Characterization of tenascin-C as a novel biomarker for asthma: utility of tenascin-C in combination with periostin or immunoglobulin E
Bibliographic record
Abstract
BACKGROUND: Extracellular matrix proteins tenascin-C (TNC) and periostin, which were identified as T-helper cell type 2 cytokine-induced genes in human bronchial epithelial cells, accumulate in the airway basement membrane of asthmatic patients. Although serum periostin has been accepted as a type 2 biomarker, serum TNC has not been evaluated as a systemic biomarker in asthma. Therefore, the objective of this study was to evaluate whether serum TNC can serve as a novel biomarker for asthma. METHODS: We evaluated 126 adult patients with mild to severe asthma. Serum TNC, periostin, and total IgE concentrations were quantified using enzyme-linked immunosorbent assays. RESULTS: Serum TNC levels were significantly higher in patients with severe asthma and high serum total IgE levels. Patients with both high serum TNC (> 37.16 ng/mL) and high serum periostin (> 95 ng/mL) levels (n = 20) or patients with both high serum TNC and high serum total IgE (> 100 IU/mL) levels (n = 36) presented higher disease severity and more severe airflow limitation than patients in other subpopulations. CONCLUSIONS: To our knowledge, this is the first study to show that serum TNC levels in asthmatic patients are associated with clinical features of asthma and that the combination of serum TNC and periostin levels or combination of serum TNC and total IgE levels were more useful for asthma than each single marker, suggesting that serum TNC can serve as a novel biomarker for asthma.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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 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".