Acute Outcomes of Cigarette Smoke and Electronic Cigarette Aerosol Inhalation in a Murine Model
Bibliographic record
Abstract
Cigarette smoking throughout life causes serious health issues in the lungs. The electronic cigarette (E‐Cig) use increased, since it was first introduced in the world. This research work compared the short‐term exposure consequences to e‐cigarette vapor and cigarette smoke in male mice. Forty‐five C57BL/6 mice were randomized into control (C) in an ambient air exposition cigarette smoke (CS) and aerosol electronic cigarette (EC), both were exposed to 120 puffs, 3 times/day during five days. Then, in the experimental protocol, the euthanized mice had their tissues removed for analysis. Our study showed that CS and EC resulted in higher cell influx into the airways, and an increase in macrophage counts in CS (209.25 ± 7.41) and EC (220.32 ± 8.15) when compared to C (108.40 ± 4.49) (p < 0.0001). The CS (1.92 ± 0.23) displayed a higher pulmonary lipid peroxidation as opposed to C (0.93 ± 0.06) and EC (1.23 ± 0.17) (p < 0.05). The EC (282.30 ± 25.68) and CS (368.50 ± 38.05) promoted increased levels of interleukin 17 when compared to C (177.20 ± 10.49) (p < 0.05). The EC developed shifts in lung histoarchitecture, characterized by a higher volume density in the alveolar air space (60.21; 55.00‐65.83) related to C (51.25; 18.75‐68.75) and CS (50.26; 43.75‐62.08) (p =0.002). The EC (185.6 ± 9.01) presented a higher respiratory rate related to CS (133.6 ± 10.2) (p < 0.002). Therefore, our findings demonstrated that the short‐term exposure to e‐cig promoted more acute inflammation comparing to cigarette smoke in the ventilatory parameters of the animals.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".