Supramolecularly Engineered Conjugate of Bacteria and Cell Membrane‐Coated Magnetic Nanoparticles for Enhanced Ferroptosis and Immunotherapy of Tumors
Bibliographic record
Abstract
Abstract Although various ferroptosis inducers including magnetic nanoparticles (Fe3O4) and iron‐organic frameworks have been applied in cancer treatment, the mild immunogenicity, low targeting efficiency to the tumor, and poor tissue penetration have limited the therapeutic efficacy. Herein, a supramolecularly engineered conjugate between living bacteria (facultative anaerobic Salmonella typhimurium VNP20009, VNP) and cancer cell membranes‐coated Fe3O4 nanoparticles is developed for improving targeted delivery of Fe3O4 nanoparticles into the tumor tissue and for synergistic ferroptosis and immunotherapy of tumor. The enhanced ferroptosis induced by both Fe3O4 nanoparticles and the loaded ferroptosis inducing agent (sulfasalazine (SAS)) effectively inhibits tumor growth and generates immune response via immunogenic cell death (ICD). The colonization of VNP in tumors also induces adaptive immune responses and further promotes ferroptosis. Fundamentally, the supramolecular conjugate of VNP and cell membranes‐coated Fe3O4 can potentiate the therapeutic capability of each other through mutually magnifying the ferroptosis and immunotherapy, resulting in significantly enhanced antitumor effects.
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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".