Decreased expression of GRIM-19 and its association with high-risk HPV infection in cervical squamous intraepithelial neoplasias and cancer
Bibliographic record
Abstract
PURPOSE: GRIM-19 has been shown to be down-regulated in cervical cancers. This study investigated the expression of GRIM-19 in cervical intra-epithelial neoplasias and its association with high-risk human papillomavirus (HR-HPV) infection. METHODS: The expression of GRIM-19 was assessed in cervical exfoliated cells and cervical intra-epithelial neoplasia tissues by immunohistochemistry, and the level of GRIM-19 was also evaluated by Western blotting using cervical exfoliated cells. HR-HPV infection of cervical exfoliated cells was detected by HC II. RESULTS: GRIM-19 is predominantly expressed in the cytoplasm of the middle layer of normal cervical epithelial cells, whereas the surface layer cells of the normal cervix showed no GRIM-19 expression. The expression of GRIM-19 gradually decreased from atypical squamous cells of undetermined significance (ASCUS), low-grade squamous intraepithelial lesion (LSIL) and high-grade squamous intraepithelial lesion (HSIL) to squamous cell carcinoma (SCC); a pattern which was also observed in cervical intra-epithelial neoplasias tissues. The reduced expression of GRIM-19 was correlated with HR-HPV infection. CONCLUSION: GRIM-19 may regulate the differentiation of normal cervical tissue, and a decrease in GRIM-19 may be the result of HR-HPV infection, which in turn leads to the malignant transformation of the cells.
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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 | 0.001 |
| 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) | 0.002 | 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".