The Use of Propidium Iodide to Assess Excitotoxic Neuronal Death in Primary Mixed Cortical Cultures
Bibliographic record
Abstract
Neurodegenerative disorders are subjects of intense scrutiny in biomedical research because of their often-debilitating effects. Currently, many laboratories are engaged in developing or testing drugs to prevent neuronal loss in a variety of these pathologies. A key to testing such drugs is the use of a fast, reliable, and easily reproducible model of neurodegeneration and neuroprotection. Our laboratory has previously used propidium iodide (PI) to assess the degree of neurodegeneration and neuroprotection under a variety of conditions. Ultimately, efforts are underway in the laboratory to prevent delayed neuronal loss following acute ischemic insults using drug therapies. It is now believed that a key mechanism of neurodegeneration following acute ischemia or anoxia is a result of excitotoxicity via N-methyl-D-aspartate receptors (NMDARs) and subsequent overproduction of nitric oxide via neuronal nitric oxide synthase (nNOS). Thus, for the purposes of this chapter, the insult used to induce cell death will be various concentrations of NMDA and the compound used to demonstrate neuroprotection will be the nonspecific NOS inhibitor No-nitro-L-arginine methyl ester (L-NAME). Assessment of neuronal death is accomplished by measuring changes in PI fluorescence using a fluorescent plate reader. This chapter will outline the necessary steps required to (1) produce primary mixed cortical cultures, (2) apply PT and NMDA to these cultures, (3) quantify the results obtained from these cultures, and (4) image these cultures in conjunction with Hoechst 33342 and immunocytochemistry using fluorescence microscopy.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".