Predicting the antigenic evolution of SARS-COV-2 with deep learning.

The relentless evolution of SARS-CoV-2 poses a significant threat to public health, as it adapts to immune pressure from vaccines and natural infections. Gaining insights into potential antigenic changes is critical but challenging due to the vast sequence space. Here, we introduce the Machine Learning-guided Antigenic Evolution Prediction (MLAEP), which combines structure modeling, multi-task learning, and genetic algorithms to predict the viral fitness landscape and explore antigenic evolution via in silico directed evolution. By analyzing existing SARS-CoV-2 variants, MLAEP accurately infers variant order along antigenic evolutionary trajectories, correlating with corresponding sampling time. Our approach identified novel mutations in immunocompromised COVID-19 patients and emerging variants like XBB1.5. Additionally, MLAEP predictions were validated through in vitro neutralizing antibody binding assays, demonstrating that the predicted variants exhibited enhanced immune evasion. By profiling existing variants and predicting potential antigenic changes, MLAEP aids in vaccine development and enhances preparedness against future SARS-CoV-2 variants.

Synergy of Small Antiviral Molecules on a Black-Phosphorus Nanocarrier: Machine Learning and Quantum Chemical Simulation Insights.

Favipiravir (FP) and Ebselen (EB) belong to a broad range of antiviral drugs that have shown active potential as medications against many viruses. Employing molecular dynamics simulations and machine learning (ML) combined with van der Waals density functional theory, we have uncovered the binding characteristics of these two antiviral drugs on a phosphorene nanocarrier. Herein, by using four different machine learning models (i.e., Bagged Trees, Gaussian Process Regression (GPR), Support Vector Regression (SVR), and Regression Trees (RT)), the Hamiltonian and the interaction energy of antiviral molecules in a phosphorene monolayer are trained in an appropriate way. However, training efficient and accurate models for approximating the density functional theory (DFT) is the final step in using ML to aid in the design of new drugs. To improve the prediction accuracy, the Bayesian optimization approach has been employed to optimize the GPR, SVR, RT, and BT models. Results revealed that the GPR model obtained superior prediction performance with an R2 of 0.9649, indicating that it can explain 96.49% of the data's variability. Then, by means of DFT calculations, we examine the interaction characteristics and thermodynamic properties in a vacuum and a continuum solvent interface. These results illustrate that the hybrid drug is an enabled, functionalized 2D complex with vigorous thermostability. The change in Gibbs free energy at different surface charges and temperatures implies that the FP and EB molecules are allowed to adsorb from the gas phase onto the 2D monolayer at different pH conditions and high temperatures. The results reveal a valuable antiviral drug therapy loaded by 2D biomaterials that may possibly open a new way of auto-treating different diseases, such as SARS-CoV, in primary terms.

Comparison of the prophylactic antithrombotic effect of indobufen and warfarin in patients with nephrotic syndrome: a randomized controlled trial.

PURPOSE: The risk of thromboembolic events is elevated in patients with nephrotic syndrome, and warfarin use has been associated with an increased risk of bleeding. Indobufen, a selective cyclooxygenase-1 inhibitor, is currently being evaluated for the prevention of thromboembolic events in nephrotic syndrome. This study aimed to compare the efficacy and safety of indobufen with that of warfarin in patients with nephrotic syndrome. MATERIALS AND METHODS: This multicenter, randomized, three-arm, open-label, parallel controlled trial involved a total of 180 adult patients with nephrotic syndrome from four centers in China. Patients were randomly assigned to receive 100 mg indobufen (bid), 200 mg indobufen (bid), and 3 mg warfarin (qd) daily for 12 weeks. The primary endpoints included thromboembolic and bleeding events, while laboratory results and adverse events constituted secondary endpoints. RESULTS: No thromboembolic events occurred in the high-/low-dose indobufen and warfarin groups. Moreover, the use of a low dose of indobufen significantly reduced the risk of minor bleeding events compared with warfarin use (2% versus 18%, p < .05). Finally, adverse events were more frequent in warfarin-treated patients. CONCLUSIONS: This study found that indobufen therapy provided equivalent effects in preventing thromboembolic events compared with warfarin therapy, while low dose of indobufen was associated with a reduced risk of bleeding events, thus it should be recommended for the prevention of thromboembolic events in clinical practice in patients with nephrotic syndrome. TRIAL REGISTRATION NUMBER: ChiCTR-IPR-17013428.

Computational network analysis of host genetic risk variants of severe COVID-19.

BACKGROUND: Genome-wide association studies have identified numerous human host genetic risk variants that play a substantial role in the host immune response to SARS-CoV-2. Although these genetic risk variants significantly increase the severity of COVID-19, their influence on body systems is poorly understood. Therefore, we aim to interpret the biological mechanisms and pathways associated with the genetic risk factors and immune responses in severe COVID-19. We perform a deep analysis of previously identified risk variants and infer the hidden interactions between their molecular networks through disease mapping and the similarity of the molecular functions between constructed networks. RESULTS: We designed a four-stage computational workflow for systematic genetic analysis of the risk variants. We integrated the molecular profiles of the risk factors with associated diseases, then constructed protein-protein interaction networks. We identified 24 protein-protein interaction networks with 939 interactions derived from 109 filtered risk variants in 60 risk genes and 56 proteins. The majority of molecular functions, interactions and pathways are involved in immune responses; several interactions and pathways are related to the metabolic and cardiovascular systems, which could lead to multi-organ complications and dysfunction. CONCLUSIONS: This study highlights the importance of analyzing molecular interactions and pathways to understand the heterogeneous susceptibility of the host immune response to SARS-CoV-2. We propose new insights into pathogenicity analysis of infections by including genetic risk information as essential factors to predict future complications during and after infection. This approach may assist more precise clinical decisions and accurate treatment plans to reduce COVID-19 complications.

A Review and Perspective of Environmental Disinfection Technology Based on Microwave Irradiation.

Purpose of Review: In the context of COVID-19 sweeping the world, the development of microbial disinfection methods in gas, liquid, and solid media has received widespread attention from researchers. As a disinfection technology that can adapt to different environmental media, microwave-assisted disinfection has the advantages of strong permeability, no secondary pollution, etc. The purpose of this review is to put forward new development requirements for future microwave disinfection strategies by summarizing current microwave disinfection methods and effects. From the perspective of the interaction mechanism of microwave and microorganisms, this review provides a development direction for more accurate and microscopic disinfection mechanism research. Recent Findings: Compared to other traditional environmental disinfection techniques, microwave-assisted disinfection means have the advantages of being more destructive, free of secondary contamination, and thorough. Currently, researchers generally agree that the efficiency of microwave disinfection is the result of a combination of thermal and non-thermal effects. However, the performance of microwave disinfection shows the differences in the face of different environmental media as well as different types of microorganisms. Summary: This review highlights the inactivation mechanism of microwave-assisted disinfection techniques used in different scenarios. Suggestions for promoting the efficiency and overcoming the limitations of low energy utilization, complex reactor design, and inaccurate monitoring methods are proposed.

A Review and Perspective of Environmental Disinfection Technology Based on Microwave Irradiation

Purpose of Review In the context of COVID-19 sweeping the world, the development of microbial disinfection methods in gas, liquid, and solid media has received widespread attention from researchers. As a disinfection technology that can adapt to different environmental media, microwave-assisted disinfection has the advantages of strong permeability, no secondary pollution, etc. The purpose of this review is to put forward new development requirements for future microwave disinfection strategies by summarizing current microwave disinfection methods and effects. From the perspective of the interaction mechanism of microwave and microorganisms, this review provides a development direction for more accurate and microscopic disinfection mechanism research. Recent Findings Compared to other traditional environmental disinfection techniques, microwave-assisted disinfection means have the advantages of being more destructive, free of secondary contamination, and thorough. Currently, researchers generally agree that the efficiency of microwave disinfection is the result of a combination of thermal and non-thermal effects. However, the performance of microwave disinfection shows the differences in the face of different environmental media as well as different types of microorganisms. Summary This review highlights the inactivation mechanism of microwave-assisted disinfection techniques used in different scenarios. Suggestions for promoting the efficiency and overcoming the limitations of low energy utilization, complex reactor design, and inaccurate monitoring methods are proposed.

Computational Studies of Auto-Active van der Waals Interaction Molecules on Ultra-Thin Black-Phosphorus Film.

Using the van der Waals density functional theory, we studied the binding peculiarities of favipiravir (FP) and ebselen (EB) molecules on a monolayer of black phosphorene (BP). We systematically examined the interaction characteristics and thermodynamic properties in a vacuum and a continuum, solvent interface for active drug therapy. These results illustrate that the hybrid molecules are enabled functionalized two-dimensional (2D) complex systems with a vigorous thermostability. We demonstrate in this study that these molecules remain flat on the monolayer BP system and phosphorus atoms are intact. It is inferred that the hybrid FP+EB molecules show larger adsorption energy due to the van der Waals forces and planar electrostatic interactions. The changes in Gibbs free energy at different surface charge fluctuations and temperatures imply that the FP and EB are allowed to adsorb from the gas phase onto the 2D film at high temperatures. Thereby, the results unveiled beneficial inhibitor molecules on two dimensional BP nanocarriers, potentially introducing a modern strategy to enhance the development of advanced materials, biotechnology, and nanomedicine.

Rapid Detection of SARS-CoV-2 Spike RBD Protein in Body Fluid: Based on Special Calcium Ion-Mediated Gold Nanoparticles Modified by Bromide Ions.

The receptor-binding domain of the SARS-CoV-2 spike mediates the key to binding the virus to the host receptor, but capturing the molecular signal of this spike RBD remains a formidable challenge. Here, we report a new surface-enhanced Raman spectroscopy (SERS) approach, which used gold nanoparticles prepared by low-speed constant-temperature centrifugation by bromine and calcium ions in two cleaning steps as the enhanced substrate to rapidly and accurately detect spike RBD large protein molecules in body fluids. The detection signal was extremely stable, and the orientation of the spike RBD on the enhanced substrate surface was also determined. This approach was specific in distinguishing different SARS-CoV-2 variants of spike RBD, including Delta, Beta, Gamma, and Omicron. Additionally, the enhanced substrate can identify biologically active or inactive spike RBD. This two-step cleaning enhanced substrate opens up opportunities not only for early diagnostics of SARS-CoV-2 virus but also for developing targeted drugs against viruses.

Online single-cell data integration through projecting heterogeneous datasets into a common cell-embedding space.

Computational tools for integrative analyses of diverse single-cell experiments are facing formidable new challenges including dramatic increases in data scale, sample heterogeneity, and the need to informatively cross-reference new data with foundational datasets. Here, we present SCALEX, a deep-learning method that integrates single-cell data by projecting cells into a batch-invariant, common cell-embedding space in a truly online manner (i.e., without retraining the model). SCALEX substantially outperforms online iNMF and other state-of-the-art non-online integration methods on benchmark single-cell datasets of diverse modalities, (e.g., single-cell RNA sequencing, scRNA-seq, single-cell assay for transposase-accessible chromatin use sequencing, scATAC-seq), especially for datasets with partial overlaps, accurately aligning similar cell populations while retaining true biological differences. We showcase SCALEX's advantages by constructing continuously expandable single-cell atlases for human, mouse, and COVID-19 patients, each assembled from diverse data sources and growing with every new data. The online data integration capacity and superior performance makes SCALEX particularly appropriate for large-scale single-cell applications to build upon previous scientific insights.

Self-supervised contrastive learning for integrative single cell RNA-seq data analysis.

We present a novel self-supervised Contrastive LEArning framework for single-cell ribonucleic acid (RNA)-sequencing (CLEAR) data representation and the downstream analysis. Compared with current methods, CLEAR overcomes the heterogeneity of the experimental data with a specifically designed representation learning task and thus can handle batch effects and dropout events simultaneously. It achieves superior performance on a broad range of fundamental tasks, including clustering, visualization, dropout correction, batch effect removal, and pseudo-time inference. The proposed method successfully identifies and illustrates inflammatory-related mechanisms in a COVID-19 disease study with 43 695 single cells from peripheral blood mononuclear cells.

Unveiling the "Template-Dependent" Inhibition on the Viral Transcription of SARS-CoV-2.

Remdesivir is one nucleotide analogue prodrug capable to terminate RNA synthesis in SARS-CoV-2 RNA-dependent RNA polymerase (RdRp) by two distinct mechanisms. Although the "delayed chain termination" mechanism has been extensively investigated, the "template-dependent" inhibitory mechanism remains elusive. In this study, we have demonstrated that remdesivir embedded in the template strand seldom directly disrupted the complementary NTP incorporation at the active site. Instead, the translocation of remdesivir from the +2 to the +1 site was hindered due to the steric clash with V557. Moreover, we have elucidated the molecular mechanism characterizing the drug resistance upon V557L mutation. Overall, our studies have provided valuable insight into the "template-dependent" inhibitory mechanism exerted by remdesivir on SARS-CoV-2 RdRp and paved venues for an alternative antiviral strategy for the COVID-19 pandemic. As the "template-dependent" inhibition occurs across diverse viral RdRps, our findings may also shed light on a common acting mechanism of inhibitors.

Facing the COVID-19 Pandemic and Developing a Sustainable Entrepreneurial Ecosystem: The Theory and Practice of Innovation and Entrepreneurship Policies in China.

Under the influence of the COVID-19 pandemic, the vitality of start-ups has been continuously suppressed, their income has been decreasing, and overall economic development has gradually declined. At this time, the government, as an effective subject, should present its due responsibility to make entrepreneurship more sustainable and form a sustainable entrepreneurship ecology that can cope with risks. This paper takes the innovation policy theory and practice from research regarding China's COVID-19 cases. One example is exploring the formation of the government's innovation entrepreneurship policy and its mechanism within industrial cluster theory. Furthermore, we explore the analysis of the practice situation and try to solve the obstacles in the process of sustainable development through the regional entrepreneurial ecosystem and platform system building. We hope to find an acceptable way for the sustainable development of entrepreneurial ecological theory research and provide effective research and practical support.

Discussion about the prevention of Coronavirus disease 19 based on the thought of treating non-disease and the theory of constitution of traditional Chinese medicine

Objective To summarize and analyze the susceptible constitution of coronavirus disease 19(COVID-19), put forward the diet prescription of traditional Chinese medicine for prevention based on the thought of treating non-disease and the theory of constitution, and to provide reference for COVID-19's prevention. Methods The relevant literatures in the past 5 years were reviewed, and the current situation of COVID-19's clinical manifestations, etiology, pathogenesis, disease location, disease transmission, susceptible physique, physique differentiation and prevention were summarized. Results The symptoms of COVID-19 were similar in the initial stage of infection, which was caused by damp pathogen. People with different physique were different in susceptibility to COVID-19 and the development and outcome of syndrome type after infection. The susceptible constitution was phlegm-dampness, dampness-heat, Qi deficiency and yang deficiency, blood stasis and Qi depression. Invigorating vital Qi, dispelling dampness, and nourishing lung are the basic treatment principles. Combined with physical differences and dialectical prevention, tonifying, invigorating spleen and stomach, and aromatic dampness-transforming drugs are major drugs, which are supplemented by drugs for promoting lung-Qi. Conclusion The formulation of preventive prescriptions of traditional Chinese medicine according to different physique can provide some guidance for the clinical prevention of COVID-19.

Discovering trends and hotspots of biosafety and biosecurity research via machine learning.

Coronavirus disease 2019 (COVID-19) has infected hundreds of millions of people and killed millions of them. As an RNA virus, COVID-19 is more susceptible to variation than other viruses. Many problems involved in this epidemic have made biosafety and biosecurity (hereafter collectively referred to as 'biosafety') a popular and timely topic globally. Biosafety research covers a broad and diverse range of topics, and it is important to quickly identify hotspots and trends in biosafety research through big data analysis. However, the data-driven literature on biosafety research discovery is quite scant. We developed a novel topic model based on latent Dirichlet allocation, affinity propagation clustering and the PageRank algorithm (LDAPR) to extract knowledge from biosafety research publications from 2011 to 2020. Then, we conducted hotspot and trend analysis with LDAPR and carried out further studies, including annual hot topic extraction, a 10-year keyword evolution trend analysis, topic map construction, hot region discovery and fine-grained correlation analysis of interdisciplinary research topic trends. These analyses revealed valuable information that can guide epidemic prevention work: (1) the research enthusiasm over a certain infectious disease not only is related to its epidemic characteristics but also is affected by the progress of research on other diseases, and (2) infectious diseases are not only strongly related to their corresponding microorganisms but also potentially related to other specific microorganisms. The detailed experimental results and our code are available at https://github.com/KEAML-JLU/Biosafety-analysis.

2'- and 3'-Ribose Modifications of Nucleotide Analogues Establish the Structural Basis to Inhibit the Viral Replication of SARS-CoV-2.

Inhibition of RNA-dependent RNA polymerase (RdRp) by nucleotide analogues with ribose modification provides a promising antiviral strategy for the treatment of SARS-CoV-2. Previous works have shown that remdesivir carrying 1'-substitution can act as a "delayed chain terminator", while nucleotide analogues with 2'-methyl group substitution could immediately terminate the chain extension. However, how the inhibition can be established by the 3'-ribose modification as well as other 2'-ribose modifications is not fully understood. Herein, we have evaluated the potential of several adenosine analogues with 2'- and/or 3'-modifications as obligate chain terminators by comprehensive structural analysis based on extensive molecular dynamics simulations. Our results suggest that 2'-modification couples with the protein environment to affect the structural stability, while 3'-hydrogen substitution inherently exerts "immediate termination" without compromising the structural stability in the active site. Our study provides an alternative promising modification scheme to orientate the further optimization of obligate terminators for SARS-CoV-2 RdRp.

Rapid detection of viruses: Based on silver nanoparticles modified with bromine ions and acetonitrile.

Nearly 200 million people have been diagnosed with COVID-19 since the outbreak in 2019, and this disease has claimed more than 5 million lives worldwide. Currently, researchers are focusing on vaccine development and the search for an effective strategy to control the infection source. This work designed a detection platform based on Surface-Enhanced Raman Spectroscopy (SERS) by introducing acetonitrile and calcium ions into the silver nanoparticle reinforced substrate system to realize the rapid detection of novel coronavirus. Acetonitrile may amplify the calcium-induced hot spots of silver nanoparticles and significantly enhanced the stability of silver nanoparticles. It also elicited highly sensitive SERS signals of the virus. This approach allowed us to capture the characteristic SERS signals of SARS-CoV-2, Human Adenovirus 3, and H1N1 influenza virus molecules at a concentration of 100 copies/test (PFU/test) with upstanding reproduction and signal-to-noise ratio. Machine learning recognition technology was employed to qualitatively distinguish the three virus molecules with 1000 groups of spectra of each virus. Acetonitrile is a potent internal marker in regulating the signal intensity of virus molecules in saliva and serum. Thus, we used the SERS peak intensity to quantify the virus content in saliva and serum. The results demonstrated a satisfactory linear relationship between peak intensity and protein concentration. Collectively, this rapid detection method has a broad application prospect in clinical diagnosis of viruses, management of emergent viral infectious diseases, and exploration of the interaction between viruses and host cells.

A novel enhanced substrate for label-free detection of SARS-CoV-2 based on surface-enhanced Raman scattering.

Accurate and sensitive detection of SARS-CoV-2 is an effective strategy for preventing the COVID-19 pandemic in the current absence of specific drug therapy. This study presents a novel enhanced substrate for label-free detection of respiratory viruses using surface-enhanced Raman Scattering. Sodium borohydride reduces silver ions to clustered silver nanoparticles to eliminate the disorganized peak signal of the traditional citrate reducing agent. Meanwhile, the study obtained the fingerprints and concentration-dependent curves of many respiratory viruses, including SARS-CoV-2, human adenovirus type 7, and H1N1 virus, with good linear relationships. The three viruses were also identified in serum and saliva within two minutes, combined with linear discriminant diagnostic analysis. Therefore, establishing this enhanced substrate is greatly valuable for the global response to the COVID-19 pandemic.

Longevity of vaccine protection: Immunological mechanism, assessment methods, and improving strategy

Vaccination represents one of the most important achievements in modern medicine. During the era of COVID‐19 pandemic, the successful vaccination for SARS‐COV‐2 is the major hope to bring the society back to normal. However, although vaccines, such as for smallpox and poliomyelitis, can trigger life‐long protection in individuals and help to generate the herd immunity resulting in the eradication of pathogens, other vaccines, with seasonal influenza vaccine as a case in point, are unable to induce sustained immunity so that repeated vaccination is required. As most vaccines were developed empirically, the immunological mechanism underlying the longevity of vaccine‐induced protection remains only partially understood. In this review, we first describe vaccine‐induced humoral immune response in which long‐lived plasma cells and memory B cells are produced. We then summarise methods using immunological correlates of protection to assess the longevity of vaccine efficacy and provide the evidence and knowledge for the duration of protection by current vaccines. Last, we discuss rationale and strategies to improve the duration of vaccine protection by targeting vaccine immunogenicity, antibody affinity, avidity and prime‐boost scheme.

Impact of computational approaches in the fight against COVID-19: an AI guided review of 17 000 studies.

SARS-CoV-2 caused the first severe pandemic of the digital era. Computational approaches have been ubiquitously used in an attempt to timely and effectively cope with the resulting global health crisis. In order to extensively assess such contribution, we collected, categorized and prioritized over 17 000 COVID-19-related research articles including both peer-reviewed and preprint publications that make a relevant use of computational approaches. Using machine learning methods, we identified six broad application areas i.e. Molecular Pharmacology and Biomarkers, Molecular Virology, Epidemiology, Healthcare, Clinical Medicine and Clinical Imaging. We then used our prioritization model as a guidance through an extensive, systematic review of the most relevant studies. We believe that the remarkable contribution provided by computational applications during the ongoing pandemic motivates additional efforts toward their further development and adoption, with the aim of enhancing preparedness and critical response for current and future emergencies.

Association Between Androgen Deprivation Therapy and Mortality Among Patients With Prostate Cancer and COVID-19.

Importance: Androgen deprivation therapy (ADT) has been theorized to decrease the severity of SARS-CoV-2 infection in patients with prostate cancer owing to a potential decrease in the tissue-based expression of the SARS-CoV-2 coreceptor transmembrane protease, serine 2 (TMPRSS2). Objective: To examine whether ADT is associated with a decreased rate of 30-day mortality from SARS-CoV-2 infection among patients with prostate cancer. Design, Setting, and Participants: This cohort study analyzed patient data recorded in the COVID-19 and Cancer Consortium registry between March 17, 2020, and February 11, 2021. The consortium maintains a centralized multi-institution registry of patients with a current or past diagnosis of cancer who developed COVID-19. Data were collected and managed using REDCap software hosted at Vanderbilt University Medical Center in Nashville, Tennessee. Initially, 1228 patients aged 18 years or older with prostate cancer listed as their primary malignant neoplasm were included; 122 patients with a second malignant neoplasm, insufficient follow-up, or low-quality data were excluded. Propensity matching was performed using the nearest-neighbor method with a 1:3 ratio of treated units to control units, adjusted for age, body mass index, race and ethnicity, Eastern Cooperative Oncology Group performance status score, smoking status, comorbidities (cardiovascular, pulmonary, kidney disease, and diabetes), cancer status, baseline steroid use, COVID-19 treatment, and presence of metastatic disease. Exposures: Androgen deprivation therapy use was defined as prior bilateral orchiectomy or pharmacologic ADT administered within the prior 3 months of presentation with COVID-19. Main Outcomes and Measures: The primary outcome was the rate of all-cause 30-day mortality after COVID-19 diagnosis for patients receiving ADT compared with patients not receiving ADT after propensity matching. Results: After exclusions, 1106 patients with prostate cancer (before propensity score matching: median age, 73 years [IQR, 65-79 years]; 561 (51%) self-identified as non-Hispanic White) were included for analysis. Of these patients, 477 were included for propensity score matching (169 who received ADT and 308 who did not receive ADT). After propensity matching, there was no significant difference in the primary end point of the rate of all-cause 30-day mortality (OR, 0.77; 95% CI, 0.42-1.42). Conclusions and Relevance: Findings from this cohort study suggest that ADT use was not associated with decreased mortality from SARS-CoV-2 infection. However, large ongoing clinical trials will provide further evidence on the role of ADT or other androgen-targeted therapies in reducing COVID-19 infection severity.