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Cellular Aging, Senescence and Autophagy Processes in Osteoarthritis

2015· review· en· W2171870699 on OpenAlexafffund
Mohamed Benderdour, Johanne Martel‐Pelletier, Jean‐Pierre Pelletier, Mohit Kapoor, Marı́a Victoria Zunzunegui, Hassan Fahmi

Bibliographic record

VenueCurrent Aging Science · 2015
Typereview
Languageen
FieldMedicine
TopicOsteoarthritis Treatment and Mechanisms
Canadian institutionsCentre Hospitalier de l’Université de Montréal
FundersCanadian Institutes of Health Research
KeywordsSenescenceAutophagyOsteoarthritisChondrocyteMedicineLife expectancyOxidative stressLongevityGerontologyBiologyBioinformaticsCartilageCell biologyInternal medicinePathologyGenetics

Abstract

fetched live from OpenAlex

Osteoarthritis (OA) is the most common form of arthritis and a huge health and financial burden. The prevalence and incidence of OA are likely to rise due to increasing life expectancy. Although the link between aging and OA is well established, little is known about the mechanisms by which aging contributes to OA development. In recent years, progress has been made in understanding the molecular mechanisms of chondrocyte aging and senescence. Aging and senescent chondrocytes display a senescence-associated secretory phenotype (SASP) associated with increased secretion of pro-inflammatory mediators, extracellular matrix degrading enzymes and oxidative stress, all of which can contribute to the development and progression of OA. There is also evidence that autophagy, an essential homeostatic process, declines with aging and during OA. This review will focus on our current understanding of chondrocyte aging, senescence, and autophagy and their potential roles in the development and progression of OA. An understanding of these processes would be very useful in devising strategies to treat OA or to delay its development.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.061
GPT teacher head0.365
Teacher spread0.304 · 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

Citations36
Published2015
Admission routes2
Has abstractyes

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