Microbiota and Neurological Disorders: A Gut Feeling
Bibliographic record
Abstract
In the past century, noncommunicable diseases have surpassed infectious diseases as the principal cause of sickness and death, worldwide. Trillions of commensal microbes live in and on our body, and constitute the human microbiome. The vast majority of these microorganisms are maternally derived and live in the gut, where they perform functions essential to our health and survival, including: digesting food, activating certain drugs, producing short-chain fatty acids (which help to modulate gene expression by inhibiting the deacetylation of histone proteins), generating anti-inflammatory substances, and playing a fundamental role in the induction, training, and function of our immune system. Among the many roles the microbiome ultimately plays, it mitigates against untoward effects from our exposure to the environment by forming a biotic shield between us and the outside world. The importance of physical activity coupled with a balanced and healthy diet in the maintenance of our well-being has been recognized since antiquity. However, it is only recently that characterization of the host-microbiome intermetabolic and crosstalk pathways has come to the forefront in studying therapeutic design. As reviewed in this report, synthetic biology shows potential in developing microorganisms for correcting pathogenic dysbiosis (gut microbiota-host maladaptation), although this has yet to be proven. However, the development and use of small molecule drugs have a long and successful history in the clinic, with small molecule histone deacetylase inhibitors representing one relevant example already approved to treat cancer and other disorders. Moreover, preclinical research suggests that epigenetic treatment of neurological conditions holds significant promise. With the mouth being an extension of the digestive tract, it presents a readily accessible diagnostic site for the early detection of potential unhealthy pathogens resident in the gut. Taken together, the data outlined herein provide an encouraging roadmap toward important new medicines and companion diagnostic platforms in a wide range of therapeutic indications.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 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".