دورية أكاديمية
أعرض في EDS

Label-free screening of brain tissue myelin content using phase imaging with computational specificity (PICS).

التفاصيل البيبلوغرافية
العنوان: Label-free screening of brain tissue myelin content using phase imaging with computational specificity (PICS).
المؤلفون: Fanous M, Shi C; Quantitative Light Imaging Laboratory, Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA., Caputo MP; Division of Nutritional Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA., Rund LA; Laboratory of Integrative Immunology & Behavior, Department of Animal Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA., Johnson RW, Das T; Abbott Nutrition, Discovery Research, Columbus, Ohio 43219, USA., Kuchan MJ; Abbott Nutrition, Strategic Research, 3300 Stelzer Road, Columbus, Ohio 43219, USA., Sobh N, Popescu G
المصدر: APL photonics [APL Photonics] 2021 Jul 01; Vol. 6 (7), pp. 076103. Date of Electronic Publication: 2021 Jul 12.
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: AIP Publishing LLC Country of Publication: United States NLM ID: 101719533 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2378-0967 (Electronic) Linking ISSN: 23780967 NLM ISO Abbreviation: APL Photonics Subsets: PubMed not MEDLINE
أسماء مطبوعة: Original Publication: Melville, NY : AIP Publishing LLC, 2016-
مستخلص: Inadequate myelination in the central nervous system is associated with neurodevelopmental complications. Thus, quantitative, high spatial resolution measurements of myelin levels are highly desirable. We used spatial light interference microcopy (SLIM), a highly sensitive quantitative phase imaging (QPI) technique, to correlate the dry mass content of myelin in piglet brain tissue with dietary changes and gestational size. We combined SLIM micrographs with an artificial intelligence (AI) classifying model that allows us to discern subtle disparities in myelin distributions with high accuracy. This concept of combining QPI label-free data with AI for the purpose of extracting molecular specificity has recently been introduced by our laboratory as phase imaging with computational specificity. Training on 8000 SLIM images of piglet brain tissue with the 71-layer transfer learning model Xception, we created a two-parameter classification to differentiate gestational size and diet type with an accuracy of 82% and 80%, respectively. To our knowledge, this type of evaluation is impossible to perform by an expert pathologist or other techniques.
(© 2021 Author(s).)
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معلومات مُعتمدة: R01 CA238191 United States CA NCI NIH HHS; R01 GM129709 United States GM NIGMS NIH HHS
تواريخ الأحداث: Date Created: 20210722 Latest Revision: 20231107
رمز التحديث: 20240628
مُعرف محوري في PubMed: PMC8278825
DOI: 10.1063/5.0050889
PMID: 34291159
قاعدة البيانات: MEDLINE
الوصف
تدمد:2378-0967
DOI:10.1063/5.0050889