Bibliographic record
Abstract
With the improvements in cancer diagnosis and therapy, clinical outcome has increased substantially, to the extent that by 2026, there will be >20M cancer survivors in the United States alone. The majority of survivors fare well; however, there is a significant proportion of patients who continue to experience the aftermath of their treatments such as radiation fibrosis (RF) or lymphedema. Until recently, there has been little understanding of the biological basis of RF, which is defined by the aberrant accumulation of extracellular matrix (ECM), leading to reduced tissue elasticity, and potential loss of organ function. The regulation of ECM production and degradation is a complex process, mediated through the production of a myriad of cytokines such as TGFB, and environmental conditions such as hypoxia. We have recently identified that metabolic dysregulation, and in particular, downregulation of fatty acid oxidation (FAO) is a key pathway driving the production and reducing the degradation of collagen, the predominant component of the ECM. Conversely, upregulation of PPAR signaling, a major mediator of FAO, reduced transcription of key ECM genes; whilst increasing internalization and degradation of extracellular collagen. Furthermore, CD36, a membrane transporter of long chain fatty acids, was discovered to play a critical role in regulating the trafficking of collagen and lysosomal degradation. Finally, using a pharmacogenomics approach, caffeic acid was identified to be a compound which can be systemically administered, and reduced RF in pre‐clinical models. These data, along with other reports in the literature, strongly point towards the use of metabolic drugs as a therapeutic strategy by which we can mitigate the deleterious effects of RF on cancer patients. More recently, we have also uncovered that metabolic dysregulation likely plays a similar role in the development of lymphedema, a common long‐term toxicity in women after breast cancer therapy. Finally, these findings have broad application to the increasing burden of fibrosis beyond cancer, wherein end‐stage renal, liver, cardiac, and pulmonary fibrosis accounts for up to 1/3 of the deaths around the world. Support or Funding Information The work is funded in part by the Canadian Institutes of Health Research, Genome Canada, the Canadian Cancer Society Research Institute, the Physicians Services Incorporated Foundation, the Harry Barberian Research Scholarship, the Mariano Elia Chair in Head and Neck Cancer Research, the Peter and Shelagh Godsoe Chair in Radiation Medicine, the Princess Margaret Cancer Centre Head and Neck Translational Program, the Princess Margaret Cancer Centre Radiation Medicine Program and the Ottawa Heart Institute Research Corporation, as well by philanthropic funding from the Jesse Rasch Foundation, the Wharton family, Joe’s Team and the Ministry of Health and Long‐Term Care.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".