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Record W4388523417 · doi:10.1038/s41598-023-44675-6

Artificial axons as a biomimetic 3D myelination platform for the discovery and validation of promyelinating compounds

2023· article· en· W4388523417 on OpenAlexfundno aff
Anna Jagielska, Kristin Radzwill, Daniela Espinosa-Hoyos, Mingyu Yang, Kavin Kowsari, Jonathan E. Farley, Stefanie Giera, Annette T. Byrne, Guoqing Sheng, Nicholas X. Fang, James C. Dodge, Carlos E. Pedraza, Krystyn J. Van Vliet

Bibliographic record

VenueScientific Reports · 2023
Typearticle
Languageen
FieldNeuroscience
TopicNeurogenesis and neuroplasticity mechanisms
Canadian institutionsnot available
FundersMassachusetts Institute of TechnologySanofiNatural Sciences and Engineering Research Council of CanadaU.S. Department of Defense
KeywordsMyelinRemyelinationMultiple sclerosisDrug discoveryAxonNeuroscienceNeurodegenerationBiologyMedicineBioinformaticsDiseaseCentral nervous systemPathologyImmunology

Abstract

fetched live from OpenAlex

Multiple sclerosis (MS), a chronic neurodegenerative disease driven by damage to the protective myelin sheath, is currently incurable. Today, all clinically available treatments modulate the immune-mediated symptoms of the disease but they fail to stop neurodegeneration in many patients. Remyelination, the regenerative process of myelin repair by oligodendrocytes, which is considered a necessary step to protect demyelinated axons and stop neuronal death, is impaired in MS patients. One of the major obstacles to finding effective remyelinating drugs is the lack of biomimetic drug screening platforms that enable quantification of compounds' potential to stimulate 3D myelination in the physiologically relevant axon-like environment. To address this need, we built a unique myelination drug discovery platform, by expanding our previously developed technology, artificial axons (AAs), which enables 3D-printing of synthetic axon mimics with the geometry and mechanical properties closely resembling those of biological axons. This platform allows for high-throughput phenotypic myelination assay based on quantification of 3D wrapping of myelin membrane around axons in response to compounds. Here, we demonstrate quantification of 3D myelin wrapping by rat oligodendrocytes around the axon mimics in response to a small library of known pro-myelinating compounds. This assay shows pro-myelinating activity for all tested compounds consistent with the published in vitro and in vivo data, demonstrating predictive power of AA platform. We find that stimulation of myelin wrapping by these compounds is dose-dependent, providing a facile means to quantify the compounds' potency and efficacy in promoting myelin wrapping. Further, the ranking of relative efficacy among these compounds differs in this 3D axon-like environment as compared to a traditional oligodendrocyte 2D differentiation assay quantifying area of deposited myelin membrane. Together, we demonstrate that the artificial axons platform and associated phenotypic myelin wrapping assay afford direct evaluation of myelin wrapping by oligodendrocytes in response to soluble compounds in an axon-like environment, providing a predictive tool for the discovery of remyelinating therapies.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.000
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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 head0.067
GPT teacher head0.300
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 source (direct Gemma or distilled Codex), 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

Citations8
Published2023
Admission routes1
Has abstractyes

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