Histopathology of nosemosis in honey bees: correlation with manual counting and comparison of staining methods
Bibliographic record
Abstract
Nosemosis, caused by Vairimorpha ( Nosema ) ceranae or V. ( Nosema ) apis , is the main fungal disease affecting the Western honey bee ( Apis mellifera ). We evaluated the use of histology in the diagnosis of disease, identified the histologic patterns, and compared the efficacy of different staining techniques. We sampled 10 hives, collecting ~80 bees per hive. Spore counts were performed on 60 bees per sample using a hemocytometer in accordance with the standard procedure. Slides of whole bees were produced from the remaining bees, stained with 15 different techniques, and observed under a light microscope at 400×. Infection in the ventriculus was graded using hematoxylin–phloxine–saffron stain; prevalence and severity of the infection were determined; and an intra-class coefficient (ICC) was calculated to correlate the histologic results with the standard counting method. Based on contrast, specificity, and sensitivity, we found hot Gram chromotrope and Ziehl–Neelsen stains offered the best approach for highlighting Vairimorpha spores. These stains were optimized to find the ideal staining times for Vairimorpha by testing different immersion durations in key steps to enhance spore contrast. There was a notable association between histologic observations and spore count, with an ICC of 0.74 (95% CI [0.36, 0.91]) and 0.82 (95% CI [0.54, 0.93]) for the percentage of infected bees and histologic grade, respectively. Lesions included distension of ventricular epithelial cells, intracellular microsporidia, reduced ciliation, and disintegration of the peritrophic membrane. No spores were detected in extra-ventricular organs.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".