Anatomically “Calibrated” Isolated Respiratory Networks from Newborn Rodents
Bibliographic record
Abstract
En bloc and transversal slice preparations from perinatal rodents are established in vitro models for studying control of breathing by neurons and neighboring glial cells in the lower brainstem. These neural “respiratory networks in the dish” show features complementary with those in recent in vivo models that enable, for example, optogenetic manipulation for studying behavioral changes in awake animals or modulation of these circuits by higher brain regions during sleep. Contrary, the in vitro models allow respiratory network analysis at the (sub)cellular level. This is currently studied using powerful analytical tools such as quantitative pharmacology and patch-clamp recording of biophysical membrane properties of respiratory interneurons and/or motoneurons (plus neighboring glia). Increasingly, these approaches are combined with fluorescence imaging of both activity and morphology of individual cells or respiratory groups. Our recent work indicates that properties of the isolated respiratory networks depend critically on both their physical dimensions and the composition of superfusates used for their study. Based on this, we recommend to use anatomically “calibrated” rhythmic en bloc and slice preparations that we have developed and to study these models in superfusate that mimics in vivo conditions as closely as possible. We show here how these preparations are generated and present examples for pharmacological and “electrophysiological imaging” analyses that revealed novel properties of neuron–glial networks involved in respiratory control.
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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 | 0.001 |
| Open science | 0.001 | 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".