Intestinal Dysbiosis and Development of Cardiometabolic Disorders in Childhood Cancer Survivors: A Critical Review
Bibliographic record
Abstract
Significance: Survivors of pediatric cancers have a high risk of developing side effects after the end of their treatments. Many potential factors have been associated with the onset of cardiometabolic disorders (CMD), including cancer disease itself, chemotherapy, hormonal treatment, radiotherapy, and genetics. However, the precise etiology and underlying mechanisms of these long-term complications are poorly understood. Recent Advances: Greater awareness is currently paid to the role of microbiota in the emergence of cancers and modulation of cancer therapies in both children and adults. Alterations in the composition and diversity of intestinal microbiota can clearly influence tumor development and progression as well as immune responses and clinical output. As dysbiosis is closely linked to the development of host metabolic diseases, including obesity, metabolic syndrome, type 2 diabetes, and non-alcoholic fatty liver disease, it may increase the risk of CMD in cancer populations. Critical Issues: Only limited studies targeting the profile of intestinal dysbiosis before and after cancer treatment have been conducted. Further, the exact contribution of intestinal dysbiosis to the development of CMD in cancer survivors is poorly appreciated. This review intends to clarify the influence of gut microbiota on CMD in childhood cancer survivors, elucidate the potential mechanisms, and evaluate the latest research on the interplay between diet/food supplement, microbiota, and cancer-related CMD. Future Directions: The implication of intestinal dysbiosis in late metabolic complications of childhood cancer survivors should be clarified. Intervention strategies could be developed to reduce the risk of survivors to CMD. Antioxid. Redox Signal. 34, 223–251.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".