Diagnostic assay and technology advancement for detecting SARS-CoV-2 infections causing the COVID-19 pandemic

Dhar, Bidhan Chandra

doi:10.1007/s00216-022-03918-7

Cited by 30 publications

(25 citation statements)

References 204 publications

(241 reference statements)

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“…RT-PCR is a molecular test that directly measures parts of the viral genome or viral transcripts [5]. The serological test detects the presence of antibodies, which are generated over days to weeks after infection exposure [6].…”

Section: Sars-cov-2 Disease (Covid-19mentioning

confidence: 99%

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Mortality Prediction of COVID-19 Patients Using Radiomic and Neural Network Features Extracted from a Wide Chest X-ray Sample Size: A Robust Approach for Different Medical Imbalanced Scenarios

et al. 2022

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Aim: The aim of this study was to develop robust prognostic models for mortality prediction of COVID-19 patients, applicable to different sets of real scenarios, using radiomic and neural network features extracted from chest X-rays (CXRs) with a certified and commercially available software. Methods: 1816 patients from 5 different hospitals in the Province of Reggio Emilia were included in the study. Overall, 201 radiomic features and 16 neural network features were extracted from each COVID-19 patient’s radiography. The initial dataset was balanced to train the classifiers with the same number of dead and survived patients, randomly selected. The pipeline had three main parts: balancing procedure; three-step feature selection; and mortality prediction with radiomic features through three machine learning (ML) classification models: AdaBoost (ADA), Quadratic Discriminant Analysis (QDA) and Random Forest (RF). Five evaluation metrics were computed on the test samples. The performance for death prediction was validated on both a balanced dataset (Case 1) and an imbalanced dataset (Case 2). Results: accuracy (ACC), area under the ROC-curve (AUC) and sensitivity (SENS) for the best classifier were, respectively, 0.72 ± 0.01, 0.82 ± 0.02 and 0.84 ± 0.04 for Case 1 and 0.70 ± 0.04, 0.79 ± 0.03 and 0.76 ± 0.06 for Case 2. These results show that the prediction of COVID-19 mortality is robust in a different set of scenarios. Conclusions: Our large and varied dataset made it possible to train ML algorithms to predict COVID-19 mortality using radiomic and neural network features of CXRs.

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Section: Sars-cov-2 Disease (Covid-19mentioning

confidence: 99%

“…The current gold standard diagnostic tool is RT-PCR, but it lacks accuracy, has limited sensitivity (71% to 98%) [10] and is time-consuming [5]. Therefore, diagnostic methods should be further developed and improved [5][6][7].…”

Section: Sars-cov-2 Disease (Covid-19mentioning

confidence: 99%

Mortality Prediction of COVID-19 Patients Using Radiomic and Neural Network Features Extracted from a Wide Chest X-ray Sample Size: A Robust Approach for Different Medical Imbalanced Scenarios

et al. 2022

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“…También debe tenerse presente que los pacientes en fase de recuperación de COVID-19 pueden tener una RT-PCR positiva, ya que la prueba no permite discriminar entre partículas virales intactas de ARN viral. Por su parte, la PCR múltiple utiliza la amplificación simultánea de distintos genes en un único tubo de reacción, lo cual aumenta la sensibilidad de la prueba, evitando los falsos negativos [37,38].…”

Section: Reacción En Cadena De La Polimerasaunclassified

“…Durante la RT-PCR, la detección de la señal fluorescente ocurre cuando hay presencia de la secuencia genómica del virus, y se debe establecer durante los primeros ciclos de amplificación una lectura basal que permita definir un umbral del número ciclos (Ct) de amplificación, a partir del cual se considera que hay presencia del gen blanco viral. El Ct se relaciona inversamente con la cantidad de ARN viral que hay en la muestra, de forma que a un número bajo de Ct, mayor carga viral [38,39]. De esta forma, se ha propuesto que valores de Ct <30 corresponden a un individuo altamente contagioso, valores entre 30 y 40 corresponden a moderadamente contagioso, valores entre 34 y 37 a zona "gris", y valores >37 a no infeccioso.…”

Section: Reacción En Cadena De La Polimerasaunclassified

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Características del SARS-CoV-2, COVID-19 y su diagnóstico en el laboratorio

et al. 2022

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La enfermedad COVID‑19 es causada por el virus SARS-CoV-2, descrito por primera vez en diciembre del 2019 en Wuhan, China, y declarada en marzo del 2020 como una pandemia mundial. Actualmente existen diversos métodos diagnósticos para COVID-19, siendo el estándar de oro la detección del material genético mediante la reacción en cadena de la polimerasa (PCR), en su variante, la RT-PCR, que detecta el material genético de tipo ARN presente en el virus. Sin embargo, es necesario disponer de pruebas rápidas con alta sensibilidad y precisión para realizarlas a gran escala y brindar un diagnóstico oportuno. Adicionalmente, se debe disponer de otras herramientas que, si bien no van a establecer un diagnóstico, le van a permitir al profesional brindar un mejor manejo clínico y epidemiológico que ayuden a predecir el agravamiento del paciente y su posible ingreso a UCI, destacando entre estas los niveles de dímero D, linfocitos, ferritina, urea y creatinina, entre otras. En esta revisión se evalúa la utilidad y limitaciones de los diferentes métodos diagnósticos para COVID-19, al igual que las características, fisiopatología y respuesta inmune al SARS-CoV-2, así como algunos aspectos preanalíticos de importancia que ayudan a minimizar errores en el diagnóstico como consecuencia de procedimientos incorrectos en la toma, transporte y conservación de la muestra, y que permiten al profesional emitir resultados veraces y confiables. Lo anterior se realizó basado en artículos originales, revisiones y guías clínicas.

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AuNP-based biosensors for the diagnosis of pathogenic human coronaviruses: COVID-19 pandemic developments

2022

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The outbreak rate of human coronaviruses (CoVs) especially highly pathogenic CoVs is increasing alarmingly. Early detection of these viruses allows treatment interventions to be provided more quickly to people at higher risk, as well as helping to identify asymptomatic carriers and isolate them as quickly as possible, thus preventing the disease transmission chain. The current diagnostic methods such as RT-PCR are not ideal due to high cost, low accuracy, low speed, and probability of false results. Therefore, a reliable and accurate method for the detection of CoVs in biofluids can become a front-line tool in order to deal with the spread of these deadly viruses. Currently, the nanomaterial-based sensing devices for detection of human coronaviruses from laboratory diagnosis to point-of-care (PoC) diagnosis are progressing rapidly. Gold nanoparticles (AuNPs) have revolutionized the field of biosensors because of the outstanding optical and electrochemical properties. In this review paper, a detailed overview of AuNP-based biosensing strategies with the varied transducers (electrochemical, optical, etc.) and also different biomarkers (protein antigens and nucleic acids) was presented for the detection of human coronaviruses including SARS-CoV-2, SARS-CoV-1, and MERS-CoV and lowly pathogenic CoVs. The present review highlights the newest trends in the SARS-CoV-2 nanobiosensors from the beginning of the COVID-19 epidemic until 2022. We hope that the presented examples in this review paper convince readers that AuNPs are a suitable platform for the designing of biosensors. Graphical abstract

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Diagnostic assay and technology advancement for detecting SARS-CoV-2 infections causing the COVID-19 pandemic

Cited by 30 publications

References 204 publications

Mortality Prediction of COVID-19 Patients Using Radiomic and Neural Network Features Extracted from a Wide Chest X-ray Sample Size: A Robust Approach for Different Medical Imbalanced Scenarios

Mortality Prediction of COVID-19 Patients Using Radiomic and Neural Network Features Extracted from a Wide Chest X-ray Sample Size: A Robust Approach for Different Medical Imbalanced Scenarios

Características del SARS-CoV-2, COVID-19 y su diagnóstico en el laboratorio

AuNP-based biosensors for the diagnosis of pathogenic human coronaviruses: COVID-19 pandemic developments

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