Aspartyl (Asparaginyl) β-Hydroxylase AABH as a serum biomarker for colorectal cancer.
Bibliographic record
Abstract
86 Background: Colorectal Cancer (CRC) is the third most common type of cancer diagnosed in the US and Canada. WHO, Canadian Cancer Society (CCS), the National Comprehensive Cancer Network (NCCN) and American Cancer Society (ACS) recommend that men and women begin CRC screening at age 50 or younger if at high risk. Recommended screening procedures: Annual occult fecal blood test (OFBT), a colonoscopy every 5 years, OFBT and colonoscopy every 5 years, or a colonoscopy every 10 years. According to The Surveillance, Epidemiology, and End Results (SEER) Program, only 39% of CRC are diagnosed in stage I, 36% are diagnosed in Stage II, 19% are diagnosed with metastasis. The corresponding 5-year survival rates are 89.8%, 67.7%, and 10.3%. Neither the CCS nor the ACS recommends a blood test be done as part of screening. This is due to the fact that, until now, there has not been a blood test with adequate sensitivity or specificity for screening. Methods: In this study we discovered that Aspartyl (Asparaginyl) β-Hydroxylase (AABH) measurement in serum can be used as an screening test for CRC. AABH has been detected by immunohistochemical staining (IHC) on the cell surface of different cancers including CRC. It has been detected by IHC in > 97% of tumor specimens tested (n > 200) but has not been found in tissue samples from normal individuals. Results: This observation and the observation that AABH is found in the serum of patients with cancer, but not in n0n cancer patients, led us to develop a Sandwich ELISA Assay to measure AABH in serum. In the current study we have quantified AABH levels in CRC patients and compared it with normal individual. Increased levels of AABH were found in the serum of 91.5% of patients with CRC in all different stages of Cancer (n = 60). In normal individuals, AABH was essentially undetectable in serum (n = 30). AABH was identified in serum from patients with CRC irrespective of cancer stage. All serum AABH levels for stages I, II, III and IV were more than 3.3 ng/mL. Conclusions: Thus, our data indicate that by measuring AABH in the serum, we should be able to detect CC at an earlier stage than it is currently detected, resulting to a much better 5-year survival for CC.
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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 | 0.001 | 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".