Remote inflammatory response in liver is dependent on the segmental level of spinal cord injury
Bibliographic record
Abstract
BACKGROUND: Traumatic spinal cord injury (SCI) triggers a systemic inflammatory response (SIR) that contributes to a high incidence of secondary organ complications, particularly after a cervical or high-level thoracic injury. Because liver plays a key role in initiating and propagating the SIR, the aim of this study was to assess the effects that SCI at differing segmental levels has on the intensity of the inflammatory response in the liver. METHODS: Using male Wistar rats, clip compression SCI was performed at the 4th thoracic (T4 SCI; high-level SCI) or the 12th thoracic (T12 SCI; low-level SCI) spinal cord segment. Sham-injured rats had a partial laminectomy, but no SCI. Leukocyte recruitment to the liver, hepatic blood flow, and hepatocellular injury/death were assessed using intravital microscopy and histology. Chemokine and cytokine concentrations were assessed in the liver. Outcomes were measured at 1.5 hours, 12 hours, and 24 hours after SCI. RESULTS: At 12 hours after injury, T4 SCI caused a threefold increase in hepatic leukocyte recruitment compared with T12 SCI (p < 0.05). T4 SCI induced 50% more hepatocyte injury than T12 SCI at 12 hours (p < 0.05). Hepatic blood flow decreased after SCI, but not after sham injury, and stayed decreased only after T4 SCI at 24 hours after injury. The T4 SCI-induced changes were accompanied by increases in the hepatic concentrations of interleukin-1β, leptin, interleukin 10, and cytokine-induced neutrophil chemoattractant-1 at 1.5 hours. CONCLUSIONS: Our findings indicate that traumatic SCI triggers an acute SIR that contributes to hepatocellular injury. SCI-induced remote injury/dysfunction to the liver appears to be transient and is more robust after an upper thoracic SCI compared with a lower thoracic 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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".