Dynamic Neutrophil Subsets and Function in Lung Transplant Recipients: Insights from a One-Year Longitudinal Pilot Study
Bibliographic record
Abstract
Introduction: Neutrophils are key innate immune cells in peripheral blood. In recent years, sub-populations of neutrophils have been identified: In addition to the normal-density neutrophils (NDN) in both healthy subjects and disease, low-density neutrophils (LDN) were described in chronic inflammation and cancer. In lung transplant (LTx), neutrophils play crucial roles in reperfusion injury, acute rejection, and chronic lung allograft dysfunction. Our pilot study examines neutrophil subsets and function in LTx recipients during the first post-transplant year. Methods: We collected blood from 11 LTx recipients at various intervals. LDN and normal-density neutrophils (NDN) were isolated. NDN's reactive oxygen species (ROS) production was measured post-PMA activation using Luminol-HRP assay. Neutrophil phenotypic markers were analyzed with flow cytometry. Results: The LDN-to-NDN ratio increased at 3 and 6 months post-transplant. Expression levels of CD62-L (aging marker), PDL-1 (immune checkpoint), CD15 (maturation), and CXCR4 (homeostasis regulator) showed modulation. Interestingly, ROS production by NDN was mildly elevated at baseline, reduced at 6 months, and returned to baseline levels by 9 months post-transplant. Conclusion: Neutrophils exhibit dynamic changes in the first post-LTx year. Investigating neutrophil plasticity could reveal clinically relevant biomarkers and facilitate the development of diagnostic and therapeutic tools in LTx.
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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".