The COVID-19 pandemic has highlighted the critical need for pathogen detection methods that offer both low detection limits and rapid results. Despite advancements in simplifying and enhancing nucleic acid amplification techniques, immunochemical methods remain the preferred methods for mass testing. These methods eliminate the need for specialized laboratories and highly skilled personnel, making home testing feasible. Here, we developed nanobody-based lateral flow assays (LFAs) for the rapid detection of SARS-CoV-2 and MERS-CoV in single and dual formats as point-of-care diagnostic tools. The developed LFAs are highly sensitive and successfully detected analytes at clinically relevant diagnostic cutoff values. Additionally, our results confirmed that the LFAs have a long shelf life and can be produced cost-effectively and with ease.

Infectious diseases impose a significant burden on global health systems due to high morbidity and mortality rates. According to the World Health Organization, millions die from infectious diseases annually, often due to delays in accurate diagnosis. Traditional diagnostic methods in clinical microbiology, primarily culture-based techniques, are time-consuming and may fail with hard-to-culture pathogens. Molecular biology advancements, notably the polymerase chain reaction (PCR), have revolutionized infectious disease diagnostics by allowing rapid and sensitive detection of pathogens' genetic material. PCR has become the gold standard for many infections, particularly highlighted during the COVID-19 pandemic. Following PCR, next-generation sequencing (NGS) has emerged, enabling comprehensive genomic analysis of pathogens, thus facilitating the detection of new strains and antibiotic resistance tracking. Innovative approaches like CRISPR technology are also enhancing diagnostic precision by identifying specific DNA/RNA sequences. However, the implementation of these methods faces challenges, particularly in low- and middle-income countries due to infrastructural and financial constraints. This review will explore the role of molecular diagnostic methods in infectious disease diagnosis, comparing their advantages and limitations, with a focus on PCR and NGS technologies and their future potential.

SARS-CoV-2, seasonal influenza, and respiratory syncytial virus (RSV) are major causes of acute respiratory infections in all age groups and responsible for an enormous socio-economic burden. The recently coined term 'tripledemic' describes co-circulation of these three viruses, a novel epidemiological paradigm that poses profound public health implications.

Real-time reverse transcription polymerase chain reaction (RT-PCR) is now considered the reference method for the diagnosis of SARS-CoV-2, influenza, and RSV infections. Syndromic-based multiplex RT-PCR panels that simultaneously detect several respiratory viruses have become increasingly common. This review explores available molecular diagnostics (MDx) platforms for the diagnosis of SARS-CoV-2, influenza, and RSV in the same biological sample. Within some limitations of the published validation and diagnostic accuracy studies, both laboratory-based and point-of-care multiplex panels proved highly performant in identifying SARS-CoV-2, influenza A, influenza B, and RSV. Improved operational efficiency and faster turnaround times make these assays potentially cost-effective or even cost-saving.

The adoption of multiplex MDx assays for the contemporary detection of SARS-CoV-2, influenza, RSV, and other respiratory pathogens will likely increase in the next few years. To maximize the clinical usefulness and cost-effectiveness of these assays, locally issued guidelines and protocols on their implementation should be adopted.

Rapid real-time PCR (generally <1 h) has broad prospects. In this study, we synthesized a new type of nanomaterial core-shell tecto-dendrimer coated with Au nanoparticles (Au CSTDs) for research in this field. The experimental results showed that Au CSTDs could significantly shorten the time of real-time PCR (from 72 to 28 min) with different templates, while the detection limit reached 10 copies and the nonspecific amplification was significantly reduced. Furthermore, experimental analyses and theoretical studies using the finite element simulation method confirmed that Au CSTDs function by synergistically enhancing electrostatic adsorption and thermal conductivity. These properties play a key role in improving real-time PCR, especially in particle-particle interactions. This study contributes an advanced method to rapid real-time PCR, which is expected to remarkably improve the efficiency, lower the detection limit, and enhance the specificity of molecular detection.

The COVID-19 pandemic has caused an unprecedented health threat globally, necessitating innovative and efficient diagnostic approaches for timely identification of infected individuals. Despite few emerging reports, the clinical utility of circulating microRNAs (miRNAs) in early and accurate diagnosis of COVID-19 is not well-evidenced. Hence, this meta-analysis aimed to explore the diagnostic potential of circulating miRNAs for COVID-19. The protocol for this study was officially recorded on PROSPERO under registration number CRD42023494959.

Electronic databases including Embase, PubMed, Scopus, and other sources were exhaustively searched to recover studies published until 16th January, 2024. Pooled specificity, sensitivity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic ratio (DOR), positive predictive value (PPV), negative predictive value (NPV), and area under the curve (AUC) were computed from the metadata using Stata 14.0 software. Risk of bias appraisal of included articles was carried out using Review Manager (Rev-Man) 5.3 package through the modified QUADAS-2 tool. Subgroup, heterogeneity, meta-regression and sensitivity analyses were undertaken. Publication bias and clinical applicability were also evaluated via Deeks' funnel plot and Fagan nomogram (scattergram), respectively.

A total of 43 studies from 13 eligible articles, involving 5175 participants (3281 COVID-19 patients and 1894 healthy controls), were analyzed. Our results depicted that miRNAs exhibit enhanced pooled specificity 0.91 (95% CI: 0.88-0.94), sensitivity 0.94 (95% CI: 0.91-0.96), DOR of 159 (95% CI: 87-288), and AUC values of 0.97 (95% CI: 0.95-0.98) with high pooled PPV 96% (95% CI: 94-97%) and NPV 88% (95% CI: 86-90%) values. Additionally, highest diagnostic capacity was observed in studies involving larger sample size (greater than 100) and those involving the African population, demonstrating consistent diagnostic effectiveness across various specimen types. Notably, a total of 12 distinct miRNAs were identified as suitable for both exclusion and confirmation of COVID-19 cases, denoting their potential clinical applicability.

Our study depicted that miRNAs show significantly high diagnostic accuracy in differentiating COVID-19 patients from healthy counterparts, suggesting their possible use as viable biomarkers. Nonetheless, thorough and wide-ranging longitudinal researches are necessary to confirm the clinical applicability of miRNAs in diagnosing COVID-19.

Surface enhanced Raman spectroscopy (SERS) is meeting the requirements in biomedical science being a highly sensitive and specific analytical tool. By employing portable Raman systems in combination with customized sample pre-treatment, point-of-care-testing (POCT) becomes feasible. Powerful SERS-active sensing surfaces with high stability and modification layers if required are available for testing and application in complex biological matrices such as body fluids, cells or tissues. This review summarizes the current state in sample collection and pretreatment in SERS detection protocols, SERS detection schemes, i.e. direct and indirect SERS as well as targeted and non-targeted SERS, and SERS-active sensing surfaces. Moreover, the recent developments and advances of SERS in biomedical application scenarios, such as infectious diseases, cancer diagnostics and therapeutic drug monitoring is given, which enables the readers to identify the sample collection and preparation protocols, SERS substrates and detection strategies that are best-suited for their specific applications in biomedicine.

The COVID-19 pandemic highlighted the crucial role of diagnostic testing in managing infectious diseases, particularly through the use of reverse transcription-quantitative polymerase chain reaction (RT-qPCR) tests. RT-qPCR has been pivotal in detecting and quantifying viral RNA, enabling the identification and management of SARS-CoV-2 infections. However, despite its widespread use, there remains a notable gap in understanding fundamental diagnostic metrics such as sensitivity and specificity among many scientists and healthcare practitioners. This gap is not merely academic; it has profound implications for interpreting test results, making public health decisions, and affecting patient outcomes. This review aims to clarify the distinctions between laboratory- and field-based metrics in the context of RT-qPCR testing for SARS-CoV-2 and summarise the global efforts that led to the development and optimisation of these tests during the pandemic. It is intended to enhance the understanding of these fundamental concepts among scientists and healthcare professionals who may not be familiar with the nuances of diagnostic test evaluation. Such knowledge is crucial for accurately interpreting test results, making informed public health decisions, and ultimately managing infectious disease outbreaks more effectively.

Loop-Mediated Isothermal Amplification (LAMP) technology is emerging as a rapid pathogen testing method, potentially challenging the RT-PCR "gold standard". Despite recent advancements, LAMP's widespread adoption remains limited. This study provides a comprehensive market overview and assesses future growth prospects to aid stakeholders in strategic decision-making and policy formulation. Using a dataset of 1134 LAMP patent documents, we analyzed lifecycle and geographic distribution, applicant profiles, CPC code classifications, and patent claims. Additionally, we examined clinical developments from 21 curated clinical trials, focusing on trends, geographic engagement, sponsor types, and the conditions and pathogens investigated. Our analysis highlights LAMP's potential as a promising rapid pathogen testing alternative, especially in resource-limited areas. It also reveals a gap between clinical research, which targets bacterial and parasitic diseases like malaria, leishmaniasis, and tuberculosis, and basic research and commercial efforts that prioritize viral diseases such as SARS-CoV-2 and influenza. European stakeholders emphasize the societal impact of addressing unmet needs in resource-limited areas, while American and Asian organizations focus more on research, innovation, and commercialization.

The "cytokine storm" often induced in COVID-19 patients contributes to the onset of "acute respiratory distress syndrome" (ARDS) accompanied by lung infection and damage, multiorgan failure, and even death. This large increase in pro-inflammatory cytokines in blood may be related to severity. Rapid, on-demand cytokine analyses can thus be critical to inform treatment plans and improve survival rates. Here, we report a sensitive, low-cost, semiautomated 3D-printed microfluidic immunoarray to detect 2 cytokines and CRP simultaneously in a single 10 μL serum sample in 25 min. Accuracy was validated by analyzing 80 COVID-19 patient serum samples, with results well correlated to a commercial Meso Scale protein immunoassay. Capture antibodies immobilized in detection microwells in a flat well plate-type flow chamber facilitate the immunoassay, with a programmable syringe pump automatically delivering reagents. Chemiluminescence signals were captured in a dark box with a CCD camera integrated for 30 s. This system was optimized to detect inflammation biomarkers IL-6, IFN-γ, and CRP simultaneously in blood serum. Ultralow limits of detection (LODs) of 0.79 fg/mL for IL-6, 4.2 fg/mL for CRP, and 2.7 fg/mL for IFN-γ with dynamic ranges of up to 100 pg/mL were achieved. ROC statistical analyses showed a relatively good diagnostic value related to the samples assigned WHO COVID-19 scores for disease severity, with the best results for IL-6 and CRP. Monitoring these biomarkers for coronavirus severity may allow prediction of disease severity as a basis for critical treatment decisions and better survival rates.

Colorimetric reverse transcription loop-mediated isothermal amplification (RT-LAMP) is a potential and relatively simple rapid diagnostics method for COVID-19 detection. This study aims to evaluate and optimize the RT-LAMP performance on saliva specimens based on a commercially available kit.Modifications on an established protocol (Protocol A) were used, including Proteinase K supplementation (Protocol B); pre-treatment using nuclease-free water and proteinase K (Protocol C); Saliva cooling (Protocol D); saliva dilution after pre-treatment (Protocol E); lastly a combination of saliva cooling and dilution (Protocol F). Protocol performances were evaluated by comparing success rates (SR), diagnostic accuracy (DA), sensitivity, specificity, and predictive values. Additionally, a correlation between the Ct value by RT-qPCR and RT-LAMP performance was analyzed.. A total of 106 specimens were used in this study. Protocols B and C showed 100% unreadable results, therefore were paused. Protocol F showed the highest SR (87.65%) compared to other protocols, with a slight compromise to DA (81.69%), sensitivity (57.14%), specificity (97.67%), PPV (94.12%), and NPV (77.78%). In the sub-analysis of the low Ct value group (Ct < 30), Protocol F demonstrated a higher success rate (86.57%) compared to protocol A (64.18%); increased 3.08% sensitivity and 2.42% NPV; comparable DA; minor reduction in specificity (A = 100%; F = 97.67%) and PPV (A = 100%; F = 92.31%). A combination of saliva cooling-dilution substantially increased the tested kit's success rate, despite a slight decrease in specificity and PPV. Findings confirmed the saliva cooling-dilution procedure was beneficial to the test's SR, sensitivity, and NPV in the low Ct value group.

The online version contains supplementary material available at 10.1007/s13337-024-00870-1.

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).

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.

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.

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

Biomarkers that accurately identify mild cognitive impairment (MCI) are of greater importance for Alzheimer's disease (AD) management and treatment. On the other hand, blood-based biomarkers are not only more practical but also less invasive than the common cerebrospinal fluid biomarkers. In their report in the Journal of Alzheimer's Disease, Wang and collaborators identified 67 upregulated and 220 downregulated long noncoding RNAs (lncRNAs). They further demonstrated that 4 of these lncRNAs could discriminate MCI from cognitively healthy individuals. Apart from their significance as potential biomarkers for MCI diagnosis, these lncRNAs can offer additional information on the cellular mechanisms of AD pathology.

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.

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.

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.

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.

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.

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.

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.

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].

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.

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.

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.

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).

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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).

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.

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.

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.

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.

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.

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.

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 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.

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.

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.

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.

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.

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).

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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

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.

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.

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.

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.