Development of a high-sensitivity and short-duration fluorescence in situ hybridization method for viral mRNA detection in HEK 293T cells
Bibliographic record
Abstract
Coronavirus disease 2019 (COVID-19) is an extremely contagious illness caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Early disease recognition of COVID-19 is crucial not only for prompt diagnosis and treatment of the patients, but also for effective public health surveillance and response. The reverse transcription-polymerase chain reaction (RT-PCR) is the most common method for the detection of SARS-CoV-2 viral mRNA and is regarded as the gold standard test for COVID-19. However, this test and those for antibodies (IgM and IgG) and antigens have certain limitations (e.g., by yielding false-negative and false-positive results). We have developed an RNA fluorescence in situ hybridization (FISH) method for high-sensitivity detection of SARS-CoV-2 mRNAs in HEK 293T cell cultures as a model. After transfection of HEK 293T cells with plasmids, Spike (S)/envelope (E) proteins and their mRNAs were clearly detected inside the cells. In addition, hybridization time could be reduced to 2 hours for faster detection when probe concentration was increased. Our approach might thus significantly improve the sensitivity and specificity of SARS-CoV-2 detection and be widely applied for the high-sensitivity single-molecular detection of other RNA viruses (e.g., Middle East respiratory syndrome coronavirus (MERS-CoV), Hepatitis A virus, all influenza viruses, and human immunodeficiency virus (HIV)) in various types of samples including tissue, body fluid, blood, and water. RNA FISH can also be utilized for the detection of DNA viruses (e.g., Monkeypox virus, human papillomavirus (HPV), and cytomegalovirus (CMV)) by detection of their mRNAs inside cells or body fluid.
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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 | 0.002 | 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) | 0.000 | 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".