Machine Learning-Based Scoring System for Early Prognosis Evaluation of Patients with Coronavirus Disease 2019

Background The global spread of coronavirus disease 2019 (COVID-19) continues to threaten human health security, exerting considerable pressure on healthcare systems worldwide. While prognostic models for COVID-19 hospitalized or intensive care patients are currently available, prognostic models developed for large cohorts of thousands of individuals are still lacking. Methods Between February 4 and April 16, 2020, we enrolled 3,974 patients admitted with COVID-19 disease in the Wuhan Huo-Shen-Shan Hospital and the Maternal and Child Hospital, Hubei Province, China. (1) Screening of key prognostic factors: A univariate Cox regression analysis was performed on 2,649 patients in the training set, and factors affecting prognosis were initially screened. Subsequently, a random survival forest model was established through machine analysis to further screen for factors that are important for prognosis. Finally, multivariate Cox regression analysis was used to determine the synergy among various factors related to prognosis. (2) Establishment of a scoring system: The nomogram algorithm established a COVID-19 patient death risk assessment scoring system for the nine selected key prognostic factors, calculated the C index, drew calibration curves and drew training set patient survival curves. (3) Verification of the scoring system: The scoring system assessed 1,325 patients in the test set, splitting them into high- and low-risk groups, calculated the C-index, and drew calibration and survival curves. Results The cross-sectional study found that age, clinical classification, sex, pulmonary insufficiency, hypoproteinemia, and four other factors (underlying diseases: blood diseases, malignant tumor;complications: digestive tract bleeding, heart dysfunction) have important significance for the prognosis of the enrolled patients with COVID-19. Herein, we report the discovery of the effects of hypoproteinemia and hematological diseases on the prognosis of COVID-19. Meanwhile, the scoring system established here can effectively evaluate objective scores for the early prognoses of patients with COVID-19 and can divide them into high- and low-risk groups (using a scoring threshold of 117.77, a score below which is considered low risk). The efficacy of the system was better than that of clinical classification using the current COVID-19 guidelines (C indexes, 0.95 vs. 0.89). Conclusions Age, clinical typing, sex, pulmonary insufficiency, hypoproteinemia, and four other factors were important for COVID-19 survival. Compared with general statistical methods, this method can quickly and accurately screen out the relevant factors affecting prognosis, provide an order of importance, and establish a scoring system based on the nomogram model, which is of great clinical significance.

Mapping the trophic state index of eastern lakes in China using an empirical model and Sentinel-2 imagery data

Lake eutrophication has become a critical environmental issue due to the global effects of anthropogenic activities and climate change, and has been comprehensively studied for many years. A series of models and indicators have been proposed to assess the trophic state of lakes. The trophic state index (TSI) is a synthetic index that integrates chlorophyll-a, water clarity, and total phosphorus and is widely used to evaluate the trophic state of aquatic environments. In this study, we collected in situ lake samples (N = 431) from typical lakes to match Sentinel-2 MultiSpectral Instrument (MSI) imagery data using the Case 2 Regional Coast Color processor. Then we developed a new empirical model, TSI = -34.04 x (band 4/band 5) - 1.114 x (band 1/band 4) + 97.376. This model is valid for all of China, with good performance and few errors (RMSE = 7.36;MAE = 6.25) for the validation dataset. Recognizing that over 94% of the Chinese population located along eastern watersheds and large lakes have competing water uses, and given the TSI model on the seasonal scales, we further estimated the mean TSI and trophic state in eastern Chinese lakes (> 100 km2) from 2019 to 2020. The results revealed that more lakes were eutrophic in autumn (94.28%) than in spring (> 77.14%), indicating a serious eutrophication of eastern lakes. Although the eastern lakes have been studied in more detail, this study found that eutrophication still has markedly negative impacts on lake ecosystems. In addition, no significant improvement was observed in spring, most likely due to the months of curfew/lockdown from January 2020 onwards due to COVID-19. This may be due to the enrichment of nutrients deposited in sediment or watershed soil, which can be characterized as "autochthonous sources" of lake eutrophication, over decades with high rates of economic development. This study demonstrates the applicability of Sentinel-2 MSI data to monitor lake eutrophication as well as the feasibility of blue/red and red/red edge combinations. The framework and TSI model used bands available on MSI sensors to develop a novel approach for generating historical eutrophication data for large-scale evaluation of and decision-making related aquatic environmental changes, even in poorly studied areas.

The relationship between high density lipoprotein cholesterol and sepsis: A clinical and genetic approach.

Sepsis accounts for one in three hospital deaths. Higher concentrations of high-density lipoprotein cholesterol (HDL-C) are associated with apparent protection from sepsis, suggesting a potential therapeutic role for HDL-C or drugs, such as cholesteryl ester transport protein (CETP) inhibitors that increase HDL-C. However, these beneficial clinical associations might be due to confounding; genetic approaches can address this possibility. We identified 73,406 White adults admitted to Vanderbilt University Medical Center with infection; 11,612 had HDL-C levels, and 12,377 had genotype information from which we constructed polygenic risk scores (PRS) for HDL-C and the effect of CETP on HDL-C. We tested the associations between predictors (measured HDL-C, HDL-C PRS, CETP PRS, and rs1800777) and outcomes: sepsis, septic shock, respiratory failure, and in-hospital death. In unadjusted analyses, lower measured HDL-C concentrations were significantly associated with increased risk of sepsis (p = 2.4 × 10-23 ), septic shock (p = 4.1 × 10-12 ), respiratory failure (p = 2.8 × 10-8 ), and in-hospital death (p = 1.0 × 10-8 ). After adjustment (age, sex, electronic health record length, comorbidity score, LDL-C, triglycerides, and body mass index), these associations were markedly attenuated: sepsis (p = 2.6 × 10-3 ), septic shock (p = 8.1 × 10-3 ), respiratory failure (p = 0.11), and in-hospital death (p = 4.5 × 10-3 ). HDL-C PRS, CETP PRS, and rs1800777 significantly predicted HDL-C (p < 2 × 10-16 ), but none were associated with sepsis outcomes. Concordant findings were observed in 13,254 Black patients hospitalized with infections. Lower measured HDL-C levels were significantly associated with increased risk of sepsis and related outcomes in patients with infection, but a causal relationship is unlikely because no association was found between the HDL-C PRS or the CETP PRS and the risk of adverse sepsis outcomes.

A potent, broadly protective vaccine against SARS-CoV-2 variants of concern.

Since the first outbreak in December 2019, SARS-CoV-2 has been constantly evolving and five variants have been classified as Variant of Concern (VOC) by the World Health Organization (WHO). These VOCs were found to enhance transmission and/or decrease neutralization capabilities of monoclonal antibodies and vaccine-induced antibodies. Here, we successfully designed and produced a recombinant COVID-19 vaccine in CHO cells at a high yield. The vaccine antigen contains four hot spot substitutions, K417N, E484K, N501Y and D614G, based on a prefusion-stabilized spike trimer of SARS-CoV-2 (S-6P) and formulated with an Alum/CpG 7909 dual adjuvant system. Results of immunogenicity studies showed that the variant vaccine elicited robust cross-neutralizing antibody responses against SARS-CoV-2 prototype (Wuhan) strain and all 5 VOCs. It further, stimulated a TH1 (T Helper type 1) cytokine profile and substantial CD4+ T cell responses in BALB/c mice and rhesus macaques were recorded. Protective efficacy of the vaccine candidate was evaluated in hamster and rhesus macaque models of SARS-CoV-2. In Golden Syrian hamsters challenged with Beta or Delta strains, the vaccine candidate reduced the viral loads in nasal turbinates and lung tissues, accompanied by significant weight gain and relieved inflammation in the lungs. In rhesus macaque challenged with prototype SARS-CoV-2, the vaccine candidate decreased viral shedding in throat, anal, blood swabs over time, reduced viral loads of bronchus and lung tissue, and effectively relieved the lung pathological inflammatory response. Together, our data demonstrated the broadly neutralizing activity and efficacy of the variant vaccine against both prototype and current VOCs of SARS-CoV-2, justifying further clinical development.

A pan-variant mRNA-LNP T cell vaccine protects HLA transgenic mice from mortality after infection with SARS-CoV-2 Beta.

Licensed COVID-19 vaccines ameliorate viral infection by inducing production of neutralizing antibodies that bind the SARS-CoV-2 Spike protein and inhibit viral cellular entry. However, the clinical effectiveness of these vaccines is transitory as viral variants escape antibody neutralization. Effective vaccines that solely rely upon a T cell response to combat SARS-CoV-2 infection could be transformational because they can utilize highly conserved short pan-variant peptide epitopes, but a mRNA-LNP T cell vaccine has not been shown to provide effective anti-SARS-CoV-2 prophylaxis. Here we show a mRNA-LNP vaccine (MIT-T-COVID) based on highly conserved short peptide epitopes activates CD8+ and CD4+ T cell responses that attenuate morbidity and prevent mortality in HLA-A*02:01 transgenic mice infected with SARS-CoV-2 Beta (B.1.351). We found CD8+ T cells in mice immunized with MIT-T-COVID vaccine significantly increased from 1.1% to 24.0% of total pulmonary nucleated cells prior to and at 7 days post infection (dpi), respectively, indicating dynamic recruitment of circulating specific T cells into the infected lungs. Mice immunized with MIT-T-COVID had 2.8 (2 dpi) and 3.3 (7 dpi) times more lung infiltrating CD8+ T cells than unimmunized mice. Mice immunized with MIT-T-COVID had 17.4 times more lung infiltrating CD4+ T cells than unimmunized mice (7 dpi). The undetectable specific antibody response in MIT-T-COVID-immunized mice demonstrates specific T cell responses alone can effectively attenuate the pathogenesis of SARS-CoV-2 infection. Our results suggest further study is merited for pan-variant T cell vaccines, including for individuals that cannot produce neutralizing antibodies or to help mitigate Long COVID.

Nano alum: A new solution to the new challenge.

Alum adjuvant has always been the first choice when designing a vaccine. Conventional aluminum adjuvant includes aluminum hydroxide, aluminum phosphate, and amorphous aluminum hydroxyphosphate (AAHS), which could effectively induce the humoral, and to a lesser extent, cellular immune responses. Their safety is widely accepted for a variety of vaccines. However, conventional alum adjuvant is not an ideal choice for a vaccine antigen with poor immunogenicity, especially the subunit vaccine in which cellular response is highly demanded. The outbreak of COVID-19 requires a delicately designed vaccine without the antibody-dependent enhancement (ADE) effect to ensure the safety. A sufficiently powerful adjuvant that can induce both Th1 and Th2 immune responses is necessary to reduce the risk of ADE. These circumstances all bring new challenges to the conventional alum adjuvant. However, turning conventional microscale alum adjuvant into nanoscale is a new solution to these problems. Nanoscale alum owns a higher surface volume ratio, can absorb much more antigens, and promote the ability to stimulate the antigen-presenting cells (APCs) via different mechanisms. In this review, the exceptional performance of nano alum adjuvant and their preparation methods will be discussed. The potential safety concern of nano alum is also addressed. Based on the different mechanisms, the potential application of nano alum will also be introduced.

Drug Repositioning for Hand, Foot, and Mouth Disease.

Hand, foot, and mouth disease (HFMD) is a highly contagious disease in children caused by a group of enteroviruses. HFMD currently presents a major threat to infants and young children because of a lack of antiviral drugs in clinical practice. Drug repositioning is an attractive drug discovery strategy aimed at identifying and developing new drugs for diseases. Notably, repositioning of well-characterized therapeutics, including either approved or investigational drugs, is becoming a potential strategy to identify new treatments for virus infections. Various types of drugs, including antibacterial, cardiovascular, and anticancer agents, have been studied in relation to their therapeutic potential to treat HFMD. In this review, we summarize the major outbreaks of HFMD and the progress in drug repositioning to treat this disease. We also discuss the structural features and mode of action of these repositioned drugs and highlight the opportunities and challenges of drug repositioning for HFMD.

Systematic review and meta-analysis of neonatal outcomes of COVID-19 vaccination in pregnancy.

BACKGROUND: The safety of coronavirus disease 2019 (COVID-19) vaccines during pregnancy is a particular concern. Here, we addressed the neonatal outcomes after maternal vaccination of COVID-19 during pregnancy. METHODS: We systematically searched PubMed, EMBASE, and the WHO COVID-19 Database for studies on neonatal outcomes after maternal COVID-19 vaccination from inception to 3 July 2022. Main neonatal outcomes were related to preterm, small for gestation (SGA), NICU admission, low Apgar score at 5 min (<7), and additional neonatal outcomes such as gestation <34 weeks, low birth weight and some neonatal morbidity were all also analyzed. RESULTS: A total of 15 studies were included. We found that maternal vaccination during pregnancy was related to the reduction rates of Preterm, SGA, Low Apgar score at 5 min (<7). In addition, there was no evidence of a higher risk of adverse neonatal outcomes after maternal vaccination of COVID-19 during pregnancy, including NICU admission, preterm birth with gestation <34 weeks, low birth weight, very low birth weight, congenital anomalies, and so on. CONCLUSIONS: COVID-19 vaccination in pregnant women does not raise significant adverse effects on neonatal outcomes and is related to a protective effect on some neonatal outcomes. IMPACT: Present study has addressed the neonatal outcomes after maternal vaccination of COVID-19 during pregnancy. COVID-19 vaccination in pregnant women does not raise significant adverse effects on neonatal outcomes and is related to a protective effect on some neonatal outcomes. The present study could encourage pregnant women to be vaccinated against COVID-19.

Remote sensing of fluorescent humification levels and its potential environmental linkages in lakes across China.

The pollution or eutrophication affected by dissolved organic matter (DOM) composition and sources of inland waters had attracted concerns from the public and government in China. Combined with remote sensing techniques, the fluorescent DOM (FDOM) parameters accounted for the important part of optical constituent as chromophoric dissolved organic matter (CDOM) was a useful tool to trace relative DOM sources and assess the trophic states for large-scale regions comprehensively and timely. Here, the objective of this research is to calibrate and validate a general model based on Landsat 8 OLI product embedded in Google Earth Engine (GEE) for deriving humification index (HIX) based on EEMs in lakes across China. The Landsat surface reflectance was matched with 1150 pairs fieldtrip samples and the nine sensitive spectral variables with good correlation with HIX were selected as the inputs in machine learning methods. The calibration of XGBoost model (R2 = 0.86, RMSE = 0.29) outperformed other models. Our results indicated that the entire dataset of HIX has a strong association with Landsat reflectance, yielding low root mean square error between measured and predicted HIX (R2 = 0.81, RMSE = 0.42) for lakes in China. Finally, the optimal XGBoost model was used to calculate the spatial distribution of HIX of 2015 and 2020 in typical lakes selected from the Report on the State of the Ecology and Environment in China. The significant decreasing of HIX from 2015 to 2020 with trophic states showed positive control of humification level of lakes based on the published document of Action plan for prevention and control of water pollution in 2015 of China. The calibrated model would greatly facilitate FDOM monitoring in lakes, and provide indicators for relative DOM sources to evaluate the impact of water protection measures or human disturbance effect from Covid-19 lockdown, and offer the government supervision to improve the water quality management for lake ecosystems.

Investigating and Resolving Cardiotoxicity Induced by COVID-19 Treatments using Human Pluripotent Stem Cell-Derived Cardiomyocytes and Engineered Heart Tissues.

Coronavirus disease 2019 continues to spread worldwide. Given the urgent need for effective treatments, many clinical trials are ongoing through repurposing approved drugs. However, clinical data regarding the cardiotoxicity of these drugs are limited. Human pluripotent stem cell-derived cardiomyocytes (hCMs) represent a powerful tool for assessing drug-induced cardiotoxicity. Here, by using hCMs, it is demonstrated that four antiviral drugs, namely, apilimod, remdesivir, ritonavir, and lopinavir, exhibit cardiotoxicity in terms of inducing cell death, sarcomere disarray, and dysregulation of calcium handling and contraction, at clinically relevant concentrations. Human engineered heart tissue (hEHT) model is used to further evaluate the cardiotoxic effects of these drugs and it is found that they weaken hEHT contractile function. RNA-seq analysis reveals that the expression of genes that regulate cardiomyocyte function, such as sarcomere organization (TNNT2, MYH6) and ion homeostasis (ATP2A2, HCN4), is significantly altered after drug treatments. Using high-throughput screening of approved drugs, it is found that ceftiofur hydrochloride, astaxanthin, and quetiapine fumarate can ameliorate the cardiotoxicity of remdesivir, with astaxanthin being the most prominent one. These results warrant caution and careful monitoring when prescribing these therapies in patients and provide drug candidates to limit remdesivir-induced cardiotoxicity.

Spike-based adenovirus vectored COVID-19 vaccine does not aggravate heart damage after ischemic injury in mice.

An unprecedented number of COVID-19 vaccination campaign are under way worldwide. The spike protein of SARS-CoV-2, which majorly binds to the host receptor angiotensin converting enzyme 2 (ACE2) for cell entry, is used by most of the vaccine as antigen. ACE2 is highly expressed in the heart and has been reported to be protective in multiple organs. Interaction of spike with ACE2 is known to reduce ACE2 expression and affect ACE2-mediated signal transduction. However, whether a spike-encoding vaccine will aggravate myocardial damage after a heart attack via affecting ACE2 remains unclear. Here, we demonstrate that cardiac ACE2 is up-regulated and protective after myocardial ischemia/reperfusion (I/R). Infecting human cardiac cells or engineered heart tissues with a spike-based adenovirus type-5 vectored COVID-19 vaccine (AdSpike) does not affect their survival and function, whether subjected to hypoxia-reoxygenation injury or not. Furthermore, AdSpike vaccination does not aggravate heart damage in wild-type or humanized ACE2 mice after I/R injury, even at a dose that is ten-fold higher as used in human. This study represents the first systematic evaluation of the safety of a leading COVID-19 vaccine under a disease context and may provide important information to ensure maximal protection from COVID-19 in patients with or at risk of heart diseases.

Mapping the Trophic State Index of Eastern Lakes in China using an Empirical Model and Sentinel-2 Imagery Data

Lake eutrophication has become a critical environmental issue due to the global effects of anthropogenic activities and climate change, and has been comprehensively studied for many years. A series of models and indicators have been proposed to assess the trophic state of lakes. The trophic state index (TSI) is a synthetic index that integrates chlorophyll-a, water clarity, and total phosphorus and is widely used to evaluate the trophic state of aquatic environments. In this study, we collected in situ lake samples (N=431) from typical lakes to match Sentinel-2 MultiSpectral Instrument (MSI) imagery data using the Case 2 Regional Coast Color processor. Then we developed a new empirical model, TSI = –34.04 × (band 4/band 5) – 1.114 × (band 1/band 4) + 97.376). This model is valid for all of China, with good performance and few errors (RMSE=7.36;MAE=6.25) for the validation dataset. Recognizing that over 94% of the Chinese population located along eastern watersheds and large lakes have competing water uses, and given the TSI model on the seasonal scales, we further estimated the mean TSI and trophic state in eastern Chinese lakes (> 100 km2) from 2019 to 2020. The results revealed that more lakes were eutrophic in autumn (94.28%) than in spring (> 77.14%), indicating a serious eutrophication of eastern lakes. Although the eastern lakes have been studied in more detail, this study found that eutrophication still has markedly negative impacts on lake ecosystems. In addition, no significant improvement was observed in spring, most likely due to the months of curfew/lockdown from January 2020 onwards due to COVID-19. This may be due to the enrichment of nutrients deposited in sediment or watershed soil, which can be characterized as “autochthonous sources” of lake eutrophication, over decades with high rates of economic development. This study demonstrates the applicability of Sentinel-2 MSI data to monitor lake eutrophication as well as the feasibility of blue/red and red/red edge combinations. The framework and TSI model used bands available on MSI sensors to develop a novel approach for generating historical eutrophication data for large-scale evaluation of and decision-making related aquatic environmental changes, even in poorly studied areas.

The Evolution and Disparities of Online Attitudes Toward COVID-19 Vaccines: Year-long Longitudinal and Cross-sectional Study.

BACKGROUND: Due to the urgency caused by the COVID-19 pandemic worldwide, vaccine manufacturers have to shorten and parallel the development steps to accelerate COVID-19 vaccine production. Although all usual safety and efficacy monitoring mechanisms remain in place, varied attitudes toward the new vaccines have arisen among different population groups. OBJECTIVE: This study aimed to discern the evolution and disparities of attitudes toward COVID-19 vaccines among various population groups through the study of large-scale tweets spanning over a whole year. METHODS: We collected over 1.4 billion tweets from June 2020 to July 2021, which cover some critical phases concerning the development and inoculation of COVID-19 vaccines worldwide. We first developed a data mining model that incorporates a series of deep learning algorithms for inferring a range of individual characteristics, both in reality and in cyberspace, as well as sentiments and emotions expressed in tweets. We further conducted an observational study, including an overall analysis, a longitudinal study, and a cross-sectional study, to collectively explore the attitudes of major population groups. RESULTS: Our study derived 3 main findings. First, the whole population's attentiveness toward vaccines was strongly correlated (Pearson r=0.9512) with official COVID-19 statistics, including confirmed cases and deaths. Such attentiveness was also noticeably influenced by major vaccine-related events. Second, after the beginning of large-scale vaccine inoculation, the sentiments of all population groups stabilized, followed by a considerably pessimistic trend after June 2021. Third, attitude disparities toward vaccines existed among population groups defined by 8 different demographic characteristics. By crossing the 2 dimensions of attitude, we found that among population groups carrying low sentiments, some had high attentiveness ratios, such as males and individuals aged ≥40 years, while some had low attentiveness ratios, such as individuals aged ≤18 years, those with occupations of the 3rd category, those with account age <5 years, and those with follower number <500. These findings can be used as a guide in deciding who should be given more attention and what kinds of help to give to alleviate the concerns about vaccines. CONCLUSIONS: This study tracked the year-long evolution of attitudes toward COVID-19 vaccines among various population groups defined by 8 demographic characteristics, through which significant disparities in attitudes along multiple dimensions were revealed. According to these findings, it is suggested that governments and public health organizations should provide targeted interventions to address different concerns, especially among males, older people, and other individuals with low levels of education, low awareness of news, low income, and light use of social media. Moreover, public health authorities may consider cooperating with Twitter users having high levels of social influence to promote the acceptance of COVID-19 vaccines among all population groups.

Development of recombinant COVID-19 vaccine based on CHO-produced, prefusion spike trimer and alum/CpG adjuvants.

COVID-19 pandemic has severely impacted the public health and social economy worldwide. A safe, effective, and affordable vaccine against SARS-CoV-2 infections/diseases is urgently needed. We have been developing a recombinant vaccine based on a prefusion-stabilized spike trimer of SARS-CoV-2 and formulated with aluminium hydroxide and CpG 7909. The spike protein was expressed in Chinese hamster ovary (CHO) cells, purified, and prepared as a stable formulation with the dual adjuvant. Immunogenicity studies showed that candidate vaccines elicited robust neutralizing antibody responses and substantial CD4+ T cell responses in both mice and non-human primates. And vaccine-induced neutralizing antibodies persisted at high level for at least 6 months. Challenge studies demonstrated that candidate vaccine reduced the viral loads and inflammation in the lungs of SARS-CoV-2 infected golden Syrian hamsters significantly. In addition, the vaccine-induced antibodies showed cross-neutralization activity against B.1.1.7 and B.1.351 variants. These data suggest candidate vaccine is efficacious in preventing SARS-CoV-2 infections and associated pneumonia, thereby justifying ongoing phase I/II clinical studies in China (NCT04982068 and NCT04990544).

Predicted Cellular Immunity Population Coverage Gaps for SARS-CoV-2 Subunit Vaccines and Their Augmentation by Compact Peptide Sets.

Subunit vaccines induce immunity to a pathogen by presenting a component of the pathogen and thus inherently limit the representation of pathogen peptides for cellular immunity-based memory. We find that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) subunit peptides may not be robustly displayed by the major histocompatibility complex (MHC) molecules in certain individuals. We introduce an augmentation strategy for subunit vaccines that adds a small number of SARS-CoV-2 peptides to a vaccine to improve the population coverage of pathogen peptide display. Our population coverage estimates integrate clinical data on peptide immunogenicity in convalescent COVID-19 patients and machine learning predictions. We evaluate the population coverage of 9 different subunits of SARS-CoV-2, including 5 functional domains and 4 full proteins, and augment each of them to fill a predicted coverage gap.

Perspectives on development of vaccines against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).

The recent outbreak of Coronavirus Disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has been characterized by the World Health Organization (WHO) as a controllable global pandemic. The spike (S) glycoprotein mediates binding to the angiotensin-converting enzyme 2 (ACE2) receptor for virus entry and also services as the target of virus-neutralizing antibodies, making it an attractive and leading viral antigen for vaccine development. No vaccine against any human coronavirus is available to date. In learning from the experience of developing Middle East respiratory syndrome coronavirus (MERS-CoV) and SARS-CoV vaccine candidates in preclinical and clinical trials, the most promising strategies for SARS-CoV-2 vaccines should employ viral-vector platforms, properly adjuvanted recombinant protein or DNA/mRNA encoding an engineered sequence of trimeric S protein in pre-fusion conformation.

Immune Checkpoints in Viral Infections

As evidence has mounted that virus-infected cells, such as cancer cells, negatively regulate the function of T-cells via immune checkpoints, it has become increasingly clear that viral infections similarly exploit immune checkpoints as an immune system escape mechanism. Although immune checkpoint therapy has been successfully used in cancer treatment, numerous studies have suggested that such therapy may also be highly relevant for treating viral infection, especially chronic viral infections. However, it has not yet been applied in this manner. Here, we reviewed recent findings regarding immune checkpoints in viral infections, including COVID-19, and discussed the role of immune checkpoints in different viral infections, as well as the potential for applying immune checkpoint blockades as antiviral therapy.

Computationally Optimized SARS-CoV-2 MHC Class I and II Vaccine Formulations Predicted to Target Human Haplotype Distributions.

We present a combinatorial machine learning method to evaluate and optimize peptide vaccine formulations for SARS-CoV-2. Our approach optimizes the presentation likelihood of a diverse set of vaccine peptides conditioned on a target human-population HLA haplotype distribution and expected epitope drift. Our proposed SARS-CoV-2 MHC class I vaccine formulations provide 93.21% predicted population coverage with at least five vaccine peptide-HLA average hits per person (≥ 1 peptide: 99.91%) with all vaccine peptides perfectly conserved across 4,690 geographically sampled SARS-CoV-2 genomes. Our proposed MHC class II vaccine formulations provide 97.21% predicted coverage with at least five vaccine peptide-HLA average hits per person with all peptides having an observed mutation probability of ≤ 0.001. We provide an open-source implementation of our design methods (OptiVax), vaccine evaluation tool (EvalVax), as well as the data used in our design efforts here: https://github.com/gifford-lab/optivax.