Evaluating Sinus Microbiology by Transplant Status in Persons With Cystic Fibrosis: A Matched Cohort Study
Bibliographic record
Abstract
Abstract Objective Sinus disease is prevalent in persons with cystic fibrosis (PwCF) and may be a reservoir of airway infection in postlung transplant (pTx) patients. The microbial composition of cystic fibrosis sinuses and its associations with chronic rhinosinusitis (CRS) is relatively unexplored. We aimed to examine the sinus and lower airway microbiome and their associations with CRS in PwCF and pTxPwCF. Study Design Prospective single‐centre study. Setting A total of 31 sex and age (±2 years) matched PwCF and pTxPwCF. Methods Demographic and clinical data along with sinus swabs and sputum were collected. CRS was assessed using Sinonasal Outcome Test‐22 (SNOT‐22) (patient reported outcome) and Lund‐McKay (computed tomography sinus) scores. Samples underwent MiSeq Illumina sequencing of the universal 16S ribosomal RNA gene. Results A total of 31 PwCF (15 pTxPwCF) were included. Aggregate airways microbiome composition was dominated by Pseudomonas (46%), Haemophilus (14%), Staphylococcus (11%), Streptococcus (10%), and Fusobacterium (6%). α‐diversity was significantly lower in post‐Tx samples across both sputum and sinus samples ( P = .005). β‐diversity was significantly different between sputum ( P = .004), but not sinus ( P = .75) samples by transplant status. While there was a trend in higher β‐diversity associated with lower SNOT‐22 score at time of first visit, this did not reach significance ( P = .05). Conclusion Sinus and airway microbiomes differed in PwCF and pTxPwCF, but the prevalent organisms remained consistent. Elucidating the relationship of the microbiome with clinical status to better understand when to intervene accordingly is needed to optimize sinus disease management in PwCF.
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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) | 0.000 | 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) | 0.001 | 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 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".