The Role of Microbiota-Derived Vitamins in Immune Homeostasis and Enhancing Cancer Immunotherapy
Bibliographic record
Abstract
Not all cancer patients who receive immunotherapy respond positively and emerging evidence suggests that the gut microbiota may be linked to treatment efficacy. Though mechanisms of microbial contributions to the immune response have been postulated, one likely function is the supply of basic co-factors to the host including selected vitamins. Bacteria, fungi, and plants can produce their own vitamins, whereas humans primarily obtain vitamins from exogenous sources, yet despite the significance of microbial-derived vitamins as crucial immune system modulators, the microbiota is an overlooked source of these nutrients in humans. Microbial-derived vitamins are often shared by gut bacteria, stabilizing bioenergetic pathways amongst microbial communities. Compositional changes in gut microbiota can affect metabolic pathways that alter immune function. Similarly, the immune system plays a pivotal role in maintaining the gut microbiota, which parenthetically affects vitamin biosynthesis. Here we elucidate the immune-interactive mechanisms underlying the effects of these microbially derived vitamins and how they can potentially enhance the activity of immunotherapies in cancer.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".