Splenic involvement in umbilical cord matrix-derived mesenchymal stromal cell-mediated effects following traumatic spinal cord injury
Bibliographic record
Abstract
BACKGROUND: The spleen plays an important role in erythrocyte turnover, adaptive immunity, antibody production, and the mobilization of monocytes/macrophages (Mφ) following tissue injury. In response to trauma, the spleen initiates production of inflammatory cytokines, which in turn recruit immune cells to the inflamed tissue, exacerbating damage. Our previous work has shown that intravenous mesenchymal stromal cell (MSC) infusion has potent immunomodulatory effects following spinal cord injury (SCI), associated with the transplanted cells homing to and persisting within the spleen. Therefore, this work aimed to characterize the relationship between the splenic inflammatory response and SCI pathophysiology, emphasizing splenic involvement in MSC-mediated effects. METHODS: Using a rodent model of cervical clip-compression SCI, secondary tissue damage and functional recovery were compared between splenectomised rodents and those with a sham procedure. Subsequently, 2.5 million MSCs from the term human umbilical cord matrix cells (HUCMCs) were infused via tail vein at 1-h post-SCI and the effects were assessed in the presence or absence of a spleen. RESULTS: Splenectomy alone had no effect on lesion volume, hemorrhage, or inflammation. There was also no significant difference between the groups in functional recovery and those in lesion morphometry. Yet, while the infusion of HUCMCs reduced spinal cord hemorrhage and increased systemic levels of IL-10 in the presence of a spleen, these effects were lost with splenectomy. Further, HUCMC infusion was shown to alter the expression levels of splenic cytokines, suggesting that the spleen is an important target and site of MSC effects. CONCLUSIONS: Our results provide a link between MSC function and splenic inflammation, a finding that can help tailor the cells/transplantation approach to enhance therapeutic efficacy.
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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 | 0.001 |
| 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".