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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8990886 | PMC |
| http://dx.doi.org/10.3389/fnut.2022.881690 | DOI Listing |
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Editorial: New insights in pediatric gastrointestinal food allergies.
Front Allergy
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Department of Translational Medical Science, University of Naples "Federico II", Naples, Italy.
Association between lifelines diet score with odds of nonalcoholic fatty liver disease and some novel anthropometric indices among adults: a case-control study.
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Background: Nonalcoholic Fatty Liver Disease (NAFLD) is a prevalent condition strongly associated with poor dietary habits and obesity. The Lifelines Diet Score (LLDS), a measure of adherence to a health-promoting diet, may reduce the risk of NAFLD. This study investigates the association between LLDS and NAFLD risk, as well as its relationship with novel anthropometric indices in adults.
View Article and Find Full Text PDFRisk and protective factors for coronavirus disease 2019 (COVID-19) in allergic rhinitis patients: a national survey in China.
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Department of Otolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
Background: Several epidemiological studies have shown that allergic rhinitis (AR) patients are more susceptible to coronavirus disease 2019 (COVID-19).
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Department of Horticulture and Life Science, Yeungnam University, Gyeongsan, Gyeongbuk, Republic of Korea.
Prioritizing cases from a multi-institutional cohort for a dataset of pathologist annotations.
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U.S. Food and Drug Administration, Center for Devices and Radiological Health, Office of Science and Engineering Laboratories, Division of Imaging, Diagnostics, and Software Reliability, Silver Spring, MD, United States of America.
Objective: With the increasing energy surrounding the development of artificial intelligence and machine learning (AI/ML) models, the use of the same external validation dataset by various developers allows for a direct comparison of model performance. Through our High Throughput Truthing project, we are creating a validation dataset for AI/ML models trained in the assessment of stromal tumor-infiltrating lymphocytes (sTILs) in triple negative breast cancer (TNBC).
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