Community analysis and co-occurrence patterns in airway microbial communities during health and disease
Bibliographic record
Abstract
Ecological relationships between bacteria are important when considering variation in bacterial communities in humans, with such variation playing an important role in both health and disease. Using high-throughput sequence data of the 16S rRNA marker-gene, we analysed the prevalence of taxa in the airways of a number of health- and disease-associated cohorts and determined the main drivers of community variance and bacterial co-occurrence. A number of facultative and obligately anaerobic bacterial taxa are commonly associated with the upper airways, forming the main “core” microbiota,e.g. Streptococcusspp.,Veillonellaspp.,Prevotellaspp.,Granulicatellaspp. andFusobacteriumspp. Opportunistic pathogenic bacteria associated with chronic airways disease, such asPseudomonasspp. (Pseudomonas aeruginosa), Burkholderiaspp. (Burkholderia cepaciacomplex) andHaemophilusspp. (Haemophilus influenzae) demonstrated poor correlation with other members of their respective communities (ρ<0.5; p>0.005), indicating probable independent acquisition and colonisation. Furthermore, our findings suggest that intra-genus variation between health and disease may affect community assemblies. Improved understanding of how bacteria assemble in time and space during health and disease will enable the future development of tailored treatment according to the patient's own signature microbiota, potentially providing benefit to patients suffering from airway diseases characterised by chronic infection.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".