Introduction of a Novel HRP Substrate-Nanogold Probe for Signal Amplification in Immunocytochemistry
Bibliographic record
Abstract
Amplification of immunological signals with catalyzed reporter deposition (CARD) allows improved detection of scarce tissue antigens in light and electron microscopy. The technique takes advantage of the oxidation ability of horseradish peroxidase (HRP), in the presence of hydrogen peroxide, to yield the accumulation of one of its specific reporter-tagged substrates. This immunocytochemical approach continues to be improved by the introduction of new reporter molecules tagged to tyramine or to other HRP substrates. In this study we introduced a novel HRP substrate tagged to Nanogold particles. The amplification protocol is based on the application of a specific primary antibody, a biotinylated secondary antibody, streptavidin-HRP, and an HRP substrate coupled to Nanogold, followed by silver intensification. In addition to amplification of immunological signals of high resolution, direct accumulation of Nanogold particles at target sites by enzymatic activity of HRP improves the efficiency of the technique compared to other amplification protocols. Moreover, this approach combines the CARD amplification potentials with the ultrasmall gold probe and the silver intensification method. Immunolabeling obtained by light and electron microscopy, as well as immunodot assay using this new amplification strategy, appear to be highly sensitive, specific, and of enhanced intensity.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".