Plant extract reduces tobacco smoke harmful effects on alveolar macrophage immune responses
Bibliographic record
Abstract
Tobacco smoke is a major factor responsible for lung cancer and chronic obstructive pulmonary disease. Although the best solution to reduce the incidence of these diseases is to quit smoking, there are still a large number of smokers. Thus, given the immunoregulatory properties of plant extracts, their capacity to reduce tobacco smoke harmful effects on alveolar macrophage (AM) functions was investigated. AM were treated with tobacco smoke extract and parenchymata tissue extract (PTE), or mesophyll cell extract (MCE) of Spinacia oleacea. The effects of tobacco smoke extract from PTE and MCE-treated cigarette filters were also investigated. AM production of tumor necrosis factor (TNF), interleukin-10 (IL-10) and macrophage chemoattractant protein-1 (MCP-1), and AM cytotoxicity were measured. Tobacco smoke extract significantly inhibited TNF, IL-10, and MCP-1 release, and AM cytotoxicity. The addition of PTE and MCE to tobacco smoke extract abrogated the inhibition of AM mediator release. However, only MCE restored AM cytotoxicity. Interestingly, tobacco smoke extract of PTE and MCE-treated cigarette filters showed reduced effects on AM functions. Tobacco smoke extract from MCE-treated (0.25%) cigarette filters did not inhibit TNF, IL-10, and MCP-1 release in contrast to tobacco smoke extract from buffer-treated cigarette filters. AM cytotoxic activity was not inhibited by the treatment with tobacco smoke extract from MCE-treated cigarette filters. Our data suggest that the presence of plant extract in cigarette filters reduces the inhibitory effects of cigarette smoke on AM functions. Thus, MCE-treated cigarette filters may help reducing lung diseases associated with smoking.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 teacher head, 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".