Viral Infections, the Microbiome, and Probiotics
Bibliographic record
Abstract
Viral infections continue to cause considerable morbidity and mortality around the world. Recent rises in these infections are likely due to complex and multifactorial external drivers, including climate change, the increased mobility of people and goods and rapid demographic change to name but a few. In parallel with these external factors, we are gaining a better understanding of the internal factors associated with viral immunity. Increasingly the gastrointestinal (GI) microbiome has been shown to be a significant player in the host immune system, acting as a key regulator of immunity and host defense mechanisms. An increasing body of evidence indicates that disruption of the homeostasis between the GI microbiome and the host immune system can adversely impact viral immunity. This review aims to shed light on our understanding of how host-microbiota interactions shape the immune system, including early life factors, antibiotic exposure, immunosenescence, diet and inflammatory diseases. We also discuss the evidence base for how host commensal organisms and microbiome therapeutics can impact the prevention and/or treatment of viral infections, such as viral gastroenteritis, viral hepatitis, human immunodeficiency virus (HIV), human papilloma virus (HPV), viral upper respiratory tract infections (URTI), influenza and SARS CoV-2. The interplay between the gastrointestinal microbiome, invasive viruses and host physiology is complex and yet to be fully characterized, but increasingly the evidence shows that the microbiome can have an impact on viral disease outcomes. While the current evidence base is informative, further well designed human clinical trials will be needed to fully understand the array of immunological mechanisms underlying this intricate relationship.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".