Microbial ecosystems therapeutics: a new paradigm in medicine?
Bibliographic record
Abstract
Increasing evidence indicates that the complex microbial ecosystem of the human intestine plays a critical role in protecting the host against disease. This review discusses gut dysbiosis (here defined as a state of imbalance in the gut microbial ecosystem, including overgrowth of some organisms and loss of others) as the foundation for several diseases, and the applicability of refined microbial ecosystem replacement therapies as a future treatment modality. Consistent with the concept of a 'core' microbiome encompassing key functions required for normal intestinal homeostasis, 'Microbial Ecosystem Therapeutics' (MET) would entail replacing a dysfunctional, damaged ecosystem with a fully developed and healthy ecosystem of 'native' intestinal bacteria. Its application in treating Clostridium difficile infection is discussed and possible applications to other diseases such as ulcerative colitis, obesity, necrotising enterocolitis, and regressive-type autism are reviewed. Unlike conventional probiotic therapies that are generally limited to a single strain or at most a few strains of bacteria 'Microbial Ecosystem Therapeutics' would utilise whole bacterial communities derived directly from the human gastrointestinal tract. By taking into account the intrinsic needs of the entire microbial ecosystem, MET would emphasise the rational design of healthy, resilient and robust microbial communities that could be used to maintain or restore human health. More than simply a new probiotic treatment, this emerging paradigm in medicine may lead to novel strategies in treating and managing a wide variety of human diseases.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".