A Single Injection of NTG-101 Reduces the Expression of Pain-Related Neurotrophins in a Canine Model of Degenerative Disc Disease
Bibliographic record
Abstract
Background: Tissue sources of pain emanating from degenerative discs remains incompletely understood. Canine intervertebral discs (IVDs) were needle puncture injured, 4-weeks later injected with either phosphate-buffered saline (PBS) or NTG-101, harvested after an additional fourteen weeks and then histologically evaluated for the expression of NGFr, BDNF, TrkB and CALCRL proteins. Quantification was performed using the HALO automated cell-counting scoring platform. Immunohistochemical analysis was also performed on human IVD tissue samples obtained from spinal surgery. Immunohistochemical analysis and quantification of neurotrophins and neuropeptides was performed using an in vivo canine model of degenerative disc disease and human degenerative disc tissue sections. Discs injected with NTG-101 showed significantly lower levels of Nerve Growth Factor receptor (NGFr/TrkA, p = 0.0001), BDNF (p = 0.009), TrkB (p = 0.002) and CALCRL (p = 0.008) relative to PBS injections. Human IVD tissue obtained from spinal surgery due to painful DDD show robust expression of NGFr, BDNF, TrkB and CALCRL proteins. A single intradiscal injection of NTG-101 significantly inhibits the expression of NGFr, BDNF, TrkB and CALCRL proteins in degenerative canine IVDs. These results strongly suggest that NTG-101 inhibits the development of neurotrophins that are strongly associated with painful degenerative disc disease and may have profound effects upon the management of patients living with discogenic pain.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".