Electronic nose versus quadrupole mass spectrometry for identifying viral hepatitis C patients
Bibliographic record
Abstract
Abstract Hepatitis C is a leading cause of liver disease and transplantation and is a significant burden on public health worldwide. This study aimed to apply the Electronic Nose (E‐Nose) and quadrupole Mass Spectrometry (MS/MS) technologies for screening blood samples from hepatitis C patients and healthy controls. We analysed volatile organic compounds (VOCs) in the headspace over blood samples to identify those VOCs characteristic for diagnosing hepatitis C patients. The study comprised 150 acute hepatitis C patients with age range: 24–59 years, and mean age ±SD: 41.5 ± 12.8 years and 150 age‐matched healthy controls (age range: 24–51 and mean age: 40.11 ± 4.89 years) from the Hospital of the Medical Research Institute, Alexandria University, Alexandria, Egypt. Collected blood samples were analysed qualitatively and quantitatively using the E‐Nose and MS/MS techniques, respectively. Principal component analysis of the E‐Nose 10‐sensor responses accurately classified blood samples from hepatitis C patients and healthy controls. The first two principal components explained over 98.35% of the variance in signals with no false‐positive (healthy controls) or false‐negative (hepatitis C patients) results. MS/MS showed two fragmentation ions at m / z of 104 and 151 Da with the positive electrospray ionization mode (ESI+) in blood samples for hepatitis C patients, but not for healthy controls or background water samples. We identified the two specific fragmentation ions at m / z 104 and m / z 151 Da as malonic acid (MF: C 3 H 4 O 4 ; MW: 104.06 g/mol) and monosaccharide pentose (MF: C 5 H 10 O 5 ; MW: 150.13 g/mol) in VOCs of the headspace over blood samples for hepatitis C patients. This provides a rationale for developing diagnostic tests for hepatitis C virus based on altered trace VOCs concentrations using the relatively inexpensive, easy‐to‐use, portable and non‐invasive E‐Nose technology.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".