Breast cancer: early diagnosis and effective treatment by drug delivery tracing

Breast cancer is the most frequent cancer in women and it is the main reason of cancer-related deaths of women worldwide. Different types of breast cancer diagnostic examinations are also available, such as mammography, MRI, biopsy, ultrasound and molecular imaging. Radionuclide-based imaging methods including SPECT and PET are useful in early diagnosis and treatment of the cancer. The radiolabeling of chemo drugs with nanoparticles should be recommended from the standpoint of an early diagnosis and effective treatment of breast cancer

A study on drug delivery tracing with radiolabeled mesoporous hydroxyapatite nanoparticles conjugated with 2DG/DOX for breast tumor cells

Background: Mesoporous nanoparticles have a great potential in targeted therapy approaches due to their ideal properties for encapsulation of various drugs, proteins and also biologically active molecules. Material and methods: We used mesoporous hydroxyapatite (HA) nanoparticles as a drug carrier and developed radiolabeled mesoporous HA containing of 2-deoxy-D-glucose (2DG) and Doxorubicin (DOX) with technetium-99m (99mTc) for imaging in in vitro and in vivo studies. Results: 2DG and DOX in presence of mesoporous HA nanoparticles more reduced the fraction of viable cells in the MDA-MB-231, MCF-7 human and MC4-L2 Balb/c mice breast cancer cells. The radiochemical purity of the nano-2DG-DOX complex with 99mTc was calculated to 96.8%. The results of cellular uptake showed a 44.77% increase in uptake of the [99mTc]-nano-2DG-DOX compared to the complex without nanoparticles (p < 0.001). Conclusion: Radioisotopic imaging demonstrated a high biochemical stability for [99mTc]-nano-2DG-DOX complex. The results demonstrated that [99mTc]-nano-2DG-DOX, may be used as an attractive candidate in cancer imaging and treatment managing.BACKGROUND: Mesoporous nanoparticles have a great potential in targeted therapy approaches due to their ideal properties for encapsulation of various drugs, proteins and also biologically active molecules. MATERIAL AND METHODS: We used mesoporous hydroxyapatite (HA) nanoparticles as a drug carrier and developed ra­diolabeled mesoporous HA containing of 2-deoxy-D-glucose (2DG) and Doxorubicin (DOX) with technetium-99m (99mTc) for imaging in in vitro and in vivo studies. RESULTS: 2DG and DOX in presence of mesoporous HA nanoparticles more reduced the fraction of viable cells in the MDA-MB-231, MCF-7 human and MC4-L2 Balb/c mice breast cancer cells. The radiochemical purity of the nano-2DG-DOX complex with 99mTc was calculated to 96.8%. The results of cellular uptake showed a 44.77% increase in uptake of the [99mTc]- nano-2DG-DOX compared to the complex without nanoparticles (p &lt; 0.001). CONCLUSIONS: Radioisotopic imaging demonstrated a high biochemical stability for [99mTc]-nano-2DG-DOX complex. The results demonstrated that [99mTc]-nano-2DG-DOX, may be used as an attractive candidate in cancer imaging and treatment managing.

Application of machine learning in predicting non-alcoholic fatty liver disease using anthropometric and body composition indices

Abstract Non-alcoholic fatty liver disease (NAFLD) is the most common chronic liver disease, which can progress from simple steatosis to advanced cirrhosis and hepatocellular carcinoma. Clinical diagnosis of NAFLD is crucial in the early stages of the disease. The main aim of this study was to apply machine learning (ML) methods to identify significant classifiers of NAFLD using body composition and anthropometric variables. A cross-sectional study was carried out among 513 individuals aged 13 years old or above in Iran. Anthropometric and body composition measurements were performed manually using body composition analyzer InBody 270. Hepatic steatosis and fibrosis were determined using a Fibroscan. ML methods including k-Nearest Neighbor (kNN), Support Vector Machine (SVM), Radial Basis Function (RBF) SVM, Gaussian Process (GP), Random Forest (RF), Neural Network (NN), Adaboost and Naïve Bayes were examined for model performance and to identify anthropometric and body composition predictors of fatty liver disease. RF generated the most accurate model for fatty liver (presence of any stage), steatosis stages and fibrosis stages with 82%, 52% and 57% accuracy, respectively. Abdomen circumference, waist circumference, chest circumference, trunk fat and body mass index were among the most important variables contributing to fatty liver disease. ML-based prediction of NAFLD using anthropometric and body composition data can assist clinicians in decision making. ML-based systems provide opportunities for NAFLD screening and early diagnosis, especially in population-level and remote areas