Types of Biobanks

Gramatiuk, Svetlana; Huppertz, Berthold

doi:10.1007/978-3-030-87637-1_3

Cited by 2 publications

(2 citation statements)

References 15 publications

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“…from several hundreds of carefully screened and well-characterized healthy individuals and patients with various cancer and auto-immune disorders [1,2]. Using biochemical, genetic, and imaging data, we have built an extensive cohort database (the Cells Line Database) that contains a wide range of imaging-associated data, including demographic and cell line viability [3,4,5]. 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.…”

Section: Introductionmentioning

confidence: 99%

“…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%

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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%

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

View full text Add to dashboard Cite

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The Regulatory Landscape of Biobanks In Europe: From Accreditation to Intellectual Property

Corradi,

Bonizzi,

Sajjadi

et al. 2025

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Biobanks are necessary resources for the storage and management of human biological materials, such as biofluids, tissues, cells, or nucleotides. They play a significant role in the development of new treatments and the advancement of basic and translational research, especially in the field of biomarkers discovery and validation. The regulatory landscape for biobanks, which is necessary to safeguard both privacy and scientific discoveries, exhibits significant heterogeneity across different countries and regions. This article outlines the standards that modern biobanks should fulfill in the European Union (EU), including general, structural, resource, process, and quality requirements. Special attention is given to the importance of transparency and donor consent following the General Data Protection Regulation (GDPR) and the ISO 20387:2018, the international standard specifies general requirements for biobanks. A dedicated section covers the preparation of donor information materials, emphasizing consent for research involvement and personal data processing. The delicate balance between donors' privacy rights and scientific research promotion is also discussed, with a focus on the patenting and economic use of biological material- derived inventions and data. Considering these factors, it would be warranted to refine legal frameworks and foster interdisciplinary collaboration to ethically and responsibly expand biobanking.

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Types of Biobanks

Cited by 2 publications

References 15 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.

The Regulatory Landscape of Biobanks In Europe: From Accreditation to Intellectual Property

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