Objective: To evaluate a reverse transcription loop-mediated isothermal amplification (RT-LAMP) assay for detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and compare it with RT-PCR. Methods: We designed primers specific to the orf1ab and S genes of SARS-CoV-2. Total viral RNA was extracted using the QIAamp Viral RNA Mini Kit. We optimized the RT-LAMP assay, and evaluated it for its sensitivity and specificity of detection using real-time turbidity monitoring and visual observation. Results: The primer sets orf1ab-4 and S-123 amplified the genes in the shortest times, the mean (±SD) times were 18 ± 1.32 min and 20 ± 1.80 min, respectively, and 63 C was the optimum reaction temperature. The sensitivities were 2 Â 10 1 copies and 2 Â 10 2 copies per reaction with primer sets orf1ab-4 and S-123, respectively. This assay showed no cross-reactivity with 60 other respiratory pathogens. To describe the availability of this method in clinical diagnosis, we collected 130 specimens from patients with clinically suspected SARS-CoV-2 infection. Among them, 58 were confirmed to be positive and 72 were negative by RT-LAMP. The sensitivity was 100% (95% CI 92.3%e100%), specificity 100% (95% CI 93.7% e100%). This assay detected SARS-CoV-2 in a mean (±SD) time of 26.28 ± 4.48 min and the results can be identified with visual observation. Conclusion: These results demonstrate that we developed a rapid, simple, specific and sensitive RT-LAMP assay for SARS-CoV-2 detection among clinical samples. It will be a powerful tool for SARS-CoV-2 identification, and for monitoring suspected patients, close contacts and high-risk groups. C.

Highlights d High-alcohol-producing strains of Klebsiella pneumoniae exist in humans d HiAlc Kpn is associated with NAFLD in a human cohort d Transplant of HiAlc Kpn into mice causes NAFLD d Feeding mice glucose led to detectable blood alcohol, suggesting a biomarker for NAFLD

Retinal screening contributes to early detection of diabetic retinopathy and timely treatment. To facilitate the screening process, we develop a deep learning system, named DeepDR, that can detect early-to-late stages of diabetic retinopathy. DeepDR is trained for real-time image quality assessment, lesion detection and grading using 466,247 fundus images from 121,342 patients with diabetes. Evaluation is performed on a local dataset with 200,136 fundus images from 52,004 patients and three external datasets with a total of 209,322 images. The area under the receiver operating characteristic curves for detecting microaneurysms, cotton-wool spots, hard exudates and hemorrhages are 0.901, 0.941, 0.954 and 0.967, respectively. The grading of diabetic retinopathy as mild, moderate, severe and proliferative achieves area under the curves of 0.943, 0.955, 0.960 and 0.972, respectively. In external validations, the area under the curves for grading range from 0.916 to 0.970, which further supports the system is efficient for diabetic retinopathy grading.

Over the past decade, the number of known eicosanoids has expanded immensely and we have now developed an ultra-performance liquid chromatography - electrospray ionization triple quadrupole mass spectrometric (UPLC-QTRAP/MS/MS) method to monitor and quantify numerous eicosanoids. The UPLC-QTRAP/MS/MS approach utilizes scheduled multiple reaction monitoring (MRM) to optimize sensitivity, number of metabolites that can be analyzed and the time requirement of the analysis. A total of 184 eicosanoids including 26 deuterated internal standards can be separated and monitored in a single 5 min UPLC run. To demonstrate a practical application, human plasma samples were analyzed following solid-phase extraction (SPE) and the recovery rate and matrix effects were determined for the 26 deuterated internal standards added to the plasma. The method was validated and shown to be sensitive with the limit of quantitation at pg levels for most compounds, accurate with recovery rates of 70-120%, and precise with a CV<30 for all compounds. Also, the method showed a linear response over a range spanning several orders of magnitude. In a QC human plasma sample, we identified and rigorously quantified over 120 eicosanoids.