Moving on from Metchnikoff: thinking about microbiome therapeutics in cancer
Bibliographic record
Abstract
Precision medicine now needs to also consider the microbiome in oncology treatment. Ingested substances, whether they are a carcinogenic or therapeutic agent, will likely come into contact with the microbiota. Even those delivered extra-intestinally can be influenced beyond xenobiotic metabolism by biochemical factors associated with the microbiota or by an immunological predisposition created by the microbiome. We need to undertake one of the largest paradigm shifts to ever occur in medicine, that is, every drug or ingested substance needs to be re-evaluated for its pharmacological effect post-microbiome interaction. The importance of the microbiome with a focus on the treatment of cancer is discussed. In the near future, it may be possible to specifically manipulate the microbial composition within cancer patients to improve the therapeutic potential of existing oncological agents. However, the current tools to do so are limited. Targeted modulation is likely to be achieved by addition, selective enhancement or depletion of specific microbial types. This may include compounds such as narrow spectrum antimicrobial agents or oligosaccharides that will kill or enhance the bacterial growth of distinct members of the microbiota, respectively. This will stimulate a new era in these fields.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".