Microbiota and Probiotics in the Treatment of Inflammatory Bowel Disease: A Summary of the Clinical Literature
Bibliographic record
Abstract
The mammalian intestinal tract contains a complex and diverse society of both pathogenic and non-pathogenic bacteria. While it is estimated that there are more than 400 bacterial species inhabiting the human intestinal tract, many of these are uncultivated microorganisms that have complex interactions with other microflora and their host. Recent advances in molecular biology and genetics will facilitate the identification of some unknown organisms through the science of metagenomics. Subsequent analyses of genetic code may inform microbiologists how to create suitable culture conditions for these unknown organisms to facilitate further study and characterization. Although environmental factors and the genetic make-up of the host can modulate the distribution of microbial strains, diet appears to be a major factor in regulating the concentration of individual species of microorganisms that colonize the gut. In addition, several gastrointestinal diseases have been associated with imbalances in the endogenous microflora population. Recent research has classified three distinct groups of intestinal microorganisms: (1) pathogenic, (2) neutral or innocuous, and (3) beneficial microorganisms. This review focuses on known microbiota imparting a protective or curative effect when the gut environment is challenged. These probiotic microorganisms have a potential therapeutic role in the maintenance of human health and the treatment of various gastrointestinal diseases. Through the examination of results obtained from high-quality open-label studies and controlled trials, the value of probiotic therapy in a variety of gastrointestinal diseases is discussed and areas for future research are identified.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".