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Record W4212791670 · doi:10.3410/f.1097790.553910

Faculty Opinions recommendation of Stem cell aging is controlled both intrinsically and extrinsically in the Drosophila ovary.

2008· dataset· en· W4212791670 on OpenAlexaff
Paul Lasko

Bibliographic record

VenueFaculty Opinions – Post-Publication Peer Review of the Biomedical Literature · 2008
Typedataset
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics, Aging, and Longevity in Model Organisms
Canadian institutionsMcGill University
Fundersnot available
KeywordsStem cellNicheBiologyCell biologyStem cell nicheOvaryGermlineDrosophila (subgenus)GeneticsGeneBiochemistryProgenitor cell

Abstract

fetched live from OpenAlex

It is widely postulated that tissue aging could be, at least partially, caused by reduction of stem cell number, activity, or both. However, the mechanisms of controlling stem cell aging remain largely a mystery. Here, we use Drosophila ovarian germline stem cells (GSCs) as a model to demonstrate that age-dependent decline in the functions of stem cells and their niche contributes to overall stem cell aging. BMP signaling activity from the niche significantly decreases with age, and increasing BMP signaling can prolong GSC life span and promote their proliferation. In addition, the age-dependent E-cadherin decline in the stem cell-niche junction also contributes to stem cell aging. Finally, overexpression of SOD, an enzyme that helps eliminate free oxygen species, in either GSCs or their niche alone can prolong GSC life span and increase GSC proliferation. Therefore, this study demonstrates that stem cell aging is controlled extrinsically and intrinsically in the Drosophila ovary. PMID: 18371381 Funding information This work was supported by: NIGMS NIH HHS, United States Grant ID: 2R01 GM64428

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)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.052
Threshold uncertainty score0.173

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.006
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0520.026

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 head0.020
GPT teacher head0.301
Teacher spread0.281 · 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 designBench or experimental
Domainnot available
GenreDataset

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
Published2008
Admission routes1
Has abstractyes

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