Evidence map›Paper›PMID 40161589›Full record

ArticlebioRxiv : the preprint server for biology2025

Point-Of-Need One-Pot Multiplexed RT-LAMP Test For Detecting Three Common Respiratory Viruses In Saliva.

Aneesh Kshirsagar, Dean DeRosa, Anthony J Politza, Tianyi Liu, Ming Dong, Weihua Guan

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2025. 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

5 · Who and what money

Authors and funding

6 authors.

Aneesh KshirsagarORCID 0000-0003-2288-5439
Dean DeRosa
Anthony J PolitzaORCID 0009-0005-8670-2357
Tianyi Liu
Weihua Guan

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Respiratory viral infections pose a significant global public health challenge, partly due to the difficulty in rapidly and accurately distinguishing between viruses with similar symptoms at the point of care, hindering timely and appropriate treatment and limiting effective infection control and prevention efforts. Here, we developed a multiplexed, non- invasive saliva-based, reverse transcription loop-mediated isothermal amplification (RT- LAMP) test that enables the simultaneous detection of three of the most common respiratory infections, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), Influenza (Flu), and respiratory syncytial virus (RSV), in a single reaction via specific probes and monitored in real-time by a machine-learning-enabled compact analyzer. Our results demonstrate that the multiplexed assay can effectively detect three target RNAs with high accuracy. Further, testing with spiked saliva samples showed strong agreement with reverse transcription polymerase chain reaction (RT-PCR) assay, with area under the curve (AUC) values of 0.82, 0.93, and 0.96 for RSV, Influenza, and SARS-CoV-2, respectively. By enabling the rapid detection of respiratory infections from easily collected saliva samples at the point of care, the device presented here offers a practical and efficient tool for improving outcomes and helping prevent the spread of contagious diseases. Significance: This research presents an innovative approach to respiratory infection diagnostics by combining a one-pot isothermal molecular test with machine learning-based analysis to simultaneously detect SARS-CoV-2, Influenza, and RSV in saliva samples. The battery- powered portable analyzer features novel machine-learning-assisted fluorescence detection for multiplexed reporter quantification, eliminating the need for traditional filter- based optical components and enabling adaptation to new targets without hardware changes. The test demonstrates high accuracy in detecting single and co-infections in spiked saliva samples, providing a rapid, cost-effective point-of-need solution. This tool can expand testing access, improve patient outcomes, and support more effective disease control, particularly in resource-limited or decentralized healthcare settings.

Identifiers

PMID40161589
PMCPMC11952292

What OpenQuestion holds

Textmetadata
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.