Evidence map›Paper›PMID 39411185›Full record

ArticlePractical laboratory medicine2024

The potential relationship between EasyNAT system Tt values and Cobas z480 Ct values in the detection of SARS-Cov-2.

Lu-Qing Zheng, Qing-Yong Wang

Abstract read
In one paragraph

Article in Practical laboratory medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Lu-Qing ZhengDepartment of Clinical Laboratory, Xiyuan Hospital, China Academy of Chinese Medical Sciences, Beijing, 100091, China.
Qing-Yong WangDepartment of Clinical Laboratory, Xiyuan Hospital, China Academy of Chinese Medical Sciences, Beijing, 100091, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: This study aimed to evaluate the potential relationship between the time threshold (Tt) values of a commercial EasyNAT system, which is based on cross priming amplification (CPA) technology, and the cycle threshold (Ct) values of the Cobas z480 analyzer, which is based on a real-time fluorescence polymerase chain reaction (PCR) method, in the detection of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) from oropharyngeal swabs. Design and Methods: Data were retrospectively collected from a clinical laboratory between December 4, 2022 and July 1, 2024. Results: A total of 277 EasyNAT-positive samples (Tt values from 3.83 to 29.5) were simultaneously investigated using the Cobas z480 analyzer (Ct values from 10.74 to 38.78). The concordance rate between the two systems was 100 %. Among the positive samples, the mean and maximum PCR Ct values of O and N genes increased in line with increasing Tt values of the left and right amplification areas of the EasyNAT system. The maximum Ct values of the O or N gene determined by the Cobas z480 analyzer were no more than 29.52 when the Tt values of the left or right amplification areas of the UC0116 analyzer were no more than 6. Conclusions: The safe, simple, fast, accurate, and automatic EasyNAT system used in conjunction with a PCR system might be a better choice for the detection of SARS-CoV-2 in hospitals, especially in settings without sophisticated PCR facilities. The Tt value (≤6) of the EasyNAT system can be a reference index for estimating the maximum Ct value (29.52) in SARS-CoV-2-positive samples.

Indexed as

Cobas z480Cycle thresholdEasyNAT systemSARS-CoV-2Time threshold

Identifiers

PMID39411185
PMCPMC11474176

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.