Bibliographic record
Abstract
Fatty liver disease has been on the rise in the past few decades, and there is no hope that it will stop. The terminology change that has been recently proposed may not be sufficient to advocate for a reduction of steatogenic foods and a change in lifestyle. A course change may be supported by the recent labeling of aspartame sweetener as a possible carcinogenic compound by the International Association for Research on Cancer (IARC), an agency of the World Health Organization (WHO). Aspartame sweeteners and other edulcorating molecular compounds besides colorings may trigger liver cancer other than fatty liver disease, despite limited data supporting it. An essential bias in human cohort studies is indeed the exclusion of all confounding factors, which may be barely impossible for human studies. In this perspective, we suggest that the activation of the NOD-like receptor-enclosing protein 3 (NLRP3) inflammasome and the stimulation of the tumor suppression gene TP53 may be critical in the progression from fatty liver to liver inflammation and liver cancer. Aspartame reduces a transcriptional coactivator, precisely the peroxisomal proliferator-initiated receptor-γ (gamma) coactivator 1-α (alpha) (or PGC1α). This coactivator upregulates mitochondrial bioformation, oxidative phosphorylation, respiratory capacity, and fatty acid β-oxidation. Aspartame acts in this way, probably through the activation of TP53. These events have been accountable for the variations in the lipid outline in serum and total lipid storage as well as for the impairment of gluconeogenesis in the liver, as supported by the downregulation of the gluconeogenic enzymes in experimental animals, and may be relevant in humans as well.
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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 | 0.040 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.012 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".