Blood and Salivary Amphiregulin Levels as Biomarkers for Asthma
Bibliographic record
Abstract
Background: Amphiregulin (Areg) expression in asthmatic airways and sputum was shown to increase and correlate with asthma severity. However, no studies were carried out to evaluate the Areg level in blood and saliva of asthmatic patients. Objective: To measure circulating Areg protein concentrations in blood and saliva from asthmatic patients and correlate its levels with asthma severity. Methods: Plasma and Saliva Areg protein concentrations were measured using ELISA in mild, moderate, and severe asthmatic patients compared to healthy controls. Primary asthmatic bronchial epithelial cells and fibroblasts were assessed for Areg mRNA expression and soluble Areg in their conditioned media. Tissue expression of Areg was evaluated using immunohistochemistry of bronchial biopsies from asthmatic patients and healthy controls. Results: Asthmatic patients had higher Areg protein levels in blood and saliva compared to control subjects. Higher mRNA expression in primary bronchial epithelial cells and higher Areg immunoreactivity in bronchial biopsies were also observed. Both blood and saliva Areg levels showed positive correlations with allergic rhinitis status, atopy status, eczema status, plasma periostin, neutrophilia, Montelukast sodium use, ACT score, FEV1, and FEV1/FVC. Areg levels can differentiate the healthy controls from non-severe asthmatic subjects with good sensitivity and specificity. Conclusion: Areg levels measured in a minimally-invasive blood sample and a noninvasive saliva sample of asthmatic patients can serve as a putative asthma biomarker. Clinical Implications This is the first report to suggest the ability of blood and saliva Areg levels to differentiate between healthy and asthmatic subjects. Therefore, Areg could be used as an adjunct bedside biomarker to support the diagnosis of asthma, specifically in non-severe cases.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".