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Record W2991678086

Legal and Policy Frameworks for Clinical-Grade Stem Cell Banking

2013· article· en· W2991678086 on OpenAlexvenueno aff
Elizabeth Ulmer, Barbara von Tigerstrom

Bibliographic record

VenueHealth law review · 2013
Typearticle
Languageen
FieldMedicine
TopicBiomedical Ethics and Regulation
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)DocumentationInduced pluripotent stem cellBusinessRegenerative medicineStem cellBiologyComputer scienceEmbryonic stem cellCell biology
DOInot available

Abstract

fetched live from OpenAlex

Introduction The potential applications of cell therapy and regenerative medicine make this area of research a dynamic, fast evolving field. To accommodate the considerable growth of cell research, banks have been created to provide researchers with access to current, high quality cell lines.' Banks accept and distribute cell lines to and from researchers, providing documentation on their use and provenance. Banks fulfill important functions to ensure the cell lines are ethically sourced and the quality of cell lines is reliable,2 helping to realize the full potential of cells. While many clinical applications of cell research remain distant possibilities, some novel cell-based therapies are beginning to be developed and tested. As these efforts continue, cell banks will increasingly take on an important role in the distribution of clinical-grade cell lines that can be used to develop therapies administered to humans. In December 2011, the first clinical-grade cells were deposited with the United Kingdom Stem Cell Bank (UKSCB),3 marking a shift to a new era in cell banking. More recently, plans were announced to establish a of clinical-grade induced pluripotent cells (iPSCs) in Japan.4 As the banking of clinical-grade cells gains momentum, this will bring new regulatory and policy issues into play. The aim of this article is to describe the legal and policy framework relevant to cell banking, with a focus on clinical-grade cell lines, and to identify some of the issues and challenges that arise in the context of clinical-grade cell banking. Stem Cell Banks The phrase stem cell bank is sometimes used to refer to both cell registries and repositories. Registries are databases that collect and provide information on derived cell lines. The European Human Embryonic Stem Cell Registry and the UMass International Stem Cell Registry are examples of cell registries.' Repositories are facilities that process, store, and maintain the actual cell lines such as the UK Stem Cell Bank (UKSCB), the Spanish National Cell Bank, and the US Wisconsin International Stem Cell Bank (WISC).Registries and repositories are not mutually exclusive and have complementary functions.' They provide researchers with information about matters such as cell provenance and culture methods, enabling more accurate and reliable research. Coordination among these functions is critical for the advancement of cell research, providing researchers with the materials and information to produce the highest quality work. This article is primarily concerned with repositories and unless otherwise indicated, the term stem cell bank in this article refers to banks that are acting as repositories and are actually handling human cells. Stem cell banks play a significant role in the advancement of cell research. Given the rapid expansion of the science, centralized cell banks are needed to provide researchers with access to cell lines!' By providing access to existing lines, banking reduces the need for derivation of new cell lines; in the case of human embryonic cells, in particular, this has ethical value because it reduces the number of human embryos that must be destroyed.' Banks also reduce unnecessary duplication in research by screening applications for use of cell lines,' and minimize waste and errors by promoting standardization.By ensuring the maintenance of only ethically sourced cells using accepted quality conditions, cell banks promote consistency, safety, and ethical conduct in cell line derivation and subsequent use in research. The benefits of using cell banks will also extend to clinical applications. There is general agreement that the use of centralized banks will serve the development of cell research, especially as it moves into clinical translation. …

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.968
Threshold uncertainty score0.261

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.106
GPT teacher head0.466
Teacher spread0.360 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2013
Admission routes1
Has abstractyes

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