Enhancing spinal cord injury repair through PTCH1-mediated neural progenitor cell differentiation induced by ion elemental-optimized layered double hydroxides
Bibliographic record
Abstract
A B S T R A C T Spinal cord injury (SCI) is associated with profound neurological impairments, and to date, efficacious therapeutic interventions remain elusive. Embryonic stem cells (ESCs) possess the totipotent capacity to differentiate into specific neuronal cell types under the influence of appropriate extrinsic signals. Notably, their induction into neural progenitor cells (NPCs) holds particular promise. These NPCs are capable of self-renewal and can differentiate into all neuronal cell types, exhibiting the ability to migrate and integrate into damaged areas of the central nervous system (CNS), thereby emerging as an ideal therapeutic strategy for neurological disorders. Layered double hydroxides (LDHs), with their lamellar architecture, are biocompatible and possess anion-exchange attributes, making them prominent in drug and nucleotide delivery for tissue engineering. Nevertheless, the investigation into the intrinsic biological effects of LDHs are rarely reported. Our research demonstrates that MgFe-LDH and MgAl-LDH promote NPCs differentiation in a dose-dependent manner, and MgAl-LDH is superior to MgFe-LDH in promoting NPCs differentiation. RNAseq revealed that the promoted NPCs differentiation by nanoparticles was primarily associated with the interaction between nanoparticles and transmembrane protein PTCH1. Furthermore, we performed PTCH1 knockdown in NPCs and observed a significant impact on the MgAl-LDH-induced NPCs differentiation. In vivo , MgAl-LDH-pretreated NPCs implantation significantly enhances the behavioral and electrophysiological performance of SCI mice, and neurons clearly observed in the lesion sites of MgAl-LDH-pretreated NPCs group. This work provides novel strategies and a theoretical foundation for the research on nanomaterial regulation of stem cells fate and neural regenerative repair.
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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".