Engineering of the Extracellular Matrix: Working toward Neural Stem Cell Programming and Neurorestoration— Concept and Progress Report
Bibliographic record
Abstract
In the concept of neurorestoration, cellular and structural elements that have been lost are replaced, and their function is restored. Central to this therapeutic strategy is the transplantation of neural progenitor cells such as clonogenically expanded stem cells. Stem cells make decisions regarding fate and patterning in response to external environmental signals. The therapeutic effectiveness of neural stem cells may be facilitated by the ability to manipulate these signals in a temporal and spatially appropriate fashion. Artificial deoxyribonucleic acid and artificial protein technology combines elements of protein engineering, molecular biology, and recombinant deoxyribonucleic acid technology to produce proteins with functional domains derived from naturally occurring proteins and represents a potentially powerful tool to modulate stem cell behavior. To this end, we have developed three artificial extracellular matrix proteins that incorporate the active domain of hJagged1 and hDelta1 into an elastin backbone. hJagged1 and hDelta1 are members of the DSL family of ligands to the Notch receptor, a signaling system that is very important in development and is the strongest known signal to instruct neural progenitor cells to choose glial fates over neuronal fates. The successful cloning of the artificial genes was confirmed by test digestions with appropriate restriction enzymes as well as direct deoxyribonucleic acid sequencing. In addition, we have demonstrated that all three artificial extracellular matrix proteins express at a high level in a prokaryotic host. This report describes the concept and progress in an entirely novel and previously unreported approach to modulate neural stem cell behavior. Its future application could include in vitro processing of stem cells before transplantation, supporting and programming the cells after transplantation, as well as the development of bioactive biomaterials.
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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 | 0.000 |
| 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) | 0.000 | 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".