An optimized immunohistochemistry protocol for detecting the guidance cue Netrin-1 in neural tissue
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Netrin-1, an axon guidance protein, is difficult to detect using immunohistochemistry. We performed a multi-step, blinded, and controlled protocol optimization procedure to establish an efficient and effective fluorescent immunohistochemistry protocol for characterizing Netrin-1 expression. Coronal mouse brain sections were used to test numerous antigen retrieval methods and combinations thereof in order to optimize the stain quality of a commercially available Netrin-1 antibody. Stain quality was evaluated by experienced neuroanatomists for two criteria: signal intensity and signal-to-noise ratio. After five rounds of testing protocol variants, we established a modified immunohistochemistry protocol that produced a Netrin-1 signal with good signal intensity and a high signal-to-noise ratio. The key protocol modifications are as follows: •Use phosphate buffer (PB) as the blocking solution solvent.•Use 1% sodium dodecyl sulfate (SDS) treatment for antigen retrieval. The original protocol was optimized for use with the Netrin-1 antibody produced by Novus Biologicals. However, we subsequently further modified the protocol to work with the antibody produced by Abcam. The Abcam protocol uses PBS as the blocking solution solvent and adds a citrate buffer antigen retrieval step.
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.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it