HIF-1alpha in lung carcinoma: Histopathological evidence of hypoxia targets in patient biopsies
Bibliographic record
Abstract
Background: Hypoxia inducible factor 1alpha (HIF-1alpha) plays a major role in the response of tumors to hypoxia, and contributes to tumor aggressiveness, invasiveness and resistance to radiotherapy and chemotherapy. Targeting HIF-1alpha is an attractive strategy, with the potential for disrupting multiple pathways crucial for tumor growth. Thus, the evaluation of HIF-1alpha in patient biopsies could be useful in personalized cancer treatment. Methodology: The current literature on HIF-1alpha immunohistochemical expression is reviewed along with the relation to clinical outcome and prognosis. In addition, the significant correlation of HIF-1alpha to vascular endothelial growth factor (VEGF) expression is reported, as well as the possible role of HIF-1alpha in predicting the therapeutic response to anti-EGFR therapies. Conclusion: Herein, an overview of the HIF-1alpha expression in lung carcinoma is presented. Since there is no consensus regarding the assessment of HIF-1alpha in tissue specimens, heterogeneous results have been reported especially regarding prognosis. In this context, methods to optimize the evaluation of HIF-1alpha in biopsies are needed in order to clarify the role of HIF-1alpha as a prognostic or predictive biomarker in lung carcinoma.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".