Transforming Growth Factor-β1 Impairs Neuropathic Pain through Pleiotropic Effects
Bibliographic record
Abstract
BACKGROUND: Understanding the underlying mechanisms of neuropathic pain caused by damage to the peripheral nervous system remains challenging and could lead to significantly improved therapies. Disturbance of homeostasis not only occurs at the site of injury but also extends to the spinal cord and brain involving various types of cells. Emerging data implicate neuroimmune interaction in the initiation and maintenance of chronic pain hypersensitivity. RESULTS: In this study, we sought to investigate the effects of TGF-beta1, a potent anti-inflammatory cytokine, in alleviating nerve injury-induced neuropathic pain in rats. By using a well established neuropathic pain animal model (partial ligation of the sciatic nerve), we demonstrated that intrathecal infusion of recombinant TGF-beta1 significantly attenuated nerve injury-induced neuropathic pain. TGF-beta1 treatment not only prevents development of neuropathic pain following nerve injury, but also reverses previously established neuropathic pain conditions. The biological outcomes of TGF-beta1 in this context are attributed to its pleiotropic effects. It inhibits peripheral nerve injury-induced spinal microgliosis, spinal microglial and astrocytic activation, and exhibits a powerful neuroprotective effect by preventing the induction of ATF3+ neurons following nerve ligation, consequently reducing the expression of chemokine MCP-1 in damaged neurons. TGF-beta1 treatment also suppresses nerve injury-induced inflammatory response in the spinal cord, as revealed by a reduction in cytokine expression. CONCLUSION: Our findings revealed that TGF-beta1 is effective in the treatment of neuropathic by targeting both neurons and glial cells. We suggest that therapeutic agents such as TGF-beta1 having multipotent effects on different types of cells could work in synergy to regain homeostasis in local spinal cord microenvironments, therefore contributing to attenuate neuropathic 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) | 0.000 | 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 | 0.001 |
| 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".