Repetitive in vivo manual loading of the spine elicits cellular responses in porcine annuli fibrosi
Bibliographic record
Abstract
Back pain and intervertebral disc degeneration are prevalent, costly, and widely treated by manual therapies, yet the underlying causes of these diseases are indeterminate as are the scientific bases for such treatments. The present studies characterize the effects of repetitive in vivo manual loads on porcine intervertebral disc cell metabolism using RNA deep sequencing. A single session of repetitive manual loading applied to the lumbar spine induced both up- and down-regulation of a variety of genes transcribed by cells in the ventral annuli fibrosi. The effect of manual therapy at the level of loading was greater than at a level distant to the applied load. Gene ontology and molecular pathway analyses categorized biological, molecular, and cellular functions influenced by repetitive manual loading, with over-representation of membrane, transmembrane, and pericellular activities. Weighted Gene Co-expression Network Analysis discerned enrichment in genes in pathways of inflammation and skeletogenesis. The present studies support previous findings of intervertebral disc cell mechanotransduction, and are the first to report comprehensively on the repertoire of gene targets influenced by mechanical loads associated with manual therapy interventions. The present study defines the cellular response of repeated, low-amplitude loads on normal healthy annuli fibrosi and lays the foundation for future work defining how healthy and diseased intervertebral discs respond to single or low-frequency manual loads typical of those applied clinically.
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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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".