Quantification of Neural Progenitor Cells From Zika Virus-Infected Zebrafish Embryos
Bibliographic record
Abstract
Zika virus (ZIKV), an arthropod-borne orthoflavivirus, has emerged as a global health concern due to its ability to cause severe fetal neurological disorders, leading to the congenital Zika syndrome (CZS) in neonates. Vertical transmission during pregnancy can alter neural progenitor cell (NPC) proliferation and differentiation and induce apoptosis, leading to microcephaly and other neurodevelopmental abnormalities. While mammalian models have been used to study the impact of ZIKV on NPC behavior, limitations such as high costs, dedicated time, and ethical constraints have fostered the exploration of alternative systems. The zebrafish embryo constitutes an advantageous in vivo model for studying ZIKV neuropathogenesis. Indeed, ZIKV infection phenocopies several features of the CZS while sharing a conserved neuroanatomical layout and offering genetic plasticity and unique accessibility to the infected brain compared to mammals. Here, we describe a protocol for characterizing ZIKV-induced defects of NPCs in this zebrafish model, relying on whole animal flow cytometry. Key features • This protocol introduces a novel and straightforward zebrafish-based system for studying ZIKV-induced neuropathogenesis. • The protocol employs an unbiased methodology for flow cytometry-based quantification of neural progenitor cells from whole embryos. • This protocol can be transposed to any transgenic reporter zebrafish lines producing specific fluorescent cell populations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".