White paper on microbial anti-cancer therapy and prevention
Bibliographic record
Abstract
In this White Paper, we discuss the current state of microbial cancer therapy. This paper resulted from a meeting ('Microbial Based Cancer Therapy') at the US National Cancer Institute in the summer of 2017. Here, we define 'Microbial Therapy' to include both oncolytic viral therapy and bacterial anticancer therapy. Both of these fields exploit tumor-specific infectious microbes to treat cancer, have similar mechanisms of action, and are facing similar challenges to commercialization. We designed this paper to nucleate this growing field of microbial therapeutics and increase interactions between researchers in it and related fields. The authors of this paper include many primary researchers in this field. In this paper, we discuss the potential, status and opportunities for microbial therapy as well as strategies attempted to date and important questions that need to be addressed. The main areas that we think will have the greatest impact are immune stimulation, control of efficacy, control of delivery, and safety. There is much excitement about the potential of this field to treat currently intractable cancer. Much of the potential exists because these therapies utilize unique mechanisms of action, difficult to achieve with other biological or small molecule drugs. By better understanding and controlling these mechanisms, we will create new therapies that will become integral components of cancer care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
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".