Flavokavains A- and B-Free Kava Enhances Resilience against the Adverse Health Effects of Tobacco Smoke in Mice
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Tobacco smoke remains a serious global issue, resulting in serious health complications, contributing to the onset of numerous preventive diseases and imposing significant health 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-smoking-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 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 protein kinase A (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 AB-free kava unique promise to mitigate tobacco smoke-related health damages. Further preclinical evaluations are warranted to fully harness the potential of AB-free kava in combating tobacco smoke-related harms in the preparation of its clinical translation.
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it