Intense terahertz pulses inhibit Ras signaling and other cancer-associated signaling pathways in human skin tissue models
Bibliographic record
Abstract
Abstract Terahertz (THz) radiation has shown unique advantages in biomedical applications for novel diagnostic technologies due to the high sensitivity to molecular structure and chemical concentration. However, emerging evidence shows that intense pulses of THz radiation can induce significant non-thermal biological effects that must be characterized. In human skin exposed to intense THz pulses, relatively large responses characterized by differential gene expression profiles are observed. These data are analyzed by signaling pathway perturbation analysis to predict phenotypic endpoints and dysregulatory effects on cancer-related processes. The activities of several important pathways that drive the initiation, development, and progression of many human cancers are predicted to be suppressed, and this effect is intensity-dependent. Some affected pathways are targets for current and emerging anti-cancer therapies. In particular, the activity of the Ras signaling and Calcium signaling pathways is predicted to be significantly inhibited. These results indicate the possibility of an additional therapeutic mechanism of intense THz pulses, due to the potential for targeted suppression of pro-mitotic activity in diseased tissue.
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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".