Assessment of Antimicrobial (Host Defense) Peptides as Anti-Cancer Agents
Bibliographic record
Abstract
Cationic antimicrobial (host defense) peptides (CAPs) are able to kill microorganisms and cancer cells, leading to their consideration as novel candidate therapeutic agents in human medicine. CAPs can physically associate with anionic membrane structures, such as those found on cancer cells, causing pore formation, intracellular disturbances, and leakage of cell contents. In contrast, normal cells are less negatively-charged and are typically not susceptible to CAP-mediated cell death. Because the interaction of CAPs with cells is based on charge properties rather than cell proliferation, both rapidly dividing and quiescent cancer cells, as well as multidrug-resistant cancer cells, are targeted by CAPs, making CAPS potentially valuable as anti-cancer agents. CAPs often exist as families of peptides with slightly different amino acid sequences. In addition, libraries of synthetic peptide variants based on naturally occurring CAP templates can be generated in order to improve upon their action. High-throughput screens are needed to quickly and efficiently assess the suitability of each CAP variant. Here we present the methods for assessing CAP-mediated cytotoxicity against cancer cells (suspension and adherent) and untransformed cells (measured using the tritiated thymidine-release or MTT assay), and for discriminating between cell death caused by necrosis (measured using lactate dehydrogenase- or (51)Cr-release assays), or apoptosis and necrosis (single-stranded DNA content measured by flow cytometry). In addition the clonogenic assay, which assesses the ability of single transformed cells to multiply and produce colonies, is described.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".