Peripheral Nerve Injury Alters Blood-Spinal Cord Barrier Functional and Molecular Integrity through a Selective Inflammatory Pathway
Bibliographic record
Abstract
Peripheral nerve lesion triggers alterations in the spinal microenvironment that contribute to the pathogenesis of neuropathic pain. While neurons and glia have been implicated in these functional changes, it remains largely underexplored whether the blood-spinal cord barrier (BSCB) is also involved. The BSCB is an important component in the CNS homeostasis, and compromised BSCB has been associated with different pathologies affecting the spinal cord. Here, we demonstrated that a remote injury on the peripheral nerve in rats triggered a leakage of the BSCB, which was independent of spinal microglial activation. The increase of BSCB permeability to different size tracers, such as Evans Blue and sodium fluorescein, was restricted to the lumbar spinal cord and prominent for at least 4 weeks after injury. The spinal inflammatory reaction triggered by nerve injury was a key player in modulating BSCB permeability. We identified MCP-1 as an endogenous trigger for the BSCB leakage. BSCB permeability can also be impaired by circulating IL-1β. In contrast, antiinflammatory cytokines TGF-β1 and IL-10 were able to shut down the openings of the BSCB following nerve injury. Peripheral nerve injury caused a decrease in tight junction and caveolae-associated proteins. Interestingly, ZO-1 and occludin, but not caveolin-1, were rescued by TGF-β1. Furthermore, our data provide direct evidence that disrupted BSCB following nerve injury contributed to the influx of inflammatory mediators and the recruitment of spinal blood borne monocytes/macrophages, which played a major role in the development of neuropathic pain. These findings highlight the importance of inflammation in BSCB integrity and in spinal cord homeostasis.
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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 | 0.001 |
| 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".