Novel Blood-based Transcriptional Biomarker Panels Predict the Late-Phase Asthmatic Response
Bibliographic record
Abstract
RATIONALE: The allergen inhalation challenge is used in clinical trials to test the efficacy of new treatments in attenuating the late-phase asthmatic response (LAR) and associated airway inflammation in subjects with allergic asthma. However, not all subjects with allergic asthma develop the LAR after allergen inhalation. Blood-based transcriptional biomarkers that can identify such individuals may help in subject recruitment for clinical trials as well as provide novel molecular insights. OBJECTIVES: To identify blood-based transcriptional biomarker panels that can predict an individual's response to allergen inhalation challenge. METHODS: We applied RNA sequencing to total RNA from whole blood (n = 36) collected before and after allergen challenge and generated both genome-guided and de novo datasets: genes, gene-isoforms (University of California, Santa Cruz, UCSC Genome Browser), Ensembl, and Trinity. Candidate biomarker panels were validated using the NanoString platform in an independent cohort of 33 subjects. MEASUREMENTS AND MAIN RESULTS: The Trinity biomarker panel consisting of known and novel biomarker transcripts had an area under the receiver operating characteristic curve of greater than 0.70 in both the discovery and validation cohorts. The Trinity biomarker panel was useful in predicting the response of subjects that elicited different responses (accuracy between 0.65 and 0.71) and subjects that elicit a dual response (accuracy between 0.70 and 0.75) upon repeated allergen inhalation challenges. CONCLUSIONS: Interestingly, the biomarker panel containing novel transcripts successfully validated compared with panels with known, well-characterized genes. These biomarker-blood tests may be used to identify subjects with asthma who develop the LAR, and may also represent members of novel molecular mechanisms that can be targeted for therapy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".