Preparation of Non-human Primate Brain Tissue for Pre-embedding Immunohistochemistry and Electron Microscopy
Bibliographic record
Abstract
Despite all the technological advances at the light microscopy level, electron microscopy remains the only tool in neuroscience to examine and characterize ultrastructural and morphological details of neurons, such as synaptic contacts. Good preservation of brain tissue for electron microscopy can be obtained by rigorous cryo-fixation methods, but these techniques are rather costly and limit the use of immunolabeling, which is crucial to understand the connectivity of identified neuronal systems. Freeze-substitution methods have been developed to allow the combination of cryo-fixation with immunolabeling. However, the reproducibility of these methodological approaches usually relies on costly freezing devices. Moreover, achieving reliable results with this technique is very time-consuming and skill-challenging. Hence, the traditional chemically fixed brain, particularly with acrolein fixative, remains a time-efficient and low-cost method to combine electron microscopy with immunohistochemistry. Here, we provide a reliable experimental protocol using chemical acrolein fixation that leads to the preservation of primate brain tissue and is compatible with pre-embedding immunohistochemistry and transmission electron microscopic examination.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.012 |
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 source (direct Gemma or distilled Codex), 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".