Sharif Digital Repository

A turn-on fluorometric assay is described for determination of the activity of enzyme telomerase. For this purpose, graphene quantum dots (GQDs) were first modified with the telomeric sequence (5′-amino-AATCCGTCGAGCAGAGTT-3′) via a condensation reaction. Injection of graphene oxide causes instant quenching of the blue fluorescence of the GQDs. Addition of cell extract containing telomerase, triggers the extension of telomer via addition of specific sequence (TTAGGG)n to its 3′ end. Fluorescence, best measured at excitation/emission wavelengths of 390/446 nm, is subsequently restored due to folding of the extended telomeric sequence into G-quadruplex structure. The method was applied to the... 

In this paper, as an application in biometrics, the electrical capacitance of normal and cancerous blood samples is experimentally determined in order to test the null hypothesis that the electrical capacitance of the two samples differs. The samples taken from healthy donors and patients diagnosed with different types of hematologic cancer are examined by a cylindrical capacitor with blood as its dielectric. The capacitance of these samples is measured at room temperature and a single frequency of 120 Hz, well below the frequency where β-dispersion starts, using a simple LCR meter device. The measurements indicate that the capacitance of the blood increases under applied electric field for... 

Breast cancer (BC) as a leading cause of cancer death among women, exhibits a wide range of genetic heterogeneity in affected individuals. Satisfactory management of BC depends on early diagnosis and proper monitoring of patients’ response to therapy. In this study, we aimed to assess the relation between the expression patterns of blood-based microRNAs (miRNAs) with demographic characteristics of the patients with BC in an attempt to find novel diagnostic markers for BC with acceptable precision in clinical applications. To this end, we performed comprehensive statistical analysis of the data of the Cancer Genome Atlas (TCGA) database and the blood miRNome dataset (GSE31309). As a result,... 

Due to the increase in the number of cancer patients, because of environmental parameters, high stress, low immunity, etc., there is an urgent need to develop cost-effective sensors for early targeted detection of cancerous cells with adequate selectivity and efficiency. Early disease diagnosis is important, as it is necessary to start treatments before disease progression. On the other hand, we need new, more efficient cancer treatment approaches with minimized side effects, more biocompatibility, and easy disposal. Nanobiotechnology is a field that can assist in developing new diagnostic and treatment approaches, specifically in fatal cancers. Herein, a study on the different applications... 

Early diagnosis of lung cancer is very important in improving patients life expectancies. Due to the high number of Computed Tomography (CT) images, fast and accurate diagnosis is difficult for radiologists. Therefore, there is an increasing demand for Computer-Aid Diagnosis (CAD) lung cancer. The core of all lung cancer detection systems is the distinction between cancer and non-cancerous tissues. This operation is performed in the false positive reduction phase, which is one of the most critical part of the lung cancer detection systems. The primary objective of this paper is to present a new method based on 3D Convolutional Neural Networks (CNN) that can reduce the false positives rate... 

In this paper, we propose using mobile nanosensors (MNSs) for early stage anomaly detection. For concreteness, we focus on the detection of cancer cells located in a particular region of a blood vessel. These cancer cells produce and emit special molecules, so-called biomarkers, which are symptomatic for the presence of anomaly, into the cardiovascular system. Detection of cancer biomarkers with conventional blood tests is difficult in the early stages of a cancer due to the very low concentration of the biomarkers in the samples taken. However, close to the cancer cells, the concentration of the cancer biomarkers is high. Hence, detection is possible if a sensor with the ability to detect... 

Glioma is a common type of tumor that develops in the brain. Due to many differences in the shape and appearance, accurate segmentation of glioma for identifying all parts of the tumor and its surrounding tissues in cancer detection is a challenging task in cancer detection. In recent researches, the combination of atlas-based segmentation and machine learning methods have presented superior performance over other automatic brain MRI segmentation algorithms. To overcome the side effects of limited existing information on atlas-based segmentation, and the long training and the time consuming phase of learning methods, we proposed a semi-supervised learning framework by introducing a...