Pseudorabies virus expressing enhanced green fluorescent protein: A tool for <i>in vitro</i> electrophysiological analysis of transsynaptically labeled neurons in identified central nervous system circuits
Bibliographic record
Abstract
Physiological properties of central nervous system neurons infected with a pseudorabies virus were examined in vitro by using whole-cell patch-clamp techniques. A strain of pseudorabies virus (PRV 152) isogenic with the Bartha strain of PRV was constructed to express an enhanced green fluorescent protein (EGFP) from the human cytomegalovirus immediate early promoter. Unilateral PRV 152 injections into the vitreous body of the hamster eye transsynaptically infected a restricted set of retinorecipient neurons including neurons in the hypothalamic suprachiasmatic nucleus (SCN) and the intergeniculate leaflet (IGL) of the thalamus. Retinorecipient SCN neurons were identified in tissue slices prepared for in vitro electrophysiological analysis by their expression of EGFP. At longer postinjection times, retinal ganglion cells in the contralateral eye also expressed EGFP, becoming infected after transsynaptic uptake and retrograde transport from infected retinorecipient neurons. Retinal ganglion cells that expressed EGFP were easily identified in retinal whole mounts viewed under epifluorescence. Whole-cell patch-clamp recordings revealed that the physiological properties of PRV 152-infected SCN neurons were within the range of properties observed in noninfected SCN neurons. Physiological properties of retinal ganglion cells also appeared normal. The results suggest that PRV 152 is a powerful tool for the transsynaptic labeling of neurons in defined central nervous system circuits that allows neurons to be identified in vitro by their expression of EGFP, analyzed electrophysiologically, and described in morphological detail.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".