Flow virometry: recent advancements, best practices, and future frontiers
Bibliographic record
Abstract
The imperative for developing robust tools to detect, analyze, and characterize viruses has become increasingly evident as they continue to threaten human health. In this review, we focus on recent advancements in studying human viruses with flow virometry (FV), an emerging technique that has gained considerable momentum over the past 5 years. These advancements include the application of FV in viral surface phenotyping, viral protein functionality, virus sorting, vaccine development, and diagnostics. With examples illustrated using primary data from our recent studies, we demonstrate that FV is a powerful yet underutilized methodology that, when employed with best practices and experimental rigor, can be highly valuable for studying individual virion heterogeneity, virus phenotypes, and virus-antibody interactions. In this review, we also address the current challenges when performing FV studies, propose strategies to overcome these obstacles, and outline best practices for both new and experienced researchers. Finally, we discuss the promising future prospects of FV within the broader context of virology research.
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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) | 0.001 | 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 | 0.001 |
| 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 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".