Diagnostic Accuracy of the LabTurbo QuadAIO Common Flu Assay for Detecting Influenza A Virus, Influenza B Virus, RSV, and SARS-CoV-2.

Background: The coronavirus disease 2019 (COVID-19) pandemic has highlighted the urgent need for rapid and accurate diagnostic tools for upper respiratory tract infections (URTIs). Nucleic acid amplification tests (NAATs) have transformed URTI diagnostics by enabling the rapid detection of multiple pathogens simultaneously, thereby improving patient management and infection control. This study aimed to evaluate the diagnostic accuracy of the LabTurbo QuadAIO Common Flu Assay compared to that of the Xpert Xpress CoV-2/Flu/RSV Plus Assay for detecting SARS-CoV-2, Influenza A, Influenza B, and respiratory syncytial virus (RSV). Methods: A retrospective diagnostic accuracy study was conducted using nasopharyngeal samples from patients. Samples were tested using the LabTurbo QuadAIO Common Flu Assay and the comparator Xpert Xpress CoV-2/Flu/RSV Plus Assay. Positive and negative percent agreements (PPA and NPA) were calculated. Results: The LabTurbo Assay demonstrated a PPA of 100% and an NPA of 100% for SARS-CoV-2, Influenza A, and Influenza B, whereas it showed a PPA of 100% and an NPA of 98.3% for RSV. Conclusions: The LabTurbo QuadAIO Assay exhibited high diagnostic accuracy for detecting multiple respiratory pathogens, including SARS-CoV-2, Influenza A, Influenza B, and RSV. Despite the slight discrepancy in the NPA for RSV, the overall performance of the LabTurbo Assay supports its integration into routine diagnostic workflows to enhance patient management and infection control.

An Advanced Machine Learning Model for a Web-Based Artificial Intelligence-Based Clinical Decision Support System Application: Model Development and Validation Study.

BACKGROUND: Breast cancer is a leading global health concern, necessitating advancements in recurrence prediction and management. The development of an artificial intelligence (AI)-based clinical decision support system (AI-CDSS) using ChatGPT addresses this need with the aim of enhancing both prediction accuracy and user accessibility. OBJECTIVE: This study aims to develop and validate an advanced machine learning model for a web-based AI-CDSS application, leveraging the question-and-answer guidance capabilities of ChatGPT to enhance data preprocessing and model development, thereby improving the prediction of breast cancer recurrence. METHODS: This study focused on developing an advanced machine learning model by leveraging data from the Tri-Service General Hospital breast cancer registry of 3577 patients (2004-2016). As a tertiary medical center, it accepts referrals from four branches-3 branches in the northern region and 1 branch on an offshore island in our country-that manage chronic diseases but refer complex surgical cases, including breast cancer, to the main center, enriching our study population's diversity. Model training used patient data from 2004 to 2012, with subsequent validation using data from 2013 to 2016, ensuring comprehensive assessment and robustness of our predictive models. ChatGPT is integral to preprocessing and model development, aiding in hormone receptor categorization, age binning, and one-hot encoding. Techniques such as the synthetic minority oversampling technique address the imbalance of data sets. Various algorithms, including light gradient-boosting machine, gradient boosting, and extreme gradient boosting, were used, and their performance was evaluated using metrics such as the area under the curve, accuracy, sensitivity, and F1-score. RESULTS: The light gradient-boosting machine model demonstrated superior performance, with an area under the curve of 0.80, followed closely by the gradient boosting and extreme gradient boosting models. The web interface of the AI-CDSS tool was effectively tested in clinical decision-making scenarios, proving its use in personalized treatment planning and patient involvement. CONCLUSIONS: The AI-CDSS tool, enhanced by ChatGPT, marks a significant advancement in breast cancer recurrence prediction, offering a more individualized and accessible approach for clinicians and patients. Although promising, further validation in diverse clinical settings is recommended to confirm its efficacy and expand its use.

Artificial intelligence-clinical decision support system for enhanced infectious disease management: Accelerating ceftazidime-avibactam resistance detection in Klebsiella pneumoniae.

BACKGROUND: Effective and rapid diagnostic strategies are required to manage antibiotic resistance in Klebsiella pneumonia (KP). This study aimed to design an artificial intelligence-clinical decision support system (AI-CDSS) using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) and machine learning for the rapid detection of ceftazidime-avibactam (CZA) resistance in KP to improve clinical decision-making processes. METHODS: Out of 107,721 bacterial samples, 675 specimens of KP with suspected multi-drug resistance were selected. These specimens were collected from a tertiary hospital and four secondary hospitals between 2022 and 2023 to evaluate CZA resistance. We used MALDI-TOF MS and machine learning to develop an AI-CDSS with enhanced speed of resistance detection. RESULTS: Machine learning models, especially light gradient boosting machines (LGBM), exhibited an area under the curve (AUC) of 0.95, indicating high accuracy. The predictive models formed the core of our newly developed AI-CDSS, enabling clinical decisions quicker than traditional methods using culture and antibiotic susceptibility testing by a day. CONCLUSIONS: The study confirms that MALDI-TOF MS, integrated with machine learning, can swiftly detect CZA resistance. Incorporating this insight into an AI-CDSS could transform clinical workflows, giving healthcare professionals immediate, crucial insights for shaping treatment plans. This approach promises to be a template for future anti-resistance strategies, emphasizing the vital importance of advanced diagnostics in enhancing public health outcomes.

Artificial Intelligence-Clinical Decision Support System in Infectious Disease Control: Combatting Multidrug-Resistant Klebsiella pneumoniae with Machine Learning.

Purpose: The World Health Organization has identified Klebsiella pneumoniae (KP) as a significant threat to global public health. The rising threat of carbapenem-resistant Klebsiella pneumoniae (CRKP) leads to prolonged hospital stays and higher medical costs, necessitating faster diagnostic methods. Traditional antibiotic susceptibility testing (AST) methods demand at least 4 days, requiring 3 days on average for culturing and isolating the bacteria and identifying the species using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS), plus an extra day for interpreting AST results. This lengthy process makes traditional methods too slow for urgent clinical situations requiring rapid decision-making, potentially hindering prompt treatment decisions, especially for fast-spreading infections such as those caused by CRKP. This research leverages a cutting-edge diagnostic method that utilizes an artificial intelligence-clinical decision support system (AI-CDSS). It incorporates machine learning algorithms for the swift and precise detection of carbapenem-resistant and colistin-resistant strains. Patients and Methods: We selected 4307 KP samples out of a total of 52,827 bacterial samples due to concerns about multi-drug resistance using MALDI-TOF MS and Vitek-2 systems for AST. It involved thorough data preprocessing, feature extraction, and machine learning model training fine-tuned with GridSearchCV and 5-fold cross-validation, resulting in high predictive accuracy, as demonstrated by the receiver operating characteristic and area under the curve (AUC) scores, laying the groundwork for our AI-CDSS. Results: MALDI-TOF MS analysis revealed distinct intensity profiles differentiating CRKP and susceptible strains, as well as colistin-resistant Klebsiella pneumoniae (CoRKP) and susceptible strains. The Random Forest Classifier demonstrated superior discriminatory power, with an AUC of 0.96 for detecting CRKP and 0.98 for detecting CoRKP. Conclusion: Integrating MALDI-TOF MS with machine learning in an AI-CDSS has greatly expedited the detection of KP resistance by approximately 1 day. This system offers timely guidance, potentially enhancing clinical decision-making and improving treatment outcomes for KP infections.

Innovative strategies against superbugs: Developing an AI-CDSS for precise Stenotrophomonas maltophilia treatment.

OBJECTIVES: The World Health Organization named Stenotrophomonas maltophilia (SM) a critical multi-drug resistant threat, necessitating rapid diagnostic strategies. Traditional culturing methods require up to 96 h, including 72 h for bacterial growth, identification with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) through protein profile analysis, and 24 h for antibiotic susceptibility testing. In this study, we aimed at developing an artificial intelligence-clinical decision support system (AI-CDSS) by integrating MALDI-TOF MS and machine learning to quickly identify levofloxacin and trimethoprim/sulfamethoxazole resistance in SM, optimizing treatment decisions. METHODS: We selected 8,662 SM from 165,299 MALDI-TOF MS-analysed bacterial specimens, collected from a major medical centre and four secondary hospitals. We exported mass-to-charge values and intensity spectral profiles from MALDI-TOF MS .mzML files to predict antibiotic susceptibility testing results, obtained with the VITEK-2 system using machine learning algorithms. We optimized the models with GridSearchCV and 5-fold cross-validation. RESULTS: We identified distinct spectral differences between resistant and susceptible SM strains, demonstrating crucial resistance features. The machine learning models, including random forest, light-gradient boosting machine, and XGBoost, exhibited high accuracy. We established an AI-CDSS to offer healthcare professionals swift, data-driven advice on antibiotic use. CONCLUSIONS: MALDI-TOF MS and machine learning integration into an AI-CDSS significantly improved rapid SM resistance detection. This system reduced the identification time of resistant strains from 24 h to minutes after MALDI-TOF MS identification, providing timely and data-driven guidance. Combining MALDI-TOF MS with machine learning could enhance clinical decision-making and improve SM infection treatment outcomes.

Monitoring coronavirus disease progression and clinical impact through quantitative viral load testing.

BACKGROUND: The viral load (VL) in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-infected individuals is critical for improving clinical treatment strategies, care, and decisions. Several studies have reported that the initial SARS-CoV-2 VL is associated with disease severity and mortality. Cycle threshold (Ct) values and/or copies/mL are often used to quantify VL. However, a multitude of platforms, primer/probe sets of different SARS-CoV-2 target genes, and reference material manufacturers may cause inconsistent interlaboratory interpretations. The first International Standard for SARS-CoV-2 RNA quantitative assays has allowed diagnostic laboratories to transition SARS-CoV-2 VL results into international units per milliliter (IU/mL). The Cobas SARS-CoV-2 Duo quantitative assay provides VL results expressed in IU/mL. MATERIALS AND METHODS: We enrolled 145 and 50 SARS-CoV-2-positive, hospitalized and 50-negative individuals at the Tri-Service General Hospital, Taiwan from January to May 2022. Each participant's electronic medical record was reviewed to determine asymptomatic, mild, moderate, and severe cases. Nasopharyngeal swabs were collected using universal transport medium. We investigated the association of SARS-CoV-2 VL with disease severity using the Cobas SARS-CoV-2 Duo quantitative assay and its functionality in clinical assessment and decision making to further improve clinical treatment strategies. Limit of detection (LOD) was assessed. RESULTS: All 50 SARS-CoV-2-negative samples confirmed negative for SARS-CoV-2, demonstrating 100 % specificity of the Cobas SARS-CoV-2 Duo assay. Patients with severe symptoms had longer hospital stays, and the length of hospital stay (30.56 days on average) positively correlated with the VL (8.22 ± 1.21 log10 IU/mL). Asymptomatic patients had the lowest VL (5.54 ± 2.06 log10 IU/mL) at admission and the shortest hospital stay (14.1 days on average). CONCLUSIONS: VL is associated with disease severity and duration of hospitalization; therefore, its quantification should be considered when making clinical care decisions and treatment strategies. The Cobas SARS-CoV-2 Duo assay provides a commutable unitage IU/mL for interlaboratory interpretations.

Host-Pathogen Interactions in K. pneumoniae Urinary Tract Infections: Investigating Genetic Risk Factors in the Taiwanese Population.

BACKGROUND: Klebsiella pneumoniae (K. pneumoniae) urinary tract infections pose a significant challenge in Taiwan. The significance of this issue arises because of the growing concerns about the antibiotic resistance of K. pneumoniae. Therefore, this study aimed to uncover potential genomic risk factors in Taiwanese patients with K. pneumoniae urinary tract infections through genome-wide association studies (GWAS). METHODS: Genotyping data are obtained from participants with a history of urinary tract infections enrolled at the Tri-Service General Hospital as part of the Taiwan Precision Medicine Initiative (TPMI). A case-control study employing GWAS is designed to detect potential susceptibility single-nucleotide polymorphisms (SNPs) in patients with K. pneumoniae-related urinary tract infections. The associated genes are determined using a genome browser, and their expression profiles are validated via the GTEx database. The GO, Reactome, DisGeNET, and MalaCards databases are also consulted to determine further connections between biological functions, molecular pathways, and associated diseases between these genes. RESULTS: The results identified 11 genetic variants with higher odds ratios compared to controls. These variants are implicated in processes such as adhesion, protein depolymerization, Ca2+-activated potassium channels, SUMOylation, and protein ubiquitination, which could potentially influence the host immune response. CONCLUSIONS: This study implies that certain risk variants may be linked to K. pneumoniae infections by affecting diverse molecular functions that can potentially impact host immunity. Additional research and follow-up studies are necessary to elucidate the influence of these risk variants on infectious diseases and develop targeted interventions for mitigating the spread of K. pneumoniae urinary tract infections.

Clinical Evaluation of Direct Reverse Transcription PCR for Detection of SARS-CoV-2 Compared to Conventional RT-PCR in Patients with Positive Rapid Antigen Test Results during Circulation of Emerging Viral Variants.

The emergence of the Omicron (B.1.1.529) variant of SARS-CoV-2 has precipitated a new global wave of the COVID-19 pandemic. The rapid identification of SARS-CoV-2 infection is imperative for the effective mitigation of transmission. Diagnostic modalities such as rapid antigen testing and real-time reverse transcription polymerase chain reaction (RT-PCR) offer expedient turnaround times of 10-15 min and straightforward implementation. This preliminary study assessed the correlation between outcomes of commercially available rapid antigen tests for home use and conventional reverse transcription polymerase chain reaction (RT-PCR) assays using a limited set of clinical specimens. Patients aged 5-99 years presenting to the emergency department for SARS-CoV-2 testing were eligible for enrollment (n = 5652). Direct PCR and conventional RT-PCR were utilized for the detection of SARS-CoV-2. The entire cohort of 5652 clinical specimens was assessed by both modalities to determine the clinical utility of the direct RT-PCR assay. Timely confirmation of SARS-CoV-2 infection may attenuate viral propagation and guide therapeutic interventions. Additionally, direct RT-PCR as a secondary confirmatory test for at-home rapid antigen test results demonstrated sensitivity comparable to conventional RT-PCR, indicating utility for implementation in laboratories globally, especially in resource-limited settings with constraints on reagents, equipment, and skilled personnel. In summary, direct RT-PCR enables the detection of SARS-CoV-2 with a sensitivity approaching that of conventional RT-PCR while offering expedient throughput and shorter turnaround times. Moreover, direct RT-PCR provides an open-source option for diagnostic laboratories worldwide, particularly in low- and middle-income countries.

Accelerating pandemic response with the emergency Omicron RT-PCR test: A comprehensive solution for COVID-19 diagnosis and tracking.

The Omicron variant of concern (VOC) has surged in many countries and replaced the previously reported VOC. To identify different Omicron strains/sublineages on a rapid, convenient, and precise platform, we report a novel multiplex real-time reverse transcriptase polymerase chain reaction (RT-PCR) method in one tube based on the Omicron lineage sequence variants' information. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) subvariants were used in a PCR-based assay for rapid identification of Omicron sublineage genotyping in 1000 clinical samples. Several characteristic mutations were analyzed using specific primers and probes for the spike gene, del69-70, and F486V. To distinguish Omicron sublineages (BA.2, BA.4, and BA.5), the NSP1:141-143del in the ORF1a region and D3N mutation in membrane protein occurring outside the spike protein region were analyzed. Results from the real-time PCR assay for one-tube accuracy were compared to those of whole genome sequencing. The developed PCR assay was used to analyze 400 SARS-CoV-2 positive samples. Ten samples determined as BA.4 were positive for NSP1:141-143del, del69-70, and F486V mutations; 160 BA.5 samples were positive for D3N, del69-70, and F486V mutations, and 230 BA.2 samples were without del69-70. Screening these samples allowed the identification of epidemic trends at different time intervals. Our novel one-tube multiplex PCR assay was effective in identifying Omicron sublineages.

Monitoring algorithm of hospitalized patients in a medical center with SARS-CoV-2 (Omicron variant) infection: clinical epidemiological surveillance and immunological assessment.

Purpose: Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a major healthcare threat worldwide. Since it was first identified in November 2021, the Omicron (B.1.1.529) variant of SARS-CoV-2 has evolved into several lineages, including BA.1, BA.2-BA.4, and BA.5. SARS-CoV-2 variants might increase transmissibility, pathogenicity, and resistance to vaccine-induced immunity. Thus, the epidemiological surveillance of circulating lineages using variant phenotyping is essential. The aim of the current study was to characterize the clinical outcome of Omicron BA.2 infections among hospitalized COVID-19 patients and to perform an immunological assessment of such cases against SARS-CoV-2. Patients and Methods: We evaluated the analytical and clinical performance of the BioIC SARS-CoV-2 immunoglobulin (Ig)M/IgG detection kit, which was used for detecting antibodies against SARS-CoV-2 in 257 patients infected with the Omicron variant. Results: Poor prognosis was noted in 38 patients, including eight deaths in patients characterized by comorbidities predisposing them to severe COVID-19. The variant-of-concern (VOC) typing and serological analysis identified time-dependent epidemic trends of BA.2 variants emerging in the outbreak of the fourth wave in Taiwan. Of the 257 specimens analyzed, 108 (42%) and 24 (9.3%) were positive for anti-N IgM and IgG respectively. Conclusion: The VOC typing of these samples allowed for the identification of epidemic trends by time intervals, including the B.1.1.529 variant replacing the B.1.617.2 variant. Moreover, antibody testing might serve as a complementary method for COVID-19 diagnosis. The combination of serological testing results with the reverse transcription-polymerase chain reaction cycle threshold value has potential value in disease prognosis, thereby aiding in epidemic investigations conducted by clinicians or the healthcare department.

Critical pediatric neurological illness associated with COVID-19 (Omicron BA.2.3.7 variant) infection in Taiwan: immunological assessment and viral genome analysis in tertiary medical center.

OBJECTIVES: Since April 2022, another wave of the Omicron epidemic has struck Taiwanese society, and children with severe neurological complications have been reported frequently. A few cases even developed acute fulminant encephalitis. To investigate the possible causes of the increased incidence of such complications in Taiwan, we reviewed several cases of pediatric patients with severe neurological symptoms. METHODS: We collected the medical records of pediatric patients with COVID-19 infection who presented with severe neurological symptoms. The COVID-19 infection was diagnosed by nasal swab reverse transcriptase-polymerase chain reaction. The remaining samples were sent for whole genome sequencing and spike (S) protein amino acid variation mapping. RESULTS: The increase of several inflammatory markers was observed in all patients included in this study. However, none of the cerebrospinal fluid samples tested positive for SARS-CoV-2. The result of whole genome sequencing showed that all the sequences belonged to the lineage BA.2.3.7. However, the sequences had a K97E mutation in the S protein that differed from other BA.2.3.7 lineage strains, which was located at the S protein N-terminal domain. CONCLUSION: The new mutation in the S protein, which had not previously been observed but was discovered in this study, potentially explains the sudden increase in incidence of extremely adverse neurological symptoms in pediatric patients.

Emergency SARS-CoV-2 variants of concern: rapidly direct RT-qPCR detection without RNA extraction, clinical comparison, cost-effective, and high-throughput.

Since the late 2020, the evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern has been characterized by the emergence of spike protein mutations, and these variants have become dominant worldwide. The gold standard SARS-CoV-2 diagnosis protocol requires two complex processes, namely, RNA extraction and real-time reverse transcriptase polymerase chain reaction (RT-PCR). There is a need for a faster, simpler, and more cost-effective detection strategy that can be utilized worldwide, especially in developing countries. We propose the novel use of direct RT-qPCR, which does not require RNA extraction or a preheating step. For the detection, retrospectively, we used 770 clinical nasopharyngeal swabs, including positive and negative samples. The samples were subjected to RT-qPCR in the N1 and E genes using two different thermocyclers. The limit of detection was 30 copies/reaction for N1 and 60 copies/reaction for E. Analytical sensitivity was assessed for the developed direct RT-qPCR; the sensitivity was 95.69%, negative predictive value was 99.9%, accuracy of 99.35%, and area under the curve was 0.978. This novel direct RT-qPCR diagnosis method without RNA extraction is a reliable and high-throughput alternative method that can significantly save cost, labor, and time during the coronavirus disease 2019 pandemic.

Clinical Comparative Evaluation of the LabTurboTM AIO® Reverse Transcription-Polymerase Chain Reaction and World Health Organization-Recommended Assays for the Detection of Emerging SARS-CoV-2 Variants of Concern.

PURPOSE: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent behind coronavirus disease-2019 (COVID-19). Single-plex reverse transcription-polymerase chain reaction (RT-PCR)-based assays are widely used for COVID-19 detection but exhibit decreased sensitivity and specificity in detecting the rapidly spreading SARS-CoV-2 variants; in contrast, multiplex RT-PCR reportedly yields better results. Here, we aimed at comparatively analyzing the clinical performance of the LabTurboTM AIO COVID-19 RNA testing kit, a multiplex quantitative RT-PCR kit, including a three-target (E, N1, and RNase P), single-reaction, triplex assay used for SARS-CoV-2 detection, with that of the WHO-recommended RT-PCR assay. MATERIALS AND METHODS: Residual, natural, nasopharyngeal swabs obtained from universal transport medium specimens at SARS-CoV-2 testing centers (n = 414) were collected from May to October 2021. For SARS-CoV-2 qRT-PCR, total viral nucleic acid was extracted. The limit of detection (LOD) and the comparative clinical performances of the LabTurboTM AIO COVID-19 RNA kit and the WHO-recommended RT-PCR assay were assessed. Statistical analysis of the correlation was performed and results with R2 values >0.9 were considered to be highly correlated. RESULTS: The LOD of the LabTurboTM AIO COVID-19 RNA kit was 9.4 copies/reaction for the target genes N1 and E. The results obtained from 102 SARS-CoV-2-positive and 312 SARS-CoV-2-negative samples showed 100% correlation with previous WHO-recommended RT-PCR assay results. CONCLUSION: Multiplex qRT-PCR is a critical tool for detecting unknown pathogens and employs multiple target genes. The LabTurboTM AIO COVID-19 RNA testing kit provides an effective and efficient assay for SARS-CoV-2 detection and is highly compatible with SARS-CoV-2 variants.

Emergency SARS-CoV-2 Variants of Concern: Novel Multiplex Real-Time RT-PCR Assay for Rapid Detection and Surveillance.

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has spread worldwide. Many variants of SARS-CoV-2 have been reported, some of which have increased transmissibility and/or reduced susceptibility to vaccines. There is an urgent need for variant phenotyping for epidemiological surveillance of circulating lineages. Whole-genome sequencing is the gold standard for identifying SARS-CoV-2 variants, which constitutes a major bottleneck in developing countries. Methodological simplification could increase epidemiological surveillance feasibility and efficiency. We designed a novel multiplex real-time reverse transcriptase PCR (RT-PCR) to detect SARS-CoV-2 variants with S gene mutations. This multiplex PCR typing method was established to detect 9 mutations with specific primers and probes (ΔHV 69/70, K417T, K417N, L452R, E484K, E484Q, N501Y, P681H, and P681R) against the receptor-binding domain of the spike protein of SARS-CoV-2 variants. In silico analyses showed high specificity of the assays. Variants of concern (VOC) typing results were found to be highly specific for our intended targets, with no cross-reactivity observed with other upper respiratory viruses. The PCR-based typing methods were further validated using whole-genome sequencing and a commercial kit that was applied to clinical samples of 250 COVID-19 patients from Taiwan. The screening of these samples allowed the identification of epidemic trends by time intervals, including B.1.617.2 in the third Taiwan wave outbreak. This PCR typing strategy allowed the detection of five major variants of concern and also provided an open-source PCR assay which could rapidly be deployed in laboratories around the world to enhance surveillance for the local emergence and spread of B.1.1.7, B.1.351, P.1, and B.1.617.2 variants and of four Omicron mutations on the spike protein (ΔHV 69/70, K417N, N501Y, P681H). IMPORTANCE COVID-19 has spread globally. SARS-CoV-2 variants of concern (VOCs) are leading the next waves of the COVID-19 pandemic. Previous studies have pointed out that these VOCs may have increased infectivity, have reduced vaccine susceptibility, change treatment regimens, and increase the difficulty of epidemic prevention policy. Understanding SARS-CoV-2 variants remains an issue of concern for all local government authorities and is critical for establishing and implementing effective public health measures. A novel SARS-CoV-2 variant identification method based on a multiplex real-time RT-PCR was developed in this study. Five SARS-CoV-2 variants (Alpha, Beta, Gamma, Delta, and Omicron) were identified simultaneously using this method. PCR typing can provide rapid testing results with lower cost and higher feasibility, which is well within the capacity for any diagnostic laboratory. Characterizing these variants and their mutations is important for tracking SAR-CoV-2 evolution and is conducive to public infection control and policy formulation strategies.

Clinical assessment of SARS-CoV-2 antigen rapid detection compared with RT-PCR assay for emerging variants at a high-throughput community testing site in Taiwan.

OBJECTIVES: With the emergence of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) B.1.1.7 lineage in the ongoing coronavirus disease 2019 (COVID-19) pandemic, Taiwan confronted a COVID-19 flare up in May 2021. Large-scale, accurate, affordable and rapid diagnostic tests such as the lateral flow assay can help to prevent community transmission, but their performance characteristics in real-world conditions and relevant subpopulations remain unclear. METHODS: The COVID-19 Antigen Rapid Test Kit (Eternal Materials, New Taipei City, Taiwan) was used in a high-throughput community testing site; the paired reverse transcription polymerase chain reaction (RT-PCR) results served as a reference for sensitivity and specificity calculations. RESULTS: Of 2096 specimens tested using the rapid antigen test, 70 (3.33%) were positive and 2026 (96.7%) were negative. This clinical performance was compared with the RT-PCR results. The sensitivity and specificity of the rapid antigen test were 76.39% [95% confidence interval (CI) 64.91-85.60%] and 99.26% (95% CI 98.78-99.58%), respectively, with high sensitivity in subjects with cycle threshold values ≤24. Further, the rapid antigen test detected the SARS-CoV-2 B.1.1.7 lineage effectively. CONCLUSIONS: Considering the short turnaround times and lower costs, this simple SARS-CoV-2 antigen detection test for rapid screening combined with RT-PCR as a double confirmatory screening tool can facilitate the prevention of community transmission during COVID-19 emergencies.

Multicenter study evaluating novel multi-specimen pooling assay for the detection of SARS-CoV-2: High sensitivity and high throughput testing.

BACKGROUND/PURPOSE: Mass screening for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is important to prevent the spread of coronavirus disease 2019 (COVID-19). Pooling samples can increase the number of tests processed. LabTurbo AIO 48 is an automated platform that allows ribonucleic acid extraction and sample analysis on the same instrument. We created a novel pooling assay on this platform for SARS-CoV-2 detection and demonstrated that the pooling strategy increases testing capacity without affecting accuracy and sensitivity. METHODS: Comparative limit of detection (LoD) assessment was performed on the LabTurbo AIO 48 platform and the current standard detection system based on real-time reverse transcription polymerase chain reaction (rRT-PCR) using 55 clinically positive samples. An additional 330 primary clinical samples were assessed. RESULTS: Six samples pooled into one reaction tube were detected in approximately 2.5 h using the World Health Organization rRT-PCR protocol. LabTurbo AIO 48 also demonstrated a higher throughput than our reference rRT-PCR assay, with an LoD of 1000 copies/mL. The overall percentage agreement between the methods for the 330 samples was 100%. CONCLUSION: We created a novel multi-specimen pooling assay using LabTurbo AIO 48 for the robust detection of SARS-CoV-2, allowing high-throughput results; this assay will aid in better control and prevention of COVID-19. The diagnostic assay was cost-effective and time-efficient; thus, the pooling strategy is a practical and effective method for diagnosing large quantities of specimens without compromising precision.

SARS-CoV-2 variants with T135I nucleocapsid mutations may affect antigen test performance.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a pandemic. Diagnostic testing for SARS-CoV-2 has continuously been challenged due to several variants with diverse spike (S) and nucleocapsid (N) protein mutations []. SARS-CoV-2 variant proliferation potentially affects N protein-targeted rapid antigen testing. In this study, rapid antigen and reverse transcription PCR (RT-PCR) tests were performed simultaneously in patients with suspected coronavirus disease 2019 (COVID-19). Direct whole genome sequencing was performed to determine the N protein variations, and the viral assemblies were uploaded to GISAID. The genomes were then compared with those of global virus strains from GISAID. These isolates belonged to the B.1.1.7 variant, exhibiting several amino acid substitutions, including D3L, R203K, G204R, and S235F N protein mutations. The T135I mutation was also identified in one variant case in which the rapid antigen test and RT-PCR test were discordantly negative and positive, respectively. These findings suggest that the variants undetected by the Panbio COVID-19 rapid antigen test may be due to the T135I mutation in the N protein, posing a potential diagnostic risk for commercially available antigen tests. Hence, we recommend concomitant paired rapid antigen tests and molecular diagnostic methods to detect SARS-CoV-2. False-negative results could be rapidly corrected using confirmatory RT-PCR results to prevent future COVID-19 outbreaks.

Multicenter study evaluating one multiplex RT-PCR assay to detect SARS-CoV-2, influenza A/B, and respiratory syncytia virus using the LabTurbo AIO open platform: epidemiological features, automated sample-to-result, and high-throughput testing.

Since the Coronavirus 19 (COVID-19) pandemic, several SARS-CoV-2 variants of concern (SARS-CoV-2 VOC) have been reported. The B.1.1.7 variant has been associated with increased mortality and transmission risk. Furthermore, cluster and possible co-infection cases could occur in the next influenza season or COVID-19 pandemic wave, warranting efficient diagnosis and treatment decision making. Here, we aimed to detect SARS-CoV-2 and other common respiratory viruses using multiplex RT-PCR developed on the LabTurbo AIO 48 open system. We performed a multicenter study to evaluate the performance and analytical sensitivity of the LabTurbo AIO 48 system for SARS-CoV-2, influenza A/B, and respiratory syncytial virus (RSV) using 652 nasopharyngeal swab clinical samples from patients. The LabTurbo AIO 48 system demonstrated a sensitivity of 9.4 copies/per PCR for N2 of SARS-CoV-2; 24 copies/per PCR for M of influenza A and B; and 24 copies/per PCR for N of RSV. The assay presented consistent performance in the multicenter study. The multiplex RT-PCR applied on the LabTurbo AIO 48 open platform provided highly sensitive, robust, and accurate results and enabled high-throughput detection of B.1.1.7, influenza A/B, and RSV with short turnaround times. Therefore, this automated molecular diagnostic assay could enable streamlined testing if COVID-19 becomes a seasonal disease.

Genomic analysis of early transmissibility assessment of the D614G mutant strain of SARS-CoV-2 in travelers returning to Taiwan from the United States of America.

BACKGROUND: There is a global pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Information on viral genomics is crucial for understanding global dispersion and for providing insight into viral pathogenicity and transmission. Here, we characterized the SARS-CoV-2 genomes isolated from five travelers who returned to Taiwan from the United States of America (USA) between March and April 2020. METHODS: Haplotype network analysis was performed using genome-wide single-nucleotide variations to trace potential infection routes. To determine the genetic variations and evolutionary trajectory of the isolates, the genomes of isolates were compared to those of global virus strains from GISAID. Pharyngeal specimens were confirmed to be SARS-CoV-2-positive by RT-PCR. Direct whole-genome sequencing was performed, and viral assemblies were subsequently uploaded to GISAID. Comparative genome sequence and single-nucleotide variation analyses were performed. RESULTS: The D614G mutation was identified in imported cases, which separated into two clusters related to viruses originally detected in the USA. Our findings highlight the risk of spreading SARS-CoV-2 variants through air travel and the need for continued genomic tracing for the epidemiological investigation and surveillance of SARS-CoV-2 using viral genomic data. CONCLUSIONS: Continuous genomic surveillance is warranted to trace virus circulation and evolution in different global settings during future outbreaks.

Clinical Comparison of Three Sample-to-Answer Systems for Detecting SARS-CoV-2 in B.1.1.7 Lineage Emergence.

PURPOSE: Accurate molecular diagnostic assays for detecting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of COVID-19, are needed for epidemiology studies and to support infection-control measures. We evaluated the analytical sensitivity and clinical performance of three sample-to-answer molecular-diagnostics systems for detecting SARS-CoV-2 using 325 nasopharyngeal swab clinical samples from symptomatic patients. METHODS: The BioFire Respiratory Panel 2.1 (RP2.1), cobas Liat SARS-CoV-2 and Influenza A/B, and Cepheid Xpert Xpress SARS-CoV-2/Flu/RSV platforms, which have been granted emergency-use authorization by the US FDA, were tested and compared. RESULTS: The positive percent agreement, negative percent agreement, and overall percent agreement among the three point of care testing systems were 98-100%, including for the wild-type SARS-CoV-2 (non-B.1.1.7) and a variant of concern (B.1.1.7). Notably, the BioFire RP2.1 may fail to detect the SARS-CoV-2 S gene in the B.1.1.7 lineage because of the spike protein mutation. CONCLUSION: All three point of care testing platforms provided highly sensitive, robust, and almost accurate results for rapidly detecting SARS-CoV-2. These automated molecular diagnostic assays can increase the effectiveness of control and prevention measures for infectious diseases.