The Upper Airway Microbiota, Environmental Exposures, Inflammation, and Disease
Bibliographic record
Abstract
Along with playing vital roles in pathogen exclusion and immune system priming, the upper airways (UAs) and their microbiota are essential for myriad physiological functions such as conditioning and transferring inhaled air. Dysbiosis, a microbial imbalance, is linked with various diseases and significantly impedes the quality of one's life. Daily inhaled exposures and/or underlying conditions contribute to adverse changes to the UA microbiota. Such variations in the microbial community exacerbate UA and pulmonary disorders via modulating inflammatory and immune pathways. Hence, exploring the UA microbiota's role in maintaining homeostasis is imperative. The microbial composition and subsequent relationship with airborne exposures, inflammation, and disease are crucial for strategizing innovating UA diagnostics and therapeutics. The development of a healthy UA microbiota early in life contributes to normal respiratory development and function in the succeeding years. Although different UA cavities present a unique microbial profile, geriatrics have similar microbes across their UAs. This lost community segregation may contribute to inflammation and disease, as it stimulates disadvantageous microbial-microbial and microbial-host interactions. Varying inflammatory profiles are associated with specific microbial compositions, while the same is true for many disease conditions and environmental exposures. A shift in the microbial composition is also detected upon the administration of numerous therapeutics, highlighting other beneficial and adverse side effects. This review examines the role of the UA microbiota in achieving homeostasis, and the impact on the UAs of environmental airborne pollutants, inflammation, and disease.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".