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Record W2128232987 · doi:10.1242/dmm.003137

Potential and pitfalls of stem cell therapy in old age

2010· article· en· W2128232987 on OpenAlexaff
David Piccin, Cindi M. Morshead

Bibliographic record

VenueDisease Models & Mechanisms · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPluripotent Stem Cells Research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsStem cellRegenerative medicineRegeneration (biology)BiologyStem cell nicheStem-cell therapyPopulationMedicineProgenitor cellCell biologyEnvironmental health

Abstract

fetched live from OpenAlex

Our increasing understanding of resident stem cell populations in various tissues of the adult body provides promise for the development of cell-based therapies to treat trauma and disease. With the sharp rise in the aging population, the need for effective regenerative medicine strategies for the aged is more important then ever. Yet, the vast majority of research fuelling our understanding of the mechanisms that control stem cell behaviour, and their role in tissue regeneration, is conducted in young animals. Evidence collected in the last several years indicates that, although stem cells remain active into old age, changes in the stem cells and their microenvironments inhibit their regenerative potential. An understanding of both the cell-intrinsic stem cell changes, as well as concomitant changes to the stem cell niche and the systemic environment, are crucial for the development of regenerative medicine strategies that might be successful in aged patients.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation 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: Review
Teacher disagreement score0.014
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.007
Scholarly communication0.0020.004
Open science0.0010.002
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0040.001

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 head0.011
GPT teacher head0.234
Teacher spread0.224 · 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 source (direct Gemma or distilled Codex), 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

Citations29
Published2010
Admission routes1
Has abstractyes

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