Oral oncolytic magnetotactic bacteria elicit anti-colorectal tumor immunity and reprogram microbiota metabolism
Bibliographic record
Abstract
Therapeutic outcomes of colorectal cancer (CRC) are influenced by intestinal microbiota and metabolites. To leverage the tumor-tropism and microbiota-regulating properties of bacteria, we developed oncolytic magnetotactic bacteria (MSR-CPT/APPs) loaded with camptothecin (CPT) and anti-PD-L1 peptide (APP) for targeted chemo-magnetothermal immunotherapy of CRC. To further achieve oral delivery, MSR-CPT/APPs were coated with mulberry leaf lipids and Pluronic F127 (LPs). When exposed to an alternating magnetic field, MSR-CPT/APP@LPs penetrated colonic mucus and reached deep-seated tumors. They elevated proinflammatory cytokine secretion, prolonged T cell recruitment, and reduced immunosuppressive cell proportions by activating the cGAS-STING pathway, inducing immunogenic cell death, and facilitating macrophage polarization to M1 phenotype. Oral MSR-CPT/APP@LPs increased the relative abundances of crucial commensal microorganisms ( e.g ., Lachnospiraceae family and Alistipes ) and influenced their metabolites, like elevating beneficial metabolites (short-chain fatty acids and citrulline) levels and decreasing harmful metabolites ( l -glutamine and kynurenic acid) amounts, thereby remodeling the tumor immune microenvironment. Overall, MSR-CPT/APP@LPs foster a supportive intestinal environment and mitigate immunosuppression, achieving the eradication of both primary and distant colorectal tumors.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".