AB-free kava enhances resilience against the adverse health effects of tobacco smoke in mice
Bibliographic record
Abstract
Tobacco smoke remains a serious global issue, resulting in serious health complications, contributing to the onsets of numerous preventive diseases, and imposing significant financial burdens. Despite regulatory policies and cessation measures aimed at curbing its usage, novel interventions are urgently needed for effective damage reduction. Our preclinical and pilot clinical studies showed that AB-free kava has the potential to reduce tobacco smoke-induced lung cancer risk, mitigate tobacco dependence, and reduce tobacco use. To understand the scope of its benefits in damage reduction and potential limitations, this study evaluated the effects of AB-free kava on a panel of health indicators in mice exposed to 2 - 4 weeks of daily tobacco smoke exposure. Our comprehensive assessments included global transcriptional profiling of the lung and liver tissues, analysis of lung inflammation, evaluation of lung function, exploration of tobacco nicotine withdrawal, and characterization of the causal PKA signaling pathway. As expected, Tobacco smoke exposure perturbed a wide range of biological processes and compromised multiple functions in mice. Remarkably, AB-free kava demonstrated the ability to globally mitigate tobacco smoke-induced deficits at the molecular and functional levels with promising safety profiles, offering a unique promise to mitigate tobacco smoke-related health damages. Further pre-clinical evaluation and clinical translation are warranted to fully harness the potential of AB-free kava in combating tobacco smoke-related harms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".