Human Spinal Cord Injury Causes Specific Increases in Surface Expression of Beta Integrins on Leukocytes
Bibliographic record
Abstract
Spinal cord injury (SCI) activates circulating leukocytes that migrate into the injured cord and bystander organs using adhesion molecule-mediated mechanisms. These cells cause oxidative damage, resulting in secondary injury to the spinal cord, as well as injury to bystander organs. This study was designed to examine, over a 6-h to 2-week period, changes in adhesion molecule surface expression on human peripheral leukocytes after SCI (9 subjects), using as controls 10 uninjured subjects and 6 general trauma patients (trauma controls, TC). Both the percentage of cells expressing a given adhesion molecule and the average level of its expression was quantified for both circulating neutrophils and monocytes. The percentage of neutrophils and monocytes expressing the selectin CD62L was unchanged in TC and SCI patients after injury compared to uninjured subjects. Concurrently, the amount of surface CD62L on neutrophils was decreased in SCI and TC subjects, and on monocytes after SCI. The percentage of neutrophils expressing α4 decreased in TC, but not in SCI, subjects. Likewise, the percentage of neutrophils and monocytes expressing CD11d decreased markedly in TC subjects, but not after SCI. In contrast, the mean surface expression of α4 and CD11d by neutrophils and monocytes increased after SCI compared with uninjured and TC subjects. The percentage of cells and surface expression of CD11b were similar in neutrophils of all three groups, whereas CD11b surface expression increased after SCI in monocytes. In summary, unlike changes found after general trauma, the proinflammatory stimulation induced by SCI increases the surface expression of adhesion molecules on circulating neutrophils and monocytes before they infiltrate the injured spinal cord and multiple organs of patients. Integrins may be excellent targets for anti-inflammatory treatment after human SCI.
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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 | 0.000 |
| 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".