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Warmt C, Nagaba J, Henkel J. Comparison of pre-labelled primers and nucleotides as DNA labelling method for lateral flow detection of Legionella pneumophila amplicons. Sci Rep 2024; 14:5018. [PMID: 38424185 PMCID: PMC10904838 DOI: 10.1038/s41598-024-55703-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2023] [Accepted: 02/27/2024] [Indexed: 03/02/2024] Open
Abstract
Labelling of nucleic acid amplicons during polymerase chain reaction (PCR) or isothermal techniques is possible by using both labelled primers and labelled nucleotides. While the former is the widely used method, the latter can offer significant advantages in terms of signal enhancement and improving the detection limit of an assay. Advantages and disadvantages of both methods depend on different factors, including amplification method, detection method and amplicon length. In this study, both methods for labelling PCR products for lateral flow assay (LFA) analysis (LFA-PCR) were analysed and compared. It was shown that labelling by means of nucleotides results in an increase in label incorporation rates. Nonetheless, this advantage is negated by the need for post-processing and competitive interactions. In the end, it was possible to achieve a detection limit of 3 cell equivalents for the detection of the Legionella-DNA used here via primer labelling. Labelling via nucleotides required genomic DNA of at least 3000 cell equivalents as starting material as well as an increased personnel and experimental effort.
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Affiliation(s)
- Christian Warmt
- Fraunhofer Institute for Cell Therapy and Immunology - Bioanalytics and Bioprocesses (IZI-BB), 14476, Potsdam, Germany.
| | - Jette Nagaba
- Fraunhofer Institute for Cell Therapy and Immunology - Bioanalytics and Bioprocesses (IZI-BB), 14476, Potsdam, Germany
| | - Jörg Henkel
- Fraunhofer Institute for Cell Therapy and Immunology - Bioanalytics and Bioprocesses (IZI-BB), 14476, Potsdam, Germany
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Wang W, Liu L, Zhu J, Xing Y, Jiao S, Wu Z. AI-Enhanced Visual-Spectral Synergy for Fast and Ultrasensitive Biodetection of Breast Cancer-Related miRNAs. ACS NANO 2024; 18:6266-6275. [PMID: 38252138 DOI: 10.1021/acsnano.3c10543] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/23/2024]
Abstract
In biomedical testing, artificial intelligence (AI)-enhanced analysis has gradually been applied to the diagnosis of certain diseases. This research employs AI algorithms to refine the precision of integrative detection, encompassing both visual results and fluorescence spectra from lateral flow assays (LFAs), which signal the presence of cancer-linked miRNAs. Specifically, the color shift of gold nanoparticles (GNPs) is paired with the red fluorescence from nitrogen vacancy color centers (NV-centers) in fluorescent nanodiamonds (FNDs) and is integrated into LFA strips. While GNPs amplify the fluorescence of FNDs, in turn, FNDs enhance the color intensity of GNPs. This reciprocal intensification of fluorescence and color can be synergistically augmented with AI algorithms, thereby improving the detection sensitivity for early diagnosis. Supported by the detection platform based on this strategy, the fastest detection results with a limit of detection (LOD) at the fM level and the R2 value of ∼0.9916 for miRNA can be obtained within 5 min. Meanwhile, by labeling the capture probes for miRNA-21 and miRNA-96 (both of which are early indicators of breast cancer) on separate T-lines, simultaneous detection of them can be achieved. The miRNA detection methods employed in this study may potentially be applied in the future for the early detection of breast cancer.
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Affiliation(s)
- Wei Wang
- School of Mechanical Engineering, Southeast University, Nanjing 211189, People's Republic of China
| | - Lei Liu
- School of Mechanical Engineering, Southeast University, Nanjing 211189, People's Republic of China
| | - Jianxiong Zhu
- School of Mechanical Engineering, Southeast University, Nanjing 211189, People's Republic of China
| | - Youqiang Xing
- School of Mechanical Engineering, Southeast University, Nanjing 211189, People's Republic of China
| | - Songlong Jiao
- School of Mechanical Engineering, Southeast University, Nanjing 211189, People's Republic of China
| | - Ze Wu
- School of Mechanical Engineering, Southeast University, Nanjing 211189, People's Republic of China
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Roche SD, Ekwunife OI, Mendonca R, Kwach B, Omollo V, Zhang S, Ongwen P, Hattery D, Smedinghoff S, Morris S, Were D, Rech D, Bukusi EA, Ortblad KF. Measuring the performance of computer vision artificial intelligence to interpret images of HIV self-testing results. Front Public Health 2024; 12:1334881. [PMID: 38384878 PMCID: PMC10880864 DOI: 10.3389/fpubh.2024.1334881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Accepted: 01/22/2024] [Indexed: 02/23/2024] Open
Abstract
Introduction HIV self-testing (HIVST) is highly sensitive and specific, addresses known barriers to HIV testing (such as stigma), and is recommended by the World Health Organization as a testing option for the delivery of HIV pre-exposure prophylaxis (PrEP). Nevertheless, HIVST remains underutilized as a diagnostic tool in community-based, differentiated HIV service delivery models, possibly due to concerns about result misinterpretation, which could lead to inadvertent onward transmission of HIV, delays in antiretroviral therapy (ART) initiation, and incorrect initiation on PrEP. Ensuring that HIVST results are accurately interpreted for correct clinical decisions will be critical to maximizing HIVST's potential. Early evidence from a few small pilot studies suggests that artificial intelligence (AI) computer vision and machine learning could potentially assist with this task. As part of a broader study that task-shifted HIV testing to a new setting and cadre of healthcare provider (pharmaceutical technologists at private pharmacies) in Kenya, we sought to understand how well AI technology performed at interpreting HIVST results. Methods At 20 private pharmacies in Kisumu, Kenya, we offered free blood-based HIVST to clients ≥18 years purchasing products indicative of sexual activity (e.g., condoms). Trained pharmacy providers assisted clients with HIVST (as needed), photographed the completed HIVST, and uploaded the photo to a web-based platform. In real time, each self-test was interpreted independently by the (1) client and (2) pharmacy provider, with the HIVST images subsequently interpreted by (3) an AI algorithm (trained on lab-captured images of HIVST results) and (4) an expert panel of three HIVST readers. Using the expert panel's determination as the ground truth, we calculated the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for HIVST result interpretation for the AI algorithm as well as for pharmacy clients and providers, for comparison. Results From March to June 2022, we screened 1,691 pharmacy clients and enrolled 1,500 in the study. All clients completed HIVST. Among 854 clients whose HIVST images were of sufficient quality to be interpretable by the AI algorithm, 63% (540/854) were female, median age was 26 years (interquartile range: 22-31), and 39% (335/855) reported casual sexual partners. The expert panel identified 94.9% (808/854) of HIVST images as HIV-negative, 5.1% (44/854) as HIV-positive, and 0.2% (2/854) as indeterminant. The AI algorithm demonstrated perfect sensitivity (100%), perfect NPV (100%), and 98.8% specificity, and 81.5% PPV (81.5%) due to seven false-positive results. By comparison, pharmacy clients and providers demonstrated lower sensitivity (93.2% and 97.7% respectively) and NPV (99.6% and 99.9% respectively) but perfect specificity (100%) and perfect PPV (100%). Conclusions AI computer vision technology shows promise as a tool for providing additional quality assurance of HIV testing, particularly for catching Type II error (false-negative test interpretations) committed by human end-users. We discuss possible use cases for this technology to support differentiated HIV service delivery and identify areas for future research that is needed to assess the potential impacts-both positive and negative-of deploying this technology in real-world HIV service delivery settings.
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Affiliation(s)
- Stephanie D. Roche
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, United States
| | - Obinna I. Ekwunife
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, United States
| | | | - Benn Kwach
- Centre for Microbiology Research, Kenya Medical Research Institute, Kisumu, Kenya
| | - Victor Omollo
- Centre for Microbiology Research, Kenya Medical Research Institute, Kisumu, Kenya
| | - Shengruo Zhang
- Department of Epidemiology, University of Washington, Seattle, WA, United States
| | | | | | | | | | | | | | - Elizabeth A. Bukusi
- Centre for Microbiology Research, Kenya Medical Research Institute, Kisumu, Kenya
- Department of Global Health, University of Washington, Seattle, WA, United States
- Department of Obstetrics and Gynecology, University of Washington, Seattle, WA, United States
| | - Katrina F. Ortblad
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, United States
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Sanchez T, Mavragani A, Álamo E, Pérez-Panizo N, Mousa A, Dacal E, Lin L, Vladimirov A, Cuadrado D, Mateos-Nozal J, Galán JC, Romero-Hernandez B, Cantón R, Luengo-Oroz M, Rodriguez-Dominguez M. A Smartphone-Based Platform Assisted by Artificial Intelligence for Reading and Reporting Rapid Diagnostic Tests: Evaluation Study in SARS-CoV-2 Lateral Flow Immunoassays. JMIR Public Health Surveill 2022; 8:e38533. [PMID: 36265136 PMCID: PMC9840096 DOI: 10.2196/38533] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 09/16/2022] [Accepted: 10/13/2022] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Rapid diagnostic tests (RDTs) are being widely used to manage COVID-19 pandemic. However, many results remain unreported or unconfirmed, altering a correct epidemiological surveillance. OBJECTIVE Our aim was to evaluate an artificial intelligence-based smartphone app, connected to a cloud web platform, to automatically and objectively read RDT results and assess its impact on COVID-19 pandemic management. METHODS Overall, 252 human sera were used to inoculate a total of 1165 RDTs for training and validation purposes. We then conducted two field studies to assess the performance on real-world scenarios by testing 172 antibody RDTs at two nursing homes and 96 antigen RDTs at one hospital emergency department. RESULTS Field studies demonstrated high levels of sensitivity (100%) and specificity (94.4%, CI 92.8%-96.1%) for reading IgG band of COVID-19 antibody RDTs compared to visual readings from health workers. Sensitivity of detecting IgM test bands was 100%, and specificity was 95.8% (CI 94.3%-97.3%). All COVID-19 antigen RDTs were correctly read by the app. CONCLUSIONS The proposed reading system is automatic, reducing variability and uncertainty associated with RDTs interpretation and can be used to read different RDT brands. The web platform serves as a real-time epidemiological tracking tool and facilitates reporting of positive RDTs to relevant health authorities.
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Affiliation(s)
| | | | | | - Nuria Pérez-Panizo
- Servicio de Geriatría, Hospital Universitario Ramon y Cajal, Madrid, Spain.,Instituto Ramón y Cajal de Investigación Sanitaria (IRYCIS), Madrid, Spain
| | | | | | - Lin Lin
- Spotlab, Madrid, Spain.,Biomedical Image Technologies, ETSI Telecomunicación, Universidad Politécnica de Madrid, Madrid, Spain
| | | | | | - Jesús Mateos-Nozal
- Servicio de Geriatría, Hospital Universitario Ramon y Cajal, Madrid, Spain.,Instituto Ramón y Cajal de Investigación Sanitaria (IRYCIS), Madrid, Spain
| | - Juan Carlos Galán
- Servicio de Microbiología, Hospital Universitario Ramon y Cajal, Madrid, Spain.,Instituto Ramón y Cajal de Investigación Sanitaria (IRYCIS), Madrid, Spain.,CIBER en Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain
| | - Beatriz Romero-Hernandez
- Servicio de Microbiología, Hospital Universitario Ramon y Cajal, Madrid, Spain.,Instituto Ramón y Cajal de Investigación Sanitaria (IRYCIS), Madrid, Spain.,CIBER en Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain
| | - Rafael Cantón
- Servicio de Microbiología, Hospital Universitario Ramon y Cajal, Madrid, Spain.,Instituto Ramón y Cajal de Investigación Sanitaria (IRYCIS), Madrid, Spain.,CIBER en Enfermedades Infecciosas (CIBERINFEC), Instituto de Salud Carlos III, Madrid, Spain
| | | | - Mario Rodriguez-Dominguez
- Servicio de Microbiología, Hospital Universitario Ramon y Cajal, Madrid, Spain.,Instituto Ramón y Cajal de Investigación Sanitaria (IRYCIS), Madrid, Spain.,CIBER en Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain
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