Standardizing Nasal Fluid Processing for Respiratory Biomarker Analysis: A Comparative Study of Homogenization and Storage Methods
Bibliographic record
Abstract
ABSTRACT Background Nasal fluid is a valuable medium for biomarker analysis in respiratory diseases due to its accessibility and proximity to relevant pathophysiological processes. However, its heterogeneous nature presents challenges for consistent biomarker detection and quantification. This study aimed to improve protein biomarker recovery and reliability by evaluating different homogenization and storage methods for nasal fluid samples. Methods Mechanical disruption techniques using syringes and commercial BioMasher units were compared for their effectiveness in analyte recovery, and the impact of pre- and post-processing of cryogenically stored nasal samples was assessed. Inflammatory protein biomarkers relevant to respiratory diseases, including interleukin (IL)-13, IL-8/ CXCL8, myeloperoxidase (MPO), CCL11/ eotaxin-1, CCL26/ eotaxin-3, elastase, myxovirus resistance protein 1 (MxA), CCL17/ TARC, CXCL10/ IP-10), and serum amyloid A (SAA), were successfully detected in the processed nasal fluid by ELISA and Luminex assays. Results Significant differences in analyte recovery were observed between homogenization methods, with greater titers of IL-5 and eotaxin-1 measured in samples processed by the syringing method. However, homogenization could be performed either before or after long-term cryogenic storage without significantly affecting protein concentrations. Spike and recovery experiments were also conducted and differences in SAA protein recovery were detected, suggesting that the matrix effects in nasal fluid can affect cytokine recovery, but variations in recovery were not significant for other biomarkers tested. Conclusions These findings demonstrate the potential of nasal fluid as a readily accessible medium with substantial potential to aid diagnosis and monitoring of respiratory diseases. However, given the significant impact homogenization protocols had on analyte recovery, the growing use of nasal fluid as a biospecimen in the respiratory field highlights the critical need for standardized protocol to ensure accuracy and reproducibility in future analyses.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".