Should RT-PCR be considered a gold standard in the diagnosis of COVID-19?

Effectiveness of a standardized quality control management procedure for COVID-19 RT-PCR testing: a large-scale diagnostic accuracy study in China

BACKGROUND

The study aimed to construct a standardized quality control management procedure (QCMP) and access its accuracy in the quality control of COVID-19 reverse transcriptase-polymerase chain reaction (RT-PCR).

METHODS

Considering the initial RT-PCR results without applying QCMP as the gold standard, a large-scale diagnostic accuracy study including 4,385,925 participants at three COVID-19 RT-PCR testing sites in China, Foshan (as a pilot test), Guangzhou and Shenyang (as validation sites), was conducted from May 21, 2021, to December 15, 2022.

RESULTS

In the pilot test, the RT-PCR with QCMP had a high accuracy of 99.18% with 100% specificity, 100% positive predictive value (PPV), and 99.17% negative predictive value (NPV). The rate of retesting was reduced from 1.98% to 1.16%. Its accuracy was then consistently validated in Guangzhou and Shenyang.

CONCLUSIONS

The RT-PCR with QCMP showed excellent accuracy in identifying true negative COVID-19 and relieved the labor and time spent on retesting.

Maternal COVID-19 Serological Changes-Comparison between Seroconversion Rate in First and Third Trimesters of Pregnancy and Subsequent Obstetric Complications: A Cohort Study

Pregnant women are especially vulnerable to respiratory diseases. We aimed to study seroconversion rates during pregnancy in a cohort of consecutive pregnancies tested in the first and third trimesters and to compare the maternal and obstetric complications in the women who seroconverted in the first trimester and those who did so in the third. This was an observational cohort study carried out at the Hospital Universitario de Torrejón, in Madrid, Spain, during the first peak of the COVID-19 pandemic. All consecutive singleton pregnancies with a viable fetus attending their 11-13-week scan between 1 January and 15 May 2020 were included and seropositive women for SARS-CoV2 were monthly follow up until delivery. Antibodies against SARS-CoV-2 (IgA and IgG) were analyzed on stored serum samples obtained from first- and third-trimester routine antenatal bloods in 470 pregnant women. Antibodies against SARS-CoV-2 were detected in 31 (6.6%) women in the first trimester and in 66 (14.0%) in the third trimester, including 48 (10.2%) that were negative in the first trimester (seroconversion during pregnancy). Although the rate of infection was significantly higher in the third versus the first trimester (p = 0.003), no significant differences in maternal or obstetric complications were observed in women testing positive in the first versus the third trimester.

SARS CoV-2 infection screening via the exhaled breath fingerprint obtained by FTIR spectroscopic gas-phase analysis. A proof of concept

The COVID-19 pandemic remains a global challenge now with the long-COVID arising. Mitigation measures focused on case counting, assessment and determination of variants and their likely targets of infection and transmission, the pursuit of drug treatments, use and enhancement of masks, social distancing, vaccination, post-infection rehabilitation, and mass screening. The latter is of utmost importance given the current scenario of infections, reinfections, and long-term health effects. Research on screening platforms has been developed to provide more sensitive, specific, and reliable tests that are accessible to the entire population and can be used to assess the prognosis of the disease as well as the subsequent health follow-up of patients with sequelae of COVID-19. Therefore, the aim of the present study was the simulation of exhaled breath of COVID-19 patients by evaluation of three identified COVID-19 indicator breath biomarkers (acetone (ACE), acetaldehyde (ACH) and nitric oxide (NO)) by gas-phase infrared spectroscopy as a proof-of-concept principle for the detection of infected patients' exhaled breath fingerprint and subsequent follow-up. The specific fingerprints of each of the compounds and the overall fingerprint were obtained. The synthetic exhaled breath evaluation concept revealed a linearity of r = 0.99 for all compounds, and LODs of 6.42, 13.81, 9.22 ppm, and LOQs of 42.26, 52.57, 69.23 ppm for NO, ACE, and ACH, respectively. This study proves the fundamental feasibility of gas-phase infrared spectroscopy for fingerprinting lung damage biomarkers in exhaled breath of patients with COVID-19. This analysis would allow faster and cheaper screening and follow-up of infected individuals, which could improve mass screening in POC settings.

Precise Detection of Viral RNA by Programming Multiplex Rolling Circle Amplification and Strand Displacement

Detection of viral infections (e.g., SARS-CoV-2) with high precision is critical to disease control and treatment. There is an urgent need to develop point-of-care detection methods to complement the gold standard laboratory-based PCR assay with comparable sensitivity and specificity. Herein, we developed a method termed mCAD to achieve ultraspecific point-of-care detection of SARS-CoV-2 RNA while maintaining high sensitivity by programming multiplex rolling circle amplification and toehold-mediated strand displacement reactions. RCA offers sufficient amplification of RNA targets for subsequent detection. Most importantly, a multilayer of detection specificity is implemented into mCAD via sequence-specific hybridization of nucleic acids across serial steps of this protocol to fully eliminate potential false-positive detections. Using mCAD, we demonstrated a highly specific, sensitive, and convenient visual detection of SARS-CoV-2 RNA from both synthetic and clinical samples, exhibiting performance comparable to qPCR. We envision that mCAD will find its broad applications in clinical prospects for nucleic acid detections readily beyond SARS-CoV-2 RNA.

A machine learning tool for the diagnosis of SARS-CoV-2 infection from hemogram parameters

Monocytes and neutrophils play key roles in the cytokine storm triggered by SARS-CoV-2 infection, which changes their conformation and function. These changes are detectable at the cellular and molecular level and may be different to what is observed in other respiratory infections. Here, we applied machine learning (ML) to develop and validate an algorithm to diagnose COVID-19 using blood parameters. In this retrospective single-center study, 49 hemogram parameters from 12,321 patients with clinical suspicion of COVID-19 and tested by RT-PCR (4239 positive and 8082 negative) were analysed. The dataset was randomly divided into training and validation sets. Blood cell parameters and patient age were used to construct the predictive model with the support vector machine (SVM) tool. The model constructed from the training set (5936 patients) achieved an accuracy for diagnosis of SARS-CoV-2 infection of 0.952 (95% CI: 0.875-0.892). Test sensitivity and specificity was 0.868 and 0.899, respectively, with a positive (PPV) and negative (NPV) predictive value of 0.896 and 0.872, respectively (prevalence 0.50). The validation set model (4964 patients) achieved an accuracy of 0.894 (95% CI: 0.883-0.903). Test sensitivity and specificity was 0.8922 and 0.8951, respectively, with a positive (PPV) and negative (NPV) predictive value of 0.817 and 0.94, respectively (prevalence 0.34). The area under the receiver operating characteristic curve was 0.952 for the algorithm performance. This algorithm may allow to rule out COVID-19 diagnosis with 94% of probability. This represents a great advance for early diagnostic orientation and guiding clinical decisions.

Determination of the Diagnostic Performance of Laboratory Tests in the Absence of a Perfect Reference Standard: The Case of SARS-CoV-2 Tests

BACKGROUND

Currently, assessing the diagnostic performance of new laboratory tests assumes a perfect reference standard, which is rarely the case. Wrong classifications of the true disease status will inevitably lead to biased estimates of sensitivity and specificity.

OBJECTIVES

Using Bayesian' latent class models (BLCMs), an approach that does not assume a perfect reference standard, we re-analyzed data of a large prospective observational study assessing the diagnostic accuracy of an antigen test for the diagnosis of SARS-CoV-2 infection in clinical practice.

METHODS

A cohort of consecutive patients presenting to a COVID-19 testing facility affiliated with a Swiss University Hospital were recruited (n = 1465). Two real-time PCR tests were conducted in parallel with the Roche/SD Biosensor rapid antigen test on nasopharyngeal swabs. A two-test (PCR and antigen test), three-population BLCM was fitted to the frequencies of paired test results.

RESULTS

Based on the BLCM, the sensitivities of the RT-PCR and the Roche/SD Biosensor rapid antigen test were 98.5% [95% CRI 94.8;100] and 82.7% [95% CRI 66.8;100]. The specificities were 97.7% [96.1;99.7] and 99.9% [95% CRI 99.6;100].

CONCLUSIONS

Applying the BLCM, the diagnostic accuracy of RT-PCR was high but not perfect. In contrast to previous results, the sensitivity of the antigen test was higher. Our results suggest that BLCMs are valuable tools for investigating the diagnostic performance of laboratory tests in the absence of perfect reference standard.

Application of clinical and CT imaging features in the evaluation of disease progression in patients with COVID-19

BACKGROUND

The Corona Virus Disease 2019(COVID-19) pandemic has strained healthcare systems worldwide, necessitating the early prediction of patients requiring critical care. This study aimed to analyze the laboratory examination indicators, CT features, and prognostic risk factors in COVID-19 patients.

METHODS

A retrospective study was conducted on 90 COVID-19 patients at the First Affiliated Hospital of Gannan Medical University between December 17, 2022, and March 17, 2023. Clinical data, laboratory examination results, and computed tomography (CT) imaging data were collected. Logistic multivariate regression analysis was performed to identify independent risk factors, and the predictive ability of each risk factor was assessed using the area under the receiver operating characteristic (ROC) curve.

RESULTS

Multivariate logistic regression analysis revealed that comorbid diabetes (odds ratio [OR] = 526.875, 95%CI = 1.384-1960.84, P = 0.053), lymphocyte count reduction (OR = 8.773, 95%CI = 1.432-53.584, P = 0.064), elevated D-dimer level (OR = 362.426, 95%CI = 1.228-984.995, P = 0.023), and involvement of five lung lobes (OR = 0.926, 95%CI = 0.026-0.686, P = 0.025) were risk factors for progression to severe COVID-19. ROC curve analysis showed the highest predictive value for 5 lung lobes (AUC = 0.782). Oxygen saturation was positively correlated with normally aerated lung volume and the proportion of normally aerated lung volume (P < 0.05).

CONCLUSIONS

The study demonstrated that comorbid diabetes, lymphocyte count reduction, elevated D-dimer levels, and involvement of the five lung lobes are significant risk factors for severe COVID-19. In CT lung volume quantification, normal aerated lung volume and the proportion of normal aerated lung volume correlated with blood oxygen saturation.

Identification and validation of fusidic acid and flufenamic acid as inhibitors of SARS-CoV-2 replication using DrugSolver CavitomiX

In this work, we present DrugSolver CavitomiX, a novel computational pipeline for drug repurposing and identifying ligands and inhibitors of target enzymes. The pipeline is based on cavity point clouds representing physico-chemical properties of the cavity induced solely by the protein. To test the pipeline's ability to identify inhibitors, we chose enzymes essential for SARS-CoV-2 replication as a test system. The active-site cavities of the viral enzymes main protease (M^pro) and papain-like protease (Pl^pro), as well as of the human transmembrane serine protease 2 (TMPRSS2), were selected as target cavities. Using active-site point-cloud comparisons, it was possible to identify two compounds-flufenamic acid and fusidic acid-which show strong inhibition of viral replication. The complexes from which fusidic acid and flufenamic acid were derived would not have been identified using classical sequence- and structure-based methods as they show very little structural (TM-score: 0.1 and 0.09, respectively) and very low sequence (~ 5%) identity to M^pro and TMPRSS2, respectively. Furthermore, a cavity-based off-target screening was performed using acetylcholinesterase (AChE) as an example. Using cavity comparisons, the human carboxylesterase was successfully identified, which is a described off-target for AChE inhibitors.

A computationally-inexpensive strategy in CT image data augmentation for robust deep learning classification in the early stages of an outbreak

Coronavirus disease 2019 (COVID-19) has spread globally for over three years, and chest computed tomography (CT) has been used to diagnose COVID-19 and identify lung damage in COVID-19 patients. Given its widespread, CT will remain a common diagnostic tool in future pandemics, but its effectiveness at the beginning of any pandemic will depend strongly on the ability to classify CT scans quickly and correctly when only limited resources are available, as it will happen inevitably again in future pandemics. Here, we resort into the transfer learning procedure and limited hyperparameters to use as few computing resources as possible for COVID-19 CT images classification. Advanced Normalisation Tools (ANTs) are used to synthesise images as augmented/independent data and trained on EfficientNet to investigate the effect of synthetic images. On the COVID-CT dataset, classification accuracy increases from 91.15% to 95.50% and Area Under the Receiver Operating Characteristic (AUC) from 96.40% to 98.54%. We also customise a small dataset to simulate data collected in the early stages of the outbreak and report an improvement in accuracy from 85.95% to 94.32% and AUC from 93.21% to 98.61%. This study provides a feasible Low-Threshold, Easy-To-Deploy and Ready-To-Use solution with a relatively low computational cost for medical image classification at an early stage of an outbreak in which scarce data are available and traditional data augmentation may fail. Hence, it would be most suitable for low-resource settings.

A highly sensitive nanobody-based immunoassay detecting SARS-CoV-2 nucleocapsid protein using all-recombinant reagents

Antigen tests have been crucial for managing the COVID-19 pandemic by identifying individuals infected with SARS-CoV-2. This remains true even after immunity has been widely attained through natural infection and vaccination, since it only provides moderate protection against transmission and is highly permeable to the emergence of new virus variants. For this reason, the widespread availability of diagnostic methods is essential for health systems to manage outbreaks effectively. In this work, we generated nanobodies to the virus nucleocapsid protein (NP) and after an affinity-guided selection identified a nanobody pair that allowed the detection of NP at sub-ng/mL levels in a colorimetric two-site ELISA, demonstrating high diagnostic value with clinical samples. We further modified the assay by using a nanobody-NanoLuc luciferase chimeric tracer, resulting in increased sensitivity (detection limit = 61 pg/mL) and remarkable improvement in diagnostic performance. The luminescent assay was finally evaluated using 115 nasopharyngeal swab samples. Receiver Operating Characteristic (ROC) curve analysis revealed a sensitivity of 78.7% (95% confidence interval: 64.3%-89.3%) and specificity of 100.0% (95% confidence interval: 94.7%-100.0%). The test allows the parallel analysis of a large number of untreated samples, and fulfills our goal of producing a recombinant reagent-based test that can be reproduced at low cost by other laboratories with recombinant expression capabilities, aiding to build diagnostic capacity.

Simultaneous Detection of Influenza A/B, Respiratory Syncytial Virus, and SARS-CoV-2 in Nasopharyngeal Swabs by One-Tube Multiplex Reverse Transcription Polymerase Chain Reaction

The treatment and outcome of respiratory virus infections differ. SARS-CoV-2, as well as other respiratory viruses such as influenza virus (A and B) and respiratory syncytial virus (RSV), require simultaneous, cost-effective, and rapid differential detection. We used a gold standard five-target single-step RT-PCR to detect influenza viruses, RSV, and SARS-CoV-2, and this method can be extended to detect influenza virus subtypes. As a result, this five-target single-step RT-PCR method is ideal for differentiating respiratory viruses. The 5' nuclease activity of Taq DNA polymerase is used in the real-time reverse transcription PCR assay. The Taq man fast viral 1-step enzyme is a 4× Master mix and five-target primer probe mix that detects influenza A, influenza B, SARS-CoV-2 ORF1ab, respiratory syncytial viruses A/B and actin. When compared with TaqMan ^TM and Invitrogen superscript ^TM III Platinum and the Meril Kit for SARS-CoV-2, the assay demonstrated 100% sensitivity, specificity, and amplification efficiency of 90.1% for target genes. In conclusion, our one-tube multiplex RT-PCR assay offers a rapid and reliable method for the simultaneous detection of influenza A/B, RSV, and SARS-CoV-2 from nasopharyngeal swabs. This assay has the potential to enhance diagnostic capabilities and improve public health responses during respiratory outbreaks, enabling timely interventions and informed decision making.

Clinical Evaluation of Commercial HARDSON COVID-19 Antigen Rapid Test Kit for Routine COVID-19 Diagnosis

Objective

This study aimed to evaluate the sensitivity, specificity, and accuracy of the commercial HARDSON COVID-19 Antigen Rapid Test Kit for diagnosing COVID-19 among the Iranian population by compared with the results of commercial RT-PCR.

Materials and Methods

Two nasopharyngeal swabs were collected from each patient. One swab was tested with HARDSON COVID-19 Antigen Rapid Test Kit, and the second swab was placed in 3 mL of a virus-transmitted inactivated media for RT-PCR testing. Then, the results of both tests were compared to investigate the diagnostic accuracy of the rapid antigen test.

Results

A total of 275 suspected COVID-19 patients' samples were collected to investigate the diagnostic accuracy of HARDSON COVID-19 Antigen Rapid Test Kit. In this study, 162 positive and 113 negative samples were evaluated. As a result, the sensitivity, specificity, and accuracy of HARDSON COVID-19 Antigen Rapid Test Kit were 90%, 100%, and 94%, respectively.

Conclusion

The diagnostic kit analyzed in this study indicated excellent specificity and a relatively good overall sensitivity for the diagnosis of COVID-19 when compared with the RT-PCR detection kit. Based on the result of this study, COVID-19 Antigen Rapid Test Kit indicated a good sensitivity (96%) in low cycle threshold (Ct) value, and it would be recommended to be integrated into routine diagnostic laboratories and used as an at-home rapid antigen test.

Community-based COVID-19 active case finding and rapid response in the Democratic Republic of the Congo: Improving case detection and response

A community-based coronavirus disease (COVID-19) active case-finding strategy using an antigen-detecting rapid diagnostic test (Ag-RDT) was implemented in the Democratic Republic of Congo (DRC) to enhance COVID-19 case detection. With this pilot community-based active case finding and response program that was designed as a clinical, prospective testing performance, and implementation study, we aimed to identify insights to improve community diagnosis and rapid response to COVID-19. This pilot study was modeled on the DRC's National COVID-19 Response Plan and the COVID-19 Ag-RDT screening algorithm defined by the World Health Organization (WHO), with case findings implemented in 259 health areas, 39 health zones, and 9 provinces. In each health area, a 7-member interdisciplinary field team tested the close contacts (ring strategy) and applied preventive and control measures to each confirmed case. The COVID-19 testing capacity increased from 0.3 tests per 10,000 inhabitants per week in the first wave to 0.4, 1.6, and 2.2 in the second, third, and fourth waves, respectively. From January to November 2021, this capacity increase contributed to an average of 10.5% of COVID-19 tests in the DRC, with 7,110 positive Ag-RDT results for 40,226 suspected cases and close contacts who were tested (53.6% female, median age: 37 years [interquartile range: 26.0-50.0)]. Overall, 79.7% (n = 32,071) of the participants were symptomatic and 7.6% (n = 3,073) had comorbidities. The Ag-RDT sensitivity and specificity were 55.5% and 99.0%, respectively, based on reverse transcription polymerase chain reaction analysis, and there was substantial agreement between the tests (k = 0.63). Despite its limited sensitivity, the Ag-RDT has improved COVID-19 testing capacity, enabling earlier detection, isolation, and treatment of COVID-19 cases. Our findings support the community testing of suspected cases and asymptomatic close contacts of confirmed cases to reduce disease spread and virus transmission.

Boosting automatic COVID-19 detection performance with self-supervised learning and batch knowledge ensembling

Problem:

Detecting COVID-19 from chest X-ray (CXR) images has become one of the fastest and easiest methods for detecting COVID-19. However, the existing methods usually use supervised transfer learning from natural images as a pretraining process. These methods do not consider the unique features of COVID-19 and the similar features between COVID-19 and other pneumonia.

Aim:

In this paper, we want to design a novel high-accuracy COVID-19 detection method that uses CXR images, which can consider the unique features of COVID-19 and the similar features between COVID-19 and other pneumonia.

Methods:

Our method consists of two phases. One is self-supervised learning-based pertaining; the other is batch knowledge ensembling-based fine-tuning. Self-supervised learning-based pretraining can learn distinguished representations from CXR images without manually annotated labels. On the other hand, batch knowledge ensembling-based fine-tuning can utilize category knowledge of images in a batch according to their visual feature similarities to improve detection performance. Unlike our previous implementation, we introduce batch knowledge ensembling into the fine-tuning phase, reducing the memory used in self-supervised learning and improving COVID-19 detection accuracy.

Results:

On two public COVID-19 CXR datasets, namely, a large dataset and an unbalanced dataset, our method exhibited promising COVID-19 detection performance. Our method maintains high detection accuracy even when annotated CXR training images are reduced significantly (e.g., using only 10% of the original dataset). In addition, our method is insensitive to changes in hyperparameters.

Conclusion:

The proposed method outperforms other state-of-the-art COVID-19 detection methods in different settings. Our method can reduce the workloads of healthcare providers and radiologists.

Optimization of the molecular diagnosis of the acute hepatitis E virus infection

To evaluate the diagnostic value of the combination of two broad-range PCR assays targeting two different and conserved regions of the viral genome for the diagnosis of acute Hepatitis E virus (HEV) infection. Patients with acute hepatitis were prospectively recruited. In all, HEV-IgM antibodies were tested together with evaluation of HEV viraemia by two PCR assays (ORF3 and ORF1). The number of individuals exhibiting negative IgM antibody results but carrying viral RNA was calculated by each PCR assay. Four-hundred and seventy individuals were included, of whom 145 (30.8%) were diagnosed as having acute HEV. Of them, 122 (84.1%) exhibited HEV-IgM antibodies, and 81 (55.8%) had detectable viral RNA for at least one PCR. Using the ORF3 molecular assay, 70 (48.3%) individuals were identified with HEV infection. When the ORF1 molecular assay was applied, 49 (33.8%) individuals were identified. The ORF3 assay detected viral RNA in 32 patients not detected by the ORF1 assay. In contrast, the ORF1 assay could amplify viral RNA in 11 patients who were not detected by the ORF3 assay. The parallel use of two broad-range PCR assays significantly increased the performance of the molecular diagnosis of HEV.

Clinical evaluation of DIAGNOVIR SARS-CoV-2 ultra-rapid antigen test performance compared to PCR-based testing

Coronavirus Disease-19 (COVID-19) is a highly contagious infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The development of rapid antigen tests has contributed to easing the burden on healthcare and lifting restrictions by detecting infected individuals to help prevent further transmission of the virus. We developed a state-of-art rapid antigen testing system, named DIAGNOVIR, based on immune-fluorescence analysis, which can process and give the results in a minute. In our study, we assessed the performance of the DIAGNOVIR and compared the results with those of the qRT-PCR test. Our results demonstrated that the sensitivity and specificity of the DIAGNOVIR were 94% and 99.2%, respectively, with a 100% sensitivity and 96.97% specificity, among asymptomatic patients. In addition, DIAGNOVIR can detect SARS‑CoV‑2 with 100% sensitivity up to 5 days after symptom onset. We observed that the DIAGNOVIR Rapid Antigen Test's limit of detection (LoD) was not significantly affected by the SARS‑CoV‑2 variants including Wuhan, alpha (B1.1.7), beta (B.1.351), delta (B.1.617.2) and omicron (B.1.1.529) variants, and LoD was calculated as 8 × 102, 6.81 × 101.5, 3.2 × 101.5, 1 × 103, and 1 × 103.5 TCID50/mL, respectively. Our results indicated that DIAGNOVIR can detect all SARS-CoV-2 variants in just seconds with higher sensitivity and specificity lower testing costs and decreased turnover time.

Leveraging transcriptomics for precision diagnosis: Lessons learned from cancer and sepsis

Diagnostics require precision and predictive ability to be clinically useful. Integration of multi-omic with clinical data is crucial to our understanding of disease pathogenesis and diagnosis. However, interpretation of overwhelming amounts of information at the individual level requires sophisticated computational tools for extraction of clinically meaningful outputs. Moreover, evolution of technical and analytical methods often outpaces standardisation strategies. RNA is the most dynamic component of all -omics technologies carrying an abundance of regulatory information that is least harnessed for use in clinical diagnostics. Gene expression-based tests capture genetic and non-genetic heterogeneity and have been implemented in certain diseases. For example patients with early breast cancer are spared toxic unnecessary treatments with scores based on the expression of a set of genes (e.g., Oncotype DX). The ability of transcriptomics to portray the transcriptional status at a moment in time has also been used in diagnosis of dynamic diseases such as sepsis. Gene expression profiles identify endotypes in sepsis patients with prognostic value and a potential to discriminate between viral and bacterial infection. The application of transcriptomics for patient stratification in clinical environments and clinical trials thus holds promise. In this review, we discuss the current clinical application in the fields of cancer and infection. We use these paradigms to highlight the impediments in identifying useful diagnostic and prognostic biomarkers and propose approaches to overcome them and aid efforts towards clinical implementation.

Diagnostic accuracy of autoverification and guidance system for COVID-19 RT-PCR results

BACKGROUND

To date, most countries worldwide have declared that the pandemic of COVID-19 is over, while the WHO has not officially ended the COVID-19 pandemic, and China still insists on the personalized dynamic COVID-free policy. Large-scale nucleic acid testing in Chinese communities and the manual interpretation for SARS-CoV-2 nucleic acid detection results pose a huge challenge for labour, quality and turnaround time (TAT) requirements. To solve this specific issue while increase the efficiency and accuracy of interpretation, we created an autoverification and guidance system (AGS) that can automatically interpret and report the COVID-19 reverse transcriptase-polymerase chain reaction (RT-PCR) results relaying on computer-based autoverification procedure and then validated its performance in real-world environments. This would be conductive to transmission risk prediction, COVID-19 prevention and control and timely medical treatment for positive patients in the context of the predictive, preventive and personalized medicine (PPPM).

METHODS

A diagnostic accuracy test was conducted with 380,693 participants from two COVID-19 test sites in China, the Hong Kong Hybribio Medical Laboratory (n = 266,035) and the mobile medical shelter at a Shanghai airport (n = 114,658). These participants underwent SARS-CoV-2 RT-PCR from March 28 to April 10, 2022. All RT-PCR results were interpreted by laboratorians and by using AGS simultaneously. Considering the manual interpretation as gold standard, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy were applied to evaluate the diagnostic value of the AGS on the interpretation of RT-PCR results.

RESULTS

Among the 266,035 samples in Hong Kong, there were 16,356 (6.15%) positive, 231,073 (86.86%) negative, 18,606 (6.99%) indefinite, 231,073 (86.86%, negative) no retest required and 34,962 (13.14%, positive and indefinite) retest required; the 114,658 samples in Shanghai consisted of 76 (0.07%) positive, 109,956 (95.90%) negative, 4626 (4.03%) indefinite, 109,956 (95.90%, negative) no retest required and 4702 (4.10%, positive and indefinite) retest required. Compared to the fashioned manual interpretation, the AGS is a procedure of high accuracy [99.96% (95%CI, 99.95-99.97%) in Hong Kong and 100% (95%CI, 100-100%) in Shanghai] with perfect sensitivity [99.98% (95%CI, 99.97-99.98%) in Hong Kong and 100% (95%CI, 100-100%) in Shanghai], specificity [99.87% (95%CI, 99.82-99.90%) in Hong Kong and 100% (95%CI, 99.92-100%) in Shanghai], PPV [99.98% (95%CI, 99.97-99.99%) in Hong Kong and 100% (95%CI, 99.99-100%) in Shanghai] and NPV [99.85% (95%CI, 99.80-99.88%) in Hong Kong and 100% (95%CI, 99.90-100%) in Shanghai]. The need for manual interpretation of total samples was dramatically reduced from 100% to 13.1% and the interpretation time fell from 53 h to 26 min in Hong Kong; while the manual interpretation of total samples was decreased from 100% to 4.1% and the interpretation time dropped from 20 h to 16 min at Shanghai.

CONCLUSIONS

The AGS is a procedure of high accuracy and significantly relieves both labour and time from the challenge of large-scale screening of SARS-CoV-2 using RT-PCR. It should be recommended as a powerful screening, diagnostic and predictive system for SARS-CoV-2 to contribute timely the ending of the COVID-19 pandemic following the concept of PPPM.

Conventional and Novel Diagnostic Tools for the Diagnosis of Emerging SARS-CoV-2 Variants

Accurate identification at an early stage of infection is critical for effective care of any infectious disease. The "coronavirus disease 2019 (COVID-19)" outbreak, caused by the virus "Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2)", corresponds to the current and global pandemic, characterized by several developing variants, many of which are classified as variants of concern (VOCs) by the "World Health Organization (WHO, Geneva, Switzerland)". The primary diagnosis of infection is made using either the molecular technique of RT-PCR, which detects parts of the viral genome's RNA, or immunodiagnostic procedures, which identify viral proteins or antibodies generated by the host. As the demand for the RT-PCR test grew fast, several inexperienced producers joined the market with innovative kits, and an increasing number of laboratories joined the diagnostic field, rendering the test results increasingly prone to mistakes. It is difficult to determine how the outcomes of one unnoticed result could influence decisions about patient quarantine and social isolation, particularly when the patients themselves are health care providers. The development of point-of-care testing helps in the rapid in-field diagnosis of the disease, and such testing can also be used as a bedside monitor for mapping the progression of the disease in critical patients. In this review, we have provided the readers with available molecular diagnostic techniques and their pitfalls in detecting emerging VOCs of SARS-CoV-2, and lastly, we have discussed AI-ML- and nanotechnology-based smart diagnostic techniques for SARS-CoV-2 detection.

Ct, IL-18 polymorphism, and laboratory biomarkers for predicting chemosensory dysfunctions and mortality in COVID-19

Aim

Patients with COVID-19 often experience chemosensory dysfunction. This research intends to uncover the association of RT-PCR Ct value with chemosensory dysfunctions and SpO2. This study also aims to investigate Ct, SpO2, CRP, D-dimer, and -607 IL-18 T/G polymorphism in order to find out predictors of chemosensory dysfunctions and mortality.

Materials & methods

This study included 120 COVID-19 patients, of which 54 were mild, 40 were severe and 26 were critical. CRP, D-dimer, RT-PCR, and IL-18 polymorphism were evaluated. Results & conclusion: Low Ct was associated with SpO2 dropping and chemosensory dysfunctions. IL-18 T/G polymorphism did not show an association with COVID-19 mortality; conversely, age, BMI, D-dimer and Ct values did.

COVID-19 detection using AIE-active iridium complexes

The highly contagious COVID-19, caused by the outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is commonly diagnosed using reverse transcription polymerase chain reaction (RT-PCR). However, despite being highly sensitive, RT-PCR is also time consuming and quite complex, which limits its use for point-of-care (POC) testing. We have developed a simple single-step fluorescence assay for SARS-CoV-2 RNA detection based on the principle of aggregation-induced emission (AIE) using iridium complexes. Our smartly designed iridium probes fluorescently "turn-on" in the presence of SARS-CoV-2 RNA and give specific results at room temperature within 10 min. The lower limit of detection (LOD) is 1.84 genome copies per reaction, and the sensitivity and specificity of the assay in 20 clinical samples are found to be 90% and 80%, respectively.

The Deceptive COVID-19: Lessons from Common Molecular Diagnostics and a Novel Plan for the Prevention of the Next Pandemic

The COVID-19 pandemic took place during the years 2020-2022 and the virus, named SARS-CoV-2, seems likely to have resulted in an endemic disease. Nevertheless, widespread COVID-19 has given rise to several major molecular diagnostics' facts and concerns that have emerged during the overall management of this disease and the subsequent pandemic. These concerns and lessons are undeniably critical for the prevention and control of future infectious agents. Furthermore, most populaces were introduced to several new public health maintenance strategies, and again, some critical events arose. The purpose of this perspective is to thoroughly analyze all these issues and the concerns, such as the molecular diagnostics' terminologies, their role, as well as the quantity and quality issues with a molecular diagnostics' test result. Furthermore, it is speculated that society will be more vulnerable in the future and prone to emerging infectious diseases; thus, a novel preventive medicine's plan for the prevention and control of future (re)emerging infectious diseases is presented, so as to aid the early prevention of future epidemics and pandemics.

Evaluation of reverse transcriptase-polymerase spiral reaction assay for rapid and sensitive detection of severe acute respiratory syndrome coronavirus 2

BACKGROUND AND AIM

Existing real-time reverse transcriptase PCR (RT-qPCR) has certain limitations for the point-of-care detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) since it requires sophisticated instruments, reagents and skilled laboratory personnel. In this study, we evaluated an assay termed the reverse transcriptase-polymerase spiral reaction (RT-PSR) for rapid and visual detection of SARS-CoV-2.

METHODS

The RT-PSR assay was optimized using RdRp gene and evaluated for the detection of SARS-CoV-2. The time of 60min and a temperature of 63°C was optimized for targeting the RNA-dependent RNA polymerase gene of SARS-CoV-2. The sensitivity of the assay was evaluated by diluting the in-vitro transcribed RNA, which amplifies as low as ten copies.

RESULTS

The specific primers designed for this assay showed 100% specificity and did not react when tested with other lung infection-causing viruses and bacteria. The optimized assay was validated with 190 clinical samples in two phases, using automated RTPCR based TrueNat test, and the results were comparable.

CONCLUSIONS

The RT-PSR assay can be considered for rapid and sensitive detection of SARS-CoV-2, particularly in resource-limited settings. To our knowledge, there is as yet no RT-PSR-based kit developed for SARS-CoV-2.

Cost-analysis of real time RT-PCR test performed for COVID-19 diagnosis at India's national reference laboratory during the early stages of pandemic mitigation

Real-time reverse transcription polymerase chain reaction (rRT-PCR) is one of the most accurate and extensively used laboratory procedures for diagnosing COVID-19. This molecular test has high diagnostic accuracy (sensitivity and specificity) and is considered as the gold standard for COVID-19 diagnosis. During COVID-19 surge in India, rRT-PCR service was encouraged and supported by the government of India through existing healthcare setup at various levels of healthcare facilities. The primary purpose of this research was to determine the per-unit cost of providing COVID-19 rRT-PCR services at the national reference laboratory at ICMR-National Institute of Virology in Pune during the early phase of COVID-19 pandemic mitigation, from the provider's perspective. The monthly cost for rRT-PCR testing as well as an estimated annual average unit cost for testing that takes account of peaks and troughs in pandemic were investigated. The time frame used to estimate unit cost was one year (July 2020-June 2021). For data collection on all resources spent during the early phase of pandemic, a conventional activity-based bottom-up costing technique was used. Capital costs were discounted and annualized over the estimated life of the item. Apportioning statistics were selected for cost heads like human resources, capital, and equipment based on time allocation, sharing of services, and utilization data. The data was also used to understand the breakdown of costs across inputs and over time and different levels of testing activity. During the initial phase of pandemic mitigation, the per unit cost of providing the COVID-19 rRT-PCR test was estimated to be ₹566 ($7.5) in the month of July 2020, where the total 56318 COVID-19 rRT-PCR tests was performed. The major proportion (87%) of funds was utilized for procuring laboratory consumables, followed by HR (10%), and it was least for stationary & allied items (0.02%). Unit cost was found to be the most sensitive to price variations in lab consumables (21.7%), followed by the number of samples tested (3.9%), salaries paid to HR (2.6%), price of equipment (0.23%), and building rental price (0.14%) in a univariate sensitivity analysis. The unit cost varies over the period of the pandemic in proportion with the prices of consumables and inversely proportional with number of tests performed. Our study would help the Government to understand the value for money they invested for laboratory diagnosis of COVID-19, budget allocation, integration and decentralization of laboratory services so as to help for achieving universal health coverage.

Validation of 3D-Printed Swabs for Sampling in SARS-CoV-2 Detection: A Pilot Study

In this pilot study, we characterize and evaluate 3D-printed swabs for the collection of nasopharyngeal and oropharyngeal secretion samples for the SARS-CoV-2 detection. Swabs are made with the fused deposition modeling technique using the biopolymer polylactic acid (PLA) which is a medical-grade, biodegradable and low-cost material. We evaluated six swabs with mechanical tests in a laboratory and in an Adult Human Simulator performed by healthcare professionals. We proved the adequacy of the PLA swab to be used in the gold standard reverse transcriptase-polymerase chain reaction (qRT-PCR) for viral RNA detection. Then, we did in vitro validation for cell collection using the 3D-printed swabs and RNA extraction for samples from 10 healthy volunteers. The 3D-printed swabs showed good flexibility and maneuverability for sampling and at the same time robustness to pass into the posterior nasopharynx. The PLA did not interfere with the RNA extraction process and qRT-PCR test. When we evaluated the expression of the reference gene (RNase P) used in the SARS-CoV-2 detection, the 3D-printed swabs showed good reproducibility in the threshold cycle values (Ct = 23.5, range 19-26) that is comparable to control swabs (Ct = 24.7, range 20.8-32.6) with p value = 0.47. The 3D-printed swabs demonstrated to be a reliable, and an economical alternative for mass use in the detection of SARS-CoV-2.

Detection of Antibody versus Antigen, Optimal Option of Different Serological Assays Based Tests for COVID-19 Diagnosis: A Meta-Analysis

Background

In this study, the diagnostic efficacy of antigen test and antibody test were assessed. Additionally, the difference of sensitivity, specificity, and diagnostic odds ratio were compared concerning efficacy of antibody test versus antigen test for Corona Virus Disease 2019 (COVID-19) diagnosis.

Methods

Online databases were searched for full-text publications and STATA software was used for data pooling and analysis before Sep 1st, 2022. Forrest plot was used to show the pooled sensitivity, specificity and diagnostic odds ratio. Combined receiver operating characteristic (ROC) curve was used to show the area of under curve of complex data.

Results

Overall, 25 studies were included. The sensitivity (0.68, 95% CI: 0.53-0.80) and specificity (0.99, 95% CI: 0.98-0.99) in antibody or antigen was calculated. The time point of test lead to heterogeneity. The area under curve (AUC) was 0.98 (95% CI: 0.96-0.99), and the diagnostic odds ratio (DOR) was 299.54 (95% CI: 135.61-661.64). Subgroup analysis indicated antibody test with sensitivity (0.59, 95% CI: 0.44-0.73) and specificity (0.98, 95% CI: 0.95-0.99) and antigen test with sensitivity of 0.77 (95% CI: 0.53-0.91) and specificity of 0.99 (95% CI: 0.98-1.00). Higher AUC and DOR were proved in antigen test.

Conclusion

The present study compared the efficacy of antibody test versus antigen test for COVID-19 diagnosis. Better diagnostic efficacy, lower heterogeneity, and less publication bias of rapid antigen testing was suggested in this study. This study would help us to make better strategy about choosing rapid and reliable testing method in diagnosis of the COVID-19 disease.

Detection of Covid-19 and other pneumonia cases from CT and X-ray chest images using deep learning based on feature reuse residual block and depthwise dilated convolutions neural network

Covid-19 has become a worldwide epidemic which has caused the death of millions in a very short time. This disease, which is transmitted rapidly, has mutated and different variations have emerged. Early diagnosis is important to prevent the spread of this disease. In this study, a new deep learning-based architecture is proposed for rapid detection of Covid-19 and other symptoms using CT and X-ray chest images. This method, called CovidDWNet, is based on a structure based on feature reuse residual block (FRB) and depthwise dilated convolutions (DDC) units. The FRB and DDC units efficiently acquired various features in the chest scan images and it was seen that the proposed architecture significantly improved its performance. In addition, the feature maps obtained with the CovidDWNet architecture were estimated with the Gradient boosting (GB) algorithm. With the CovidDWNet+GB architecture, which is a combination of CovidDWNet and GB, a performance increase of approximately 7% in CT images and between 3% and 4% in X-ray images has been achieved. The CovidDWNet+GB architecture achieved the highest success compared to other architectures, with 99.84% and 100% accuracy rates, respectively, on different datasets containing binary class (Covid-19 and Normal) CT images. Similarly, the proposed architecture showed the highest success with 96.81% accuracy in multi-class (Covid-19, Lung Opacity, Normal and Viral Pneumonia) X-ray images and 96.32% accuracy in the dataset containing X-ray and CT images. When the time to predict the disease in CT or X-ray images is examined, it is possible to say that it has a high speed because the CovidDWNet+GB method predicts thousands of images within seconds.

Screening and confirmation tests for SARS-CoV-2: benefits and drawbacks

Background

Coronavirus disease 2019 is a pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection that emerged in late 2019 and has activated an ongoing international public health emergency. SARS-CoV-2 was discovered in Wuhan, China, in December 2019 and rapidly spread to other cities and countries. Currently, SARS-CoV-2 diagnostic tests have relied heavily on detecting viral genes, antigens, and human antibodies. Hence, this review discusses and analyses the existing screening and confirmation tests for SARS-CoV-2, including the real-time reverse transcriptase polymerase chain reaction (RT-PCR), lateral flow immunoassay (LFIA), and enzyme-linked immunosorbent assay (ELISA).

Main body

The illustrations of each testing were presented to provide the readers with an understanding of the scientific principles behind the testing methods. The comparison was made by highlighting the advantages and disadvantages of each testing. ELISA is ideal for performing the maximum population screening to determine immunological capacity, although its inability to provide reliable results on the status of the infection. Recently, LFIA has been approved as a quicker way of determining whether a patient is infected at the analysis time without using particular instruments and non-laboratory settings. RT-PCR is the gold-standard approach in terms of sensitivity and specificity.

Conclusion

However, the combination of LFIA or ELISA with RT-PCR is also proposed in this review to obtain an adequate level of sensitivity and specificity.

Graphic Abstract

Direct Nasal Swab for Rapid Test and Saliva as an Alternative Biological Sample for RT-PCR in COVID-19 Diagnosis

Accurate and early diagnoses are prerequisites for prompt treatment. For coronavirus disease 2019 (COVID-19), it is even more crucial. Currently, choice of methods include rapid diagnostic tests and reverse transcription polymerase chain reaction (RT-PCR) using samples mostly of respiratory origin and sometimes saliva. We evaluated two rapid diagnostic tests with three specimen types using viral transport medium (VTM) containing naso-oropharyngeal (NOP) swabs, direct nasal and direct nasopharyngeal (NP) samples from 428 prospective patients. We also performed RT-PCR for 428 NOP VTM and 316 saliva samples to compare results. The sensitivity of the SD Biosensor Standard Q COVID-19 antigen (Ag) test kit drastically raised from an average of 65.55% (NOP VTM) to 85.25% (direct nasal samples), while RT-PCR was the gold standard. For the CareStart kit, the sensitivity was almost similar for direct NP swabs; the average was 84.57%. The specificities were ≥95% for both SD Biosensor Standard Q and CareStart COVID-19 Ag tests in all platforms. The kits were also able to detect patients with different variants as well. Alternatively, RT-PCR results from saliva and NOP VTM samples showed high sensitivities of 96.45% and 95.48% with respect to each other as standard. The overall results demonstrated high performance of the rapid tests, indicating the suitability for regular surveillance at clinical facilities when using direct nasal or direct NP samples rather than NOP VTM. Additionally, the analysis also signifies not showed that RT-PCR of saliva can be used as an choice of method to RT-PCR of NOP VTM, providing an easier, non-invasive sample collection method. IMPORTANCE There are several methods for the diagnosis of coronavirus disease 2019 (COVID-19), and the choice of methods depends mostly on the resources and level of sensitivity required by the user and health care providers. Still, reverse transcription polymerase chain reaction (RT-PCR) has been chosen as the best method using direct naso-oropharyngeal swabs. There are also other methods of fast detection, such as rapid diagnostic tests (RDTs), which offer result within 15 to 20 min and have become quite popular for self-testing and in the clinical setting. The major drawback of the currently used RT-PCR method is compliance, as it may cause irritation, and patients often refuse to test in such a way. RDTs, although inexpensive, suffer from low sensitivity due to technical issues. In this article, we propose saliva as a noninvasive source for RT-PCR samples and evaluate various specimen types at different times after infection for the best possible output from COVID-19 rapid tests.

Structural Attacks and Defenses for Flow-Based Microfluidic Biochips

Flow-based microfluidic biochips (FMBs) have seen rapid commercialization and deployment in recent years for point-of-care and clinical diagnostics. However, the outsourcing of FMB design and manufacturing makes them susceptible to susceptible to malicious physical level and intellectual property (IP)-theft attacks. This work demonstrates the first structure-based (SB) attack on representative commercial FMBs. The SB attacks maliciously decrease the heights of the FMB reaction chambers to produce false-negative results. We validate this attack experimentally using fluorescence microscopy, which showed a high correlation ( R² = 0.987) between chamber height and related fluorescence intensity of the DNA amplified by polymerase chain reaction. To detect SB attacks, we adopt two existing deep learning-based anomaly detection algorithms with  ∼ 96% validation accuracy in recognizing such deliberately introduced microstructural anomalies. To safeguard FMBs against intellectual property (IP)-theft, we propose a novel device-level watermarking scheme for FMBs using intensity-height correlation. The countermeasures can be used to proactively safeguard FMBs against SB and IP-theft attacks in the era of global pandemics and personalized medicine.

Risk factors and survival in patients with COVID-19 in northeastern Brazil

BACKGROUND

Knowledge about the epidemiology and risk factors surrounding COVID-19 contributes to developing better health strategies to combat the disease.

OBJECTIVE

This study aimed to establish a survival analysis and identify the risk factors for patients with COVID-19 in an upper middle-income city in Brazil.

METHODS

A retrospective cohort study was conducted with 280 hospitalized patients with COVID-19. The eCOVID platform provided data to monitor COVID-19 cases and help the communication between professionals.

RESULTS

Age ≥ 65 years was associated with decreased survival (54.8%), and females had a lower survival rate than males (p = 0.01). Regarding risk factors, urea concentration (p<0.001), hospital length of stay (p = 0.002), oxygen concentration (p = 0.005), and age (p = 0.02) were associated with death.

CONCLUSION

Age, hospital length of stay, high blood urea concentration, and low oxygen concentration were associated with death by COVID-19 in the studied population. These findings corroborate with studies conducted in research centers worldwide.

Host transcriptional responses in nasal swabs identify potential SARS-CoV-2 infection in PCR negative patients

We analyzed RNA sequencing data from nasal swabs used for SARS-CoV-2 testing. 13% of 317 PCR-negative samples contained over 100 reads aligned to multiple regions of the SARS-CoV-2 genome. Differential gene expression analysis compares the host gene expression in potential false-negative (FN: PCR negative, sequencing positive) samples to subjects with multiple SARS-CoV-2 viral loads. The host transcriptional response in FN samples was distinct from true negative samples (PCR & sequencing negative) and similar to low viral load samples. Gene Ontology analysis shows viral load-dependent changes in gene expression are functionally distinct; 23 common pathways include responses to viral infections and associated immune responses. GO analysis reveals FN samples had a high overlap with high viral load samples. Deconvolution of RNA-seq data shows similar cell content across viral loads. Hence, transcriptome analysis of nasal swabs provides an additional level of identifying SARS-CoV-2 infection.

Oral saliva swab reverse transcription PCR for Covid-19 in the paediatric population

OBJECTIVES

To evaluate the performance of oral saliva swab (OSS) reverse transcription PCR (RT-PCR) compared with RT-PCR and antigen rapid diagnostic test (Ag-RDT) on nasopharyngeal swabs (NPS) for SARS-CoV-2 in children.

DESIGN

Cross-sectional multicentre diagnostic study.

SETTING

Study nested in a prospective, observational cohort (EPICO-AEP) performed between February and March 2021 including 10 hospitals in Spain.

PATIENTS

Children from 0 to 18 years with symptoms compatible with Covid-19 of ≤5 days of duration were included. Two NPS samples (Ag-RDT and RT-PCR) and one OSS sample for RT-PCR were collected.

MAIN OUTCOME

Performance of Ag-RDT and RT-PCR on NPS and RT-PCR on OSS sample for SARS-CoV-2.

RESULTS

1174 children were included, aged 3.8 years (IQR 1.7-9.0); 73/1174 (6.2%) patients tested positive by at least one of the techniques. Sensitivity and specificity of OSS RT-PCR were 72.1% (95% CI 59.7 to 81.9) and 99.6% (95% CI 99 to 99.9), respectively, versus 61.8% (95% CI 49.1 to 73) and 99.9% (95% CI 99.4 to 100) for the Ag-RDT. Kappa index was 0.79 (95% CI 0.72 to 0.88) for OSS RT-PCR and 0.74 (95% CI 0.65 to 0.84) for Ag-RDT versus NPS RT-PCR.

CONCLUSIONS

RT-PCR on the OSS sample is an accurate option for SARS-CoV-2 testing in children. A less intrusive technique for younger patients, who usually are tested frequently, might increase the number of patients tested.

Reverse Transcription-Loop-Mediated Isothermal Amplification-CRISPR-Cas13a Technology as a Promising Diagnostic Tool for SARS-CoV-2

At the end of 2019, a new coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), caused a pandemic that persists to date and has resulted in more than 6.2 million deaths. In the last couple of years, researchers have made great efforts to develop a diagnostic technique that maintains high levels of sensitivity and specificity, since an accurate and early diagnosis is required to minimize the prevalence of SARS-CoV-2 infection. In this context, CRISPR-Cas systems are proposed as promising tools for development as diagnostic techniques due to their high specificity, highlighting that Cas13 endonuclease discriminates single nucleotide changes and displays collateral activity against single-stranded RNA molecules. With the aim of improving the sensitivity of diagnosis, this technology is usually combined with isothermal preamplification reactions (SHERLOCK, DETECTR). Based on this, we developed a reverse transcription-loop-mediated isothermal amplification (RT-LAMP)-CRISPR-Cas13a method for SARS-CoV-2 virus detection in nasopharyngeal samples without using RNA extraction that exhibits 100% specificity and 83% sensitivity, as well as a positive predictive value (PPV) of 100% and negative predictive values (NPVs) of 100%, 81%, 79.1%, and 66.7% for cycle threshold (CT) values of <20, 20 to 30, >30 and overall, respectively. IMPORTANCE The coronavirus disease 2019 (COVID-19) crisis has driven the development of innovative molecular diagnosis methods, including CRISPR-Cas technology. In this work, we performed a protocol, working with RNA extraction kit-free samples and using RT-LAMP-CRISPR-Cas13a technology; our results place this method at the forefront of rapid and specific diagnostic methods for COVID-19 due to the high specificity (100%), sensitivity (83%), PPVs (100%), and NPVs (81% for high viral loads) obtained with clinical samples.

COVID-19 Rapid Antigen Tests: Bibliometric Analysis of the Scientific Literature

As the COVID-19 pandemic continues to disrupt health systems worldwide, conducting Rapid Antigen Testing (RAT) at specified intervals has become an essential part of many people's lives around the world. We identified and analyzed the academic literature on COVID-19 RAT. The Web of Science electronic database was queried on 6 July 2022 to find relevant publications. Publication and citation data were retrieved directly from the database. VOSviewer, a bibliometric software, was then used to relate these data to the semantic content from the titles, abstracts, and keywords. The analysis was based on data from 1000 publications. The most productive authors were from Japan and the United States, led by Dr. Koji Nakamura from Japan (n = 10, 1.0%). The most academically productive countries were in the North America, Europe and Asia, led by the United States of America (n = 266, 26.6%). Sensitivity (n = 32, 3.2%) and specificity (n = 23, 2.3%) were among the most frequently recurring author keywords. Regarding sampling methods, "saliva" (n = 54, 5.4%) was mentioned more frequently than "nasal swab" (n = 32, 3.2%) and "nasopharyngeal swab" (n = 22, 2.2%). Recurring scenarios that required RAT were identified: emergency department, healthcare worker, mass screening, airport, traveler, and workplace. Our bibliometric analysis revealed that COVID-19 RAT has been utilized in a range of studies. RAT results were cross-checked with RT-PCR tests for sensitivity and specificity. These results are consistent with comparable exchanges of methods, results or discussions among laboratorians, authors, institutions and publishers in the involved countries of the world.

The Agreement Between Reverse Transcriptase-Polymerase Chain Reaction (RT-PCR) and Rapid Antigen Test (RAT) in Diagnosing COVID-19

Background

False-negative results derived from RT-PCR tests for diagnosing coronavirus disease (COVID-19) have raised questions about whether to consider them the gold standard for the detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Using an imperfect gold standard to assess other diagnostic tests would never let the other tests show better diagnostic performance. The best strategy in such cases is to do an agreement analysis, and this study aims to estimate the agreement between real-time reverse transcriptase-polymerase chain reaction (RT-PCR) and rapid antigen test (RAT) for COVID-19 detection.

Methods

A retrospective study was done using paired data of individuals tested for COVID-19, both by RT-PCR and RAT, obtained from the virology laboratory of Government Bundelkhand Medical College, Sagar, Madhya Pradesh, India. A sample size of 93 was calculated, and the data were abstracted in a data abstraction sheet. Variables included were results of RT-PCR and RAT, age, gender, presence of symptoms, test kit used, and the time duration between sampling for RT-PCR and RAT. Apart from descriptive statistics, keeping in mind the binary outcome of RT-PCR and RAT, Cohen’s kappa was calculated for agreement analysis. A p-value of <0.05 was considered significant.

Results

The data on 100 participants suspected to be infected with COVID-19 (58 male and 42 female) with a mean age of 39.8 (±19.0) years were analysed. The number of discordant pairs was eight. Cohen’s kappa showed substantial agreement between RT-PCR and RAT, κ=0.646, (95% CI 0.420 to 0.871), p<0.001.

Conclusion

Considering the ease of conducting RAT with quick results and substantial agreement with RT-PCR, RAT could be a better choice in detecting SARS-CoV-2 and, hence, COVID-19 disease on a large scale.

Utility of in silico-identified-peptides in spike-S1 domain and nucleocapsid of SARS-CoV-2 for antibody detection in COVID-19 patients and antibody production

SARS-CoV-2 contains four structural proteins, two of which, the spike and nucleocapsid, are commonly used for the standardization of novel methods for antibody detection; however, some limitations in their use have been observed due to the homology of this virus with other phylogenetically-related viruses. We performed in silico analysis to search for novel immunogenic and antigenic peptides. A total of twenty-five peptides were preliminarily selected, located in the 3D structure of both proteins. Finally, eight peptides were selected: one located in the N protein and seven in the S1 domain of the spike protein. Additionally, the localization of selected peptides in 2D structures and possible changes in the sequences of these peptides in SARS-CoV-2 variants of concern were analyzed. All peptides were synthetized in MAP8 format, and recombinant S (trimer and RBD) and N proteins were used as antigens to search for antibodies in serum samples derived from COVID-19 patients, and for antibody response in New Zealand rabbits. Results showed high recognition of the serum derived from COVID-19 patients to all selected peptides; however, only the RBD3 peptide induced antibody production. In conclusion, this work provides evidence for a new strategy in peptide selection and its use for antibody detection or antibody production in animals.

Diagnostic Approaches For COVID-19: Lessons Learned and the Path Forward

Coronavirus disease 2019 (COVID-19) is a transmitted respiratory disease caused by the infection of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Although humankind has experienced several outbreaks of infectious diseases, the COVID-19 pandemic has the highest rate of infection and has had high levels of social and economic repercussions. The current COVID-19 pandemic has highlighted the limitations of existing virological tests, which have failed to be adopted at a rate to properly slow the rapid spread of SARS-CoV-2. Pandemic preparedness has developed as a focus of many governments around the world in the event of a future outbreak. Despite the largely widespread availability of vaccines, the importance of testing has not diminished to monitor the evolution of the virus and the resulting stages of the pandemic. Therefore, developing diagnostic technology that serves as a line of defense has become imperative. In particular, that test should satisfy three criteria to be widely adopted: simplicity, economic feasibility, and accessibility. At the heart of it all, it must enable early diagnosis in the course of infection to reduce spread. However, diagnostic manufacturers need guidance on the optimal characteristics of a virological test to ensure pandemic preparedness and to aid in the effective treatment of viral infections. Nanomaterials are a decisive element in developing COVID-19 diagnostic kits as well as a key contributor to enhance the performance of existing tests. Our objective is to develop a profile of the criteria that should be available in a platform as the target product. In this work, virus detection tests were evaluated from the perspective of the COVID-19 pandemic, and then we generalized the requirements to develop a target product profile for a platform for virus detection.

Multicenter Study on COVID-19 Lung Computed Tomography Segmentation with varying Glass Ground Opacities using Unseen Deep Learning Artificial Intelligence Paradigms: COVLIAS 1.0 Validation

Variations in COVID-19 lesions such as glass ground opacities (GGO), consolidations, and crazy paving can compromise the ability of solo-deep learning (SDL) or hybrid-deep learning (HDL) artificial intelligence (AI) models in predicting automated COVID-19 lung segmentation in Computed Tomography (CT) from unseen data leading to poor clinical manifestations. As the first study of its kind, “COVLIAS 1.0-Unseen” proves two hypotheses, (i) contrast adjustment is vital for AI, and (ii) HDL is superior to SDL. In a multicenter study, 10,000 CT slices were collected from 72 Italian (ITA) patients with low-GGO, and 80 Croatian (CRO) patients with high-GGO. Hounsfield Units (HU) were automatically adjusted to train the AI models and predict from test data, leading to four combinations—two Unseen sets: (i) train-CRO:test-ITA, (ii) train-ITA:test-CRO, and two Seen sets: (iii) train-CRO:test-CRO, (iv) train-ITA:test-ITA. COVILAS used three SDL models: PSPNet, SegNet, UNet and six HDL models: VGG-PSPNet, VGG-SegNet, VGG-UNet, ResNet-PSPNet, ResNet-SegNet, and ResNet-UNet. Two trained, blinded senior radiologists conducted ground truth annotations. Five types of performance metrics were used to validate COVLIAS 1.0-Unseen which was further benchmarked against MedSeg, an open-source web-based system. After HU adjustment for DS and JI, HDL (Unseen AI) > SDL (Unseen AI) by 4% and 5%, respectively. For CC, HDL (Unseen AI) > SDL (Unseen AI) by 6%. The COVLIAS-MedSeg difference was < 5%, meeting regulatory guidelines.Unseen AI was successfully demonstrated using automated HU adjustment. HDL was found to be superior to SDL.

Point-of-care contrast enhanced lung ultrasound and COVID-19

OBJECTIVES

Bedside lung ultrasound has been indispensable during the coronavirus disease 2019 (COVID-19) pandemic, allowing us to rapidly assess critically unwell patients. We demonstrate the unique application of contrast-enhanced ultrasound with the aim of further understanding this disease.

METHODS

Patient demographics were recorded alongside recent cross-sectional imaging and inflammatory markers. Ultrasound was conducted by experienced operators in a portable setting. Conventional six-point lung ultrasound method was used to evaluate B-lines, small (subpleural) consolidation and the pleura. Areas of small consolidation were targeted after intravenous administration of ultrasound contrast.

RESULTS

The areas of small consolidations, a potential sign of pneumonia on B-mode lung ultrasound, usually enhance on contrast-enhanced ultrasound. Our study revealed these areas to be avascular, indicating an underlying thrombotic/infarction process. Findings were present in 100% of the patients we examined. We have also shown that the degree of infarction correlates with CT severity (r = 0.4) and inflammatory markers, and that these areas improve as patients recover.

CONCLUSIONS

We confirmed the theory of immune thrombus by identifying the presence of microthrombi in the lungs of 100% of our patients, despite 79% having had a recent negative CT pulmonary angiogram study. contrast-enhanced ultrasound can be utilised to add confidence to an uncertain COVID-19 diagnosis and for prognosticating and monitoring progress in confirmed COVID-19 patients. Contrast-enhanced ultrasound is clearly very different to CT, the gold standard, and while there are specific pathologies that can only be detected on CT, contrast-enhanced ultrasound has many advantages, most notability the ability to pick up microthrombi at the periphery of the lungs.

Technical performance of a lateral flow immunoassay for detection of anti-SARS-CoV-2 IgG in the outpatient follow-up of non-severe cases and at different times after vaccination: comparison with enzyme and chemiluminescent immunoassays

This study assessed the technical performance of a rapid lateral flow immunochromatographic assay (LFIA) for the detection of anti-SARS-CoV-2 IgG and compared LFIA results with chemiluminescent immunoassay (CLIA) results and an in-house enzyme immunoassay (EIA). To this end, a total of 216 whole blood or serum samples from three groups were analyzed: the first group was composed of 68 true negative cases corresponding to blood bank donors, healthy young volunteers, and eight pediatric patients diagnosed with other coronavirus infections. The serum samples from these participants were obtained and stored in a pre-COVID-19 period, thus they were not expected to have COVID-19. In the second group of true positive cases, we chose to replace natural cases of COVID-19 by 96 participants who were expected to have produced anti-SARS-CoV-2 IgG antibodies 30-60 days after the vaccine booster dose. The serum samples were collected on the same day that LFIA were tested either by EIA or CLIA. The third study group was composed of 52 participants (12 adults and 40 children) who did or did not have anti-SARS-CoV-2 IgG antibodies due to specific clinical scenarios. The 12 adults had been vaccinated more than seven months before LFIA testing, and the 40 children had non-severe COVID-19 diagnosed using RT-PCR during the acute phase of infection. They were referred for outpatient follow-up and during this period the serum samples were collected and tested by CLIA and LFIA. All tests were performed by the same healthcare operator and there was no variation of LFIA results when tests were performed on finger prick whole blood or serum samples, so that results were grouped for analysis. LFIA's sensitivity in detecting anti-SARS-CoV-2 IgG antibodies was 90%, specificity 97.6%, efficiency 93%, PPV 98.3%, NPV 86.6%, and likelihood ratio for a positive or a negative result were 37.5 and 0.01 respectively. There was a good agreement (Kappa index of 0.677) between LFIA results and serological (EIA or CLIA) results. In conclusion, LFIA analyzed in this study showed a good technical performance and agreement with reference serological assays (EIA or CLIA), therefore it can be recommended for use in the outpatient follow-up of non-severe cases of COVID-19 and to assess anti-SARS-CoV-2 IgG antibody production induced by vaccination and the antibodies decrease over time. However, LFIAs should be confirmed by using reference serological assays whenever possible.

Evaluation and Clinical Validation of Guanidine-Based Inactivation Transport Medium for Preservation of SARS-CoV-2

WHO declared the outbreak of COVID-19, caused by SARS-CoV-2, a pandemic in March 2020. More than 223 million cases and approximately 4.6 million deaths have been confirmed. Early diagnosis and immediate treatment became a priority during this pandemic. However, COVID-19 diagnostic testing resources are limited, especially early in the pandemic. Apart from being limited, the COVID-19 diagnostic tests using reverse transcription polymerase chain reaction (RT-PCR) have encountered storage, transportation, and safety issues. These problems are mainly experienced by developing poor countries, countries in the equatorial region, and archipelagic countries. VITPAD® is a guanidine-based inactivation transport medium (ITM) formulated to maintain the RNA quality of SARS-CoV-2 during transportation without cold chains. This study, conducted from September 2020 to March 2021, performed clinical validation of VITPAD® by comparing its performance with a globally commercially available ITM from the NEST brand. Its stability at room temperature, safety, and resistance at high temperatures was also tested using RT-PCR analysis. VITPAD® can reduce the infectious nature of the specimen, preserve the SARS-CoV-2 for 18 days at an ambient temperature, and resist high temperatures (40°C for 3 hours). A guanidine-based transport medium, such as VITPAD®, is compatible and recommended for RT-PCR-based molecular diagnosis of COVID-19.

Rapid self-test of unprocessed viruses of SARS-CoV-2 and its variants in saliva by portable wireless graphene biosensor

We have developed a DNA aptamer-conjugated graphene field-effect transistor (GFET) biosensor platform to detect receptor-binding domain (RBD), nucleocapsid (N), and spike (S) proteins, as well as viral particles of original Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) coronavirus and its variants in saliva samples. The GFET biosensor is a label-free, rapid (≤20 min), ultrasensitive handheld wireless readout device. The limit of detection (LoD) and the limit of quantitation (LoQ) of the sensor are 1.28 and 3.89 plaque-forming units (PFU)/mL for S protein and 1.45 and 4.39 PFU/mL for N protein, respectively. Cognate spike proteins of major variants of concern (N501Y, D614G, Y453F, Omicron-B1.1.529) showed sensor response ≥40 mV from the control (aptamer alone) for fM to nM concentration range. The sensor response was significantly lower for viral particles and cognate proteins of Middle East Respiratory Syndrome (MERS) compared to SARS-CoV-2, indicating the specificity of the diagnostic platform for SARS-CoV-2 vs. MERS viral proteins. During the early phase of the pandemic, the GFET sensor response agreed with RT-PCR data for oral human samples, as determined by the negative percent agreement (NPA) and positive percent agreement (PPA). During the recent Delta/Omicron wave, the GFET sensor also reliably distinguished positive and negative clinical saliva samples. Although the sensitivity is lower during the later pandemic phase, the GFET-defined positivity rate is in statistically close alignment with the epidemiological population-scale data. Thus, the aptamer-based GFET biosensor has a high level of precision in clinically and epidemiologically significant SARS-CoV-2 variant detection. This universal pathogen-sensing platform is amenable for a broad range of public health applications and real-time environmental monitoring.

Eight pruning deep learning models for low storage and high-speed COVID-19 computed tomography lung segmentation and heatmap-based lesion localization: A multicenter study using COVLIAS 2.0

BACKGROUND

COVLIAS 1.0: an automated lung segmentation was designed for COVID-19 diagnosis. It has issues related to storage space and speed. This study shows that COVLIAS 2.0 uses pruned AI (PAI) networks for improving both storage and speed, wiliest high performance on lung segmentation and lesion localization.

METHOD

ology: The proposed study uses multicenter ∼9,000 CT slices from two different nations, namely, CroMed from Croatia (80 patients, experimental data), and NovMed from Italy (72 patients, validation data). We hypothesize that by using pruning and evolutionary optimization algorithms, the size of the AI models can be reduced significantly, ensuring optimal performance. Eight different pruning techniques (i) differential evolution (DE), (ii) genetic algorithm (GA), (iii) particle swarm optimization algorithm (PSO), and (iv) whale optimization algorithm (WO) in two deep learning frameworks (i) Fully connected network (FCN) and (ii) SegNet were designed. COVLIAS 2.0 was validated using "Unseen NovMed" and benchmarked against MedSeg. Statistical tests for stability and reliability were also conducted.

RESULTS

Pruning algorithms (i) FCN-DE, (ii) FCN-GA, (iii) FCN-PSO, and (iv) FCN-WO showed improvement in storage by 92.4%, 95.3%, 98.7%, and 99.8% respectively when compared against solo FCN, and (v) SegNet-DE, (vi) SegNet-GA, (vii) SegNet-PSO, and (viii) SegNet-WO showed improvement by 97.1%, 97.9%, 98.8%, and 99.2% respectively when compared against solo SegNet. AUC > 0.94 (p < 0.0001) on CroMed and > 0.86 (p < 0.0001) on NovMed data set for all eight EA model. PAI <0.25 s per image. DenseNet-121-based Grad-CAM heatmaps showed validation on glass ground opacity lesions.

CONCLUSIONS

Eight PAI networks that were successfully validated are five times faster, storage efficient, and could be used in clinical settings.

COVID-19 Diagnosis: A Comprehensive Review of the RT-qPCR Method for Detection of SARS-CoV-2

The world is grappling with the coronavirus disease 2019 (COVID-19) pandemic, the causative agent of which is severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). COVID-19 symptoms are similar to the common cold, including fever, sore throat, cough, muscle and chest pain, brain fog, dyspnoea, anosmia, ageusia, and headache. The manifestation of the disease can vary from being asymptomatic to severe life-threatening conditions warranting hospitalization and ventilation support. Furthermore, the emergence of mutecated variants of concern (VOCs) is paramount to the devastating effect of the pandemic. This highly contagious virus and its emergent variants challenge the available advanced viral diagnostic methods for high-accuracy testing with faster result yields. This review is to shed light on the natural history, pathology, molecular biology, and efficient diagnostic methods of COVID-19, detecting SARS-CoV-2 in collected samples. We reviewed the gold standard RT-qPCR method for COVID-19 diagnosis to confer a better understanding and application to combat the COVID-19 pandemic. This comprehensive review may further develop awareness about the management of the COVID-19 pandemic.

Prevalence of the Clinical Symptoms and PCR Test Results on Patients With COVID-19 in South of Tehran

Background:

The first human case of COVID-19 was reported on 19 February 2020 in Iran, since then the number of infected cases has dramatically increased to about 1.800 million and about 62 000 deaths, also elderly people have accounted for the largest frequency of mortality.

Objective:

This study aims to evaluate the prevalence of Covid-19 RT-PCR positive among Iranian patients and to evaluate the most important clinical symptoms of identified patients based on age, gender, and their background diseases to be used as an aid for early diagnosis.

Methods:

During 9 months, swab samples of 11 034 patients’ nasopharynx and oropharynx secretions were obtained and were referred to the laboratory for Covid-19 RT-PCR test. In addition, the history of signs and symptoms from patients was recorded.

Results:

In total, 11 034 Covid-19 RT-PCR tests that performed, 3358 samples had positive results (30.4%). Headache was the most commonly reported, which was found in 42.5% of our cases. Fever was the second most common symptom among the patients studied, with a prevalence rate of 36.2%.

Conclusion:

In this study, almost 30% of symptomatic patients had positive Covid-19 RT-PCR test results. Headache was the most common symptom; also diarrhea and nausea were the least common symptoms among patients under this study.

COVLIAS 2.0-cXAI: Cloud-Based Explainable Deep Learning System for COVID-19 Lesion Localization in Computed Tomography Scans

Background: The previous COVID-19 lung diagnosis system lacks both scientific validation and the role of explainable artificial intelligence (AI) for understanding lesion localization. This study presents a cloud-based explainable AI, the “COVLIAS 2.0-cXAI” system using four kinds of class activation maps (CAM) models. Methodology: Our cohort consisted of ~6000 CT slices from two sources (Croatia, 80 COVID-19 patients and Italy, 15 control patients). COVLIAS 2.0-cXAI design consisted of three stages: (i) automated lung segmentation using hybrid deep learning ResNet-UNet model by automatic adjustment of Hounsfield units, hyperparameter optimization, and parallel and distributed training, (ii) classification using three kinds of DenseNet (DN) models (DN-121, DN-169, DN-201), and (iii) validation using four kinds of CAM visualization techniques: gradient-weighted class activation mapping (Grad-CAM), Grad-CAM++, score-weighted CAM (Score-CAM), and FasterScore-CAM. The COVLIAS 2.0-cXAI was validated by three trained senior radiologists for its stability and reliability. The Friedman test was also performed on the scores of the three radiologists. Results: The ResNet-UNet segmentation model resulted in dice similarity of 0.96, Jaccard index of 0.93, a correlation coefficient of 0.99, with a figure-of-merit of 95.99%, while the classifier accuracies for the three DN nets (DN-121, DN-169, and DN-201) were 98%, 98%, and 99% with a loss of ~0.003, ~0.0025, and ~0.002 using 50 epochs, respectively. The mean AUC for all three DN models was 0.99 (p < 0.0001). The COVLIAS 2.0-cXAI showed 80% scans for mean alignment index (MAI) between heatmaps and gold standard, a score of four out of five, establishing the system for clinical settings. Conclusions: The COVLIAS 2.0-cXAI successfully showed a cloud-based explainable AI system for lesion localization in lung CT scans.

An all-in-one point-of-care testing device for multiplexed detection of respiratory infections

The impact of the COVID-19 pandemic has reinforced the need for rapid, cost-effective, and reliable point-of-care testing (POCT) devices for massive population screening. The co-circulation of SARS-CoV-2 with several seasonal respiratory viruses highlights the need for multiplexed biosensing approaches. Herein, we present a fast and robust all-in-one POCT device for parallel viral antigen and serological analysis. The biosensing approach consists of a functionalized polycarbonate disc-shaped surface with microfluidic structures, where specific bioreagents are immobilized in microarray format, and a portable optoelectronic analyzer. The biosensor quantifies the concentration of viral antigens and specific immunoglobulins G and M for SARS-CoV-2, influenza A/B, adenovirus, and respiratory syncytial virus, using 30 μL of a sample. The semi-automated analysis of 6 samples is performed in 30 min. Validation studies performed with 135 serum samples and 147 nasopharyngeal specimens reveal high diagnostic sensitivity (98–100%) and specificity (84–98%), achieving an excellent agreement (κ = 0.937) with commercial immunoassays, which complies with the World Health Organization criteria for POC COVID-19 diagnostic tests. The versatility of the POCT device paves the way for the detection of other pathogens and analytes in the incoming post-pandemic world, integrating specific bioreagents against different variants of concerns and interests.

Building a predictive model to identify clinical indicators for COVID-19 using machine learning method

Although some studies tried to identify risk factors for COVID-19, the evidence comparing COVID-19 and community-acquired pneumonia (CAP) is inconclusive, and CAP is the most common pneumonia with similar symptoms as COVID-19. We conducted a case–control study with 35 routine-collected clinical indicators and demographic factors to identify predictors for COVID-19 with CAP as controls. We randomly split the dataset into a training set (70%) and testing set (30%). We built Explainable Boosting Machine to select the important factors and built a decision tree on selected variables to interpret their relationships. The top five individual predictors of COVID-19 are albumin, total bilirubin, monocyte count, alanine aminotransferase, and percentage of monocyte with the importance scores ranging from 0.078 to 0.567. The top systematic predictors for COVID-19 are liver function, monocyte increasing, plasma protein, granulocyte, and renal function (importance scores ranging 0.009–0.096). We identified five combinations of important indicators to screen COVID-19 patients from CAP patients with differentiating abilities ranging 83.3–100%. An online predictive tool for our model was published. Certain clinical indicators collected routinely from most hospitals could help screen and distinguish COVID-19 from CAP. While further verification is needed, our findings and predictive tool could help screen suspected COVID-19 cases.

Combining rapid antigen testing and syndromic surveillance improves community-based COVID-19 detection in a low-income country

Diagnostics for COVID-19 detection are limited in many settings. Syndromic surveillance is often the only means to identify cases but lacks specificity. Rapid antigen testing is inexpensive and easy-to-deploy but can lack sensitivity. We examine how combining these approaches can improve surveillance for guiding interventions in low-income communities in Dhaka, Bangladesh. Rapid-antigen-testing with PCR validation was performed on 1172 symptomatically-identified individuals in their homes. Statistical models were fitted to predict PCR-status using rapid-antigen-test results, syndromic data, and their combination. Under contrasting epidemiological scenarios, the models’ predictive and classification performance was evaluated. Models combining rapid-antigen-testing and syndromic data yielded equal-to-better performance to rapid-antigen-test-only models across all scenarios with their best performance in the epidemic growth scenario. These results show that drawing on complementary strengths across rapid diagnostics, improves COVID-19 detection, and reduces false-positive and -negative diagnoses to match local requirements; improvements achievable without additional expense, or changes for patients or practitioners.

Rapid antigen tests and syndromic surveillance for identification of COVID-19 cases are limited by low sensitivity and specificity, respectively. Here, the authors use data from Bangladesh and show that combining the two methods improves diagnostic accuracy in a range of epidemiological scenarios.