Expression of integrins and Toll-like receptors in cervical cancer: Effect of infectious agents
Bibliographic record
Abstract
We hypothesized that development of cervical cancer is associated with alterations in the expression of innate immune receptors, i.e. integrins and TLRs, and that these alterations can be induced by infectious agents. We have studied the expression of these proteins in cervical biopsy tissues and cervical cancer-derived cell lines HeLa, CaSki, SiHa, C-33 A, and ME180. Immunohistochemistry analysis demonstrated an increase in integrin αv, β3, β4, and β6 expression in the epithelium during the development of cervical cancer. A clear trend towards higher expression of integrin β6 in cell lines harbouring human papillomavirus (HPV) genetic material, compared to HPV-negative C-33 A, was observed. To investigate whether bacterial infection can alter the expression of TLRs and integrins, we infected HeLa cells by two pathogens, Escherichia coli and Pseudomonas aeruginosa, using a common bacterium of the female genital tract, Lactobacillus reuteri, as a control. Infection with E. coli or P. aeruginosa, but not with L. reuteri, significantly altered the expression of TLR and integrins, particularly of TLR4 and integrin β6. Considering that both integrin β6 and TLR4 play important roles in tumorigenesis, our data suggest that bacterial infection may trigger cancer development in HPV-infected cervical epithelium.
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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 | 0.000 | 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) | 0.001 | 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".