Carbon black nanoparticle intratracheal installation results in large and sustained changes in the expression of miR‐135b in mouse lung
Bibliographic record
Abstract
MicroRNAs (miRNA) are important noncoding regulatory molecules that bind target messenger RNA (mRNA), primarily affecting their translation into protein. Because miRNAs can simultaneously target hundreds of mRNAs, subtle changes in their expression can elicit important cellular effects. Little is known about the role of miRNAs in pulmonary responses to inhaled particulate matter. We studied pulmonary global miRNA responses to Printex 90 carbon black nanoparticles in (1) nonpregnant C57BL/6 female mice instilled with vehicle or a single dose of 0.162 mg and euthanized 1, 3, and 28 days post-exposure, and (2) C57BL/6Bom Tac dams instilled with vehicle or a cumulative dose of 0.268 mg (four separate instillations of vehicle or 0.067 mg Printex 90 during pregnancy) and euthanized at weaning (26-27 days postexposure). We measured similar expression profiles in both exposure scenarios, with marked increases in miR-135b and subtle changes in miR-21 and miR-146b. All three miRNAs were confirmed in nonpregnant females by RT-PCR, whereas only miR-135b was confirmed in the dams. Target analysis revealed no concomitant changes in established and predicted targets of miR-135b, miR-21, or miR-146b. Analysis of potentially perturbed pathways did not reveal changes that would suggest down-stream miRNA effects. The reasons for the lack of association between miRNA and transcript profiles may be related to the complexity of miRNA function and fate, or to the possibility that targets may differ from those already established or predicted in silico. We hypothesize that changes in the expression of these miRNAs may be associated with resolution of pulmonary inflammation, but future work will be necessary to precisely identify specific targets of these miRNAs in lungs.
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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 | 0.001 |
| 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".