Pulmonary inflammation caused by chitosan microparticles
Bibliographic record
Abstract
Chitosan is a cationic biopolymer derived from chitin with potential therapeutic applications such as controlled drug delivery to mucosal-epithelial surfaces in the body. Inhaled chitosan microparticles (CM), for example, are of potential interest in pulmonary pharmacotherapy. In this context, we examine some basic reactions of lung tissue to CM. Inhaled CM (2-10 mg/kg of particles) induce dose-dependent proinflammatory effects in rat lungs; these effects are documented in increases in bronchoalveolar lavage fluid protein (BALF-P) and lactate dehydrogenase activity (BALF-LDH) and increases in lung tissue myeloperoxidase (MPO) activity and leukocyte migration. Overall, the biochemical parameters (i.e., average of BALF-P, BALF-DH, and MPO) indicate that the inflammation response is 1.8-fold greater than controls without CM; the same inflammation parameters, however, are 1.9-fold lower with CM compared with the proinflammatory effects of lipopolysaccharide (LPS). Cytological examination of BALF shows a large infiltration of polymorphonuclear neutrophils to lung tissue: more than a sixfold increase in this population of inflammatory cells, after inhalation of CM relative to air inhalation controls. Thus, the results indicate that inhaled CM can have significant proinflammatory effects on lung tissues; these effects are mild relative to LPS but need to be considered in the context of therapeutic applications via pulmonary delivery if such concentrations of CM are used.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| 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".