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Record W4220664179 · doi:10.1002/bit.28083

Tissue‐engineered in vitro modeling of the impact of Schwann cells in amyotrophic lateral sclerosis

2022· article· en· W4220664179 on OpenAlexafffund
Aurélie Louit, Marie‐Josée Beaudet, François Gros‐Louis, François Berthod

Bibliographic record

VenueBiotechnology and Bioengineering · 2022
Typearticle
Languageen
FieldMedicine
TopicAmyotrophic Lateral Sclerosis Research
Canadian institutionsUniversité Laval
FundersFondation Brain CanadaALS Society of Canada
KeywordsAmyotrophic lateral sclerosisMicrogliaSOD1Schwann cellIn vitroCell biologyBiologyRegeneration (biology)NeurosciencePathologyMedicineImmunologyInflammationDiseaseBiochemistry

Abstract

fetched live from OpenAlex

Abstract Amyotrophic Lateral Sclerosis (ALS) is a devastating neurodegenerative disease affecting upper and lower motor neurons (MNs). To investigate whether Schwann cells could be involved in the disease pathogenesis, we developed a tissue‐engineered three‐dimensional (3D) in vitro model that combined MNs cocultured with astrocytes and microglia seeded on top of a collagen sponge populated with epineurium fibroblasts to enable 3D axonal migration. C2C12 myoblasts were seeded underneath the sponge in the presence or absence of Schwann cells. To reproduce an ALS cellular microenvironment, MNs, astrocytes, and microglia were extracted from SOD1 G93A mice recapitulating many aspects of the human disease. This 3D ALS in vitro model was compared with a 3D control made of cells isolated from SOD1 WT mice. We showed that normal Schwann cells strongly enhanced MN axonal migration in the 3D control model but had no effect in the ALS model. However, ALS‐derived Schwann cells isolated from SOD1 G93A mice failed to significantly improve axonal migration in both models. These results suggest that a cell therapy using healthy Schwann cells may not be effective in promoting axonal regeneration in ALS. In addition, this 3D ALS model could be used to study the impact of other cell types on ALS by various combinations of normal and diseased cells.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.361
Threshold uncertainty score0.405

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.022
GPT teacher head0.254
Teacher spread0.233 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations10
Published2022
Admission routes2
Has abstractyes

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