The importance of reporting house dust mite endotoxin abundance: impact on the lung transcriptome
Bibliographic record
Abstract
The abundance of lipopolysaccharide (LPS) in house dust mite (HDM) preparations is broad and mirrors the variability seen in the homes of people with asthma. LPS in commercially available stocks ranges from 31 to 5,2000 endotoxin units. The influence of vastly different LPS loads on the mechanisms that define the immune and inflammatory phenotype of HDM-challenged mice has not been defined. This aim of the study was to understand the lung phenotype of mice challenged with HDM extract containing high or low levels of LPS. Female BALB/c mice were sensitized for 2 wk with commercial HDM extract containing either high (36,000 endotoxin units; HHDM) or low (615 endotoxin units; LHDM) levels of LPS. Lung phenotype was characterized by measuring lung function, total and differential cell counts, cytokine abundance, and the lung transcriptome by RNA-sequencing. LPS levels in HDM stocks used for preclinical asthma research in mice remain poorly reported. In 2019, only 14% of papers specified LPS concentration in HDM lots. Specific differences existed in airway responsiveness between mice challenged with HHDM or LHDM. HHDM- and LHDM-induced cytokine profiles of bronchial lavage were significantly different and the lung transcriptome was differentially enriched for genes involved in DNA damage repair or cilium movement, following HHDM or LHDM challenge, respectively. The abundance of LPS in commercially available HDM influences the phenotype of allergic airways inflammation in mice. Failure to report the level of LPS in HDM extracts used in animal models of airway disease will lead to inconsistency in reproducibility and reliability of published data.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".