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Applications of AI in Medical Science and Drug Development
Published in Mark Chang, Artificial Intelligence for Drug Development, Precision Medicine, and Healthcare, 2020
Pathology: Pathology is the medical specialty that is concerned with the diagnosis of disease based on the laboratory analysis of bodily fluids and tissues. Machine vision and other machine learning technologies can enhance the efforts traditionally left only to pathologists with microscopes.
Trends in Biotechnology
Published in Firdos Alam Khan, Biotechnology Fundamentals, 2020
The branch of pathology that determines the cause of death by examination of the victim’s body tissues is called forensic pathology. The post-mortem autopsy is performed by the forensic pathologist at the request of a medical doctor or examiner. The forensic pathologist processes the tissues using biochemical and microscopic techniques to determine the cause of death. The biochemical and cytological data obtained from the victim’s body are compared with those of normal tissues to conclude what was the cause of the death. Additionally, forensic pathologists write a report to approve the identity of a victim.
Disease-Inspired Feature Design for Computer-Aided Diagnosis of Breast Cancer Digital Pathology Images
Published in de Azevedo-Marques Paulo Mazzoncini, Mencattini Arianna, Salmeri Marcello, Rangayyan Rangaraj M., Medical Image Analysis and Informatics: Computer-Aided Diagnosis and Therapy, 2018
Pathology is the study of bodily tissues and cells under magnification in order to make a judgment on the state of disease. Histological pathology analysis (histopathology) is done using high-power microscopes and thinly sliced tissue samples, extracted from the region(s). Samples are colored using a complementary set of stains, highlighting components of the tissue which are pertinent to the examination. The pathologist then identifies regions of the section which are diseased, if any, and employs semi-quantitative scoring schemes to quantify the severity of disease. In particular, two main types of slides are used commonly in breast cancer diagnosis, treatment planning, and patient management: immunohistochemistry (IHC) and hematoxylin and eosin (H&E).
An Efficient Hybrid Model for Acute Myeloid Leukaemia detection using Convolutional Bi-LSTM based Recurrent Neural Network
Published in Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 2023
Arunachalam (2020) proposed an SVM as the classifier for the detection of leukaemia and its subtypes. The data set was collected the blood smear images from leukaemia as well as non-leukaemia patients. The segmentation methods, such as clustering, marker-controlled watershed segmentation and Hue Saturation Value (HSV) colour-based segmentation, were utilised for the image segmentation. It was difficult to detect the subtypes of leukaemia accurately (Arunachalam 2020). Suryani et al. (2017) suggested the K-fold validation for the classification of AML to examine the WBC with leukaemia. The results of the feature extraction process were utilised for testing and training, and the AML was classified into M2 and M3. The images obtained from clinical pathology in Dr. Moewardi Hospital were utilised for the analysis (Suryani et al. 2017).
Combined high-intensity interval training as an obesity-management strategy for adolescents
Published in European Journal of Sport Science, 2023
António Videira-Silva, Megan Hetherington-Rauth, Luís B. Sardinha, Helena Fonseca
Biochemical analyses were performed in the laboratory of clinical pathology at the HSM. Blood samples were collected after overnight fasting (12 h) in the presence of one of the parents/caregivers, and after a local application of a topical anesthesia patch (EMLA). Blood glucose levels were determined using hexokinase method and insulin was assessed using a chemiluminescence immunoassay technique. Insulin resistance was derived from the homeostasis model assessment (HOMA) method. Total cholesterol, triglycerides, and high-density lipoprotein cholesterol (HDL-C) were determined using enzymatic, GPO-trinder, and direct methods, respectively. Low-density lipoprotein cholesterol (LDL-C) was calculated based on total cholesterol and HDL-C levels (Dansethakul, Thapanathamchai, Saichanma, Worachartcheewan, & Pidetcha, 2015). Alanine aminotransferase (ALT) levels were assessed with modified IFCC method. C-reactive protein was determined using a turbidimetric immunoassay (Siemens, ADVIA 2400, Newark, DE, USA).
Mutation patterns of epidermal growth factor receptor gene in non-small cell lung cancer among Egyptian patients
Published in Egyptian Journal of Basic and Applied Sciences, 2022
Wafaa H. Elmetnawy, Mona Qenawi, Salwa Sabet, Heba Bassiony
The relationship between the mutational state of EGFR and clinical pathology in NSCLCs was investigated in the present study. The most frequently mutated exons linked to the development of NSCLC were exons 19 and 21 of the TK domain. EGFR gene mutations are the first recognized as targetable driver mutations described in about 17% and 50% of lung adenocarcinoma in Caucasians and Asians; respectively [20,21,33]. In our study, 40.8% of the patients were EGFR mutation positive The incidence differed from published data in the PIONEER Trial which reported lower rate of EGFR mutations among Americans (15%) and higher incidence among Asians (62%) [22]. This prospective, international and epidemiological study correlated the incidence of EGFR mutations to country, gender, ethnicity, smoking status, pack-years, disease stage and histological type. Another study reported that the frequency of EGFR mutations in Middle Eastern and African patients was higher than in white Caucasians, but still lower than in Asian populations [23]. Furthermore, a recent study reported that; the incidence rates based on the population can provide a full picture of the risk of EGFR mutation-positive lung cancer, where the age-specific incidence rates (ASRs) of EGFR mutation-positive NSCLC were around 3.5 times higher for Pacifica and Asians, and two times higher for Maori than New Zealand Europeans [24].