Evaluation of Three Automated Extraction Systems for the Detection of SARS-CoV-2 from Clinical Respiratory Specimens

Severe acute respiratory syndrome coronavirus (SARS-CoV-2) is highly contagious and causes coronavirus disease 2019 (COVID-19). Reverse transcription quantitative polymerase chain reaction (RT-qPCR) is the most accurate and reliable molecular assay to detect active SARS-CoV-2 infection. However, a rapid increase in test subjects has created a global bottleneck in testing capacity. Given that efficient nucleic acid extraction greatly affects reliable and accurate testing results, we compared three extraction platforms: MagNA Pure 96 DNA and Viral NA Small Volume kit on MagNA Pure 96 (Roche, Basel, Switzerland), careGENETM Viral/Pathogen HiFi Nucleic Acid Isolation kit (WELLS BIO Inc., Seoul, Korea) on KingFisher Flex (Thermo Fisher Scientific, Rocklin, CA, USA), and SGRespiTM Pure kit (Seegene Inc., Seoul, Korea) on Maelstrom 9600 (Taiwan Advanced Nanotech Inc., Taoyuan, Taiwan). RNA was extracted from 245 residual respiratory specimens from the different types of samples (i.e., NPS, sputum, and saliva) using three different kits. The 95% limits of detection of median tissue culture infectious dose per milliliter (TCID50/mL) for the MagNA Pure 96, KingFisher Flex, and Maelstrom 9600 were 0.37–3.15 × 101, 0.41–3.62 × 101, and 0.33–1.98 × 101, respectively. The KingFisher Flex platform exhibited 99.2% sensitivity and 100% specificity, whereas Maelstrom 9600 exhibited 98.3–100% sensitivity and 100% specificity. Bland–Altman analysis revealed a 95.2% concordance between MagNA Pure 96 and KingFisher Flex and 95.4% concordance between MagNA Pure 96 and Maelstrom 9600, indicating that all three platforms provided statistically reliable results. This suggests that two modifying platforms, KingFisher Flex and Maelstrom 9600, are accurate and scalable extraction platforms for large-scale SARS-CoV-2 clinical detection and could help the management of COVID-19 patients.

Neuroendocrine Tumors of the Pancreas

Pancreatic neuroendocrine tumors (PNETs) comprise a diverse, heterogeneous group of tumours that range in presentation and biologic behavior, including small, asymptomatic, incidentally discovered, nonfunctional neoplasms, functional tumors (both localizable and unlocalizable) with associated clinical syndromes, and diffuse metastatic disease. Based on its functional status, the malignancy of a PNET can vary, from the benign (insulinoma) to that which is commonly malignant more than 50% of the time (gastrinoma, somatostatinoma). According to a recent study, PNETs appear to be increasing in incidence or at least in clinical detection; currently the disorder accounts for 1 to 2% of pancreatic tumors and with a reported clinical incidence of one to five cases per million persons annually in the United States. Nonfunctional PNETs make up the majority of cases, and comprise 2% of all pancreatic malignancies. Treatment has been primarily done through surgical management, particularly via resection. However, medical management has played a more increased role for patients where the disease is advanced, encompassing biotherapy, chemotherapy, and targeted therapies such as peptide receptor radionuclide therapy (PRRT). For nonfunctional PNETs—insulinomas, gastrinomas, glucagonomas, somatostatinomas, and VIPomas—the epidemiology, biology of disease, clinical presentation and diagnosis, localization of tumor, operative management considerations, surgical management of primary tumor, and prognosis and outcomes of each are discussed. This review contains 6 figures, 18 tables, and 58 references Keywords: Pancreas, neuroendocrine tumor, gastrinoma, somatostatinoma, VIPoma, insulinoma, incidentaloma, glucagonoma, enucleation

COVID-19 infection dynamics revealed by SARS-CoV-2 wastewater sequencing analysis and deconvolution

The use of RNA sequencing from wastewater samples is proven to be a valuable way for estimating infection dynamics and circulating lineages of SARS-CoV-2. This approach has the advantage of being independent from patient population testing and symptomatic disease courses. However, it is equally important to develop easily accessible and scalable tools which can highlight critical changes in infection rates and dynamics over time across different locations given the sequencing data from the wastewater. Here we provide the first analysis of variant dynamics in Germany using wastewater sequencing and present PiGx SARS-CoV-2, a bit-by-bit reproducible end-to-end pipeline with comprehensive reports. To our knowledge, this is the first pipeline that includes all steps from raw-data to shareable reports, additional taxonomic analysis, deconvolution and geospatial time series analysis. Using our pipeline on a dataset of wastewater samples, from different locations across Berlin, over the time period from February 2021 to June 2021, we could reconstruct the dynamic of the Variant of Concern (VoC) B.1.1.7 (alpha). Additionally, we detected the unique signature mutation M:T26767C for the VoC B.1.617.2 (delta) and its raise in early June. We also show that SARS-CoV-2 mutation load measured from wastewater sequencing is correlated with actual case numbers and it has potential to be used in a predictive manner. All in all, our study provides additional evidence that systematic wastewater analysis using sequencing and computational methods can be used for modeling the infection dynamics of SARS-CoV-2. In addition, the results show that our tool can be used to tease out new mutations and to detect any emerging new lineages of concern before clinical detection. Our approach can support efforts for establishing continuous monitoring and early-warning projects for COVID-19 or any other infectious disease.

Quantitative Electroencephalography (EEG) Predicting Acute Neurologic Deterioration in the Pediatric Intensive Care Unit: A Case Series

Introduction: Continuous neurologic assessment in the pediatric intensive care unit is challenging. Current electroencephalography (EEG) guidelines support monitoring status epilepticus, vasospasm detection, and cardiac arrest prognostication, but the scope of brain dysfunction in critically ill patients is larger. We explore quantitative EEG in pediatric intensive care unit patients with neurologic emergencies to identify quantitative EEG changes preceding clinical detection. Methods: From 2017 to 2020, we identified pediatric intensive care unit patients at a single quaternary children's hospital with EEG recording near or during acute neurologic deterioration. Quantitative EEG analysis was performed using Persyst P14 (Persyst Development Corporation). Included features were fast Fourier transform, asymmetry, and rhythmicity spectrograms, “from-baseline” patient-specific versions of the above features, and quantitative suppression ratio. Timing of quantitative EEG changes was determined by expert review and prespecified quantitative EEG alert thresholds. Clinical detection of neurologic deterioration was defined pre hoc and determined through electronic medical record documentation of examination change or intervention. Results: Ten patients were identified, age 23 months to 27 years, and 50% were female. Of 10 patients, 6 died, 1 had new morbidity, and 3 had good recovery; the most common cause of death was cerebral edema and herniation. The fastest changes were on “from-baseline” fast Fourier transform spectrograms, whereas persistent changes on asymmetry spectrograms and suppression ratio were most associated with morbidity and mortality. Median time from first quantitative EEG change to clinical detection was 332 minutes (interquartile range: 201-456 minutes). Conclusion: Quantitative EEG is potentially useful in earlier detection of neurologic deterioration in critically ill pediatric intensive care unit patients. Further work is required to quantify the predictive value, measure improvement in outcome, and automate the process.

Monoclonal Antibodies against Nucleocapsid Protein of SARS-CoV-2 Variants for Detection of COVID-19

Mitigation strategies of the coronavirus disease 2019 (COVID-19) pandemic have been greatly hindered by the continuous emergence of SARS-CoV-2 variants. New sensitive, rapid diagnostic tests for the wide-spectrum detection of viral variants are needed. We generated a panel of 41 monoclonal antibodies against the SARS-CoV-2 nucleocapsid protein (NP) by using mice hybridoma techniques. Of these mAbs, nine exhibited high binding activities and were applied in latex-based lateral flow immunoassays (LFIAs). The LFIAs utilizing NP-mAb-7 and -40 had the best sensitivity and lowest limit of detection: 8 pg for purified NP and 625 TCID50/mL for the authentic virus (hCoV-19/Taiwan/4/2020). The specificity tests showed that the NP-mAb-40/7 LFIA strips did not cross-react with five human coronavirus strains or 20 other common respiratory pathogens. Importantly, we found that 10 NP mutants, including alpha (B.1.1.7), beta (B.1.351), gamma (P.1), and delta (B.1.617.2) variants, could be detected by NP-mAb-40/7 LFIA strips. A clinical study (n = 60) of the NP-mAb-40/7 LFIA strips demonstrated a specificity of 100% and sensitivity of 90% in infected individuals with cycle threshold (Ct) values < 29.5. These anti-NP mAbs have strong potential for use in the clinical detection of SARS-CoV-2 infection, whether the virus is wild-type or a variant of concern.

Existing and Emerging Breast Cancer Detection Technologies and Its Challenges: A Review

Breast cancer is the most leading cancer occurring in women and is a significant factor in female mortality. Early diagnosis of breast cancer with Artificial Intelligent (AI) developments for breast cancer detection can lead to a proper treatment to affected patients as early as possible that eventually help reduce the women mortality rate. Reliability issues limit the current clinical detection techniques, such as Ultra-Sound, Mammography, and Magnetic Resonance Imaging (MRI) from screening images for precise elucidation. The capability to detect a tumor in early diagnosis, expensive, relatively long waiting time due to pandemic and painful procedure for a patient to perform. This article aims to review breast cancer screening methods and recent technological advancements systematically. In addition, this paper intends to explore the progression and challenges of AI in breast cancer detection. The next state of the art between image and signal processing will be presented, and their performance is compared. This review will facilitate the researcher to insight the view of breast cancer detection technologies advancement and its challenges.

Detection of Klebsiella pneumoniae DNA by PCR-Based CRISPR-lbCas12a System

Klebsiella pneumonia (K. pneumoniae) is a Gram-negative bacterium that causes nosocomial infections in the lung, bloodstream, and urinary tract. Therefore, detecting K. pneumoniae in early time is important in preventing severe infections. However, clinical detection of K. pneumoniae requires a long time of agar plate culture. Nucleic acid detection like qPCR is precise but requires expensive equipment. Recent research reveals that collateral cleavage activity of CRISPR-LbCas12a has been applied in nucleic acid detection. In this study, PCR combined with CRISPR-LbCas12a targeting the K. pneumoniae system was established. This system showed excellent detection specificity and sensitivity in both bench work and clinical samples. Due to its advantages, its application can meet different detection requirements in health centers where qPCR is not accessible.