Three‐dimensional quantification of oxytocin neurons in the hypothalamic paraventricular nucleus reveals sex‐ and subregion‐specific differences in two genetic mouse models of autism
Bibliographic record
Abstract
Oxytocin (OXT), a neuropeptide hormone essential to a wide range of social functions, has drawn increasing attention as a crucial contributor to the neurobiology of autism spectrum disorder (ASD). Central OXT system disruptions have been reported in several genetic mouse models of ASD; however, a detailed and systematic characterization of these phenotypes, and cross-model identification of shared and distinct features, are presently lacking. We integrated whole-brain OXT immunolabeling, SHIELD tissue clearing, light-sheet microscopy, and three-dimensional (3D) machine learning-based cell detection to establish a high-throughput, intact-tissue pipeline and quantified OXT immunopositive (OXT+) neurons across subregions of the paraventricular nucleus of the hypothalamus (PVN) in two genetic mouse models of ASD: Cntnap2 and Fmr1 knockout (KO) mice. We validated this pipeline alongside conventional immunohistochemistry using tissue sections. We show subregion- and sex-specific differences in PVN OXT+ cell counts in the two KO models. Notably, whole-PVN analysis revealed additional subregion- and sex-specific differences that were not evident in section-based quantification. These results identify subregion- and sex-specific differences in PVN OXT+ neuronal distribution as a shared phenotype in two genetic mouse models of ASD. This work highlights the importance of region-specific, high-resolution 3D approaches in intact tissue for quantifying cell populations within anatomically complex brain regions.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".