Disc Related Cytokines Inhibit Axonal Outgrowth From Dorsal Root Ganglion Cells In Vitro
Bibliographic record
Abstract
STUDY DESIGN: Application of nucleus pulposus and disc related cytokines in vitro on cultured dorsal root ganglion (DRG) cells. OBJECTIVES: To study if tumor necrosis factor (TNF) and interleukin-1beta (IL-1beta) may induce similar inhibition of axonal outgrowth from cultured DRG cells as application of nucleus pulposus and to compare a new assessment method to previous data. SUMMARY OF BACKGROUND DATA: Pro-inflammatory cytokines related to the intervertebral disc have been suggested to affect adversely neurons following local application, with implications for the nucleus pulposus-induced nerve injury seen in various studies. Nucleus pulposus is known to inhibit axonal outgrowth from cultured DRG cells, thereby indicating a neurotoxic potential. The mechanisms were not understood, but it was suspected that the effect was mediated by pro-inflammatory cytokines produced by the nucleus pulposus. METHODS: DRG were harvested from newborn rats and put in culture. The axonal outgrowth was determined 24 hours after starting the culture. Twenty-four hours after exposing the cultured cells to nucleus pulposus, frozen nucleus pulposus, TNF, or IL-1beta, the axonal outgrowth was reassessed, and the outgrowth during the exposure time was calculated. RESULTS: Nucleus pulposus clearly reduced the axonal outgrowth. Also, application of TNF and IL-1beta reduced the outgrowth but not as pronounced as the nucleus pulposus. Frozen nucleus pulposus had no effects on the outgrowth. Overall, the data were similar regarding frozen and nonfrozen nucleus pulposus compared to a previous study. CONCLUSIONS: It was evident that the 2 studied cytokines inhibited the outgrowth of axons from cultured DRG cells, thus suggesting a neurotoxic potential. However, the inhibition was not as pronounced as for nucleus pulposus. These data may increase our understanding for cytokine induced nerve injury, with implications for future treatment strategies for such conditions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".