Luís Fernandes, H. Silva, Inês H Martins, S. Carvalho, I. Carneiro, R. Henrique, V. Tuchin, Luís Manuel Couto Oliveira
{"title":"Tissue Spectroscopy and Optical Clearing of Colorectal Mucosa in the Pursuit of New Cancer Diagnostic Approaches","authors":"Luís Fernandes, H. Silva, Inês H Martins, S. Carvalho, I. Carneiro, R. Henrique, V. Tuchin, Luís Manuel Couto Oliveira","doi":"10.18287/JBPE21.07.040302","DOIUrl":"https://doi.org/10.18287/JBPE21.07.040302","url":null,"abstract":"In this paper we present three studies that demonstrate the applicability of spectroscopy methods and optical clearing treatments in pathology identification and monitoring. In the first study, by obtaining the absorption spectra of human healthy and pathological (adenocarcinoma) colorectal mucosa tissues, it was possible to identify a higher content of a pigment in the diseased tissues. This study also shows that machine learning methods can be used to reach the same differentiated results in vivo through diffuse reflectance spectroscopy. In the second study, the combination of collimated transmittance spectroscopy with optical clearing treatments allowed to obtain the diffusion coefficients of glucose in healthy and pathological colorectal mucosa as: D glucose =5.8x 10 – 7 cm 2 /s and D glucose =4.4x 10 – 7 cm 2 /s, respectively. This study also demonstrated that the diseased tissues contains about 5% more mobile water than the healthy tissues. The third study was performed to evaluate the protein dissociation mechanism of optical clearing. By treating both healthy and pathological colorectal mucosa tissues with 93%-glycerol, a protein dissociation rate of about 3 times higher was obtained for the pathological mucosa. All the discriminating parameters that result from these studies can be obtained in the in vivo situation through diffuse reflectance spectroscopy and further studies to evaluate their values in different stages of cancer progression are of great importance to develop disease monitoring protocols.","PeriodicalId":52398,"journal":{"name":"Journal of Biomedical Photonics and Engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47205091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A Review of EEG Signal Analysis for Diagnosis of Neurological Disorders using Machine Learning","authors":"Vandana Joshi, Nirali R. Nanavati","doi":"10.18287/10.18287/JBPE21.07.040201","DOIUrl":"https://doi.org/10.18287/10.18287/JBPE21.07.040201","url":null,"abstract":"Neurological disorders are diseases that affect the brain and the central autonomic nervous systems. These disorders take a huge toll on an individual's health and general well-being. After cardiovascular diseases, neurological disorders are the main cause of death. These disorders include epilepsy, Alzheimer ’ s disease, dementia, cerebrovascular diseases including stroke, migraine, Parkinso n ’s disease and numerous other disorders. This manuscript presents a state-of-the-art consolidated review of research on the diagnosis of the three most common neurological disorders using electroencephalogram (EEG) signals with machine learning techniques. The disorders discussed in this manuscript are the more prevalent disorders like epilepsy, Attention-deficit/hyperactivity disorder (ADHD), and Alzheimer’s disease. This manuscript helps in understanding the details about EEG signal processing for diagnosis and analysis of neurological disorders along with a discussion of the datasets, limitations, results and research scope of the various techniques.","PeriodicalId":52398,"journal":{"name":"Journal of Biomedical Photonics and Engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48807425","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}