Quantitative Caveats of Standard Immunohistochemical Procedures: Implications for Optical Disector–based Designs
Bibliographic record
Abstract
Immunohistochemistry is a ubiquitous technique in histology. Often, the goal of such studies is the quantification of some parameter associated with a particular antigen. When used correctly, the optical disector offers a statistically relevant approach to achieve this goal without bias from cell size, shape, or orientation. This three-dimensional counting probe is virtually embedded within the depth of the tissue section, thus avoiding sampling near the cut surfaces of the section, where cells are often lost during the cutting and subsequent processing steps. It follows that the probability that a cell could be immunolabeled should be equal throughout the section depth to correctly employ the optical disector. In this report, we demonstrate that parameters commonly used in immunohistochemistry often leave the middle of the section unlabeled. Furthermore, the degree of incomplete penetration varies among antibodies but can be overcome in some cases by extending the incubation time of the secondary antibody. The detection of this phenomenon in immunofluorescence preparations and the implications of these findings for quantitative stereology using the optical disector are discussed.
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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 | 0.269 | 0.403 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.009 | 0.007 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".