Analysis of axonal growth in organotypic neural cultures
Bibliographic record
Abstract
Organotypic cultures are multicellular in vitro models that preserve both cytoarchitecture and cell interactions that form the tissue, providing a closer approximation to in vivo models in comparison with dissociated cell cultures.Previous studies in our lab proposed a method of dorsal root ganglia \(DRG) and spinal cord slice \(SC) organotypic 3D cultures to study motor and sensory axonal regeneration.Although these models are useful to study how different factors and substrates affect axonal growth, manual sample analysis can be inaccurate, tiresome and high time-consuming.Therefore, we have developed a computer-aided method, using the Neurite-J plug-in, to analyze the neurite outgrowth in organotypic cultures, that can also be applied to other types of explants.This program, implemented as a plug-in for ImageJ software, markedly reduces the time needed in the manual analysis, improves the accuracy and increases the amount of information obtained from each sample.Therefore, these organotypic 3D cultures and the computed aired method are a powerful and useful tool to obtain valuable data of neurite growth in different conditions.The objective of the present work is to provide the protocol of our DRG and SC slice cultures, from the animal to the image analysis, that will allow studying neurite outgrowth in a reliable in vitro model.See gure in Figures section.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".