Dynamics of the inflammatory response after murine spinal cord injury revealed by flow cytometry
Bibliographic record
Abstract
Spinal cord injury (SCI) triggers a robust inflammatory response that contributes in part to the secondary degeneration of spared tissue. Here, we use flow cytometry to quantify the inflammatory response after SCI. Besides its objective evaluation, flow cytometry allows for levels of particular markers to be documented that further aid in the identification of cellular subsets. Analyses of blood from SCI mice for CD45 (common leukocyte antigen), CD11b (complement receptor-3), Gr-1 (neutrophil/monocyte marker), and CD3 (T-cell marker) revealed a marked increase in circulating neutrophils (CD45(high):Gr-1(high)) at 12 hr compared with controls. Monocyte density in blood increased at 24 hr, and in contrast, lymphocyte numbers were significantly decreased. Mirroring the early increase in neutrophils within the blood, flow analysis of the spinal cord lesion site revealed a significant (P < 0.01) and maintained increase in blood-derived leukocytes (CD45(high):CD11b(high)) from 12 to 96 hr compared with sham-injured and naive controls. Importantly, this technique clearly distinguishes blood-derived neutrophils (CD45:Gr-1(high):F4/80(negative)) and monocyte/macrophages (CD45(high)) from resident microglia (CD45(low)) and revealed that the majority of the blood-derived infiltrate were neutrophils. Our results highlight an assumed, but previously uncharacterized, marked and transient increase in leukocyte populations in blood early after SCI followed by the orchestrated invasion of neutrophils and monocytes into the injured cord. In contrast to mobilization of neutrophils, SCI induces lymphopenia that may contribute negatively to the overall outcome after spinal cord trauma.
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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 | 0.001 | 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 | 0.001 |
| 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".