Diagnostic parameters and reliability of four rapid immunochromatographic tests for dengue 4

Mata, Verónica Elizabeth; Passos, Sônia Regina Lambert; Santos, Maria Angélica Borges dos; Buonora, Sibelle Nogueira; Andrade, Carlos Augusto Ferreira de; Lima, Monique da Rocha Queiroz; Costa, Betina Moreira; Hökerberg, Yara Hahr Marques

doi:10.1016/j.bjid.2019.12.004

Cited by 4 publications

(2 citation statements)

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“…While some studies have found different sensitivities of detection of NS1 antigen by infecting serotype, others have not [64, 66, 70, 75, 77, 79–84]. The serotypes performing better or worse than others vary between studies, though DENV2 and DENV4 were commonly mentioned.…”

Section: Ns1 Antigen Detectionmentioning

confidence: 99%

Dengue: A review of laboratory diagnostics in the vaccine age

Frazer,

Norton

2024

Journal of Medical Microbiology

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Background. Dengue is an important arboviral infection of considerable public health significance. It occurs in a wide global belt within a variety of tropical regions. The timely laboratory diagnosis of Dengue infection is critical to inform both clinical management and an appropriate public health response. Vaccination against Dengue virus is being introduced in some areas. Discussion. Appropriate diagnostic strategies will vary between laboratories depending on the available resources and skills. Diagnostic methods available include viral culture, the serological detection of Dengue-specific antibodies in using enzyme immunoassays (EIAs), microsphere immunoassays, haemagglutination inhibition or in lateral flow point of care tests. The results of antibody tests may be influenced by prior vaccination and exposure to other flaviviruses. The detection of non-structural protein 1 in serum (NS1) has improved the early diagnosis of Dengue and is available in point-of-care assays in addition to EIAs. Direct detection of viral RNA from blood by PCR is more sensitive than NS1 antigen detection but requires molecular skills and resources. An increasing variety of isothermal nucleic acid detection methods are in development. Timing of specimen collection and choice of test is critical to optimize diagnostic accuracy. Metagenomics and the direct detection by sequencing of viral RNA from blood offers the ability to rapidly type isolates for epidemiologic purposes. Conclusion. The impact of vaccination on immune response must be recognized as it will impact test interpretation and diagnostic algorithms.

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Section: Ns1 Antigen Detectionmentioning

confidence: 99%

Dengue: A review of laboratory diagnostics in the vaccine age

Frazer,

Norton

2024

Journal of Medical Microbiology

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“…However, despite the prevalence of DF, [22–27] there is a lack of developed applications that can effectively examine symptoms and predict the presence of the disease. Although the nonparametric HT fit statistic [28,29] has been used to develop algorithms for parents in 1 study on DF in children, [1] alternative diagnostic methods are needed for comparison, such as convolutional neural networks (CNN), [30–33] artificial neural networks (ANN), [34,35] K-nearest neighbors algorithms (KNN), [36] and/or logistic regression algorithms (LR).…”

Section: Introductionmentioning

confidence: 99%

Detecting dengue fever in children using online Rasch analysis to develop algorithms for parents: An APP development and usability study

Chow

Chien

et al. 2023

Medicine

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Background: Dengue fever (DF) is a significant public health concern in Asia. However, detecting the disease using traditional dichotomous criteria (i.e., absent vs present) can be extremely difficult. Convolutional neural networks (CNNs) and artificial neural networks (ANNs), due to their use of a large number of parameters for modeling, have shown the potential to improve prediction accuracy (ACC). To date, there has been no research conducted to understand item features and responses using online Rasch analysis. To verify the hypothesis that a combination of CNN, ANN, K-nearest-neighbor algorithm (KNN), and logistic regression (LR) can improve the ACC of DF prediction for children, further research is required. Methods: We extracted 19 feature variables related to DF symptoms from 177 pediatric patients, of whom 69 were diagnosed with DF. Using the RaschOnline technique for Rasch analysis, we examined 11 variables for their statistical significance in predicting the risk of DF. Based on 2 sets of data, 1 for training (80%) and the other for testing (20%), we calculated the prediction ACC by comparing the areas under the receiver operating characteristic curve (AUCs) between DF + and DF− in both sets. In the training set, we compared 2 scenarios: the combined scheme and individual algorithms. Results: Our findings indicate that visual displays of DF data are easily interpreted using Rasch analysis; the k-nearest neighbors algorithm has a lower AUC (<0.50); LR has a relatively higher AUC (0.70); all 3 algorithms have an almost equal AUC (=0.68), which is smaller than the individual algorithms of Naive Bayes, LR in raw data, and Naive Bayes in normalized data; and we developed an app to assist parents in detecting DF in children during the dengue season. Conclusion: The development of an LR-based APP for the detection of DF in children has been completed. To help patients, family members, and clinicians differentiate DF from other febrile illnesses at an early stage, an 11-item model is proposed for developing the APP.

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