The triad: respiratory microbiome – virus – immune response in the pathophysiology of pulmonary viral infections
Bibliographic record
Abstract
INTRODUCTION: The longstanding dogma that the healthy lung is sterile has been refuted by recent advances in culture-independent analyses of airway samples. The respiratory microbiome comprises all airway and lung tissue-associated microbes. These micro-organisms occur throughout the upper and lower respiratory tracts, with different populations and distinct burdens at specific sites and can be classified as pathogenic or commensal. AREAS COVERED: The majority of studies investigating the respiratory microbiome have focused on bacteria; however, emerging evidence has revealed the composition of the lung virome, the global viral communities present in the lung tissue. In this review, we searched PubMed and used keywords such as airway microbiome. We restricted outputs to English language and did not limit by any dates. We summarize the up-to-date knowledge on how the microbiome interacts with the host immune system and influences the pathogenesis of pulmonary viral infections. EXPERT OPINION: The relationship between colonizing microbes and the host is complex and various factors need to be considered in order to appreciate its pathophysiological consequences. Understanding these intricate mechanisms of interaction among the respiratory microbiome, viruses and the immune response may lead to the development of better therapies to treat or prevent respiratory viral infections.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".