Incomplete Spinal Cord Injury Reverses the Level-Dependence of Spinal Cord Injury Immune Deficiency Syndrome
Bibliographic record
Abstract
Spinal cord injury (SCI) is associated with an increased susceptibility to infections, such as pneumonia, which is the leading cause of death in these patients. This phenomenon is referred to as SCI immune deficiency syndrome (SCI-IDS), and has been shown to be more prevalent after high-level transection in preclinical SCI models. Despite the high prevalence of contusion SCIs, the effects of this etiology have not been studied in the context of SCI-IDS. Compared to transection SCIs, which involve a complete loss of supraspinal input and lead to the disinhibition of spinally-generated activity, contusion SCIs may cause significant local deafferentation, but only a partial disruption of sympathetic tone below the level of injury. In this work, we investigate the effects of thoracic (T6-7) and cervical (C6-7) moderate-severe contusion SCIs on the spleen by characterizing splenic norepinephrine (NE) and cortisol (CORT), caspase-3, and multiple inflammation markers at 3- and 7-days post-SCI. In contrary to the literature, we observe an increase in splenic NE and CORT that correspond to an increase in caspase-3 after thoracic SCI relative to cervical SCI. Further, we found differences in expression of leptin, eotaxin, IP-10, and IL-18 that implicate alterations in splenocyte recruitment and function. These results suggest that incomplete SCI drastically alters the level-dependence of SCI-IDS.
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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 | 0.000 |
| 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 | 0.001 |
| 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".