Functional engineering of human <scp>iPSC</scp> ‐derived parasympathetic neurons enhances responsiveness to gastrointestinal hormones
Bibliographic record
Abstract
Food‐derived biological signals are transmitted to the brain via peripheral nerves through the paracrine activity of gastrointestinal (GI) hormones. The signal transduction circuit of the brain–gut axis has been analyzed in animals; however, species‐related differences and animal welfare concerns necessitate investigation using in vitro human experimental models. Here, we focused on the receptors of five GI hormones (CCK, GLP1, GLP2, PYY, and serotonin (5‐HT)), and established human induced pluripotent stem cell (iPSC) lines that functionally expressed each receptor. Compared to the original iPSCs, iPSCs expressing one of the receptors did not show any differences in global mRNA expression, genomic stability, or differentiation capacities of the three germ layers. We induced parasympathetic neurons from these established iPSC lines to assess vagus nerve activity. We generated GI hormone receptor‐expressing neurons (CCKAR, GLP1R, and NPY2R‐neuron) and tested their responsiveness to each ligand using Ca 2+ imaging and microelectrode array recording. GI hormone receptor‐expressing neurons (GLP2R and HTR3A) were generated directly by gene induction into iPSC‐derived peripheral nerve progenitors. These receptor‐expressing neurons promise to contribute to a better understanding of how the body responds to GI hormones via the brain–gut axis, aid in drug development, and offer an alternative to animal studies.
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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) | 0.002 | 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".