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Record W4415007154 · doi:10.1101/2025.10.08.681005

Senolytic Therapy as a Preventive Strategy for Low Back Pain

2025· preprint· en· W4415007154 on OpenAlexafffund
Saber Ghazizadeh Darband, Hosni Cherif, Matthew Mannarino, Juiena Sagir, Magali Millecamps, Jean Ouellet, Laura S. Stone, Lisbet Haglund

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2025
Typepreprint
Languageen
FieldMedicine
TopicBiochemical effects in animals
Canadian institutionsMcGill University
FundersCanadian Institutes of Health Research
KeywordsDegeneration (medical)Low back painInflammationIntervertebral discSenescenceBack painIntervention (counseling)

Abstract

fetched live from OpenAlex

ABSTRACT Cell senescence drives inflammation and tissue breakdown and is a key hallmark of aging. Low back pain is strongly linked to age-related degeneration of spine tissues, and with an accumulation of senescent. Here we show that preventive administration of the senolytic agents o-vanillin and RG-7112 prevent the development of pain-related behaviour in young sparc -/- mice. Treated mice exhibit a reduction of senescence markers in the intervertebral discs, vertebral endplates, vertebral bone, and spinal cord, alongside a dampening of pro-inflammatory senescence-associated secretory factors in these tissues. This early senolytic intervention also preserves intervertebral disc volume and vertebral bone microarchitecture, indicating protection against structural degeneration of the spine. These findings demonstrate that targeting cellular senescence at an early stage can mitigate degenerative changes and pain, supporting senolytic therapy as a promising preventive strategy for musculoskeletal decline.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.027
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.020
GPT teacher head0.278
Teacher spread0.258 · 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.

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

Citations0
Published2025
Admission routes2
Has abstractyes

Explore more

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