The M.I.O.C. (Microbiota, Inflammation, Obesity, Cancer) Network
Bibliographic record
Abstract
Overweight and obesity are associated with an increased risk of metabolic developmental disorders, such as diabetes and cardiovascular disease. However, in addition to these metabolic diseases, excess body weight is associated with various cancers, including cancer of the gastrointestinal tract, such as liver, pancreatic and colon cancers. Inflammation is a common feature of obesity and cancer. In addition to diet and physical factors that contribute to the intestinal barrier (mucus, epithelial cell renewal and tight junction proteins), is important to consider the immune intestinal system. Similar to obesity and metabolic disorders, inflammation is recognized as the enabler of cancer development, providing support for multiple hallmark features of cancers, including the supply of bioactive molecules, such as growth, survival, and pro-angiogenic factors. At an evolutionary level, the relationship between humans and bacteria is so close that we can think of our body as a sort of superorganism made up of human and microbial cells. It is not only the action of individual microorganisms that defines a possible pathological condition. Even the general composition of the microbiota can contribute to the development of a tumor and one of the most studied conditions, known for its influence on the intestinal bacterial community, is obesity which is associated with a reduction of variability within the microbiota composition. In the future, bacteria could therefore be a valuable ally in the fight against cancer.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.198 | 0.098 |
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".