Surfactant protein SP-D to the rescue of NETosis and NET-induced lung surfactant inactivation
Bibliographic record
Abstract
In response to airway infections caused by pathogens such as bacteria, alveolar neutrophils are activated by the recognition of bacterial components like LPS. As a consequence, neutrophils release neutrophil extracellular traps (NETs) to fight and clear those invading pathogens. The NETosis mechanism has to be regulated because an overaccumulation of NETs can turn into damaging effects, causing pulmonary dysfunction. Surfactant protein SP-D is a lung collectin, which participates in the innate immune defense of the lungs. SP-D binds to certain types of LPS and interacts with NETs. SP-D also interacts with pulmonary surfactant lipids, which are the essential components to facilitate lung mechanics. Up to date, whether SP-D modulates LPS-induced NETosis and NET-related alterations of surfactant function is unknown. Using human neutrophils, purified human SP-D and SP-D-deficient mice, we have showed that SP-D suppresses LPS-induced NETosis, in a LPS-binding dependent manner. Analyses of mouse lung bronchoalveolar lavages have shown that the airways of LPS-instilled SP-D deficient mice have increased NETs and lung surfactant with reduced biophysical activity – impaired lung compliance-, compared to wild type mice. Moreover, NETs inhibit the biophysical functions of lung surfactant, as assessed under physiologically meaningful conditions, and purified SP-D prevents NET-mediated lung surfactant inactivation. Therefore, excess NETs are deleterious for the biophysical activity of surfactant and SP-D protects surfactant from the negative effect of NETs.
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) | 0.001 | 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".