A New Pre-Embedding Immunogold Method That Permits to Obtain a Very High Signal with a Very Good Ultrastucture
Bibliographic record
Abstract
Abstract The ultimate goal of the immunocytochemistry is to get the best signal with the lowest background while preserving ultrastructure. Unfortunaly too often the experimentator faces a compromise between density of the signal and ultrastructure preservation (1). with the advent of hight resolution confocal light microscopy, two photon imaging and other new technologies there is a need to provide the best immunocytochemical complementary results with the high resolution of the electron microscope. But often for exemple the high level of labelling observed by immunofluorescence microscopy is not matched by immunocytochemistry (2). One approach to obtain comparable levels of labelling is to use comparable protocols. We propose here such a new immunogold method. to illustrate our point we used a very convenient biological material, the adherent cells grown on 35 mm plastic culture dishes. The following procedure was applied : Confluent endothelial cells were fixed in situ for 3 hours with the following freshly prepared and filtered solution : 1 % 1-lysine, 4% paraformaldehyde, 0.04% glutaraldehyde, 0.25% sodium metaperiodate in 0.04M sodium cacodylate buffer, pH 7.4.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".