Evidence map›Paper›PMID 42521679›Full record

ArticleMicrosystems & nanoengineering2026

ViraLite: an ultracompact HIV viral load self-testing system with internal quality control.

Aneesh Kshirsagar, Anthony J Politza, Tianyi Liu, Md Ahasan Ahamed, Ming Dong, Muhammad Asad Ullah Khalid, Roland Jones, Uttara Seshu, Kathryn Risher, Casey N Pinto and 3 more

Abstract read
In one paragraph

Article in Microsystems & nanoengineering, 2026. 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

13 authors.

Aneesh Kshirsagar *Department of Intelligent Systems Engineering, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, 47408, USA.
Anthony J Politza *Department of Biomedical Engineering, The Pennsylvania State University, University Park, PA, 16802, USA.
Tianyi LiuDepartment of Electrical Engineering, The Pennsylvania State University, University Park, PA, 16802, USA.
Md Ahasan AhamedDepartment of Intelligent Systems Engineering, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, 47408, USA.
Ming DongDepartment of Electrical Engineering, The Pennsylvania State University, University Park, PA, 16802, USA.
Muhammad Asad Ullah KhalidDepartment of Intelligent Systems Engineering, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, 47408, USA.
Roland JonesDepartments of Pathology and Laboratory Medicine and Pharmacology, Milton S. Hershey Medical Center and The Pennsylvania State University College of Medicine, Hershey, PA, 17033, USA.
Uttara SeshuDepartment of Public Health Sciences, The Pennsylvania State University College of Medicine, Hershey, PA, 17033, USA.
Kathryn RisherDepartment of Public Health Sciences, The Pennsylvania State University College of Medicine, Hershey, PA, 17033, USA.
Casey N PintoDepartment of Public Health Sciences, The Pennsylvania State University College of Medicine, Hershey, PA, 17033, USA.
Yusheng ZhuDepartments of Pathology and Laboratory Medicine and Pharmacology, Milton S. Hershey Medical Center and The Pennsylvania State University College of Medicine, Hershey, PA, 17033, USA.
Samir K GuptaDivision of Infectious Diseases, Indiana University School of Medicine, Indianapolis, IN, 46202, USA.
Weihua GuanDepartment of Intelligent Systems Engineering, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, 47408, USA. guanw@iu.edu.ORCID http://orcid.org/0000-0002-8435-9672

Funding

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
National Science Foundation (NSF) 2319913National Science Foundation (NSF) 2528103NIAID NIH HHS R33 AI147419NIAID NIH HHS R61 AI147419U.S. Department of Health & Human Services | National Institutes of Health (NIH) R33AI147419U.S. Department of Health & Human Services | National Institutes of Health (NIH) R61AI147419
6 · The paper itself

Abstract

Effective antiretroviral therapy has transformed HIV into a manageable chronic condition, provided viral suppression is maintained through routine viral load (VL) monitoring. Access to frequent VL testing remains limited, particularly outside centralized clinical settings. Decentralized and at-home HIV VL testing requires low-volume systems designed for patient operation. These systems must distinguish true viral suppression from test failure. Many approaches lack internal process verification, which makes negative results ambiguous. Here, we present ViraLite, an ultracompact, battery-powered HIV VL monitoring system that integrates reverse transcription loop-mediated isothermal amplification (RT-LAMP) with an RNase P internal process control, machine learning-assisted fluorescence analysis for one-pot multiplexing, and smartphone-guided operation. We evaluated ViraLite using 45 clinically archived plasma samples and benchmarked it against reverse transcription quantitative polymerase chain reaction (RT-qPCR). The internal process control identified 17 inconclusive tests that would otherwise be misclassified as negative. Among valid tests, ViraLite achieved 93.3% sensitivity and 100% specificity versus RT-qPCR. ViraLite enables decentralized HIV VL testing with low sample volume and interpretable negative results. This capability can expand monitoring beyond traditional clinic workflows and addresses a key barrier to patient-operated VL testing.

Identifiers

PMID42521679
PMCPMC13415889

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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.