CELL SENESCENCE AND SENOLYTIC TREATMENT FOR LOW BACK PAIN
Bibliographic record
Abstract
Low back pain (LBP) is a leading global cause of disability, imposing personal and economic costs that exceed $100 billion annually in the U.S. alone. LBP is often linked to intervertebral disc (IVD) degeneration, with senescent cells (SnCs) playing a key role in its progression. SnCs, which accumulate due to aging and cellular stress, adopt a senescence-associated secretory phenotype (SASP), releasing inflammatory and degenerative factors that drive age-related diseases like LBP. In human IVD tissue and cell cultures, SnCs have been shown to contribute to LBP, and their removal decreases the expression of inflammatory and pain-associated SASP factors. Similarly, Sparc-/- mice, which mimic human IVD degeneration, show SnC accumulation with age. Oral senolytic treatments in these animals reduced LBP, eliminated SnCs from the IVD and spinal cord, and decreased SASP factor release. Additionally, treatment improved vertebral bone quality, reduced IVD degeneration and lowered spinal cord pain marker expression. This study demonstrates that systemic oral senolytic drugs, such as RG-7112 and o-Vanillin, effectively reduce behavioural indicators of LBP, suppress SASP, and mitigate degenerative changes in spinal tissues. Combined treatments yielded more robust therapeutic effects, suggesting senolytics could be a promising novel therapy for LBP and other disorders linked to cellular senescence.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".