Accurate Estimation of Viral Abundance by Epifluorescence Microscopy
Bibliographic record
Abstract
Virus enumeration by epifluorescence microscopy (EFM) is routinely done on preserved, refrigerated samples. Concerns about obtaining accurate and reproducible estimates led us to examine procedures for counting viruses by EFM. Our results indicate that aldehyde fixation results in rapid decreases in viral abundance. By 1 h postfixation, the abundance dropped by 16.4% +/- 5.2% (n = 6), and by 4 h, the abundance was 20 to 35% lower. The average loss rates for glutaraldehyde- and formaldehyde-fixed samples over the first 2 h were 0.12 and 0.13 h(-1), respectively. By 16 days, viral abundance had decreased by 72% (standard deviation, 6%; n = 6). Aldehyde fixation of samples followed by storage at 4 degrees C, for even a few hours, resulted in large underestimates of viral abundance. The viral loss rates were not constant, and in glutaraldehyde- and formaldehyde-fixed samples they decreased from 0.13 and 0.17 h(-1) during the first hour to 0.01 h(-1) between 24 and 48 h. Although decay rates changed over time, the abundance was predicted by using separate models to describe decay over the first 8 h and decay beyond 8 h. Accurate estimates of abundance were easily made with unfixed samples stained with Yo-Pro-1, SYBR Green I, or SYBR Gold, and slides could be stored at -20 degrees C for at least 2 weeks or, for Yo-Pro-1, at least 1 year. If essential, samples can be fixed and flash frozen in liquid nitrogen upon collection and stored at -86 degrees C. Determinations performed with fixed samples result in large underestimates of abundance unless slides are made immediately or samples are flash frozen. If protocols outlined in this paper are followed, EFM yields accurate estimates of viral abundance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".