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Novel innovations in cell and gene therapies for spinal cord injury

2020· preprint· en· W3018783050 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueF1000Research · 2020
Typepreprint
Languageen
FieldNeuroscience
TopicNerve injury and regeneration
Canadian institutionsToronto Western HospitalUniversity of TorontoUniversity Health Network
FundersCanadian Institutes of Health Research
KeywordsSpinal cord injuryNeuroscienceRegeneration (biology)Spinal cordMedicineNeuroprotectionNeural stem cellBiologyStem cellBioinformaticsCell biology

Abstract

fetched live from OpenAlex

Spinal cord injury (SCI) leads to chronic and multifaceted disability, which severely impacts the physical and mental health as well as the socio-economic status of affected individuals. Permanent disabilities following SCI result from the failure of injured neurons to regenerate and rebuild functional connections with their original targets. Inhibitory factors present in the SCI microenvironment and the poor intrinsic regenerative capacity of adult spinal cord neurons are obstacles for regeneration and functional recovery. Considerable progress has been made in recent years in developing cell and molecular approaches to enable the regeneration of damaged spinal cord tissue. In this review, we highlight several potent cell-based approaches and genetic manipulation strategies (gene therapy) that are being investigated to reconstruct damaged or lost spinal neural circuits and explore emerging novel combinatorial approaches for enhancing recovery from SCI.

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.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.701

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.221
GPT teacher head0.426
Teacher spread0.205 · 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