β-glucan enhances LPS-induced acute lung injury via alveolar macrophages reprogramming
Bibliographic record
Abstract
Introduction: Acute respiratory distress syndrome is responsible for 400,000 deaths per year worldwide. Despite five decades of research, no immunomodulatory treatment has been proven to be effective. Trained immunity (TI) is the ability for the innate immune system to be reprogrammed by a first insult, without persistent inflammation, then providing an increased response to a second insult. The aim of this study is to confirm that TI could protect from acute lung injury (ALI). Methods: twelve weeks old C57BL/6J mice were intra-nasally instilled with 50μg and 100μg of LPS and with 50μg of poly(I:C), seven or thirty days after intra-peritoneal injection of a training agent (1mg of β-1,3-(D)-glucan). Alveolar macrophages (AM) depletion was obtained by intra-nasal clodronate instillation. AM adoptive transfer was performed in Csf2rb-/- pups. ALI was assessed by lung imaging, histology, alveolar-capillary permeability, pro-inflammatory cytokines production and inflammatory cells recruitment. AM reprogramming was assessed ex vivo by pro-inflammatory cytokines production, metabolism exploration and transcriptomics. Results: Lung injury is significantly increased in β-glucan trained mice compared to controls in LPS and poly(I:C) models with long-term persistence. Depletion of AM alleviates this increase in ALI which is restored after adoptive transfer of trained AM, demonstrating the direct role of AM in increased inflammatory responses. AM reprogramming was confirmed ex vivo with increased pro-inflammatory cytokines production after LPS stimulation and switch to glycolytic metabolism. Conclusion: β-glucan reprograms alveolar macrophages promoting acute lung injury after LPS challenge.
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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".