Functional immobilization of interferon‐gamma induces neuronal differentiation of neural stem cells
Bibliographic record
Abstract
Stem cell transplantation provides significant promise to regenerative strategies after injury in the central nervous system. Neural stem/progenitor cells (NSPCs) have been studied in terms of their regenerative capacity and their ability to differentiate into neurons when exposed to various soluble factors. In this study, interferon-gamma (IFN-gamma) was compared with brain-derived neurotrophic factor (BDNF) and erythropoietin and was shown to be the best single growth factor for inducing neuronal differentiation from adult rat brain-derived NSPCs. Next, IFN-gamma was surface immobilized to a methacrylamide chitosan (MAC) scaffold that was specifically designed to match the modulus of brain tissue and neuronal differentiation of NSPCs was examined in vitro by immunohistochemistry. Bioactive IFN-gamma was successfully immobilized and quantified by ELISA. Both soluble and immobilized IFN-gamma on MAC surfaces showed dose dependent neuronal differentiation with soluble saturation occurring at 100 ng/mL and the most effective immobilized IFN-gamma dose at 37.5 ng/cm(2), where significantly more neurons resulted compared with controls including soluble IFN-gamma.
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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) | 0.001 | 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".