99mTc labeled genistein kits: development methods and quality control for breast cancer radiotracer applications

Objective. Selective estrogen receptor modulators (SERMs) have been widely used to treat breast cancer, osteoporosis, and postmenopausal symptoms. SERMs have an affinity for estrogen receptors (ER) in target tissues and resist stimulation of the breast, bone, and endometrium. Genistein as an isoflavone compound that has a high affinity for ERβ targets makes it a potential target or prognostic marker for breast cancer. This study was carried out to develop 99mTc-genistein kit that can be used to detect breast cancer. Methods. The synthesis process and quality control were investigated to obtain the optimal formula for the ratio of a substance, reducing agent, optimal conditions of the synthesis reaction, physicochemical properties of the kit, and its stability to meet the requirements of radiochemical purity. Results. The radiochemical purity in the development of the radiopharmaceutical kit was 93.25% ± 0.30%. The physicochemical properties of the kit preparations showed hydrophilic properties, good plasma protein binding, no electrical charge, and were stable at storage temperatures. Conclusions. The radiochemical purity of the radiopharmaceutical kits meets the requirements of the United State Pharmacopeia and has good physicochemical properties to be developed into kits.

Disease Diagnostic System Using Deep Learning

Early disease detection cannot be neglected in the healthcare domain and especially in the diseases where a person's life is at stake. According to the WHO, if the diseases are predicted on time, then the death rates could reduce. The paper's goal is to find out how to detect Breast Cancer, Skin Cancer, Lung Cancer, and Brain Tumor at the early stages with the help of Deep Learning techniques. The authors of different papers have used different techniques and Algorithms like Adaptive Median Filters, Gaussian Filters, CNN algorithms, etc. Keywords: Breast Cancer, Skin Cancer, Brain Tumor, Lung Cancer, Deep Learning, CNN, SVM, Random Forest

Classification of Breast cancer tumors using Feature Selection and CNN

Breast cancer is one of the most dangerous diseases in the world and almost two million new cases are diagnosed every year. It starts from the breasts tissue and then spreads to other parts of the body. Early detection of breast cancer is important to save the life of a woman as it is related with a risen number of available treatment options. Benign and malignant are the major types of tumors and they are cancerous and non-cancerous, respectively. Benign is not dangerous since it does not destroy the nearby tissues and cannot spread or grow. Malignant tumor invades neighbouring tissues, blood vessels and spreads to other parts of the body by metastasis. Therefore, differentiating malignant from benign will help to detect breast cancer in its early stage. Nowadays, machine learning techniques are used to classify the tumor types hence the quality of lift is increased.

Use of components analysis to identify internal heat in breast dynamic thermal images

<div>I suggest a method for biomedical imaging with heat using principal and independent components analysis. This method produces novel results suggesting physiologic mechanisms. When using thermal imaging to detect breast cancer, the dominant heat signature is of indirect heat transported by the blood away from the tumor location into the skin. Interpretation is usually based on vascular patterns and not by observing the direct cancerous heat. In this new method one uses a sequence of thermal images of the patient’s breast following external temperature change. Data are recorded and analyzed using independent component analysis (ICA) and principal component analysis (PCA). ICA separates the image sequence into new independent images having a common characteristic time behavior. Using the Brazilian visual lab mastology data set, I observed three types of component images: Images corresponding to a minimum change as a function of applied temperature or time, which suggests an association with the cancer generated heat, images in which a moderate temperature dependence is associated with veins affected by vasomodulation, and images of complex time behavior indicating heat absorption due to high perfusion of the tumor. All components appear to clearly and distinctly represent underlying physiology.</div>

Use of components analysis to identify internal heat in breast dynamic thermal images

<div>I suggest a method for biomedical imaging with heat using principal and independent components analysis. This method produces novel results suggesting physiologic mechanisms. When using thermal imaging to detect breast cancer, the dominant heat signature is of indirect heat transported by the blood away from the tumor location into the skin. Interpretation is usually based on vascular patterns and not by observing the direct cancerous heat. In this new method one uses a sequence of thermal images of the patient’s breast following external temperature change. Data are recorded and analyzed using independent component analysis (ICA) and principal component analysis (PCA). ICA separates the image sequence into new independent images having a common characteristic time behavior. Using the Brazilian visual lab mastology data set, I observed three types of component images: Images corresponding to a minimum change as a function of applied temperature or time, which suggests an association with the cancer generated heat, images in which a moderate temperature dependence is associated with veins affected by vasomodulation, and images of complex time behavior indicating heat absorption due to high perfusion of the tumor. All components appear to clearly and distinctly represent underlying physiology.</div>

Design of a Start-Up Sequence Controller for a Mammography Machine

Mammography, which is also calledMastography, is the process of using low-energy X-rays to inspect the human breast for screening and diagnostics. The purpose of mammography is to detect breast cancer early, usually by looking for specific lumps or microcalcifications. The X-rays used are usually around 30 kVp. Excessive voltage to such a machine would be harmful to the patient. Proper monitoring of temperature and pressure needs to be ensured. To ensure this, a start-up sequence module is developed. The start-up sequence module reads the digitized voltage, pressure, and temperature reading from the sensor and asserts all the outputs to ensure that the machine is ready. The scan chain is formed of 13 scan flip-flops in this configuration. The synthesis mapped the design to 484 instances of cells in the open-source PDK technology. The design had a total area of 594 μm2, with a cell width of 0.297 μm, and a height of 0.99 μm.

Breast Cancer Detection from a Urine Sample by Dog Sniffing: A Preliminary Study for the Development of a New Screening Device and a Literature Review

Background: Breast cancer is a leading cause of cancer death worldwide. Several studies have demonstrated that dogs can sniff and detect cancer in the breath or urine sample of a patient. This study aims to assess whether the urine sample can be used for breast cancer screening by its fingerprints of volatile organic compounds using a single trained sniffer dog. This is a preliminary study for developing the “electronic nose” for cancer screening. Methods: A nine-year-old female Labrador Retriever was trained to identify cancer from urine samples of breast cancer patients. Urine samples from patients histologically diagnosed with primary breast cancer, those with non-breast malignant diseases, and healthy volunteers were obtained, and a double-blind test was performed. Total of 40 patients with breast cancer, 142 patients with non-breast malignant diseases, and 18 healthy volunteers were enrolled, and their urine samples were collected. Results: In 40 times out of 40 runs of a double-blind test, the trained dog could correctly identify urine samples of breast cancer patients. Sensitivity and specificity of this breast cancer detection method using dog sniffing were both 100%. Conclusions: The trained dog in this study could accurately detect breast cancer from urine samples of breast cancer patients. These results indicate the feasibility of a method to detect breast cancer from urine samples using dog sniffing in the diagnosis of breast cancer. Although the methodological standardization is still an issue to be discussed, the current result warrants further study for developing a new breast cancer screening method based on volatile organic compounds in urine samples.