Rapid Cytological Diagnosis With Evaluation of Pre- and Post-Therapeutic Fungal Morphological Characteristics in Mucormycosis
Bibliographic record
Abstract
Background: Mucormycosis necessitates rapid diagnosis and treatment. Microscopy and culture have been considered the gold standard for diagnosis but both take time of 3 - 5 days. KOH mount is another method for fungal identification that takes 1 - 2 h, but it has its own limitations. This study evaluated crush smear as a means of rapid cytological diagnosis. Methods: Biopsy tissue (pre-treatment) from clinically suspicious mucormycosis patients (n = 52) was received in normal saline and crush/imprint smears were prepared; the remaining tissue was processed as routine biopsy specimen. After the rapid initial cytological identification, the patients were managed according to the standard clinical protocol. Random post-therapeutic biopsy samples of some of these patients (n = 19) were also obtained and again evaluated cytologically. Results: Crush smears showed sensitivity/specificity of 77.7%/75.0% with histopathology and 72.2%/62.5% with culture, respectively, while KOH mount had values of 71.4%/70.5% with histopathology and 79.3%/69.5% with culture, respectively. Degenerative fungal morphological characteristics and cellular inflammatory infiltrate (predominantly neutrophilic) in the vicinity of fungal hyphae were compared in pre- and post-treatment groups, and we found a statistically significant difference (P < 0.05) between them. Conclusion: Our preliminary results suggest that crush smear cytology is a simple, rapid, cost-effective and easily available method for diagnosing mucormycosis. Moreover, crush smears also demonstrated morphological alteration in hyphal structure and accompanying immune cell infiltration which may provide valuable insights into mechanism of therapy/host immune response against fungal pathogen.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".