Browsing by Subject "Early diagnosis"

Now showing 1 - 4 of 4

Open Access
Advances in biosensor technologies for acute kidney injury
(American Chemical Society, 2021-12-20) Derin, Esma; İnci, Fatih
Acute kidney injury (AKI) is one of the most prevalent and complex clinical syndromes with high morbidity and mortality. The traditional diagnosis parameters are insufficient regarding specificity and sensitivity, and therefore, novel biomarkers and their facile and rapid applications are being sought to improve the diagnostic procedures. The biosensors, which are employed on the basis of electrochemistry, plasmonics, molecular probes, and nanoparticles, are the prominent ways of developing point-of-care devices, along with the mutual integration of efficient surface chemistry strategies. In this manner, biosensing platforms hold pivotal significance in detecting and quantifying novel AKI biomarkers to improve diagnostic interventions, potentially accelerating clinical management to control the injury in a timely manner. In this review, novel diagnostic platforms and their manufacturing processes are presented comprehensively. Furthermore, strategies to boost their effectiveness are also indicated with several applications. To maximize these efforts, we also review various biosensing approaches with a number of biorecognition elements (e.g., antibodies, aptamers, and molecular imprinting molecules), as well as benchmark their features such as robustness, stability, and specificity of these platforms.
Open Access
Development and validation of methods for the diagnosis of lung cancer via serological biomarkers
(2019-02) Akçay, Abbas Güven
Over 10% of all new cancer cases are lung cancer. Moreover, estimates till 2030 indicate that already increasing lung cancer incidences will keep increasing, especially in developing countries like Turkey. Lung cancer, the leading cause of cancer deaths, has two large divisions: Small Cell Lung Cancer (SCLC) and Non-Small Cell Lung Cancer (NSCLC). SCLC is the most aggressive subtype of lung cancer. And although, the treatment options and median survival time is more favorable in Limited Disease (LD), high tumor growth rate and metastatic tendency of SCLC even in the early stages, makes the diagnosis troublesome. Similarly, if NSCLC is diagnosed in early stages, surgery option is open and this increases the patient survival rate. However, current methods in screening and diagnosis, such as computed tomography (CT) and positron emission tomography (PET), are all limited by false positivity rates. Additionally, biopsy methods used in histological evaluations are both invasive and prone to false negativity. Therefore, new diagnostic tools which are cheap, accurate and non-invasive are in high demand. Autologous antibodies are abundantly elicited and stably exist in patient sera years before the clinical diagnosis of disease. Several such antibodies were reported by our group and other groups in lung cancer. Therefore, new diagnostic methods incorporating autologous antibodies can be a huge step forward in early diagnosis of lung cancer. Moreover, miRNAs, with their unique hormone like features such as circulation in serum and their regulatory effects in cell, are another good candidate for the early diagnosis of lung cancer. Therefore, in this study I aimed to develop a reliable, robust and automated evaluation method to re-evaluate custom Protein Array (cPA) screenings previously performed in our lab, and to determine the autologous antibodies with highest discriminatory power between SCLC patients & healthy controls. Moreover, I aimed to develop a Quartz Crystal Microbalance with Dissipation (QCM-D) based immunoassay to be incorporated later in the validation of cPA results. Lastly, in a parallel study I aimed to identify and validate novel miRNA biomarkers NSCLC. My results indicate that cPAs can have better sensitivity and specificity than ELISA and that QCM-D can be developed as an alternative to ELISA. miRNAs identified in silico, can also be validated ex vivo. Previously, Protein Arrays (PAs) and cPAs were screened using 49 SCLC patient’s and 50 healthy serums in our laboratory, incorporating visual and manual evaluations. Sensitivity and specificity values were calculated for individual autologous-antibodies and a number of autologous-antibody panels. Moreover, validations of cPA results were carried via ELISA. However, large discrepancies between cPA and ELISA results, as well as inconsistencies among ELISA results urged me to consider re-evaluation of cPA results with a more robust way, and to focus on developing a method superior to ELISA in autologous-antibody evaluations. Therefore, I incorporated AIDA to generate numeric values out of cPA screening images and filtered low quality data with optimized cut-off values. Several Receiver Operating Characteristic (ROC) curves were plotted using evaluated data. Improved results were evident by the increased Area Under Curve (AUC) values in both individual and combined ROC curves. Moreover, I developed a QCM based immunosensor for detection of anti-SOX2 antibody to be incorporated later in validation of cPA results. Binding interaction between anti-SOX2 antibody and SOX2 protein was modelled using 1:1 Langmuir Isothermal Binding and standard curves generated in QCM. In a parallel study, I also investigated miRNAs significantly upregulated in NSCLC when compared to high risk controls. For that purpose, miRNA expression datasets were gathered from GEO. Selected 2 datasets with the same sample type were analyzed for common significantly upregulated miRNAs among these two datasets. Significantly upregulated miRNAs were subjected to logistic regression analysis with LASSO regularization (error metrics: AUC and MSE) to select best panel of miRNAs that can distinguish NSCLC patients from healthy controls in given datasets. Moreover, selected miRNAs were analyzed with qRT-PCR to validate the panel. I was able to re-evaluate cPA results by eliminating low quality data from numeric values generated via AIDA software from cPA images. I identified a panel of 4 autologous antibodies (FKBP8 – P53 – SOX2 – POLB) which resulted in 60% sensitivity at 100% specificity in discrimination of SCLC from controls. ROC of this autologous antibody panel had an AUC of 95.04%. Given panel surpassed diagnostic power of the only commercially available diagnostic kit of the same kind; EarlyCDT-Lung. Moreover, proof of concept for measurements of anti-protein antibodies were carried successfully in QCM, using anti-SOX2 antibody-SOX2 protein pair in PBS buffer as an example for it. Early results of anti-SOX2 mAb QCM indicate a linear assay range comparable to ELISA. Langmuir Isothermal Binding model revealed a strong interaction between antibody and protein in our QCM anti-SOX2 measurement experiments. Lastly, I was able to select 5 miRNAs using logistic regression and LASSO regularization that can best discriminate between NSCLC patients and high risk controls. However, validation experiments using qRT-PCR needs to be repeated as low Ct values and prominent hemolysis in serum samples prevented drawing meaningful conclusions.
Open Access
Kelime histogram modeli ile histopatolojik görüntü sınıflandırılması
(IEEE, 2011-04) Özdemir, Erdem; Sökmensüer, C.; Gündüz-Demir, Çiğdem
Colon cancer, which is one of the most common cancer type, could be cured with its early diagnosis. In the current practice of medicine, there are many screening techniques such as colonoscopy, sigmoidoscopy, and stool test, however the most effective and most widely used method for cancer diagnosis is to take tissue sections with biopsy and examine them under a microscope. As this examination is based on visual interpretation, it may lead to subjective decisions and diagnostic differences among pathologists. The need of reducing inter-variability in cancer diagnosis has led to studies for extraction of features from biopsy images and development of algorithms that give objective results. In this paper, we propose a method for the automated classification of a colon tissue image with the features extracted from a histogram that models the existence of image regions determined in an unsupervised way. Experiments on colon tissue images show that the proposed method leads to more successful results compared to its counterparts. Moreover, the proposed method, which uses color intensities for feature extraction, has the potential of giving better results with the use of additional features. © 2011 IEEE.
Open Access
Segmentation of cervical cell images
(IEEE, 2010) Kale, A.; Aksoy, S.
The key step of a computer-assisted screening system that aims early diagnosis of cervical cancer is the accurate segmentation of cells. In this paper, we propose a two-phase approach to cell segmentation in Pap smear test images with the challenges of inconsistent staining, poor contrast, and overlapping cells. The first phase consists of segmenting an image by a non-parametric hierarchical segmentation algorithm that uses spectral and shape information as well as the gradient information. The second phase aims to obtain nucleus regions and cytoplasm areas by classifying the segments resulting from the first phase based on their spectral and shape features. Experiments using two data sets show that our method performs well for images containing both a single cell and many overlapping cells. © 2010 IEEE.