Methods of Implementation and Set-Up of National Biobanking Networks

Gramatiuk, Svetlana; Huppertz, Berthold; Alekseenko, Mykola; Hartl, Gabriele; Macheiner, Tanja; Sarkisian, Tamara; Kozlakidis, Zisis; Steinfelder, Erik; Mitchell, Christine; Sargsyan, Karine

doi:10.1007/978-3-030-87637-1_7

Cited by 2 publications

(1 citation statement)

References 3 publications

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“…The Biobanks are the associated collection of biological specimens from the cohort, including saliva, blood, and in some instances, urine and hair samples, which allow for additional biochemical and genetic analyses. Stem cell products should be generated in compliance with Good Manufacturing Practices (GMP) [5,6,7,8]. No uniform global guidelines exist for producing and clinically applying stem cell products.…”

Section: Introductionmentioning

confidence: 99%

The artificial intelligence to analyze and characterize cell lines based on the processes of visible spectrum image data.

Gramatiuk,

Kryvoruchko,

Ivanova

et al. 2023

Preprint

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In particular, as part of the extensive Stem Line project Mito-Cell-UAB073 «Stem cell lines - Quality control», we have taken a specific interest in a new and complementary CQ approach to cell line and stem cell line intelligence in Biobank. We have combined computer vision image processing methods and deep learning techniques to create the non-invasive Life Cell AI UAB model for robust prediction of cell line viability, using single static images obtained from standard optical light microscope systems. The Life Cell AI UAB model showed a sensitivity of 82.1% for viable cell lines while maintaining a specificity of 67.5% for non-viable cell lines across three independent blind test sets from different biotechnology laboratories. The weighted overall accuracy in each blind test set was >63%, with a combined accuracy of 64.3% across both viable and non-viable cell lines, demonstrating model robustness and generalizability beyond the result expected from chance. Distributions of predictions showed clear separation of correctly and incorrectly classified cell lines. Binary comparison of viable/non-viable embryo classification demonstrated an improvement of 21.9% over cell lines accuracy (P = 0.042, n = 2, Student's t-test), and SOP procedure of QC comparison demonstrated an improvement of 42.0% over embryologists (P = 0.026, n = 2, Student's t-test). The superior accuracy of the Life Cell AI UAB model could lead to improved quality control assessments of samples in Biobank. It could also assist in standardizing QC methods of cell lines and stem cells across multiple environments while eliminating the need for complex time-lapse imaging equipment.

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Section: Introductionmentioning

confidence: 99%

The artificial intelligence to analyze and characterize cell lines based on the processes of visible spectrum image data.

Gramatiuk,

Kryvoruchko,

Ivanova

et al. 2023

Preprint

Self Cite

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Twinning for the Armenian research infrastructure on cancer research

Sargsyan,

Kinkorova,

Hartl

et al. 2024

Open Res Europe

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The incidence of cancer is expected to rise globally, with low-and middle-income countries affected disproportionally. One of those countries, Armenia, also faces the challenge of exhibiting one of the lowest research and scientific publication rates within Europe on cancer research. This report presents the experiences of the Twinning for the Armenian Research Infrastructure on Cancer Research (ARICE) program, funded by the European Commission from 2019 – 2024. The project brought together experts from three leading research-intensive, cancer centres: the Medical University of Graz, Austria; the Charles University, Czechia; and the International Agency for Research on Cancer, World Health Organization (IARC/WHO), in partnership with the Yerevan State Medical University. The aim was to enhance the research capacity in Armenia in the field of infrastructure development for clinical genetic research, in particular targeting the collection and use of biological materials, and the data integration for downstream analyses in relation to chronic, non-communicable diseases such as cancer. To this end, training initiatives and educational programmes were developed throughout the duration of the grant and are discussed within their local context.

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Methods of Implementation and Set-Up of National Biobanking Networks

Cited by 2 publications

References 3 publications

The artificial intelligence to analyze and characterize cell lines based on the processes of visible spectrum image data.

The artificial intelligence to analyze and characterize cell lines based on the processes of visible spectrum image data.

Twinning for the Armenian research infrastructure on cancer research

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