Potential Therapies for Infectious Diseases Based on Targeting Immune Evasion Mechanisms That Pathogens Have in Common With Cancer Cells
Bibliographic record
Abstract
Many global infectious diseases are not well-controlled, underlining a critical need for new, more effective therapies. Pathogens and pathogen-infected host cells, like cancer cells, evade immune surveillance via immune evasion mechanisms. The present study indicates that pathogenic bacteria, endoparasites, and virus-infected host cells can have immune evasion mechanisms in common with cancers. These include entry into dormancy and metabolic reprogramming to aerobic glycolysis leading to excessive secretion of lactic acid and immobilization of local host immunity. The latter evasion tactic provides a therapeutic target for cancer, as shown by our recent finding that patient-derived cancer xenografts can be growth-arrested, without major host toxicity, by inhibiting their lactic acid secretion (as mediated by the MCT4 transporter)-with evidence of host immunity restoration. Accordingly, the multiplication of bacteria, endoparasites, and viruses that primarily depend on metabolic reprogramming to aerobic glycolysis for survival may be arrested using cancer treatment strategies that inhibit their lactic acid secretion. Immune evasion mechanisms shared by pathogens and cancer cells likely represent fundamental, evolutionarily-conserved mechanisms that may be particularly critical to their welfare. As such, their targeting may lead to novel therapies for infectious diseases.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".