Gut-lung axis, probiotics, and prebiotics: insights on dysbiosis, mechanism, and prevention of lung cancer
Bibliographic record
Abstract
Background/objectives: Science continues to unravel the intricacies of the human body that affect health and well-being. The human gastrointestinal tract is inhabited by microbiota responsible for various bodily functions and reactions. This review provides an overview of lung-related diseases and the role of the gut-lung axis (GLA) related to lung cancer. Integral to the discussion are the roles that prebiotics and probiotics play in critical aspects of the GLA. The review aims to bridge the conventional knowledge with recent knowledge of therapeutic agents. Methods: A literature review was performed using the keywords "gut-lung axis," "prebiotics," "probiotics," and "lung cancer" on Google Scholar, PubMed, and JSTOR. Results: Probiotic bacteria have a positive effect in maintaining beneficial bacteria in the gastrointestinal tract, which subsequently shows significant effects in maintaining lung health. Coupled with the efficacy of probiotics is a diet rich in prebiotics required to maintain a healthy gastrointestinal tract microbiota. Probiotics and prebiotics have specific mechanisms of action to prevent lung health-related diseases such as lung cancer. Recent advances have shown the potential of non-toxic and sustainable therapeutic agents for reversal of dysbiosis in GLA. Conclusion: Research suggests that the gastrointestinal tract microbiota has a crucial role in the onset and prevention of lung cancer. Lung cancer, which is prevalent in almost every country around the globe, is found to be associated with the occurrence of various other co-morbidities. Seeking new methods to maintain lung health and prevent lung diseases, including lung cancer, remains urgent, especially in the post-COVID era.
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 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) | 0.000 | 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 | 0.001 | 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 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".