Characterization of the Upper Respiratory Tract Microbiomes of Patients with Pandemic H1N1 Influenza
Bibliographic record
Abstract
The upper respiratory tract microbiome has an important role in respiratory health. Influenza A is a common viral infection that challenges that health, and a well-recognized sequela is bacterial pneumonia. Given this connection, we sought to characterize the upper respiratory tract microbiota of individuals suffering from the pandemic H1N1 influenza A outbreak of 2009 and determine if microbiome profiles could be correlated with patient characteristics. We determined the microbial profiles of 65 samples from H1N1 patients by cpn60 universal target amplification and sequencing. Profiles were examined at the phylum and nearest neighbor "species" levels using the characteristics of patient gender, age, originating health authority, sample type and designation (STAT/non-STAT). At the phylum level, Actinobacteria-, Firmicutes- and Proteobacteria-dominated microbiomes were observed, with none of the patient characteristics showing significant profile composition differences. At the nearest neighbor "species" level, the upper respiratory tract microbiomes were composed of 13-20 "species" and showed a trend towards increasing diversity with patient age. Interestingly, at an individual level, most patients had one to three organisms dominant in their microbiota. A limited number of discrete microbiome profiles were observed, shared among influenza patients regardless of patient status variables. To assess the validity of analyses derived from sequence read abundance, several bacterial species were quantified by quantitative PCR and compared to the abundance of cpn60 sequence read counts obtained in the study. A strong positive correlation between read abundance and absolute bacterial quantification was observed. This study represents the first examination of the upper respiratory tract microbiome using a target other than the 16S rRNA gene and to our knowledge, the first thorough examination of this microbiome during a viral infection.
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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 | 0.000 |
| 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".