Radioproteomics modeling of metformin-enhanced radiosensitivity: an animal study.

PURPOSE: Metformin is considered as radiation modulator in both tumors and healthy tissues. Radiomics has the potential to decode biological mechanisms of radiotherapy response. The aim of this study was to apply radiomics analysis in metformin-induced radiosensitivity and finding radioproteomics associations of computed tomography (CT) imaging features and proteins involved in metformin radiosensitivity signaling pathways. MATERIALS AND METHODS: A total of 32 female BALB/c mice were used in this study and were subjected to injection of breast cancer cells. When tumors reached a mean volume of 150 mm3, mice were randomly divided into the four groups including Control, Metformin, Radiation, and Radiation + Metformin. Western blot analysis was performed after treatment to measure expression of proteins including AMPK-alpha, phospho-AMPK-alpha (Thr172), mTOR, phospho-mTOR (Ser2448), phospho-4EBP1 (Thr37/46), phospho-ACC (Ser79), and ß-actin. CT imaging was performed before treatment and at the end of treatment in all groups. Radiomics features extracted from segmented tumors were selected using Elastic-net regression and were assessed in terms of correlation with expression of the proteins. RESULTS: It was observed that proteins including phospho-mTOR, phospho-4EBP1, and mTOR had positive correlations with changes in tumor volumes in days 28, 24, 20, 16, and 12, while tumor volume changes at these days had negative correlations with AMPK-alpha, phospho-AMPK-alpha, and phospho-ACC proteins. Furthermore, median feature had a positive correlation with AMPK-alpha, phospho-ACC, and phospho-AMPK-alpha proteins. Also, Cluster shade feature had positive correlations with mTOR and p-mTOR. On the other hand, LGLZE feature had negative correlations with AMPK-alpha and phospho-AMPK-alpha. CONCLUSION: Radiomics features can decode proteins that involved in response to metformin and radiation, although further studies are warranted to investigate the optimal way to integrate radiomics into biological experiments.

A Computed Tomography-based Radiomics Analysis of Low-energy Proximal Femur Fractures in the Elderly Patients.

INTRODUCTION: Low-energy proximal femur fractures in elderly patients result from factors, like osteoporosis and falls. These fractures impose high rates of economic and social costs. In this study, we aimed to build predictive models by applying machine learning (ML) methods on radiomics features to predict low-energy proximal femur fractures. METHODS: Computed tomography scans of 40 patients (mean ± standard deviation of age = 71 ± 6) with low-energy proximal femur fractures (before a fracture occurs) and 40 individuals (mean ± standard deviation of age = 73 ± 7) as a control group were included. The regions of interest, including neck, trochanteric, and intertrochanteric, were drawn manually. The combinations of 25 classification methods and 8 feature selection methods were applied to radiomics features extracted from ROIs. Accuracy and the area under the receiver operator characteristic curve (AUC) were used to assess ML models' performance. RESULTS: AUC and accuracy values ranged from 0.408 to 1 and 0.697 to 1, respectively. Three classification methods, including multilayer perceptron (MLP), sequential minimal optimization (SMO), and stochastic gradient descent (SGD), in combination with the feature selection method, SVM attribute evaluation (SAE), exhibited the highest performance in the neck (AUC = 0.999, 0.971 and 0.971, respectively; accuracy = 0.988, 0.988, and 0.988, respectively) and the trochanteric (AUC = 1, 1 and 1, respectively; accuracy = 1, 1 and 1, respectively) regions. The same methods demonstrated the highest performance for the combination of the 3 ROIs' features (AUC = 1, 1 and 1, respectively; accuracy =1, 1 and 1, respectively). In the intertrochanteric region, the combination methods, MLP + SAE, SMO + SAE, and SGD + SAE, as well as the combination of the SAE method and logistic regression (LR) classification method exhibited the highest performance (AUC = 1, 1, 1 and 1, respectively; accuracy= 1, 1, 1 and 1, respectively). CONCLUSION: Applying machine learning methods to radiomics features is a powerful tool to predict low-energy proximal femur fractures. The results of this study can be verified by conducting more research on bigger datasets.

Zinc oxide nanoparticles enhance expression of maspin in human breast cancer cells.

Toxic and apoptotic impacts of zinc oxide nanoparticle (ZNP) on different cancer cells have been reported. Maspin (a mammary serine protease inhibitor) as a tumor suppressor gene can inhibit tumor growth and metastasis. The expression of maspin is modulated by p53, Bcl-2 family genes, and estrogen receptor α (ER-α). This study aimed to assess the ZNP effects on maspin expression in MCF-7 cells (a breast cancer cell). Experimental groups (ZNP5, ZNP10, and ZNP20) received 5, 10, and 20 µM/mL ZNP for 48 h, respectively. 17-ß-estradiol (E2) was used to evaluate the role of ER-α in the anticancer impact of ZNP. Cell viability, Annexin V, migration assay, gene expression, and western blotting methods were applied to evaluate ZNP effects on the MCF-7 cells. ZNP at the concentrations of 10 and 20 µM/mL could significantly decrease the viability and migration rate, and significantly increase apoptosis percentage in the MCF-7 cells. ZNP significantly enhanced mRNA expression and protein level of maspin in MCF-7 cells in a concentration-dependent way. ZNP concentration-dependently elevated mRNA expression and protein level of p53 and Bax while reduced the expression of Bcl-2 and ER-α. E2 promoted cancer cell growth by enhancing survival and migration rates. E2 treatment reduced mRNA expression and protein level of maspin and p53, and elevated Bcl-2 expression. ZNP considerably changed these events induced by E2 in the MCF-7 cells. It is concluded that the maspin overexpression is one of the toxic mechanisms of the ZNP on the ER-α-positive breast cancer cells, and can suppress the migration of these cells.

In Vitro Toxic Effects of Zinc Oxide Nanoparticles on Rat Adipose Tissue-Derived Mesenchymal Stem Cells.

OBJECTIVE: Zinc oxide nanoparticles (ZnO-NPs) are increasingly used in sunscreens, bio- sensors, food additives, pigments, manufacture of rubber products, and electronic materi- als. There are several studies about the effects of NPs on dermal fibroblast or keratino- cytes, but very little attention has been directed towards adipose-derived mesenchymal stem cells (ASCs). A previous study has revealed that ZnO-NPs restricted the migration capability of ASCs. However, the potential toxicity of these NPs on ASCs is not well un- derstood. This study intends to evaluate the effects of ZnO-NPs on subcutaneous ASCs. MATERIALS AND METHODS: In this experimental study, In order to assess toxicity, we ex- posed rat ASCs to ZnO-NPs at concentrations of 10, 50, and 100 µg/ml for 48 hours. Tox- icity was evaluated by cell morphology changes, cell viability assay, as well as apoptosis and necrosis detection. RESULTS: ZnO-NPs concentration dependently reduced the survival rates of ASCs as re- vealed by the trypan blue exclusion and 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazo- lium-bromide (MTT) tests. ZnO-NPs, at concentrations of 10 and 50 µg/ml, induced a significant increase in apoptotic indices as shown by the annexin V test. The concentration of 10 µg/ml of ZnO-NPs was more toxic. CONCLUSION: Lower concentrations of ZnO-NPs have toxic and apoptotic effects on subcutaneous ASCs. We recommend that ZnO-NPs be used with caution if there is a dermatological problem.