Depletion of Ly6G/Gr-1 Leukocytes after Spinal Cord Injury in Mice Alters Wound Healing and Worsens Neurological Outcome
Bibliographic record
Abstract
Spinal cord injury (SCI) induces a robust inflammatory response and the extravasation of leukocytes into the injured tissue. To further knowledge of the functions of neuroinflammation in SCI in mice, we depleted the early arriving neutrophils using an anti-Ly6G/Gr-1 antibody. Complete blood counts revealed that neutrophils increased approximately 3-fold over uninjured controls and peaked at 6-12 h after injury, and that anti-Ly6G/Gr-1 treatment reduced circulating neutrophils by >90% at these time points. Intravital and spinning disk confocal microscopy of the exposed posterior vein and postcapillary venules showed a significant reduction in rolling and adhering neutrophils in vivo after anti-Ly6G/Gr-1 treatment; this was accompanied by a parallel reduction in neutrophil numbers within the injured spinal cord at 24 and 48 h as determined by flow cytometry. The evolution of astrocyte reactivity, a wound healing response, was reduced in anti-Ly6G/Gr-1-treated mice, which also had less spared white matter and axonal preservation compared with isotype controls. These histological outcomes may be caused by alterations of growth factors and chemokines important in promoting wound healing. Importantly, anti-Ly6G/Gr-1 treatment worsened behavioral outcome as determined using the Basso Mouse Scale and subscores. Although the spectrum of cells affected by anti-Ly6G/Gr-1 antibody treatment cannot be fully ascertained at this point, the correspondence of neutrophil depletion and worsened recovery suggests that neutrophils promote recovery after SCI through wound healing and protective events that limit lesion propagation.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".