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Integration of Digital Phenotyping and Genomics for Dry Eye Disease: Protocol for a Prospective Cohort Study

Integration of Digital Phenotyping and Genomics for Dry Eye Disease: Protocol for a Prospective Cohort Study

The collected medical samples and data have established a foundation for a large-scale biobank [40-42]. This study conducts an add-on study to integrate DED-related personalized digital data, such as lifestyle factors and activity patterns, with the existing TMM databases by providing the Dry Eye Rhythm app to the participants of TMM cohort studies. These TMM databases include genomics, epidemiologic data, medical history, medical sample analyses, and physiological function testing results.

Ken Nagino, Yasutsugu Akasaki, Nobuo Fuse, Soichi Ogishima, Atsushi Shimizu, Akira Uruno, Yoichi Sutoh, Yayoi Otsuka-Yamasaki, Fuji Nagami, Jun Seita, Tomohiro Nakamura, Satoshi Nagaie, Makiko Taira, Tomoko Kobayashi, Ritsuko Shimizu, Atsushi Hozawa, Shinichi Kuriyama, Atsuko Eguchi, Akie Midorikawa-Inomata, Masahiro Nakamura, Akira Murakami, Shintaro Nakao, Takenori Inomata

JMIR Res Protoc 2025;14:e67862

A Biobanking System for Diagnostic Images: Architecture Development, COVID-19–Related Use Cases, and Performance Evaluation

A Biobanking System for Diagnostic Images: Architecture Development, COVID-19–Related Use Cases, and Performance Evaluation

In this study, we performed an experimental evaluation of an implementation of the digital biobank Bio Check Up Srl (BCU) Imaging Biobank (BCU-IB), derived from the Extensible Neuroimaging Archive Toolkit (XNAT) open-source platform [26] operated by BCU. BCU-IB has been part of the Biobanking and Biomolecular Resources Research Infrastructure-Education Information Resources Center (BBMRI-ERIC), and its national node BBMRI.it.

Giuseppina Esposito, Ciro Allarà, Marco Randon, Marco Aiello, Marco Salvatore, Giuseppe Aceto, Antonio Pescapè

JMIR Form Res 2023;7:e42505

COVID-19 Surveillance in the Biobank at the Colorado Center for Personalized Medicine: Observational Study

COVID-19 Surveillance in the Biobank at the Colorado Center for Personalized Medicine: Observational Study

Uniquely, for this analysis, we were able to combine clinical data from EHRs with self-reported information collected via an online survey that was offered to all Biobank participants. We present here results from our analysis of self-reported survey data and clinical data recorded in EHRs for Biobank participants.

Randi K Johnson, Katie M Marker, David Mayer, Jonathan Shortt, David Kao, Kathleen C Barnes, Jan T Lowery, Christopher R Gignoux

JMIR Public Health Surveill 2022;8(6):e37327

Uncovering Clinical Risk Factors and Predicting Severe COVID-19 Cases Using UK Biobank Data: Machine Learning Approach

Uncovering Clinical Risk Factors and Predicting Severe COVID-19 Cases Using UK Biobank Data: Machine Learning Approach

Here we made use of the UK Biobank (UKBB) data to build ML models to predict severity and fatality from COVID-19, and evaluated the contributing risk factors. We built prediction models not only for patients infected but also at a general population level. While predictive performance is the main concern in most previous studies, we argue that ML models can also provide important insights into individual contributing factors and the pattern of complex relationships between risk factors and the outcome.

Kenneth Chi-Yin Wong, Yong Xiang, Liangying Yin, Hon-Cheong So

JMIR Public Health Surveill 2021;7(9):e29544