Specificity of Antibodies for the Identification of Annexin 1 (ANXA1) Protein in Various Types of Cancer
Bibliographic record
Abstract
Annexins are a well‐characterized multigene family of phospholipid‐binding and membrane‐bound proteins that are Ca2+‐regulated. ANXA1 expression is variable in tumor cells, ranging from high levels to none, which is why it is helpful to know the staining potential of various antibodies against the protein. Our research aims to evaluate the specificity of five antibodies used to detect ANXA1 expression in selecting different types of cancer and normal tissue and the differential specificity between the two groups. We examined a data set composed of 2177 samples from The Human Protein Atlas (HPA) as mentioned, cancer patients:1904 samples, and healthy persons: 273 samples. We examined the specificity of five different antibody types of detecting ANXA1 expression (HPA011271, HPA011272, CAB013023, CAB035987, and CAB058693). The specificity of each antibody against ANXA1 protein was evaluated using a staining scale of 0 ‐ 3.00, where 0 indicates no staining, 0.01‐1.0 low staining, 1.01 ‐ 2.0 medium staining, and 2.01 ‐ 3.00 high staining. Results CAB013023 had the highest staining values for both cancer and healthy samples; thyroid cancer and endometrial cancer had the highest staining values (2.75 and 2.80, respectively). Breast, head and neck, and bladder normal tissues stained the most intensely in healthy samples (3.00 in all three cases). The antibody's specificity for identifying ANXA1 expression suggests that this may be used as a prognosis and treatment marker in cancer.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".