Anti-tumor effects of the bacterium caulobacter crescentus in murine tumor models
Bibliographic record
Abstract
Caulobacter crescentus is a gram negative, non-pathogenic bacterium, common in aquatic and soil environments. One feature of note is a protein surface layer (S-layer) composed of a single protein, organized as a self-assembled crystalline array that coats the bacterium. In the course of efforts to express cancer-associated peptides as genetic insertions into the S-layer, we noted a tumor suppressive effect of the unmodified bacterium. C. crescentus was examined for anti-tumor activity against three transplantable tumor mouse models: Lewis lung carcinoma cells transfected with the MUC1 gene in C57BL/6, murine mammary carcinoma (EMT-6) in BALB/c (both in prophylactic and therapeutic mode) and murine leukemia cells (L1210) in DBA2. Mice were immunized three times i.p. with C. crescentus (2 x 10(7) cells/mouse). In prophylactic mode, the mice were challenged with tumor cells two weeks after the last immunization. Immunization with live C. crescentus resulted in anti-tumor activity in all three transplantable tumor models, as measured by prolonged survival, reduced tumor mass or reduced number of lung nodules, compared to saline control groups. In the Lewis lung and the EMT-6 mammary carcinoma murine models the number of lung nodules as well as the tumor weight was lower in mice treated with C. crescentus, compared to the control group; for EMT-6, this was observed in prophylactic and therapeutic modes. In the murine leukemia and Lewis lung carcinoma models prolonged survival was observed in the groups of mice immunized with Caulobacters. In most cases the live C. crescentus cells were markedly more efficacious than heat killed or formalin fixed cells, despite the fact that they do not grow or persist in mice. The results suggest that C. crescentus may be a safe, bacterial immunomodulator for the treatment of 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".