Exploring the link between dioxin exposure and diabetes risk: contributions of the islet aryl hydrocarbon receptor
Bibliographic record
Abstract
1.0 The epidemiology literature reveals disaster-exposure sex differences in the association between dioxin exposure and T2D incidence…………………………..……………….1231.1 T2D incidence in the global population is subject to sex differences...…...…124 1.2 AHR-regulated genes show sex differences in expression and activity across species, tissues, and time…..………………………….………….……….….…125 1.3 AHR crosstalk with sex hormone receptors is established but incompletely understood……………………………………………………………….……...126 1.3.1 AHR crosstalk with sex hormone receptors may contribute to sexdifferences seen experimentally…..……………………....…..………...127 2.0 AHR-HIF1α crosstalk impacts CYP1A1 expression in SC-islets and human donor islets…………………….………………………..……………………………………..128 2.1 AHR-HIF1α crosstalk has not been previously examined in islets…….…..129 2.2 AHR-HIF1α crosstalk in islets is dominated by HIF1α ……..….……........130 3.0 G6PC2 expression in islets is associated with fasting blood glucose..…………….130ix 3.1 k expression in pancreatic islets was shown experimentally to be impacted by TCDD exposure and hypoxia………………………………….…..131 4.0 TCDD activates the AHR in the developing SC-islet……………………...……..….133 4.1 G6PC2 expression in developing pancreatic islets is repressed by TCDD exposure…………………………………………….……………….……...…..134 4.2 Differentiated AHR KO SC-islets show reduced G6PC2 expression…..……135 4.2.1 TCDD exposure and AHR KO both interfere with endogenous AHR ligand signalling……………………..….………………….……….…..135 4.2.2There is evidence for AHR activation in the islet mediating G6PC2 expression………………….………………………….………………..
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".