Elevated CXCL10 (IP-10) in Bronchoalveolar Lavage Fluid Is Associated With Acute Cellular Rejection After Human Lung Transplantation
Bibliographic record
Abstract
BACKGROUND: CXCL10 (IP-10) is a potent chemoattractant for T cells that has been postulated to play a role in infection and acute cellular rejection (ACR) in animal models. We measured CXCL10 (IP-10) (and other cytokines previously implicated in the pathogenesis of ACR) in the bronchoalveolar lavage (BAL) of lung transplant recipients (LTRs) to determine the association between CXCL10 (IP-10) and ACR in LTRs. METHODS: In a prospective study of 85 LTRs, expression of cytokines (tumor necrosis factor, interferon-γ, interleukin [IL]-6, IL-8, IL-15, IL-16, IL-17, CXCL10 [IP-10], and MCP-1 [CCL2]) in BAL samples (n=233) from patients with episodes of ACR (n=44), infection ("Infect"; n=25), concomitant "Infect+ACR" (n=10), and "No Infect and No ACR" (n=154) were analyzed. RESULTS: The levels of both CXCL10 (IP-10) and IL-16 were significantly increased in histologically proven ACR compared with the "No Infect and No ACR" group (CXCL10 [IP-10]: 107.0 vs. 31.9 pg/mL [P=0.001] and IL-16: 472.1 vs. 283.01 pg/mL [P=0.01]). However, in a linear mixed-effects model, significant association was found only between CXCL10 (IP-10) and ACR. A one-log increase of CXCL10 (IP-10) was associated with a 40% higher risk of ACR (odds ratio, 1.4; 95% confidence interval, 1.12-1.84). CONCLUSION: Higher values of CXCL10 (IP-10) in BAL fluid are associated with ACR in LTRs, suggesting a potential mechanistic role in the pathogenesis of ACR in LTRs. These results suggest that therapeutic strategies to inhibit CXCL10 (IP-10) and or its cognate receptor, CXCR3, warrant investigation to prevent and/or treat ACR in clinical lung transplantation.
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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) | 0.002 | 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".