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Parkinson's Disease Pre-Diagnosis Using Smart Technologies
Published in Chinmay Chakraborty, Digital Health Transformation with Blockchain and Artificial Intelligence, 2022
Mohammad Yasser Chuttur, Azina Nazurally
As cited in Cova et al. [19], the “NIH Biomarkers Definitions Working Group” defines a biomarker as “a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes or pharmacologic responses to a therapeutic intervention”. There can be different types of biomarkers or markers, with each having a special purpose. For example, a diagnostic marker can be used to diagnose a specific condition in a patient. In contrast, a staging marker is used to evaluate the severity of a disease in a person. The prognostic markers are another type of biomarker that can forecast the risk or progression of a disease. When it comes to PD, diagnostic markers are essential to detect early signs of the disease before symptoms of the disease become more apparent in patients. The most reliable markers for PD are based on motors symptoms [19]. This section will describe common motor symptoms markers and recent works that have used those markers to develop PD pre-diagnosis models using machine learning.
Nanocarriers in Early Diagnosis of Cancer
Published in Bhupinder Singh, Rodney J. Y. Ho, Jagat R. Kanwar, NanoBioMaterials, 2018
Gurpal Singh, Rajneet Kaur Khurana, Atul Jain, Taranvir Kaur, Bhupinder Singh
Cancer markers help in detecting the presence of certain types of cancer in the body, and in monitoring the progress of cancer treatment. Cancer markers are substances found in the blood, body fluids or tissues that are produced by cancer cells. If a certain cancer marker is found in the body, it can indicate that the cancer is still present and ongoing treatment may still be recommended. Cancer biomarkers also helpful in establishing a specific diagnosis. This is particularly the case when there is a need to determine whether the tumors are of primary or metastatic origin. Some of the cancer biomarkers are enlisted in Table 5.2 (Tokumaru and Coyle, 1992; Salomon et al., 1995; Alivisatos, 1996; Bruchez et al., 1998; Chan and Nie, 1998; Hammarstrom, 1999; Greenlee et al., 2000; Dubertret et al., 2002; Sidransky, 2002; Suzuki et al., 2008; Anajwala et al., 2010; Xiaoxiao et al., 2010; Jana et al., 2014; labtestsonline.org).
Introducing a modern chemotherapeutic drug formulated by iron nanoparticles for the treatment of human lung cancer
Published in Journal of Experimental Nanoscience, 2021
Junfeng Bai, Xin Gongsun, Liangliang Xue, Mohammad Mahdi Zangeneh
Investigation of cell proliferation and survival is one of the most important and basic techniques in cell laboratories. This study requires accurate quantification of the number of living cells in the cell culture medium. Therefore, cell survival calculation methods are necessary to optimize cell culture conditions, evaluate cell growth factors, detect antibiotics and anticancer drugs, evaluate the toxic effects of environmental pollutants, and study apoptosis. Many methods can be used for such purposes, but indirect methods using fluorescent or dye (chromogenic) markers provide very fast large-scale methods. Among these methods, measurement of cell survival by MTT method (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) is the most widely used method [19]. In this research, we used the following cell lines to evaluating anti-human lung cancer and cytotoxicity effects of FeCl3, A. maurorum leaf aqueous extract, and FeNPs using an MTT method. Human lung cancer cell lines: Lung well-differentiated bronchogenic adenocarcinoma (HLC-1), and lung poorly differentiated adenocarcinoma (PC-14).Normal cell line: HUVEC.
Longitudinal MRI data analysis in presence of measurement error but absence of replicates
Published in IISE Transactions on Healthcare Systems Engineering, 2018
Chitta Ranjan, Kamran Paynabar, Martin Reuter, Kourosh Jafari-Khouzani, the ADNI
Next, we analyze differences in degeneration rates of MCI-NC patients with MCI-C, and OC patients. Figure 8 shows the (log) p-values of their differences. We can see that the proposed method detects the difference with much higher confidence compared to the traditional Multilevel and Latent Growth models. These results demonstrate that hippocampal volume loss is an excellent marker for disease progression, as it is capable of differentiating the various groups at different stages. Finding these markers is important as they can then be used to quantify effects of disease modifying therapies or for computer-aided diagnosis. Overall, these results are quite useful for medical studies, such as differentiating various groups at different stages.