An Evaluation of SPARC Protein as a Serum Biomarker of Chronic Rhinosinusitis
Bibliographic record
Abstract
OBJECTIVE: Precision medicine initiatives for chronic rhinosinusitis (CRS) management suggest tailoring treatment to the patient's individual disease profile; however, serum biomarkers for evaluation of disease activity or predicting response to therapy are lacking in CRS. Epithelial-to-mesenchymal transition (EMT) has been described as a component of barrier dysfunction in CRS. SPARC (secreted protein acidic and rich in cysteine) is a marker of EMT that has previously been identified in sinus epithelium by gene expression profiling. We wished to determine if SPARC could represent a serum biomarker for CRS by verifying (1) if SPARC could be detected in serum, (2) whether levels were sensitive to disease burden reduction following surgery, and (3) if it could predict response to therapy. STUDY DESIGN: Prospective. SETTING: Tertiary care center. SUBJECTS: Patients with CRS undergoing endoscopic sinus surgery (ESS). METHODS: Twenty-six patients undergoing ESS for CRS were prospectively recruited. Serum was collected at the time of surgery and 4 months following ESS and SPARC level measured using enzyme-linked immunosorbent assay. Postoperative outcome was characterized as "remission" or "unfavorable" based on symptomatology and endoscopy. RESULTS: SPARC could be detected and measured in serum in all subjects. Following ESS, SPARC levels decreased by 33% ( P = .005) but did not predict evolution at 4 months postsurgery ( P = .94). CONCLUSION: SPARC may be an interesting serum biomarker of disease activity in CRS, as it can be reliably measured and decreases following successful reduction of disease burden after surgery. However, it does not predict post-ESS evolution, suggesting that the link between EMT and outcome is not linear.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".