Nuclear and Nucleolar Morphometric Changes in MGP Stained Oral Epithelial Cells In Cigarette Smokers and Non Smokers
Bibliographic record
Abstract
Aim: To compare nuclear and nucleolar morphometric changes in methyl green pyronin stained oral epithelial cells in cigarette smokers, passive smokers and non smokers Study design: A comparative cross sectional study Place and duration: Carried out in Postgraduate Medical Institute, Lahore. Duration was 6 months, March 2022 to August 2022. Methods: 120 subjects were equally divided into three main groups, cigarette smokers, passive smokers and non smokers, each fulfilling the inclusion and exclusion criteria. Buccal smears were collected by exfoliative cytology of mucosa and then stained by methyl green pyronin stain. The quantitative nuclear morphometric parameters i.e., nuclear and nucleolar diameter and area, and number of nucleoli, were measured. Results: Themedian of all nuclear parameters of smokers was significantly higher as compared to passive smokers and non-smokers in MGP stain (p ≤ 0.05). Further, effect of duration of smoking on morphometric variables showed significant (p< 0.05) results amongst the smokers in MGP stain. However, no significant differences were seen in all morphometric parameters with respect to frequency of cigarette smoking. Conclusion: The study confirms nuclear morphometric changes in oral epithelial cells of cigarette smokers indicating that smoking does influence cellular alterations. The simplicity of cytology technique with the use of methyl green pyronin stain is valuable screening tool for detecting early oral mucosal changes in high risk patients. Keywords: smoking, exfoliative cytology, nuclear morphometric parameters, methyl green pyronin stain.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".