Probiotics in Treatment of Viral Respiratory Infections and Neuroinflammatory Disorders
Bibliographic record
Abstract
Inflammation is a biological response to the activation of the immune system by various infectious or non-infectious agents, which may lead to tissue damage and various diseases. Gut commensal bacteria maintain a symbiotic relationship with the host and display a critical function in the homeostasis of the host immune system. Disturbance to the gut microbiota leads to immune dysfunction both locally and at distant sites, which causes inflammatory conditions not only in the intestine but also in the other organs such as lungs and brain, and may induce a disease state. Probiotics are well known to reinforce immunity and counteract inflammation by restoring symbiosis within the gut microbiota. As a result, probiotics protect against various diseases, including respiratory infections and neuroinflammatory disorders. A growing body of research supports the beneficial role of probiotics in lung and mental health through modulating the gut-lung and gut-brain axes. In the current paper, we discuss the potential role of probiotics in the treatment of viral respiratory infections, including the COVID-19 disease, as major public health crisis in 2020, and influenza virus infection, as well as treatment of neurological disorders like multiple sclerosis and other mental illnesses.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.003 | 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".