Harnessing the Secretome of Mesenchymal Stromal Cells for Traumatic Spinal Cord Injury: Multicell Comparison and Assessment of In Vivo Efficacy
Bibliographic record
Abstract
Cell therapy offers significant promise for traumatic spinal cord injury (SCI), which despite many medical advances, has limited treatment strategies. Able to address the multifactorial and dynamic pathophysiology of SCI, cells present various advantages over standard pharmacological approaches. However, the use of live cells is also severely hampered by logistical and practical considerations. These include specialized equipment and expertise, standardization of cell stocks, sustained cell viability post-thawing, and cryopreservation-induced delayed-onset cell death. For this reason, we suggest a novel and clinically translatable alternative to live-cell systemic infusion, which retains the efficacy of the latter while overcoming many of its limitations. This strategy involves the administration of concentrated cell secretome and exploits the trophic mechanism by which stromal cells function. In this study, we compare the efficacy of intravenously delivered concentrated conditioned media (CM) from human umbilical cord matrix cells (HUCMCs), bone marrow mesenchymal stromal cells, as well as newborn and adult fibroblasts in a rat model of moderately severe cervical clip compression/contusion injury (C7--T1, 35 g). This is further paired with a thorough profile of the CM cytokines, chemokines, and angiogenic factors. The HUCMC-derived CM was most effective at limiting acute (48 h post-SCI) vascular pathology, specifically lesion volume, and functional vascularity. Principle component analysis (PCA), hierarchical clustering, and interaction analysis of proteins highly expressed in the HUCMC secretome suggest involvement of the MAPK/ERK, JAK/STAT, and immune cell migratory pathways. This "secretotherapeutic" strategy represents a novel and minimally invasive method to target multiple organ systems and several pathologies shortly after traumatic SCI.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".