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Record W4415312859 · doi:10.1002/bmm2.70025

New opportunities for bioscaffold‐enabled spinal cord injury repair

2025· article· en· W4415312859 on OpenAlexaff
Xiaoqing Qi, Yicheng Fu, Zhaoliang Su, Li Li, Subrata Chakrabarti, Peng Li, Yilun Wu, Fang Liu, Teng Gao, Zhifeng Dong, Lei Liu, Pei Cao

Bibliographic record

VenueBMEMat · 2025
Typearticle
Languageen
FieldMaterials Science
TopicElectrospun Nanofibers in Biomedical Applications
Canadian institutionsWestern University
FundersScience and Technology Planning Social Development Project of Zhenjiang CityNational Natural Science Foundation of China
KeywordsSpinal cord injuryRegeneration (biology)Stem cellAxonReview articleTissue engineeringSpinal cordTissue repair

Abstract

fetched live from OpenAlex

Abstract Spinal cord injury (SCI) leads to high rates of central nervous system impairment and imposes a significant treatment burden, highlighting the need for effective repair strategies. Bioscaffolds are considered to be multifunctional materials composed of bioactive polymers and signaling molecules, showing potential comparable to tissue engineering approaches utilizing exogenous stem cells. These bioscaffolds, which act as biological frameworks, can modulate intrinsic neuronal regeneration and the external microenvironment to facilitate SCI repair. This review explores the current status and future prospects of three‐dimensional bioscaffolds for SCI repair, covering the pathophysiology of spinal cord injury, associated repair mechanisms, and key bioscaffold properties influencing repair efficiency. Notably, the review highlights new insights into the use of therapeutic bioscaffolds to promote endogenous stem cell differentiation, enhance axon growth, regulate the injury microenvironment, and support SCI repair. Finally, expert opinions are discussed, summarizing design principles for effective SCI‐repair bioscaffolds and underscoring their significant potential for clinical applications.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.275
Threshold uncertainty score0.503

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.041
GPT teacher head0.330
Teacher spread0.288 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

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".

Quick stats

Citations3
Published2025
Admission routes1
Has abstractyes

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