LPS-RS attenuation of lipopolysaccharide-induced acute lung injury involves NF-κB inhibition
Bibliographic record
Abstract
In this study, we studied the effect of lipopolysaccharide from Rhodobacter sphaeroides (LPS-RS), an inhibitor of Toll-like receptor 4 (TLR4), in LPS-induced acute lung injury (ALI). Male Sprague-Dawley rats were treated with LPS-RS (0.1 mg/kg body mass, by intraperitoneal (i.p.) injection) 1 h before LPS injection (10 mg/kg, i.p.). Bronchoalveolar lavage fluid (BALF) and lung tissues were collected 24 h later to determine total and differential cell count, total protein content, levels of lactate dehydrogenase (LDH), histopathological changes, markers of oxidative stress, and mRNA expression of the inhibitory protein nuclear factor kappaB-α (NFκBIA) and TLR4. Additionally, rings of pulmonary artery were isolated for measuring vascular reactivity. LPS-induced ALI was indicated by increases in total and differential cell count, total protein, and LDH in BALF, and increased lung levels of malondialdehyde (MDA), as well as decreased activity of reduced glutathione (GSH) and superoxide dismutase (SOD). Moreover, LPS increased pulmonary artery contraction in response to phenylephrine (PE). Additionally, LPS downregulated mRNA expression of NFκBIA and upregulated mRNA expression of TLR4. LPS caused a marked inflammation in the lung tissue, with tubercular granuloma and numerous neutrophils. Pretreatment with LPS-RS protected against LPS-induced ALI by decreasing total and differential cell count, total protein, and LDH in BALF, and increased pulmonary GSH content and SOD activity without affecting MDA content. Additionally, it decreased the elevated PE-induced pulmonary artery contraction. LPS-RS upregulated mRNA expression of NFκBIA and downregulated mRNA expression of TLR4. Moreover, LPS-RS prevented inflammation in lung tissues. In conclusion, pretreatment with LPS-RS protects against LPS-induced ALI in rats through its anti-inflammatory effects, possibly by decreasing the mRNA expression of TLR4 and increasing that of NFκBIA.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".