Cancer Evolution–Development: Experience of Hepatitis B Virus–Induced Hepatocarcinogenesis
Bibliographic record
Abstract
Here, we present the basic concept and theoretical framework of a scientific hypothesis called Cancer Evolution-Development ("Cancer Evo-Dev"), based on our recent studies of the molecular mechanisms by which chronic infection with the hepatitis B virus induces hepatocarcinogenesis, together with related advances in that field. Several aspects central to our hypothesis are presented: ■ Immune imbalance-caused by the interaction of genetic predispositions and environmental exposures such as viral infection-is responsible for the maintenance of chronic non-resolving inflammation. Non-resolving inflammation promotes the occurrence and progression of cancers, characterized by an evolutionary process of "mutation-selection-adaptation" for both viruses and host cells.■ Under a microenvironment of non-resolving inflammation, proinflammatory factors promote mutations in viral or host genomes by transactivation of the expression of cytidine deaminases and their analogues. Most cells with genomic mutations and mutated viruses are eliminated in the competition for survival in the inflammatory microenvironment. Only a small percentage of the mutated cells that alter their survival signal pathways and exhibit the characteristics of "stem-ness" can survive and function as cancer-initiating cells.■ Cancers generally develop with properties of "backward evolution" and "retro-differentiation," indicating the indispensability of stem-like signal pathways in the evolution and development of cancers. The hypothesis of Cancer Evo-Dev not only lays the theoretical foundation for understanding the mechanisms by which inflammation promotes the development of cancers, but also plays an important role in specific prophylaxis, prediction, early diagnosis, and targeted treatment of cancers.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".