An Open-Source and Highly Adaptable Rodent Limited Bedding and Nesting Apparatus for Chronic Early Life Stress
Bibliographic record
Abstract
Early life stress (ELS) increases susceptibility to cognitive and socioemotional dysfunction by disrupting the neurobiological systems that regulate these behaviors. Animal models provide a valuable tool for investigating the underlying mechanisms, enabling precise manipulation of stress exposure during development. The limited bedding and nesting (LBN) model, which induces maternal stress by restricting access to bedding and nesting materials in rodents, has been instrumental in advancing our understanding of chronic ELS. While this paradigm has been widely adopted, variations in apparatus designs and subtle differences in methodologies could impact consistency across studies. Here, we provide standardized guidelines for a cost-effective open-source mouse LBN apparatus design, which could further enhance the model's utility while supporting pup survival. We additionally present our findings observed during the duration of the LBN paradigm, which spans from postnatal day (PND) 2 to 10, for both dams and pups. We observe comparable corticosterone in control and LBN dams from PND 3 to 5. However, from PND 6 to 10, corticosterone remains elevated in LBN dams, while control dams show a decline. Notably, the LBN paradigm disrupts maternal care, as LBN dams exhibit more frequent nest exits and stereotypic behaviors during the dark phase. At PND 10, pups exhibit significantly reduced blood serum corticosterone levels and lower body weight compared with those reared under control conditions. By providing open-source equipment and detailed experimental protocols, our work aims to build on existing LBN paradigms to further enhance the accessibility and reproducibility of chronic ELS models.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".