Cytokine profile in lung transplant recipients with <i>Aspergillus</i> spp colonization
Bibliographic record
Abstract
We studied cytokine profiles in BAL of LTRs with Aspergillus spp colonization who did not progress to IPA in the absence of antifungal prophylaxis. This was a retrospective, single center case-control study. BAL samples were analyzed for cytokines. Patients with Aspergillus spp in BAL who did not receive prophylaxis and did not develop IPA were compared to LTRs with Aspergillus spp that received prophylaxis, LTRs with IPA and controls. Twenty-one patients with Aspergillus colonization who did not develop IPA, seven patients with suspected IPA who received prophylaxis, 4 IPA and 19 controls were included. IPA group had significantly higher levels (median [IQR]) of MIP-1 beta compared to the Suspected IPA group (5 vs 5 P: 0.03). The Suspected IPA group had significantly higher levels of IL-12 (11.38 vs 1 P: 0.0001), IL-1 RA (86.11 vs 23.98 P: 0.0118), IP-10 (22.47 vs 0.86 P: 0.0151), HGF (40.92 vs 16.82 P: 0.0055), and MIG (169.62 vs 5 P: 0.0005) than Colonization group. We have identified a unique cytokine signature in patients with Aspergillus colonization that do not develop IPA. Our study forms basis for a larger study to use these cytokines profile to identify patients at a lower risk of developing IPA.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 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".