In vivo bioluminescence imaging for viable human neural stem cells incorporated within in situ gelatin hydrogels
Bibliographic record
Abstract
BACKGROUND: Three-dimensional (3D) hydrogel-based stem cell therapies contribute to enhanced therapeutic efficacy in treating diseases, and determining the optimal mechanical strength of the hydrogel in vivo is important for therapeutic success. We evaluated the proliferation of human neural stem cells incorporated within in situ-forming hydrogels and compared the effect of hydrogels with different elastic properties in cell/hydrogel-xenografted mice. METHODS: The gelatin-polyethylene glycol-tyramine (GPT) hydrogel was fabricated through enzyme-mediated cross-linking reaction using horseradish peroxidase (HRP) and hydrogen peroxide (H2O2). RESULTS: The F3-effluc encapsulated within a soft 1,800 pascal (Pa) hydrogel and stiff 5,800 Pa hydrogel proliferated vigorously in a 24-well plate until day 8. In vitro and in vivo kinetics of luciferase activity showed a slow time-to-peak after D-luciferin administration in the stiff hydrogel. When in vivo proliferation of F3-effluc was observed up to day 21 in both the hydrogel group and cell-only group, F3-effluc within the soft hydrogel proliferated more vigorously, compared to the cells within the stiff hydrogel. Ki-67-specific immunostaining revealed highly proliferative F3-effluc with compactly distributed cell population inside the 1,800 Pa or 5,800 Pa hydrogel. CONCLUSIONS: We examined the in vivo effectiveness of different elastic types of hydrogels encapsulating viable neural stem cells by successfully monitoring the proliferation of implanted stem cells incorporated within a 3D hydrogel scaffold.
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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".