Bacteria in Cancer Therapeutics: A Framework for Effective Therapeutic Bacterial Screening and Identification
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
By 2030, the global incidence of cancer is expected to increase by approximately 50%. However, most conventional therapies still lack cancer selectivity, which can have severe unintended side effects on healthy body tissue. Despite being an unconventional and contentious therapy, the last two decades have seen a significant renaissance of bacterium-mediated cancer therapy (BMCT). Although promising, most present-day therapeutic bacterial candidates have not shown satisfactory efficacy, effectiveness, or safety. Furthermore, therapeutic bacterial candidates are available to only a few of the approximately 200 existing cancer types. Excitingly, the recent surge in BMCT has piqued the interest of non-BMCT microbiologists. To help advance these interests, in this paper we reviewed important aspects of cancer, present-day cancer treatments, and historical aspects of BMCT. Here, we provided a four-step framework that can be used in screening and identifying bacteria with cancer therapeutic potential, including those that are uncultivable. Systematic methodologies such as the ones suggested here could prove valuable to new BMCT researchers, including experienced non-BMCT researchers in possession of extensive knowledge and resources of bacterial genomics. Lastly, our analyses highlight the need to establish and standardize quantitative methods that can be used to identify and compare bacteria with important cancer therapeutic traits.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 it