Generation of three-dimensional human neuronal cultures: application to modeling CNS viral infections
Bibliographic record
Abstract
BACKGROUND: A variety of neurological disorders including neurodegenerative diseases and infection by neurotropic viruses can cause structural and functional changes in the central nervous system (CNS), resulting in long-term neurological sequelae. An improved understanding of the pathogenesis of these disorders is important for developing efficacious interventions. Human induced pluripotent stem cells (hiPSCs) offer an extraordinary window for modeling pathogen-CNS interactions, and other cellular interactions, in three-dimensional (3D) neuronal cultures that can recapitulate several aspects of in vivo brain tissue. METHODS: Herein, we describe a prototype of scaffold-free hiPSC-based adherent 3D (A-3D) human neuronal cultures in 96-well plates. To test their suitability for drug screening, A-3D neuronal cultures were infected with herpes simplex virus type 1 (HSV-1) with or without acyclovir. RESULTS: The half maximal inhibitory concentration (IC50) of acyclovir was 3.14 μM and 3.12 μM determined using flow cytometry and the CX7 High Content Screening platform, respectively. CONCLUSIONS: Our A-3D neuronal cultures provide an unprecedented opportunity for high-content drug screening programs to treat human CNS infections.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".