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 significantly obstructs neuroregeneration. Neural progenitor cell (NPC) therapies for repair and regeneration have demonstrated favourable outcomes not only via cell replacement, but trophic support. In an effort to strategically enhance this secretory potential, we utilized the regenerative aptitude of hepatocyte growth factor (HGF) as a cellular preconditioning agent and assessed the capacity of modified secretome to attenuate astrocyte reactivity in vitro. HGF-preconditioned NPCs demonstrated increased levels of tissue inhibitor of metalloproteinases-1 and reduced vascular endothelial growth factor compared to untreated NPCs. In reactive astrocytes, HGF-enhanced NPC secretome reduced glial fibrillary acidic protein expression and chondroitin sulfate proteoglycan deposition to a greater extent than either treatment alone, and enhanced neurite outgrowth of co-cultured neurons. These findings have important translational implications for modification of the post-injury milieu and optimization of cell-based strategies for CNS injury.
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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 | 0.000 |
| 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".