Integration of Salmonella into Combination Cancer Therapy
Bibliographic record
Abstract
Current modalities of cancer treatment have limitations related to poor target selectivity, resistance to treatment, and low response rates in patients. Accumulating evidence over the past few decades has demonstrated the capacity of several strains of bacteria to exert anti-tumor activities. Salmonella is the most extensively studied entity in bacterial-mediated cancer therapy, and has a good potential to induce direct tumor cell killing and manipulate the immune components of the tumor microenvironment in favor of tumor inhibition. In addition, Salmonella possesses some advantages over other approaches of cancer therapy, including high tumor specificity, deep tissue penetration, and engineering plasticity. These aspects underscore the potential of utilizing Salmonella in combination with other cancer therapeutics to improve treatment effectiveness. Herein, we describe the advantages that make Salmonella a good candidate for combination cancer therapy and summarize the findings of representative studies that aimed to investigate the therapeutic outcome of combination therapies involving Salmonella. We also highlight issues associated with their application in clinical use.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".