New Strategy to Reduce the Harmful Effects of Smoking: Reducing the Level of <i>N</i>‐nitrosamines in Mainstream Smoke by NaA Zeolite and In vitro and In vivo Investigations
Bibliographic record
Abstract
Abstract A new effort to control the pollution caused by smoking by use of zeolite sieving the N‐nitrosamines in smoke is depicted and assessed in this paper, which is a first preliminary approach to demonstrate the performance characteristics of zeolite for the reduction of the concentration of N‐nitrosamines in the cigarette smoke. Smoking causes global pollution and is difficult to control because smoke is a complex system containing thousands of components. A new strategy is presented here for the reduction of the harmful effects of smoking through dispersion of zeolite into the tobacco rod of cigarettes to eliminate the N‐nitrosamines once they form in the smoke, and both in vitro and in vivo tests were employed to check the efficiency of this strategy. The impact of zeolite on the combustion of tobacco and the total toxicity of smoke are evaluated for the first time. Lower mutagenic activity and higher CHO cell livability were found for the test cigarette containing zeolite in comparison with the control, and no significant damage was detected in 30‐day animal experiments when exposed to the cigarette smoke. This confirms the feasibility of the new strategy and promotes the potential application of zeolite for protecting public health.
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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 | 0.000 | 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".