P.182 Self-Assembling Peptide Biomaterial to Optimize Human Stem Cell-Based Regeneration of the Injured Spinal Cord
Bibliographic record
Abstract
Background: Human induced pluripotent stem cell-derived neural stem cells (hiPS-NSCs) are a promising therapeutic approach to regenerate after spinal cord injury (SCI) as they can differentiate to myelinating oligodendrocytes, synaptically-active neurons, and supportive astrocytes. Unfortunately, most chronically injured patients develop ex vacuo microcystic cavitations which prevent regenerative cell migration and neurite outgrowth. QL6 is a novel, pH-neutral, biomaterial which can self-assembles into a supportive extracellular matrix (ECM)-like matrix in vivo. This work assesses QL6’s ability to support hiPS-NSC-based regeneration. Methods:In Vitro:hiPS-NSCs were extensively characterized by EDTA assay, qPCR, and immunocytochemistry(ICC), electron microscopy(EM) and neurosphere formation assays. In Vivo:Immunodeficient rats received clinically-relevant chronic C6-7 injuries. Animals were randomized: (1)vehicle, (2)hiPS-NSCs, (3)QL6, (4)QL6+hiPS-NSCs. All rats underwent treadmill rehabilitation and behavioural testing. A subset underwent single-cell RNA sequencing(scRNAseq). Results: hiPS-NSCs proliferated robustly on QL6(Ki67+/DAPI+; 29%vs6%; p<0.01). EDTA assay showed hiPS-NSC binding to QL6 to be driven by calcium-independent mechanisms. Importantly, QL6 enhanced adherent neurosphere formation. EM-imaging provided the first images of the hiPS-NSC/QL6 interaction. Behavioural assessments demonstrate synergistic improvements with combinatorial treatment. High-throughput scRNAseq differential gene expression analyses suggest QL6 is altering lineage signalling in the human graft post-transplantation. Conclusions: This work provides key proof-of-concept data that QL6 can support translationally-relevant human iPS-NSCs in traumatic SCI.
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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 | 0.001 |
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".