A rapid, visual, ultrasensitive and highly specific method for detecting adeno-associated virus 2020 based on the LAMP-CRISPR/Cas12a system
Bibliographic record
Abstract
Avian parvovirus infection would lead to growth retardation, weight loss, physical deformities and increased mortality in poultry, causing substantial economic losses to the poultry industry. Therefore, the development of a rapid, visual, ultrasensitive and highly specific method is essential for timely diagnosis and effective control of the avian parvovirus infection. In this study, we developed a detection platform based on loop-mediated isothermal amplification (LAMP) combined with the clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 12a (Cas12a) system. Firstly, we have identified a novel avian parvovirus strain from diseased Muscovy ducks. Through genome sequencing, sequence assembly and phylogenetic tree analysis, we have identified this novel avian parvovirus as an adeno-associated virus (AAA) belonging to the family Parvoviridae, subfamily Parvovirinae and genus Dependoparvovirus. So, the novel virus strain was named AAV-2020. Next, specific sgRNAs and LAMP primers targeting the 3 capsid proteins (Cap) genes of AAV-2020 were designed and optimized. Moreover, the CRISPR/Cas12a-based system demonstrated a limit of detection as low as 2 copies/μL for AAV-2020. Importantly, the system could effectively distinguish AAV-2020 from 3 closely related AAV strains with high sequence similarity, indicating excellent specificity. In summary, we developed a novel, rapid, visual, ultrasensitive and highly specific detection system for AAV-2020, offering a reliable tool for early diagnosis and on-site detection of avian parvovirus infections, which would aid in the prevention and control of avian parvovirus infection in poultry industry.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".