Evaluation of Decalcification Techniques for Rat Femurs Using HE and Immunohistochemical Staining
Bibliographic record
Abstract
Aim . In routine histopathology, decalcification is an essential step for mineralized tissues. The purpose of this study is to evaluate the effects of different decalcification solutions on the morphological and antigenicity preservation in Sprague Dawley (SD) rat femurs. Materials and Methods . Four different decalcification solutions were employed to remove the mineral substances from rat femurs, including 10% neutral buffered EDTA, 3% nitric acid, 5% nitric acid, and 8% hydrochloric acid/formic acid. Shaking and low temperature were used to process the samples. The stainings of hematoxylin-eosin (HE) and immunohistochemical (IHC) were employed to evaluate the bone morphology and antigenicity. Key Findings . Different decalcification solutions may affect the quality of morphology and the staining of paraffin-embedded sections in pathological examinations. Among four decalcifying solutions, 3% nitric acid is the best decalcifying agent for HE staining. 10% neutral buffered EDTA and 5% nitric acid are the preferred decalcifying agents for IHC staining. Significance . The current study investigated the effects of different decalcifying agents on the preservation of the bone structure and antigenicity, which will help to develop suitable protocols for the analyses of the bony tissue.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".