Maternal Caffeine Intake Disrupts Eggshell Integrity and Retards Larval Development by Reducing Yolk Production in a Caenorhabditis elegans Model
Bibliographic record
Abstract
During pregnancy, most women are exposed to caffeine, which is a widely consumed psychoactive substance. However, the consequences of maternal caffeine intake on the child remain largely unknown. Here, we investigated the intergenerational effects of maternal caffeine intake on offspring in a Caenorhabditis elegans model. We treated a young mother (P0) with 10 mM of caffeine equivalent to 2–5 cans of commercial energy drinks and examined its reproduction and growth rate from P0 to F2 generation. The fertility decreased and embryonic lethality increased by defective oocytes and eggshell integrity in caffeine-ingested mothers, and F1 larval development severely retarded. These results were due to decreased production of vitellogenin protein (yolk) in caffeine-ingested mothers. Furthermore, effects of RNA interference of vitellogenin (vit) genes, vit-1 to vit-6, in P0 mothers can mimic those by caffeine-ingested mothers. In addition, RNA interference (RNAi) depletion of unc-62 (human Meis homeobox), a transcriptional activator for vit genes, also showed similar effects induced by caffeine intake. Taken together, maternal caffeine intake reduced yolk production mediated by the UNC-62 transcription factor, thereby disrupting oocyte and eggshell integrity and retarding larval development. Our study suggests the clinical significance of caffeine intake for prospective mothers.
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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 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".