Gender differences in murine pulmonary responses elicited by cellulose nanocrystals
Bibliographic record
Abstract
BACKGROUND: Cellulose-based materials have been used for centuries to manufacture different goods derived from forestry and agricultural sources. In the growing field of nanocellulose applications, its uniquely engineered properties are instrumental for inventive products coming to competitive markets. Due to their high aspect ratio and stiffness, it is speculated that cellulose nanocrystals (CNC) may cause similar pulmonary toxicity as carbon nanotubes and asbestos, thus posing a potential negative impact on public health and the environment. METHODS: The present study was undertaken to investigate the pulmonary outcomes induced by repeated exposure to respirable CNC. C57BL/6 female and male mice were exposed by pharyngeal aspiration to CNC (40 μg/mouse) 2 times a week for 3 weeks. Several biochemical endpoints and pathophysiological outcomes along with gene expression changes were evaluated and compared in the lungs of male and female mice. RESULTS: Exposure to respirable CNC caused pulmonary inflammation and damage, induced oxidative stress, elevated TGF-β and collagen levels in lung, and impaired pulmonary functions. Notably, these effects were markedly more pronounced in females compared to male mice. Moreover, sex differences in responses to pulmonary exposure to CNC were also detected at the level of global mRNA expression as well as in inflammatory cytokine/chemokine activity. CONCLUSIONS: Overall, our results indicate that there are considerable differences in responses to respirable CNC based on gender with a higher pulmonary toxicity observed in female mice.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".