Quantifying Blood-Spinal Cord Barrier Permeability after Peripheral Nerve Injury in the Living Mouse
Bibliographic record
Abstract
BACKGROUND: Genetic polymorphisms, gender and age all influence the risk of developing chronic neuropathic pain following peripheral nerve injury (PNI). It is known that there are significant inter-strain differences in pain hypersensitivity in strains of mice after PNI. In response to PNI, one of the earliest events is thought to be the disruption of the blood-spinal cord barrier (BSCB). The study of BSCB integrity after PNI may lead to a better understanding of the mechanisms that contribute to chronic pain. RESULTS: Here we used in vivo dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to establish a timecourse for BSCB permeability following PNI, produced by performing a spared nerve injury (SNI). From this longitudinal study, we found that the SNI group had a significant increase in BSCB permeability over time throughout the entire spinal cord. The BSCB opening had a delayed onset and the increase in permeability was transient, returning to control levels just over one day after the surgery. We also examined inter-strain differences in BSCB permeability using five mouse strains (B10, C57BL/6J, CD-1, A/J and BALB/c) that spanned the range of pain hypersensitivity. We found a significant increase in BSCB permeability in the SNI group that was dependent on strain but that did not correlate with the reported strain differences in PNI-induced tactile hypersensitivity. These results were consistent with a previous experiment using Evans Blue dye to independently assess the status of the BSCB permeability. CONCLUSIONS: DCE-MRI provides a sensitive and non-invasive method to follow BSCB permeability in the same group of mice over time. Examining differences between mouse strains, we demonstrated that there is an important genetically-based control of the PNI-induced increase in BSCB permeability and that the critical genetic determinants of BSCB opening after PNI are distinct from those that determine genetic variability in PNI-induced pain hypersensitivity.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".