Alpha-fetoprotein as a tumor marker in hepatocellular carcinoma: investigations in south Indian subjects with hepatotropic virus and aflatoxin etiologies
Bibliographic record
Abstract
OBJECTIVES: The prevalence of hepatitis B virus (HBV) is reportedly the main cause of hepatocellular carcinoma (HCC) in India, where hepatitis C virus (HCV)-associated HCC is believed to be relatively less prevalent. We verified the usefulness of alpha-fetoprotein (AFP) as a tumor marker and analyzed the influence of viral etiology on AFP levels in HCC. METHODS: Of a total of 1012 cases with liver disease, 202 were investigated for the presence of AFP (142 HCC cases, 30 cirrhosis cases, and 30 chronic liver disease (CLD) cases). In addition, serum samples from 30 healthy patients, 30 hepatitis B surface antigen (HBsAg) carriers, and 30 acute viral hepatitis cases were included as controls. AFP was quantitatively determined using a commercial ELISA (Quorum Diagnostics, Canada). Out of the 142 HCC cases screened for AFP, aflatoxin B1 (AFB1) detection was carried out in 38 HCC cases using an in-house immunoperoxidase test. RESULTS: In HBV and HCV co-infected HCC cases, the AFP positivity was 85.7%. In HBV alone-associated HCC, the positivity was 62.9%, and 54.5% of AFB1 positive HCC cases showed AFP positivity. In HBV and HCV negative HCC cases, the positivity was 20.5%, and in HCV-associated HCC it was 17.6%. The HBV/HCV co-infected group and HBV alone positive HCC cases had significantly elevated levels of AFP. When AFP positivity was analyzed based on the marker profile of HBV, 89.7% of AFP positive cases were HBV-DNA positive. CONCLUSIONS: The overall positivity pattern of AFP in HCC does indicate that higher levels of AFP are observed with hepatitis virus positivity, especially with HBV. Further studies must be carried out to correlate the serum levels of AFP with the size, number, and degree of differentiation of HCC nodules.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".