Voltage-Clamp Analysis of Synaptic Transmission at the<i>Drosophila</i>Larval Neuromuscular Junction
Bibliographic record
Abstract
Although it is particularly valuable in revealing membrane potential changes, intracellular recording has a number of limitations. Primarily, it does not offer information on the kinetics of membrane currents associated with ion channels or synaptic receptors responsible for the potential change. Furthermore, the resting potential of the Drosophila body wall muscle varies naturally such that the driving force also varies considerably, making it difficult to accurately compare the amplitude of miniature synaptic potentials (minis) or evoked excitatory junction potentials (EJPs). Finally, accurate determination of quantal content based on minis and EJPs is possible only under low-release conditions when nonlinear summation is not a major issue. As the EJP amplitude increases, it creates a “ceiling effect,” because the same amount of transmitter will be less effective in depolarizing the membrane when the potential is approaching the reversal potential of glutamate receptors/channels. To overcome these limitations, the voltage-clamp technique can be used, which uses negative feedback mechanisms to keep the cell membrane potential steady at any reasonable set points. In voltage-clamp mode, the amplitude and kinetics of membrane currents can be determined. In the large larval muscle cells of Drosophila , the two-electrode voltage-clamp (TEVC) method is used, in which one electrode monitors the cell membrane potential while the other electrode passes electric currents. This protocol introduces the application of TEVC in analysis of synaptic currents using the larval neuromuscular junction preparation.
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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 | 0.001 | 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".