Delineation of disease phenotypes associated with esophageal adenocarcinoma by MALDI-IMS-MS analysis of serum N-linked glycans
Bibliographic record
Abstract
N-Linked glycans, extracted from patient sera and healthy control individuals, are analyzed by Matrix-assisted laser desorption ionization (MALDI) in combination with ion mobility spectrometry (IMS), mass spectrometry (MS) and pattern recognition methods. MALDI-IMS-MS data were collected in duplicate for 58 serum samples obtained from individuals diagnosed with Barrett's esophagus (BE, 14 patients), high-grade dysplasia (HGD, 7 patients), esophageal adenocarcinoma (EAC, 20 patients) and disease-free control (NC, 17 individuals). A combined mobility distribution of 9 N-linked glycans is established for 90 MALDI-IMS-MS spectra (training set) and analyzed using a genetic algorithm for feature selection and classification. Two models for phenotype delineation are subsequently developed and as a result, the four phenotypes (BE, HGD, EAC and NC) are unequivocally differentiated. Next, the two models are tested against 26 blind measurements. Interestingly, these models allowed for the correct phenotype prediction of as many as 20 blinds. Although applied to a limited number of blind samples, this methodology appears promising as a means of discovering molecules from serum that may have capabilities as markers of disease.
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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 | 0.000 |
| 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) | 0.000 | 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".