetes.

Background: Pre-diabetes precedes Diabetes Mellitus (DM) disease and is a critical period for hyperglycemia treatment, especially for menopausal women, considering all metabolic alterations due to hormonal changes. Recently, the literature has demonstrated the role of physical exercise in epigenetic reprogramming to modulate the gene expression patterns of metabolic conditions, such as hyperglycemia, and prevent DM development. In the present study, we hypothesized that physical exercise training could modify the epigenetic patterns of women with poor glycemic control. Methods: 48 post-menopause women aged 60.3 ± 4.5 years were divided according to their fasting blood glucose levels into two groups: Prediabetes Group, PG (n=24), and Normal Glucose Group, NGG (n=24). All participants performed 14 weeks of physical exercise three times a week. The Infinium Methylation EPIC BeadChip measured the participants' Different Methylated Regions (DMRs). Results: Before the intervention, the PG group had 12 DMRs compared to NGG. After the intervention, five DMRs remained different. Interestingly, when comparing the PG group before and after training, 118 DMRs were found. The enrichment analysis revealed that the genes were related to different biological functions such as energy metabolism, cell differentiation, and tumor suppression. Conclusion: Physical exercise is a relevant alternative in treating hyperglycemia and preventing DM in post-menopause women with poor glycemic control.

Patient-specific IMRT QA verification using machine learning and gamma radiomics.

Gamma function is the standard methodology for comparing dose distributions. It is calculated in dedicated software, and its results verification is not performed. Thus we developed an automatic tool for patient-specific QA results verification through high accuracy machine learning (ML) models based on the radiomics characteristics extraction from gamma images. We used 158 patient-specific QA tests and extracted 105 radiomics features from each gamma image. Three random forest models were developed (ML I, ML II, and ML III). ML I and ML II verified the gamma image approval using criteria of 2%/2mm/15% threshold and 3%/3mm/15% threshold, respectively. ML III verified if the gamma analyzes software recommended protocol was followed to detect if the TPS grid modification step was done. The models were based on the most important features selected using the mean decreased impurity, and their performances were evaluated. ML I included 25 features. Its accuracy was 0.85 using the test set and 0.84 using dataset B. ML II included 10 features, and its accuracy with the test set was 0.98; the same value was achieved using the never seen data (dataset B). The First-order 10th percentile feature was identified as a feature strongly related to the approved classification. ML III selected 23 features with an accuracy of 0.99 for test set and 0.98 for dataset B. An automatic workflow example for gamma analyses QA results verification could be proposed combining the models to detect grid inconsistencies on software evaluation, followed by the test approval classification.

Constancy checks of HDR well-type ionization chambers in the linac era: Potential use of a 90Sr source.

A methodology using a90Sr source, originally designed for constancy checks of parallel-plate ionization chambers, was proposed for constancy checks of HDR brachytherapy well-type ionization chamber (WTIC). The 90Sr source was positioned on the top of the WTIC, a reference reading value was established, and the constancy variation was calculated by the difference between each subsequent measurement and the reference value. This methodology was validated by calibrated 192Ir sources measurements and an acceptable tolerance limit for constancy variation of 1.5% was suggested.