Evidence map›Paper›PMID 39764454›Full record

ArticleFrontiers in microbiology2024

Deciphering dengue: novel RNA barcoding segments for enhanced serotype-specific identification and global surveillance of dengue viruses.

Shuai Jiang, Gaili Zhao, Yunyun Ding, Shunxing Ye, Zeqi Li, Changqiao You, Yan Yin, Xinhong Guo

Abstract read
In one paragraph

Article in Frontiers in microbiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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

8 authors.

Shuai Jiang *College of Biology, Hunan University, Changsha, China.
Gaili Zhao *College of Biology, Hunan University, Changsha, China.
Yunyun Ding *College of Biology, Hunan University, Changsha, China.
Shunxing YeCollege of Bioscience and Biotechnology, Hunan Agricultural University, Changsha, China.
Zeqi LiCollege of Biology, Hunan University, Changsha, China.
Changqiao YouCollege of Biology, Hunan University, Changsha, China.
Yan YinCollege of Biology, Hunan University, Changsha, China.
Xinhong GuoCollege of Biology, Hunan University, Changsha, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Dengue viruses (DENVs), the causative agents of dengue hemorrhagic fever and dengue shock syndrome, undergo genetic mutations that result in new strains and lead to ongoing global re-infections. Objectives: To address the growing complexity of identifying and tracking biological samples, this study screened RNA barcode segments for the four DENV serotypes, ensuring high specificity and recall rates for DENV identification using segments. Results: Through analyzing complete genome sequences of DENVs, we screened eight barcode segments for DENV, DENV-1, DENV-2, DENV-3, and DENV-4 identification. Comparing the screened barcode segments to sequences of known strains and determining the proportion of correctly or incorrectly identified nucleotides, these segments demonstrated an average recall rate at nucleotide level of 91.34% for four DENV serotypes, a specificity of 99.50% at species level within the Conclusion: This work established a standard framework for DENV identification and barcode segment selection, promising significant advancements in the real-time management and control of DENVs, thereby enhancing surveillance capabilities and facilitating targeted interventions in dengue outbreak-prone regions.

Indexed as

four DENV serotypesgenetic information visualizationpopulation genetic testsRNA barcoding technologyspecies-specific molecule markers

Identifiers

PMID39764454
PMCPMC11701003

What OpenQuestion holds

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Registered trials

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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.