Revealing oral microbiota composition and functionality associated with heavy cigarette smoking
Bibliographic record
Abstract
BACKGROUND: Heavy tobacco smoking, a hallmark feature of lung cancer, is drastically predominant in Middle Eastern populations. The precise links between nicotine dependence and the functional contribution of the oral microbiota remain unknown in these populations. METHODS: We evaluated the composition and functional capabilities of oral microbiota with relation to cigarette smoking in 105 adults through shotgun metagenomics using buccal swabs. RESULTS: The oral microbiota composition in our study subjects was dominated by the phyla Firmicutes, Proteobacteria, Actinobacteria, and Bacteroidetes, in addition to the genera Prevotella and Veillonella, similar to previously described westernized cohorts. Furthermore, the smoker's oral microbiota represented a significant abundance of Veillonella dispar, Leptotrichia spp. and Prevotella pleuritidis when compared to non-smokers. Within the smoking groups, differential relative abundance testing unveiled relative abundance of Streptobacillus hongkongensis, Fusobacterium massiliense, Prevotella bivia in high nicotine dependent compared to low nicotine dependent profiles based on Fagerström Test for Nicotine Dependence. Functional profiling showed marked differences between smokers and non-smokers. Smokers exhibited an enrichment of Tricarballylate utilization and Lactate racemization when compared to the non-smokers. According to their nicotine dependence, enrichment of Xanthosine utilization, p-Aminobenzoyl-Glutamate utilization, and multidrug efflux pump in Campylobacter jejuni biosynthesis modules were detected in the high nicotine dependent group. CONCLUSIONS: These compositional and functional differences may provide critical insight on how variations in the oral microbiota could predispose to respiratory illnesses and smoke cessation relapse in cigarette smokers. In particular, the observed enrichment of Fusobacterium and Prevotella in the oral microbiota possibly suggests an intriguing linkage to gut and lung cancers.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".