Gut Microbiota and Health: A Review With Focus on Metabolic and Immunological Disorders and Microbial Remediation
Bibliographic record
Abstract
Understanding and defining health is an important yet fuzzy topic. Despite several attempts, health is not a well-defined concept, therefore we seek to understand health from the perspective of the microbiome. Gut microbiota are an essential component in the modern concept of human health. However, the precise patterns of composition and functional characteristics of a healthy gut microbiome remain ill-defined. Microbial colonization patterns associated with disease states have been documented with the advancement of sequencing technologies. Several prebiotics and probiotics have been reported to restore the normal gut flora after being disrupted by various factors. Fecal microbial transplantation from healthy individuals into recipients suffering from diseases related to gut dysbiosis has also been reported to be effective in restoring the normal makeup of gut microbiota, as shown by its efficacy in treating Clostridium difficile infection, colitis, constipation, irritable bowel syndrome, and neurological conditions such as multiple sclerosis and Parkinson`s disease. In this review we attempt to define the parameters of healthy human gut flora and its disruption in diseased conditions, and restoration through administration of prebiotics, probiotics, and fecal microbial transplantation.
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".