Monkeypox virus quadrivalent mRNA vaccine induces immune response and protects against vaccinia virus.

Monkeypox has been declared a public health emergency by the World Health Organization. There is an urgent need for efficient and safe vaccines against the monkeypox virus (MPXV) in response to the rapidly spreading monkeypox epidemic. In the age of COVID-19, mRNA vaccines have been highly successful and emerged as platforms enabling rapid development and large-scale preparation. Here, we develop two MPXV quadrivalent mRNA vaccines, named mRNA-A-LNP and mRNA-B-LNP, based on two intracellular mature virus specific proteins (A29L and M1R) and two extracellular enveloped virus specific proteins (A35R and B6R). By administering mRNA-A-LNP and mRNA-B-LNP intramuscularly twice, mice induce MPXV specific IgG antibodies and potent vaccinia virus (VACV) specific neutralizing antibodies. Further, it elicits efficient MPXV specific Th-1 biased cellular immunity, as well as durable effector memory T and germinal center B cell responses in mice. In addition, two doses of mRNA-A-LNP and mRNA-B-LNP are protective against the VACV challenge in mice. And, the passive transfer of sera from mRNA-A-LNP and mRNA-B-LNP-immunized mice protects nude mice against the VACV challenge. Overall, our results demonstrate that mRNA-A-LNP and mRNA-B-LNP appear to be safe and effective vaccine candidates against monkeypox epidemics, as well as against outbreaks caused by other orthopoxviruses, including the smallpox virus.

Rational preparation and application of a mRNA delivery system with cytidinyl/cationic lipid.

The messenger RNA (mRNA)-based therapy, especially mRNA vaccines, has shown its superiorities in versatile design, rapid development and scale production, since the outbreak of coronavirus disease 2019 (COVID-19). Although the Pfizer-BioNTech and Moderna COVID-19 mRNA vaccines had been approved for application, unexpected adverse events were reported to be most likely associated with the mRNA delivery systems. Thus, the development of mRNA delivery system with good efficacy and safety remains a challenge. Here, for the first time, we report that the neutral cytidinyl lipid, 2-(4-amino-2-oxopyrimidin-1-yl)-N-(2,3-dioleoyl-oxypropyl) acetamide (DNCA), and the cationic lipid, dioleoyl-3,3'-disulfanediylbis-[2-(2,6-diaminohexanamido)] propanoate (CLD), could encapsulate and deliver the COVID-19 mRNA-1096 into the cytoplasm to induce robust adaptive immune response. In the formulation, the molar ratio of DNCA/CLD to a single nucleotide of COVID-19 mRNA-1096 was about 0.9: 0.5: 1 (the N/P ratio was about 7: 1). The DNCA/CLD-mRNA-1096 lipoplexes were rationally prepared by the combination of the lipids DNCA/CLD with the aqueous mRNA solution under mild sonication to stimulate multiple interactions, including H-bonding, π-stacking and electrostatic force between the lipids and the mRNA. After intramuscular applications of the DNCA/CLD-mRNA-1096 lipoplexes, robust neutralizing antibodies and long-lived Th1-biased SARS-CoV-2-specific cell immunity were detected in the immunized mice, thus suggesting the DNCA/CLD a promising mRNA delivery system. Moreover, our study might also inspire better ideas for developing mRNA delivery systems.

Clinical longitudinal evaluation of COVID-19 patients and prediction of organ-specific recovery using artificial intelligence.

Within COVID-19 there is an urgent unmet need to predict at the time of hospital admission which COVID-19 patients will recover from the disease, and how fast they recover to deliver personalized treatments and to properly allocate hospital resources so that healthcare systems do not become overwhelmed. To this end, we have combined clinically salient CT imaging data synergistically with laboratory testing data in an integrative machine learning model to predict organ-specific recovery of patients from COVID-19. We trained and validated our model in 285 patients on each separate major organ system impacted by COVID-19 including the renal, pulmonary, immune, cardiac, and hepatic systems. To greatly enhance the speed and utility of our model, we applied an artificial intelligence method to segment and classify regions on CT imaging, from which interpretable data could be directly fed into the predictive machine learning model for overall recovery. Across all organ systems we achieved validation set area under the receiver operator characteristic curve (AUC) values for organ-specific recovery ranging from 0.80 to 0.89, and significant overall recovery prediction in Kaplan-Meier analyses. This demonstrates that the synergistic use of an artificial intelligence (AI) framework applied to CT lung imaging and a machine learning model that integrates laboratory test data with imaging data can accurately predict the overall recovery of COVID-19 patients from baseline characteristics.

A deep-learning pipeline for the diagnosis and discrimination of viral, non-viral and COVID-19 pneumonia from chest X-ray images.

Common lung diseases are first diagnosed using chest X-rays. Here, we show that a fully automated deep-learning pipeline for the standardization of chest X-ray images, for the visualization of lesions and for disease diagnosis can identify viral pneumonia caused by coronavirus disease 2019 (COVID-19) and assess its severity, and can also discriminate between viral pneumonia caused by COVID-19 and other types of pneumonia. The deep-learning system was developed using a heterogeneous multicentre dataset of 145,202 images, and tested retrospectively and prospectively with thousands of additional images across four patient cohorts and multiple countries. The system generalized across settings, discriminating between viral pneumonia, other types of pneumonia and the absence of disease with areas under the receiver operating characteristic curve (AUCs) of 0.94-0.98; between severe and non-severe COVID-19 with an AUC of 0.87; and between COVID-19 pneumonia and other viral or non-viral pneumonia with AUCs of 0.87-0.97. In an independent set of 440 chest X-rays, the system performed comparably to senior radiologists and improved the performance of junior radiologists. Automated deep-learning systems for the assessment of pneumonia could facilitate early intervention and provide support for clinical decision-making.