Hepatocyte Growth Factor-Preconditioned Neural Progenitor Cells Attenuate Astrocyte Reactivity and Promote Neurite Outgrowth
Bibliographic record
Abstract
The astroglial scar is a defining hallmark of secondary pathology following central nervous system (CNS) injury that, despite its role in limiting tissue damage, presents a significant barrier to neuroregeneration. Neural progenitor cell (NPC) therapies for tissue repair and regeneration have demonstrated favorable outcomes, the effects of which are ascribed not only to direct cell replacement but trophic support. Cytokines and growth factors secreted by NPCs aid in modifying the inhibitory and cytotoxic post-injury microenvironment. In an effort to harness and enhance the reparative potential of NPC secretome, we utilized the multifunctional and pro-regenerative cytokine, hepatocyte growth factor (HGF), as a cellular preconditioning agent. We first demonstrated the capacity of HGF to promote NPC survival in the presence of oxidative stress. We then assessed the capacity of this modified conditioned media (CM) to attenuate astrocyte reactivity and promote neurite outgrowth in vitro . HGF pre-conditioned NPCs demonstrated significantly increased levels of tissue inhibitor of metalloproteinases-1 and reduced vascular endothelial growth factor compared to untreated NPCs. In reactive astrocytes, HGF-enhanced NPC-CM effectively reduced glial fibrillary acidic protein (GFAP) expression and chondroitin sulfate proteoglycan deposition to a greater extent than either treatment alone, and enhanced neurite outgrowth of co-cultured neurons. in vivo , this combinatorial treatment strategy might enable tactical modification of the post-injury inhibitory astroglial environment to one that is more conducive to regeneration and functional recovery. These findings have important translational implications for the optimization of current cell-based therapies for CNS injury.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".