Early proteomic analysis may allow noninvasive identification of hepatitis C response to treatment with pegylated interferon α-2b and ribavirin
Bibliographic record
Abstract
BACKGROUND AND AIM: Chronic hepatitis C virus (HCV) infection represents a significant disease burden worldwide. Approximately 170 million people are chronically infected. HCV can lead to liver fibrosis, cirrhosis and hepatocellular carcinoma. Current standard treatment with pegylated interferon and ribavirin is suboptimal and up to 60% of patients fail to respond. Week 4 and 12 HCV RNA is used as a marker of response with nonresponders at 12 weeks discontinuing treatment. Earlier identification of nonresponders using novel biomarkers would be beneficial in preventing unnecessary toxicities and cost. This study profiled the proteomic response to treatment in HCV patients within the first 24 h using surface-enhanced laser desorption-ionization time-of-flight mass spectrometry (SELDI-TOF MS). METHODS: Serum from 25 HCV infected individuals during the initial 24 h of treatment was profiled using SELDI-TOF MS. Arrays were analyzed on the ProteinChip Reader and time-of-flight spectra were generated. Peak detection was performed by Biomarker Wizard software and analyzed using BioConductor packages. RESULTS: Significant differences were seen between the proteomic profiles of responders and nonresponders to treatment. Overall 70 peaks differentiated responders from nonresponders. A random forest classifier identified a panel of 20 peaks, which differentiated responders from nonresponders with 87.4% accuracy. The CM10 chip revealed 16 peaks identifying genotype 1 responders from nonresponders. CONCLUSION: This study identifies early proteomic spectra as potential predictors of HCV treatment response using SELDI-TOF MS. This illustrates the importance of early biomarkers in the prediction of response within the first 24 h, which may aid in tailoring HCV treatment regimens.
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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) | 0.001 | 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) | 0.002 | 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".