Linking the westernised oropharyngeal microbiome to the immune response in Chinese immigrants
Bibliographic record
Abstract
BACKGROUND: Human microbiota plays a fundamental role in modulating the immune response. Western environment and lifestyle are envisaged to alter the human microbiota with a new microbiome profile established in Chinese immigrants, which fails to prime the immune system. Here, we investigated how differences in composition of oropharyngeal microbiome may contribute to patterns of interaction between the microbiome and immune system in Chinese immigrants living in Australia. METHODS: We recruited 44 adult Chinese immigrants: newly-arrived (n = 22, living in Australia < 6 months) and long-term Chinese immigrants (n = 22, living in Australia > 5 years), with age and gender matched. Oropharyngeal swabs, serum and whole blood were collected. The 16 s ribosomal RNA gene from the swabs was sequenced on the Illumina MiSeq platform. Innate immune responses were determined by 23 Toll-like receptors (TLR) pathway cytokines, while adaptive immune responses were determined by IgG-associated response to specific microbial/viral pathogens. RESULTS: were negatively correlated with cytokine IL-6 in long-term Chinese immigrants (rho range: - 0.46 ~ - 0.73). With respect to adaptive immunity, several microbial taxa were significantly associated with IgG1 responsiveness to microbial antigens in long-term immigrants, while a significant correlation with IgG1 responsiveness to viral antigens was detected in newly-arrived immigrants. CONCLUSIONS: The composition of the oropharyngeal microbiome varies between newly-arrived and long-term Chinese immigrants. Specific microbial taxa are significantly associated with immunological parameters but with different association patterns between newly-arrived and long-term Chinese immigrants.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".