Plasma proteomics can discriminate isolated early from dual responses in asthmatic individuals undergoing an allergen inhalation challenge
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE: This proteomics study was designed to determine the utility of iTRAQ MALDI-TOF/TOF technology to compare plasma samples from carefully phenotyped mild, atopic asthma subjects undergoing allergen inhalation challenge. EXPERIMENTAL DESIGN: Eight adult subjects with mild, allergic asthma (four early responders (ERs) and four dual responders (DRs)) participated in the allergen inhalation challenge. Blood samples were collected prior to and 2 h after the inhalation challenge. Sixteen plasma samples (two per subject), technical replicates, and pooled controls were analyzed using iTRAQ. Technical validation was performed using LC-MRM/MS. Moderated robust regression was used to determine differentially expressed proteins. RESULTS: Although this study did not show significant differences between pre- and post-challenge samples, discriminant analysis indicated that certain proteins responded differentially to allergen challenge with respect to responder type. At pre-challenge, fibronectin was significantly elevated in DRs compared to ERs and remained significant in the multiple reaction monitoring validation. CONCLUSIONS AND CLINICAL RELEVANCE: This proof of principle demonstration has shown that iTRAQ can uncover differences in the human plasma proteome between two endotypes of asthma and merits further application of iTRAQ to larger cohorts of asthma and other respiratory diseases.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 it