Upper and Lower Respiratory Tract Compartmentalization in Pediatric Stem Cell Transplantation
Bibliographic record
Abstract
RATIONALE: Lung injury after hematopoietic stem cell transplantation (HCT) occurs due to infection, chemotherapy toxicity, and alloreactive inflammation. Analyses of bronchoalveolar lavage (BAL) fluid have revealed dominant pathobiologic signatures, but minimally-invasive diagnostics are needed. OBJECTIVES: To determine whether microbiome and gene expression perturbations are shared along the respiratory tract or isolated to the alveoli in pediatric HCT patients with lung injury. METHODS: We performed bulk RNA sequencing on 206 paired nasal and BAL samples from 160 HCT patients and 17 healthy controls enrolled at 28 children's hospitals (2016-2025). Microbial and human transcripts were compared using multivariable models accounting for age, sex, and paired sampling. MEASUREMENTS AND MAIN RESULTS: HCT BAL and nasal transcriptomes differed across 13,698 genes, 48 cellular components, and network interactions linking inflammation, reactive oxygen species, and immunometabolism. Minimal BAL-nasal correlation was observed in gene expression levels (median ρ = 0.03, IQR -0.03 to 0.08) or fractional abundance of key cells such as neutrophils and CD8 + T-cells. BAL microbiomes harbored fewer commensal bacteria and more fungi and DNA viruses. BAL bacterial RNA was associated with diminished immune signaling whereas nasal bacterial RNA aligned with inflammatory gene expression. Further, only BAL microbial RNA was linked to transcriptional shifts in epithelial injury response, keratinization, and collagen deposition. Finally, BAL commensal microbiome depletion, epithelial injury, and immune dysregulation signatures were associated with death or prolonged mechanical ventilation, whereas nasal samples provided minimal prognostic information. CONCLUSIONS: These data support alveolar compartmentalization in pediatric HCT and emphasize the ongoing need for minimally-invasive but informative diagnostics.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".