Dysbiosis of the Gut Microbiome in Lung Cancer
Bibliographic record
Abstract
Dear Editor: We wish to submit a manuscript entitled “Dysbiosis of the gut microbiome in lung cancer ” to Frontiers in Cellular and Infection Microbiology, Microbiome in Health and Disease section, for your consideration to publish as a Research Article. In this study, we examined the gut microbiome between 30 lung cancer patients and 30 healthy controls via next generation sequencing of 16S rDNA to uncover potential biomarkers for early diagnosis and targeted intervention. Previous analyses of the microbe-lung cancer relationship have been focused on lung microbiome. To our knowledge, this is the first study of lung cancer from the perspective of gut microbiome. Our findings support the hypothesis of an lung cancer-specific compositional structure of gut bacterial populations. Our work suggests that the dysbiosis of the gut microbiome in lung cancer as reflected by reduced abundance of Actinobacteria and Bifidobacterium and elevated levels of Enterococcus had facilitated the development of lung cancer and may help develop novel strategies for early prevention and targeted intervention against lung cancer. The authors claim that none of the materials in the paper has been published or is under consideration for publication elsewhere. Thank you very much for your consideration and we look forward to hearing from you soon. Yours Sincerely He Zhuang and Shu-Lin Liu Genomics Research Center Harbin Medical University 157 Baojian Road Harbin, 150081 China
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".