ArticleJournal of biophotonics2017
Development of a classification model for non-alcoholic steatohepatitis (NASH) using confocal Raman micro-spectroscopy.
Article in Journal of biophotonics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed, 12 citations in OpenAlex.
- Discrimination of lipid composition and cellular localization in human liver tissues by stimulated Raman scattering microscopy.Journal of biomedical optics · 2024Article
- Fully connected neural network-based serum surface-enhanced Raman spectroscopy accurately identifies non-alcoholic steatohepatitis.Hepatology international · 2023Article
- Microarray Expression Profiling and Raman Spectroscopy Reveal Anti-Fatty Liver Action of Berberine in a Diet-Induced Larval Zebrafish Model.Frontiers in pharmacology · 2019Article
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Authors and funding
11 authors at 6 institutions in 2 countries.
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Abstract
Non-alcoholic fatty liver disease (NAFLD) is the most common liver disorder in developed countries [1]. A subset of individuals with NAFLD progress to non-alcoholic steatohepatitis (NASH), an advanced form of NAFLD which predisposes individuals to cirrhosis, liver failure and hepatocellular carcinoma. The current gold standard for NASH diagnosis and staging is based on histological evaluation, which is largely semi-quantitative and subjective. To address the need for an automated and objective approach to NASH detection, we combined Raman micro-spectroscopy and machine learning techniques to develop a classification model based on a well-established NASH mouse model, using spectrum pre-processing, biochemical component analysis (BCA) and logistic regression. By employing a selected pool of biochemical components, we identified biochemical changes specific to NASH and show that the classification model is capable of accurately detecting NASH (AUC=0.85-0.87) in mice. The unique biochemical fingerprint generated in this study may serve as a useful criterion to be leveraged for further validation in clinical samples.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.