Gut microbiota changes in airway diseases: a systematic review
Bibliographic record
Abstract
Introduction: studies have highlighted the importance of gut microbiota (GM) to the host immune defenses, influencing the host development and physiology. Changes in the composition and diversity of GM have been detected in some disease and could be implicated in the pathophysiological mechanisms of them. Objective: the purpose of this study was to show an overview of the current knowledge about the GM of patients with airway diseases (AD). Methodology: the literature search was performed in four databases, using a combination of the descriptors: “Gastrointestinal Microbiome”, “Gut Microbiome”, “Gut Microbiota”, “Cystic Fibrosis” (CF), “Asthma”, “Pulmonary Hypertension” (HP) and/or “Chronic Obstructive Pulmonary Disease” (COPD). Results: fifteen studies were herein included: ten of CF and five of asthma. No study about other AD matched the inclusion criteria. In all studies about CF, changes were detected in GM, particularly quantitative and qualitative microbial changes. For asthma, data showed changes in GM also including a reduction of microbial richness, evenness and diversity and in the Bacteroidetes/Firmicutes ratio. Conclusions: the current data indicate the existence of GM changes in AD. However, due to the few studies for asthma and the lack of investigations on HP and COPD, it was not possible to confirm whether these GM changes are observed in other AD. Furthermore, this review shows the necessity of more studies in this area to characterize dysbiosis and which alterations are more frequent observed in AD patients.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".