Potential markers and metabolic processes involved in the mechanism of radiation-induced heart injury
Bibliographic record
Abstract
Irradiation of normal tissues leads to acute increase in reactive oxygen/nitrogen species that serve as intra- and inter-cellular signaling to alter cell and tissue function. In the case of chest irradiation, it can affect the heart, blood vessels, and lungs, with consequent tissue remodelation and adverse side effects and symptoms. This complex process is orchestrated by a large number of interacting molecular signals, including cytokines, chemokines, and growth factors. Inflammation, endothelial cell dysfunction, thrombogenesis, organ dysfunction, and ultimate failing of the heart occur as a pathological entity - "radiation-induced heart disease" (RIHD) that is major source of morbidity and mortality. The purpose of this review is to bring insights into the basic mechanisms of RIHD that may lead to the identification of targets for intervention in the radiotherapy side effect. Studies of authors also provide knowledge about how to select targeted drugs or biological molecules to modify the progression of radiation damage in the heart. New prospective studies are needed to validate that assessed factors and changes are useful as early markers of cardiac damage.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".