Hyaluronan (HA) and H11 Antigen (HABP) Expression in Cervical Cancer Sections and Their Significances as Prominent Markers
Bibliographic record
Abstract
This article has been retracted. Background : Cancer is a disease characterized by a population of cells that grow and divide without respect to normal limits, invade and destroy adjacent tissues, and may spread to distant anatomic sites through a process called metastasis . These malignant properties of cancers differentiate them from benign tumors, which are self-limited in their growth and do not invade or metastasize for the development and progression of tumor cells. Cancer is a result of multistep process involving accumulation of genetic alternation, which results in loss of cell-cell interaction, increasing invasive migration, loss of control in cell division and abnormal matrix assembly. Carcinoma of the cervix is one of the most common malignancies. Cervical differentiation requires changes in the composition and structure of extracellular matrix. However, the biological roles of Hyaluronan (HA) in cervical remodeling are still under investigation and HA-receptors are not studied during cervical pathological remodeling. Hence, in the current study we investigated the expression and association of extracellular matrix components namely hyaluronan-hyaluronic acid binding protein (H 11 antigen) (HA-HABP) in normal and invasive lesion in cervical tissue section slides. Methods and Results : Cervical tissues from normal and suspected cancer patients from hospitals were examined. Immunohistochemistry was performed with specific HA probe, b. PG (biotinylated proteoglycan) and anti-HABP antibody H 11 B 2 C 2 for H 11 antigen. HA and H 11 antigen are overexpressed in well-differentiated tumors during progression. Conclusions : We conclude that the expression of HA and H 11 antigen and their interaction are very important in identifying cervical cancers and probably H 11 antigen can be used as a biomarker in identifying the tumor cells in cervical lesions. doi:10.4021/wjon287e
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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".