Aerosol treatment with MNEI suppresses bacterial proliferation in a model of chronicPseudomonas aeruginosa lung infection
Bibliographic record
Abstract
Neutrophil elastase is present at high levels in airway fluid of patients with cystic fibrosis (CF), and is responsible for considerable inflammatory damage. Human monocyte/neutrophil elastase inhibitor (MNEI), a 42-kDa serpin protein, is an effective inhibitor of neutrophil elastase, cathepsin G, and proteinase-3, related proteases released from inflammatory neutrophils. We hypothesized that recombinant MNEI would reduce inflammatory damage and enhance bacterial clearance from the lung in an animal model of chronic Pseudomonas aeruginosa infection. In vitro studies showed that MNEI causes dose-dependent inhibition of the activity of rat neutrophil elastase. Recombinant MNEI was administered daily by aerosolization to rats previously inoculated with agar beads containing P. aeruginosa to initiate chronic infection. Administered MNEI was partially recovered in lavage fluid of treated rats as a 66-kDa complex with protease indicative of in vivo inhibition of elastase or a related protease. Aerosol treatment with MNEI significantly decreased the extent of inflammatory injury, quantified as the histopathology score. MNEI, which had no bactericidal effect on P. aeruginosa in vitro, significantly enhanced clearance of bacteria from infected rat lungs. The reduction of histopathology scores and enhancement of bacterial killing were evident 6 hr after a single aerosol treatment with MNEI. These findings indicate an important function of MNEI in protecting innate antimicrobial defense. Similar results were previously obtained for aerosolized prolastin (alpha1-antitrypsin), indicating that enhanced bacterial clearance by MNEI is due to inhibition of neutrophil protease. These findings demonstrate the value of this nonantibiotic protease inhibitor as an adjunct for the treatment and prevention of the infection component of CF lung disease.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".