The severity of the pulmonary manifestations of COVID-19 can be quantitatively evaluated from chest CT using a deep-learning method. There were significant differences in lung opacification percentage, as measured by the deep learning algorithm, among patients with different clinical severity. This automated tool for quantification of lung involvement may be used to monitor the disease progression and understand the temporal evolution of COVID-19. Abbreviations ARDS = acute respiratory distress syndrome, COVID-19 = coronavirus disease 19, GGO = ground glass opacity, HRCT = high resolution computed tomography, RT-PCR = reverse transcription-polymerase chain reaction, SARS-Cov-2 = severe acute respiratory syndrome coronavirus 2, SpO2 =pulse oxygen saturation

We report the crystal structure of an NH2-terminal 388-residue fragment of T4 DNA polymerase (protein N388) refined at 2.2 A resolution. This fragment contains both the 3'-5' exonuclease active site and part of the autologous mRNA binding site (J. D. Karam, personal communication). The structure of a complex between the apoprotein N388 and a substrate, p(dT)3, has been refined at 2.5 A resolution to a crystallographic R-factor of 18.7%. Two divalent metal ion cofactors, Zn(II) and Mn(II), have been located in crystals of protein N388 which had been soaked in solutions containing Zn(II), Mn(II), or both. The structure of the 3'-5' exonuclease domain of protein N388 closely resembles the corresponding region in the Klenow fragment despite minimal sequence identity. The side chains of four carboxylate residues that serve as ligands for the two metal ions required for catalysis are located in geometrically equivalent positions in both proteins with a rms deviation of 0.87 A. There are two main differences between the 3'-5' exonuclease active site regions of the two proteins: (I) the OH of Tyr-497 in the Klenow fragment interacts with the scissile phosphate in the active site whereas the OH of the equivalent tyrosine (Tyr-320) in protein N388 points away from the active center; (II) different residues form of the binding pocket for the 3'-terminal bases of the substrate. In the protein N388 complex the 3'-terminal base of p(dT)3 is rotated approximately 60 degrees relative to the position that the corresponding base occupies in the p(dT)3 complex with the Klenow fragment. Finally, a separate domain (residues 1-96) of protein N388 may be involved in mRNA binding that results in translational regulation of T4 DNA polymerase (Pavlov & Karam, 1994).

Large DNA constructs of arbitrary sequences can currently be assembled with relative ease by joining short synthetic oligodeoxynucleotides (oligonucleotides). The ability to mass produce these synthetic genes readily will have a significant impact on research in biology and medicine. Presently, high-throughput gene synthesis is unlikely, due to the limits of oligonucleotide synthesis. We describe a microfluidic PicoArray method for the simultaneous synthesis and purification of oligonucleotides that are designed for multiplex gene synthesis. Given the demand for highly pure oligonucleotides in gene synthesis processes, we used a model to improve key reaction steps in DNA synthesis. The oligonucleotides obtained were successfully used in ligation under thermal cycling conditions to generate DNA constructs of several hundreds of base pairs. Protein expression using the gene thus synthesized was demonstrated. We used a DNA assembly strategy, i.e. ligation followed by fusion PCR, and achieved effective assembling of up to 10 kb DNA constructs. These results illustrate the potential of microfluidics-based ultra-fast oligonucleotide parallel synthesis as an enabling tool for modern synthetic biology applications, such as the construction of genome-scale molecular clones and cell-free large scale protein expression.

Background Prompt identification of patients suspected to have COVID-19 is crucial for disease control. We aimed to develop a deep learning algorithm on the basis of chest CT for rapid triaging in fever clinics. Methods We trained a U-Net-based model on unenhanced chest CT scans obtained from 2447 patients admitted to Tongji Hospital (Wuhan, China) between Feb 1, 2020, and March 3, 2020 (1647 patients with RT-PCR-confirmed COVID-19 and 800 patients without COVID-19) to segment lung opacities and alert cases with COVID-19 imaging manifestations. The ability of artificial intelligence (AI) to triage patients suspected to have COVID-19 was assessed in a large external validation set, which included 2120 retrospectively collected consecutive cases from three fever clinics inside and outside the epidemic centre of Wuhan (Tianyou Hospital [Wuhan, China; area of high COVID-19 prevalence], Xianning Central Hospital [Xianning, China; area of medium COVID-19 prevalence], and The Second Xiangya Hospital [Changsha, China; area of low COVID-19 prevalence]) between Jan 22, 2020, and Feb 14, 2020. To validate the sensitivity of the algorithm in a larger sample of patients with COVID-19, we also included 761 chest CT scans from 722 patients with RT-PCR-confirmed COVID-19 treated in a makeshift hospital (Guanggu Fangcang Hospital, Wuhan, China) between Feb 21, 2020, and March 6, 2020. Additionally, the accuracy of AI was compared with a radiologist panel for the identification of lesion burden increase on pairs of CT scans obtained from 100 patients with COVID-19. Findings In the external validation set, using radiological reports as the reference standard, AI-aided triage achieved an area under the curve of 0·953 (95% CI 0·949–0·959), with a sensitivity of 0·923 (95% CI 0·914–0·932), specificity of 0·851 (0·842–0·860), a positive predictive value of 0·790 (0·777–0·803), and a negative predictive value of 0·948 (0·941–0·954). AI took a median of 0·55 min (IQR: 0·43–0·63) to flag a positive case, whereas radiologists took a median of 16·21 min (11·67–25·71) to draft a report and 23·06 min (15·67–39·20) to release a report. With regard to the identification of increases in lesion burden, AI achieved a sensitivity of 0·962 (95% CI 0·947–1·000) and a specificity of 0·875 (95 %CI 0·833–0·923). The agreement between AI and the radiologist panel was high (Cohen's kappa coefficient 0·839, 95% CI 0·718–0·940). Interpretation A deep learning algorithm for triaging patients with suspected COVID-19 at fever clinics was developed and externally validated. Given its high accuracy across populations with varied COVID-19 prevalence, integration of this system into the standard clinical workflow could expedite identification of chest CT scans with imaging indications of COVID-19. Funding Special Project for Emergency of the Science and Technology Department of Hubei Province, China.

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Solution reactions using photogenerated reagents (Gao, X.; Yu, P.; LeProust, E.; Sonigo, L.; Pellois, J. P.; Zhang, H. J. Am. Chem. Soc. 1998, 120, 12698) are a potentially powerful means for combinatorial parallel synthesis of addressable molecular microarrays. In this report, we demonstrate that this chemistry permits combinatorial screening of reaction conditions on a microarray platform. Using this method of optimization and our reaction apparatus, efficient photogenerated acids and reaction conditions suitable for removal of the acid labile protection group on 5'-O of nucleotides are identified in a short period of time. The chemistry platform demonstrated opens new avenues for rapid, simultaneous investigation of multiple reactions using different reagents and reaction parameters directly on a solid support (e.g., a glass plate). The combinatorial screening method described may be extended to include general organic reactions employing photogenerated and conventional reagents as well as a microarray reaction device. This should be especially valuable for efficient synthesis of addressable organic compound libraries.

Background: Green tea polyphenol (GTP) is a polyphenol source from green tea that has drawn wide attention owing to epidemiological evidence of its beneficial effects in the prevention of cardiovascular disease; the underlying molecular mechanisms of these effects are not well understood. This study aimed to investigate the effects of GTP treatment on autophagy regulation in the vessel wall and lipid metabolism of HFD-fed male ApoE-knockout mice. Methods: Adult male ApoE-knockout mice (n = 30) fed with a high-fat diet (HFD) were treated with either vehicle or GTP (3.2 or 6.4 g/L) administered via drinking water for 15 weeks, and C57BL/6J mice fed with standard chow diet (STD) were used as the control group. Metabolic parameters, expression of key mRNAs and proteins of hepatic lipid metabolism and autophagy in the vessel wall of mice were determined after the 15-week treatment. Results: A HFD induced atherosclerosis formation and lipid metabolism disorders as well as reduced autophagy expression in the vessel wall of ApoE-knockout mice, but GTP treatment alleviated the lipid metabolism disorders, decreased the oxLDL levels in serum, and increased the mRNA and protein expressions of hepatic PPARα and autophagy markers (LC3, Beclin1 and p62) in the vessel wall of ApoE-knockout mice. Conclusions: Our findings suggest that GTP supplementation showed marked suppression of atherogenesis through improved lipid metabolism as well as through a direct impact on oxLDL and autophagy flux in the vessel wall.