Detection and Identification of Human Metapneumovirus Infection in ShenZhen, China
Bibliographic record
Abstract
Background: Human metapneumovirus (HMPV) is a newly discovered and identified negative-sense RNA virus thought to be associated with respiratory disease in 2001. Acute respiratory tract infections with HMPV have been reported in Europe, America, Australia, Japan, China, Canada, Tailand, HangKong. The clinical syndrome of the infected children ranges from mild respiratory problems to bronchiolitis and pneumonitis. In this study, for rapid, multiplex detection of respiratory tract virus in clinical specimens with respiratory infections caused by HMPV. HMPV was identified by molecular biology technique. Methods: 7 respiratory tract virus (11 typing) were deteced by using multiplex PCR technology and a flexible Multi-Analyte Profiling (suspension array). Human Metapneumovirus was identified by using a real-time reverse transcriptase PCR (RT-PCR) assay and RNA sequences Results: The virus were detected in 40.23% (19/47) of. 47 samples collected from clinical respiratory tract infections, including 8 (42.11%) HRSV, 36.84%) Influenza virus, 1 (5.26%) Parainfluenza virus, Rhinovirus, Coxsackievirus and Human Metapneumovirus infections. This hMPV was the first deteced from clinical samples in ShenZhen. The N genes amplified of hMPV from specimens was identified by sequencing and was compared with GenBank. The ShenZhen hMPV N genes nucleotides similarities were over 98% with JPS03–187, JPS03–176, JPS03–240, JPS03–178, BJ1887, NED01–22, NED01–17. Conclusion: Multiplex PCR technology and flexible Multi-Analyte Profiling were high sensitive and throughput and increased assay speed for multiplex detecting respiratory pathogens in clinical specimens. It is useful tool for epidemiology yet.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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 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".