Evidence map›Paper›PMID 40614098›Full record

ArticleACS sensors2025

Multiplexed Salivary miRNA Quantification for Predicting Severe COVID-19 Symptoms in Children Using Ligation-RPA Amplification Assay.

Md Ahasan Ahamed, Zhikun Zhang, Aneesh Kshirsagar, Anthony J Politza, Usha Sethuraman, Srinivasan Suresh, Steven Hicks, Feng Guo, Weihua Guan

Abstract read
In one paragraph

Article in ACS sensors, 2025. 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

5 · Who and what money

Authors and funding

9 authors.

Md Ahasan AhamedDepartment of Intelligent Systems Engineering, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, Indiana 47408, United States.ORCID 0009-0006-9962-2994
Zhikun ZhangDepartment of Electrical Engineering, Pennsylvania State University, University Park, Pennsylvania 16802, United States.
Aneesh KshirsagarDepartment of Electrical Engineering, Pennsylvania State University, University Park, Pennsylvania 16802, United States.ORCID 0000-0003-2288-5439
Anthony J PolitzaDepartment of Biomedical Engineering, Pennsylvania State University, University Park, Pennsylvania 16802, United States.ORCID 0009-0005-8670-2357
Usha SethuramanDivision of Emergency Medicine, Department of Pediatrics, Children's Hospital of Michigan/Central Michigan University, Detroit, Michigan 48201, United States.
Srinivasan SureshDivisions of Health Informatics and Emergency Medicine, Department of Pediatrics, University of Pittsburgh School of Medicine and UPMC Children's Hospital of Pittsburgh, Pittsburgh, Pennsylvania 15224, United States.
Steven HicksDepartment of Pediatrics, Division of Academic General Pediatrics, Penn State Neuroscience Institute, Pennsylvania State University, Hershey, Pennsylvania 17033, United States.
Feng GuoDepartment of Intelligent Systems Engineering, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, Indiana 47408, United States.ORCID 0000-0001-9103-3235
Weihua GuanDepartment of Intelligent Systems Engineering, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, Indiana 47408, United States.ORCID 0000-0002-8435-9672

Funding

Severity Predictors Integrating salivary Transcriptomics and proteomics with Multi neural network Intelligence in SARS-CoV2 infection in Children (SPITS MISC)R33HD105610 · NICHD · CENTRAL MICHIGAN UNIVERSITY · PI HICKS, STEVEN DANIEL, SETHURAMAN, USHA · 2023 to 2023
$1.4M
Severity Predictors Integrating salivary Transcriptomics and proteomics with Multi neural network Intelligence in SARS-CoV2 infection in Children (SPITS MISC)R61HD105610 · NICHD · CENTRAL MICHIGAN UNIVERSITY · PI HICKS, STEVEN DANIEL, SETHURAMAN, USHA · 2021 to 2022
$1.4M
Home Fingerprick Blood-Based HIV Self-Test For Quantitative Monitoring Of Viral ReboundR33AI147419 · NIAID · PENNSYLVANIA STATE UNIVERSITY, THE · PI GUAN, WEIHUA · 2023 to 2024
$1.3M
Home Fingerprick Blood-Based HIV Self-Test For Quantitative Monitoring Of Viral ReboundR61AI147419 · NIAID · PENNSYLVANIA STATE UNIVERSITY, THE · PI GUAN, WEIHUA · 2020 to 2022
$1.0M
NIAID NIH HHS R33 AI147419NIAID NIH HHS R61 AI147419NICHD NIH HHS R33 HD105610NICHD NIH HHS R61 HD105610
6 · The paper itself

Abstract

While most children with COVID-19 experience mild symptoms or remain asymptomatic, some may develop severe complications. Early identification of children at risk for severe outcomes is essential to ensuring timely and effective intervention. Recent studies have identified alterations in salivary microRNA (miRNA) expression levels as promising biomarkers for predicting severe complications in children. However, there remains a need for a rapid, noninvasive, and quantitative method to detect miRNA expression level changes, as their upregulation or downregulation serves as a hallmark of various diseases, providing an alternative to sequencing-based methods. Here, we developed a highly specific and sensitive ligation-coupled recombinase polymerase amplification (RPA) assay for quantitatively detecting multiplex severe and nonsevere miRNAs on a portable platform. The assay begins with an miRNA-templated annealing and ligation reaction of miR-1273, miR-296, and miR-29, followed by an RPA reaction. We quantified 100 pM to 1 fM, resolving 1 fM, with 100% specificity. Next, we validated portable extraction against benchtop extraction, achieving

Indexed as

COVID-19MicroRNAsNucleic Acid Amplification TechniquesSalivaChildChild, PreschoolHumansSARS-CoV-2MicroRNAsligation-RPA reactionpoint-of-careportable analyzerportable centrifugerecombinase polymerase amplificationsalivary miRNAsSARS-CoV-2severe COVID-19

Identifiers

PMID40614098
PMCPMC13046176

What OpenQuestion holds

Textmetadata
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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.