Effect of Human Umbilical Cord Perivascular Cell-Conditioned Media in an Adult Zebrafish Model of Traumatic Brain Injury
Bibliographic record
Abstract
The pathophysiological events of secondary brain injury contribute to poor outcome after traumatic brain injury (TBI). The neuroprotective effects of mesenchymal cells have been extensively studied and evidence suggests that their effects are mostly mediated through paracrine effects. Human umbilical cord perivascular cells (HUCPVCs) are mesenchymal stem cells with potential therapeutic value in TBI. In this study, we assessed the effect of HUCPVC-conditioned media (CM) in an established adult zebrafish model of TBI induced by pulsed high-intensity focused ultrasound (pHIFU). This model demonstrates similarities to mammalian outcome after TBI. Administration of HUCPVC-CM 1 h postinjury (hpi) resulted in improved outcome after pHIFU-induced TBI. Western blot and immunohistochemistry results demonstrated that the HUCPVC-CM reduced (p < 0.05) reactive astrogliosis at 24 hpi. Moreover, at 24 hpi, the HUCPVC-CM treatment resulted in reduced apoptosis in HUCPVC-CM-treated zebrafish. Behavioral analysis demonstrated improvement in locomotor activity (p < 0.05) and anxiety (p < 0.05) at 6 and 24 hpi following HUCPVC-CM treatment. Overall, HUCPVC-CM treatment improved acute outcome measures in pHIFU-injured zebrafish. Collectively, the data demonstrate a cell-free treatment approach for traumatic brain injuries.
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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) | 0.003 | 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".