Gut microbiota: next frontier in understanding human health and development of biotherapeutics
Bibliographic record
Abstract
The gut microbiota is a remarkable asset for human health. As a key element in the development and prevention of specific diseases, its study has yielded a new field of promising biotherapeutics. This review provides comprehensive and updated knowledge of the human gut microbiota, its implications in health and disease, and the potentials and limitations of its modification by currently available biotherapeutics to treat, prevent and/or restore human health, and future directions. Homeostasis of the gut microbiota maintains various functions which are vital to the maintenance of human health. Disruption of the intestinal ecosystem equilibrium (gut dysbiosis) is associated with a plethora of human diseases, including autoimmune and allergic diseases, colorectal cancer, metabolic diseases, and bacterial infections. Relevant underlying mechanisms by which specific intestinal bacteria populations might trigger the development of disease in susceptible hosts are being explored across the globe. Beneficial modulation of the gut microbiota using biotherapeutics, such as prebiotics, probiotics, and antibiotics, may favor health-promoting populations of bacteria and can be exploited in development of biotherapeutics. Other technologies, such as development of human gut models, bacterial screening, and delivery formulations eg, microencapsulated probiotics, may contribute significantly in the near future. Therefore, the human gut microbiota is a legitimate therapeutic target to treat and/or prevent various diseases. Development of a clear understanding of the technologies needed to exploit the gut microbiota is urgently required.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".