Improved Molecular Diagnosis of COVID-19 by the Novel, Highly Sensitive and Specific COVID-19-RdRp/Hel Real-Time Reverse Transcription-PCR Assay Validated In Vitro and with Clinical Specimens.

On 31 December 2019, the World Health Organization was informed of a cluster of cases of pneumonia of unknown etiology in Wuhan, China. Subsequent investigations identified a novel coronavirus, now named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), from the affected patients. Highly sensitive and specific laboratory diagnostics are important for controlling the rapidly evolving SARS-CoV-2-associated coronavirus disease 2019 (COVID-19) epidemic. In this study, we developed and compared the performance of three novel real-time reverse transcription-PCR (RT-PCR) assays targeting the RNA-dependent RNA polymerase (RdRp)/helicase (Hel), spike (S), and nucleocapsid (N) genes of SARS-CoV-2 with that of the reported RdRp-P2 assay, which is used in >30 European laboratories. Among the three novel assays, the COVID-19-RdRp/Hel assay had the lowest limit of detection in vitro (1.8 50% tissue culture infective doses [TCID50]/ml with genomic RNA and 11.2 RNA copies/reaction with in vitro RNA transcripts). Among 273 specimens from 15 patients with laboratory-confirmed COVID-19 in Hong Kong, 77 (28.2%) were positive by both the COVID-19-RdRp/Hel and RdRp-P2 assays. The COVID-19-RdRp/Hel assay was positive for an additional 42 RdRp-P2-negative specimens (119/273 [43.6%] versus 77/273 [28.2%]; P < 0.001), including 29/120 (24.2%) respiratory tract specimens and 13/153 (8.5%) non-respiratory tract specimens. The mean viral load of these specimens was 3.21 × 104 RNA copies/ml (range, 2.21 × 102 to 4.71 × 105 RNA copies/ml). The COVID-19-RdRp/Hel assay did not cross-react with other human-pathogenic coronaviruses and respiratory pathogens in cell culture and clinical specimens, whereas the RdRp-P2 assay cross-reacted with SARS-CoV in cell culture. The highly sensitive and specific COVID-19-RdRp/Hel assay may help to improve the laboratory diagnosis of COVID-19.

Prognostic Model of COVID-19 Severity and Survival among Hospitalized Patients Using Machine Learning Techniques.

We conducted a statistical study and developed a machine learning model to triage COVID-19 patients affected during the height of the COVID-19 pandemic in Hong Kong based on their medical records and test results (features) collected during their hospitalization. The correlation between the values of these features is studied against discharge status and disease severity as a preliminary step to identify those features with a more pronounced effect on the patient outcome. Once identified, they constitute the inputs of four machine learning models, Decision Tree, Random Forest, Gradient and RUSBoosting, which predict both the Mortality and Severity associated with the disease. We test the accuracy of the models when the number of input features is varied, demonstrating their stability; i.e., the models are already highly predictive when run over a core set of (6) features. We show that Random Forest and Gradient Boosting classifiers are highly accurate in predicting patients' Mortality (average accuracy ∼99%) as well as categorize patients (average accuracy ∼91%) into four distinct risk classes (Severity of COVID-19 infection). Our methodical and broad approach combines statistical insights with various machine learning models, which paves the way forward in the AI-assisted triage and prognosis of COVID-19 cases, which is potentially generalizable to other seasonal flus.

Prenatal sonography of placental abscess and prolonged antibiotic treatment for Serratia marcescens bacteremia.

Termination of pregnancy is indicated for Serratia marcescens bacteremia, a major cause of mortality. Our present case was highly challenging because the patient wished to continue with her pregnancy, and the ultrasonography showed features of a placental abscess. Although the outcomes were good after prolonged antibiotic treatment, this was an exceptional case.

Increased Serum Hyaluronic Acid and Heparan Sulfate in Dengue Fever: Association with Plasma Leakage and Disease Severity.

Plasma leakage is a major pathogenic mechanism of severe dengue, but the etiology remains unclear. The association between endothelial glycocalyx integrity and vascular permeability in older adults with dengue has not been evaluated. A prospective cohort study of adults with undifferentiated fever screened for dengue by RT-PCR or NS1 antigen testing was performed. Patients were assessed daily while symptomatic and at convalescence. Serum hyaluronic acid (HA), heparan sulfate (HS) and selected cytokines (TNF-α, IL-6, IL-10) were measured on enrollment and convalescence. Patients were diagnosed as dengue fever (DF, n = 30), dengue hemorrhagic fever (DHF, n = 20) and non-dengue (ND) febrile illness (n = 11). Acute HA and HS levels were significantly higher in all dengue patients compared to ND (p = 0.0033 and p = 0.0441 respectively), but not different between DF and DHF (p = 0.3426 and p = 0.9180 respectively). Enrolment HA inversely correlated with serum albumin, protein and platelets in all dengue and DHF (p < 0.05). HA and HS in all dengue patients decreased significantly at convalescence. Serum IL-10 was significantly associated with HA in all dengue patients (p = 0.002). Serum HA and HS levels were increased in adult dengue and HA was associated with markers of disease severity. Endothelial glycocalyx damage may have a role in vascular leakage in dengue.

Shewanella-Related Bacteremia and Fournier's Gangrene: A Case Report.

Shewanella algae and Shewanella putrefaciens have been implicated for causing serious infections in humans, including disseminated infection. We report the possible first case of Shewanella-related Fournier's gangrene and bacteremia caused in a 65-year-old Chinese male with nephrotic syndrome. He was successfully managed by surgical debridement and antibiotic therapy.