Modélisation<i>in vitro</i>du système nerveux par génie tissulaire
Bibliographic record
Abstract
The nervous system is extraordinarily complex and exposed to various trauma and degenerative diseases that remain difficult to treat. To facilitate its study, in vitro models were developed by culturing neurons and glial cells in monolayer cultures, or through organotypic cultures of brain or spinal cord slices. These in vitro models were, and are still very helpful for the advancement of neurosciences. However, they are for some studies, either overly simplified, or too complex. The application of tissue engineering to neurosciences offers a new and highly versatile approach to develop accurate models of the nervous system. These models can be engineered in three-dimensions while choosing for each individual component, cellular and molecular, that will compose it. The level of complexity of the model can be adjusted from the simplest to the more complete as needed. For example, through the use of a three-dimensional tissue-engineered model of the spinal cord, it was possible to reproduce the process of myelin sheath formation around motor neuron axons for the first time in vitro. This breakthrough shows the promising potential of tissue engineering in the development of powerful in vitro models of the nervous system. The combination of these models with the use of human adult neurons and glial cells obtained from the differentiation of neural precursor cells isolated from accessible tissues from patients (skin, fat, bone marrow), opens promising perspectives to better understand -neurodegenerative diseases.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 teacher head, 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".