The Gut-Brain Axis, the Human Gut Microbiota and Their Integration in the Development of Obesity
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Obesity is a global epidemic, placing socioeconomic strain on public healthcare systems, especially within the so-called Western countries, such as Australia, United States, United Kingdom and Canada. Obesity results from an imbalance between energy intake and energy expenditure, where energy intake exceeds expenditure. Current non-invasive treatments lack efficacy in combating obesity, suggesting that obesity is a multi-faceted and more complex disease than previously thought. This has led to an increase in research exploring energy homeostasis and the discovery of a complex bidirectional communication axis referred to as the gut-brain axis. The gut-brain axis is comprised of various neurohumoral components that allow the gut and brain to communicate with each other. Communication occurs within the axis via local, paracrine and/or endocrine mechanisms involving a variety of gut-derived peptides produced from enteroendocrine cells (EECs), including glucagon-like peptide 1 (GLP1), cholecystokinin (CCK), peptide YY3-36 (PYY), pancreatic polypeptide (PP) and oxyntomodulin. Neural networks, such as the enteric nervous system (ENS) and vagus nerve also convey information within the gut-brain axis. Emerging evidence suggests the human gut microbiota, a complex ecosystem residing in the gastrointestinal tract (GIT), may influence weight-gain through several inter-dependent pathways including energy harvesting, short-chain fatty-acids (SCFA) signalling, behaviour modifications, controlling satiety and modulating inflammatory responses within the host. Hence, the gut-brain axis, the microbiota and the link between these elements and the role each plays in either promoting or regulating energy and thereby contributing to obesity will be explored in this review.
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it