Diet as a Potential Moderator for Genome Stability and Immune Response in Pediatric Leukemia
Bibliographic record
Abstract
Pediatric leukemias are the most prevalent cancers affecting children in developed societies, with childhood acute lymphoblastic leukemia (ALL) being the most common subtype. As diet is a likely modulator of many diseases, this review focuses on the potential for diet to influence the incidence and progression of childhood ALL. In particular, the potential effect of diets on genome stability and immunity during the prenatal and postnatal stages of early childhood development are discussed. Maternal diet plays an integral role in shaping the bodily composition of the newborn, and thus may influence fetal genome stability and immune system development. Indeed, higher birth weights of newborns are associated with increased risk of ALL, which suggests in-utero biology may shape the evolution of preleukemic clones. Postnatally, the ingestion of maternal breastmilk both nourishes the infant, and provides essential components that strengthen and educate the developing immune system. Consistently, breast-feeding associates with decreased risk of ALL development. For children already suffering from ALL, certain dietary regimens have been proposed. These regimens, which have been validated in both animals and humans, alter the internal hormonal environment. Thus, hormonal regulation by diet may shape childhood metabolism and immunity in a manner that is detrimental to the evolution or expansion of preleukemic and leukemic ALL clones.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".