In Vitro Preparations for Electrophysiological Study of Peptide Neurons and Actions
Bibliographic record
Abstract
The value of in vivo electrophysiological studies for revealing information about peptidergic neurons and peptide actions has been discussed by Ferguson and Renaud in this volume. Nevertheless, carrying out pharmacological studies in vivo raises a number of problems with respect to both interpretation of data and the ease with which data can be obtained. In particular, the following considerations pertain to working in vivo: (1) the use of anesthetics is often required and their presence may alter the neuronal responses to peptide application; (2) it can be very difficult to manipulate the environment of the cell in order to differentiate between, for example, pre- and postsynaptic effects or to determine the ionic fluxes underlying a peptide action; (3) it is difficult to perform intracellular experiments because pulsation caused by blood pressure and respiration make it difficult to keep the electrode in the cell. Because of these problems, some investigators have turned to simpler in vitro animal models, such as the Aplysia, a marine mollusc, the utility of which is described by Murphy and Lukowiak elsewhere in this volume. The evident success achieved by investigators using simple invertebrate preparations has prompted a general movement over recent years to simpler in vitro neuronal preparations for the study of peptide actions in vertebrate tissue.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 0.019 |
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".