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Record W2214848565 · doi:10.1096/fj.15-278846

Antisenescence effect of mouse embryonic stem cell conditioned medium through a PDGF/FGF pathway

2015· article· en· W2214848565 on OpenAlexaff
Yun‐Ui Bae, Joon Hyuk Choi, András Nagy, Hoon‐Ki Sung, Jae‐Ryong Kim

Bibliographic record

VenueThe FASEB Journal · 2015
Typearticle
Languageen
FieldMedicine
TopicTelomeres, Telomerase, and Senescence
Canadian institutionsHospital for Sick ChildrenUniversity of TorontoLunenfeld-Tanenbaum Research InstituteMount Sinai Hospital
FundersNational Research Foundation of Korea
KeywordsSenescenceEmbryonic stem cellCell biologyFibroblast growth factorBiologyGrowth factorPlatelet-derived growth factor receptorStem cellRegenerative medicineCancer researchGeneticsReceptor

Abstract

fetched live from OpenAlex

Cellular senescence, an irreversible state of growth arrest, underlies organismal aging and age-related diseases. Recent evidence suggests that aging intervention based on inhibition of cellular senescence might be a promising strategy for treatment of aging and age-related diseases. Embryonic stem cells (ESCs) and ESC conditioned medium (CM) have been suggested as a desirable source for regenerative medicine. However, effects of ESC-CM on cellular senescence remain to be determined. We found that treatment of senescent human dermal fibroblasts with CM from mouse ESCs (mESCs) decreases senescence phenotypes. We found that platelet-derived growth factor BB in mESC-CM plays a critical role in antisenescence effect of mESC-CM through up-regulation of fibroblast growth factor 2. We confirmed that mESC-CM treatment accelerates the wound-healing process by down-regulating senescence-associated p53 expression in in vivo models. Taken together, our results suggest that mESC-CM has the ability to suppress cellular senescence and maintain proliferative capacity. Therefore, this strategy might emerge as a novel therapeutic strategy for aging and age-related diseases.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.654

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 head0.027
GPT teacher head0.266
Teacher spread0.239 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations37
Published2015
Admission routes1
Has abstractyes

Explore more

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