Advancing Non-Invasive Respiratory Diagnostics: Multiplex Nasal Biomarker Profiling for Stratification of Airway Inflammatory Diseases
Bibliographic record
Abstract
ABSTRACT This study explores the potential of nasal secretions to serve as a source of biomarkers for diagnosing respiratory conditions. A total of 40 inflammatory biomarkers were detected and quantified in participants with upper respiratory diseases, including chronic rhinosinusitis (CRS), allergic rhinitis, and viral and bacterial infections. The different expression levels of various biomarkers could distinguish CRS participants with and without nasal polyps (i.e. CRP, GzmB, IL-4, MMP-1, MMP-8, SAA and TREM-1), and healthy and rhinitis participants (i.e. CRP, EGF, Eotaxin-1, Fractalkine, IL-1RA, IL-5, IP-10 and TRAIL). CRP, G-CSF, GzmA, IL-1, IL-2, IL-5, IL-8, IL-9, MMP-1, TNFa and TREM-1 protein expression differed between the healthy, viral and bacterial-infected individuals, and EGF, G-CSF, MCP-1, MIP1a and MIP-1b protein expression differed between type 2 and non-type 2 inflammatory cohorts. Significant correlations were also noted between SNOT-22 scores and specific cytokines, such as IP-10 and TRAIL. Despite the heterogeneity of patient diagnoses, these findings highlight the potential of nasal fluid as a readily accessible reflection of respiratory health. Future studies with larger cohorts and standardized methodologies are needed to validate these biomarkers and potentially enable precision diagnosis and improved treatment of respiratory conditions.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".