Tissue Engineered Neural Constructs Composed of Neural Precursor Cells, Recombinant Spidroin and PRP for Neural Tissue Regeneration
Bibliographic record
Abstract
We have designed a novel two-component matrix (SPRPix) for the encapsulation of directly reprogrammed human neural precursor cells (drNPC). The matrix is comprised of 1) a solid anisotropic complex scaffold prepared by electrospinning a mixture of recombinant analogues of the spider dragline silk proteins - spidroin 1 (rS1/9) and spidroin 2 (rS2/12) - and polycaprolactone (PCL) (rSS-PCL), and 2) a "liquid matrix" based on platelet-rich plasma (PRP). The combination of PRP and spidroin promoted drNPC proliferation with the formation of neural tissue organoids and dramatically activated neurogenesis. Differentiation of drNPCs generated large numbers of βIII-tubulin and MAP2 positive neurons as well as some GFAP-positive astrocytes, which likely had a neuronal supporting function. Interestingly the SPRPix microfibrils appeared to provide strong guidance cues as the differentiating neurons oriented their processes parallel to them. Implantation of the SPRPix matrix containing human drNPC into the brain and spinal cord of two healthy Rhesus macaque monkeys showed good biocompatibility: no astroglial and microglial reaction was present around the implanted construct. Importantly, the human drNPCs survived for the 3 month study period and differentiated into MAP2 positive neurons. Tissue engineered constructs based on SPRPix exhibits important attributes that warrant further examination in spinal cord injury treatment.
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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) | 0.000 | 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 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".