Single‐molecule methods, activation‐induced cytidine deaminase, and quantum mechanical approach to explore and prevent carcinogenesis
Bibliographic record
Abstract
Abstract Recent advancements in single‐molecule methods have not only made it possible to obtain precise measurements for complex biological processes but also to produce simple mathematical models for intricate biochemical mechanisms, which would otherwise be speculative. These developments have strengthened our ability to respond through mathematical modeling to concepts of protein‒protein and protein‒DNA interactions on a nanometer level and address‐related questions. In this article, we examine an intriguing biological phenomenon in which a protein and an enzyme co‐jointly encounter carcinogenic adducts during transcription. We are focusing mainly on the dysregulation of the protein involved and the possible consequences that may arise. By providing a quantum mechanical model, we have demonstrated that the presence of carcinogenic adducts in a transcriptional bubble deregulates the protein that could cause lethal mutations. Next, we present a case study to explore carcinogenesis by suggesting an alternative experimental design. Our quantum mechanical model emphasizes the use of a quantized energies approach for specific mechanisms within the living cells. Radiation‐induced carcinogenicity can be prevented if radiation interacting with tissue is not given the energies that satisfy the quantization conditions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".