Early warning of emerging infectious diseases based on multimodal data

The coronavirus disease 2019 (COVID-19) pandemic has dramatically increased the awareness of emerging infectious diseases. The advancement of multiomics analysis technology has resulted in the development of several databases containing virus information. Several scientists have integrated existing data on viruses to construct phylogenetic trees and predict virus mutation and transmission in different ways, providing prospective technical support for epidemic prevention and control. This review summarized the databases of known emerging infectious viruses and techniques focusing on virus variant forecasting and early warning. It focuses on the multi-dimensional information integration and database construction of emerging infectious viruses, virus mutation spectrum construction and variant forecast model, analysis of the affinity between mutation antigen and the receptor, propagation model of virus dynamic evolution, and monitoring and early warning for variants. As people have suffered from COVID-19 and repeated flu outbreaks, we focused on the research results of severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) and influenza viruses. This review comprehensively viewed the latest virus research and provided a reference for future virus prevention and control research.

Safety and immunogenicity of heterologous boosting with orally aerosolised or intramuscular Ad5-nCoV vaccine and homologous boosting with inactivated vaccines (BBIBP-CorV or CoronaVac) in children and adolescents: a randomised, open-label, parallel-controlled, non-inferiority, single-centre study.

BACKGROUND: Heterologous booster immunisation with orally administered aerosolised Ad5-nCoV vaccine (AAd5) has been shown to be safe and highly immunogenic in adults. Here, we aimed to assess the safety and immunogenicity of heterologous booster immunisation with orally administered AAd5 in children and adolescents aged 6-17 years who had received two doses of inactivated vaccine (BBIBP-CorV or CoronaVac). METHODS: We did a randomised, open-label, parallel-controlled, non-inferiority study to assess the safety and immunogenicity of heterologous booster immunisation with AAd5 (0·1 mL) or intramuscular Ad5-nCoV vaccine (IMAd5; 0·3 mL) and homologous booster immunisation with inactivated vaccine (BBIBP-CorV or CoronaVac; 0·5 mL) in children (aged 6-12 years) and adolescents (aged 13-17 years) who had received two doses of inactivated vaccine at least 3 months earlier in Hunan, China. Children and adolescents who were previously immunised with two-dose BBIBP-CorV or CoronaVac were recruited for eligibility screening at least 3 months after the second dose. A stratified block method was used for randomisation, and participants were stratified by age and randomly assigned (3:1:1) to receive AAd5, IMAd5, or inactivated vaccine. The study staff and participants were not masked to treatment allocation. Laboratory and statistical staff were masked during the study. In this interim analysis, adverse events within 14 days and geometric mean titre (GMT) of serum neutralising antibodies on day 28 after the booster vaccination, based on the per-protocol population, were used as the primary outcomes. The analysis of non-inferiority was based on comparison using a one-sided 97·5% CI with a non-inferiority margin of 0·67. This study was registered at ClinicalTrials.gov, NCT05330871, and is ongoing. FINDINGS: Between April 17 and May 28, 2022, 436 participants were screened and 360 were enrolled: 220 received AAd5, 70 received IMAd5, and 70 received inactivated vaccine. Within 14 days after booster vaccination, vaccine-related adverse reactions were reported: 35 adverse events (in 13 [12%] of 110 children and 22 [20%] of 110 adolescents) in 220 individuals in the AAd5 group, 35 (in 18 [51%] of 35 children and 17 [49%] of 35 adolescents) in 70 individuals in the IMAd5 group, and 13 (in five [14%] of 35 children and eight [23%] of 35 adolescents) in 70 individuals in the inactivated vaccine group. Solicited adverse reactions were also reported: 34 (13 [12%] of 110 children and 21 [10%] of 110 adolescents) in 220 individuals in the AAd5 group, 34 (17 [49%] of 35 children and 17 [49%] of 35 adolescents) in 70 individuals in the IMAd5 group, and 12 (five [14%] of 35 children and seven [20%] of 35 adolescents) in 70 individuals in the inactivated vaccine group. The GMTs of neutralising antibodies against ancestral SARS-CoV-2 Wuhan-Hu-1 (Pango lineage B) in the AAd5 group were significantly higher than the GMTs in the inactivated vaccine group (adjusted GMT ratio 10·2 [95% CI 8·0-13·1]; p<0·0001). INTERPRETATION: Our study shows that a heterologous booster with AAd5 is safe and highly immunogenic against ancestral SARS-CoV-2 Wuhan-Hu-1 in children and adolescents. FUNDING: National Key R&D Program of China.

Using Machine Learning Methods in Identifying Genes Associated with COVID-19 in Cardiomyocytes and Cardiac Vascular Endothelial Cells.

Corona Virus Disease 2019 (COVID-19) not only causes respiratory system damage, but also imposes strain on the cardiovascular system. Vascular endothelial cells and cardiomyocytes play an important role in cardiac function. The aberrant expression of genes in vascular endothelial cells and cardiomyocytes can lead to cardiovascular diseases. In this study, we sought to explain the influence of respiratory syndrome coronavirus 2 (SARS-CoV-2) infection on the gene expression levels of vascular endothelial cells and cardiomyocytes. We designed an advanced machine learning-based workflow to analyze the gene expression profile data of vascular endothelial cells and cardiomyocytes from patients with COVID-19 and healthy controls. An incremental feature selection method with a decision tree was used in building efficient classifiers and summarizing quantitative classification genes and rules. Some key genes, such as MALAT1, MT-CO1, and CD36, were extracted, which exert important effects on cardiac function, from the gene expression matrix of 104,182 cardiomyocytes, including 12,007 cells from patients with COVID-19 and 92,175 cells from healthy controls, and 22,438 vascular endothelial cells, including 10,812 cells from patients with COVID-19 and 11,626 cells from healthy controls. The findings reported in this study may provide insights into the effect of COVID-19 on cardiac cells and further explain the pathogenesis of COVID-19, and they may facilitate the identification of potential therapeutic targets.

Effects of mild/asymptomatic COVID-19 on semen parameters and sex-related hormone levels in men: a systematic review and meta-analysis.

Coronavirus disease 2019 (COVID-19) has yet to be proven to alter male reproductive function, particularly in the majority of mild/asymptomatic patients. The purpose of this study was to explore whether mild/asymptomatic COVID-19 affects semen quality and sex-related hormone levels. To find suitable comparative studies, a systematic review and meta-analysis was done up to January 22, 2022, by using multiple databases (Web of Science, PubMed, and Embase). Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were used to identify and choose the studies. Meta-analysis was used to examine the semen parameters and sex-related hormones of mild/asymptomatic COVID-19 patients before and after infection. The effects of semen collection time, fever, and intensity of verification on semen following infection were also investigated. A total of 13 studies (n = 770) were included in the analysis, including three case-control studies, six pre-post studies, and four single-arm studies. A meta-analysis of five pre-post studies showed that after infection with COVID-19, sperm concentration (I2 = 0; P = 0.003), total sperm count (I2 = 46.3%; P = 0.043), progressive motility (I2 = 50.0%; P < 0.001), total sperm motility (I2 = 76.1%; P = 0.047), and normal sperm morphology (I2 = 0; P = 0.001) decreased. Simultaneously, a systematic review of 13 studies found a significant relationship between semen collection time after infection, inflammation severity, and semen parameter values, with fever having only bearing on semen concentration. Furthermore, there was no significant difference in sex-related hormone levels before and after infection in mild/asymptomatic patients. Mild/asymptomatic COVID-19 infection had a significant effect on semen quality in the short term. It is recommended to avoid initiating a pregnancy during this period of time.

Identification of genes related to immune enhancement caused by heterologous ChAdOx1–BNT162b2 vaccines in lymphocytes at single-cell resolution with machine learning methods

The widely used ChAdOx1 nCoV-19 (ChAd) vector and BNT162b2 (BNT) mRNA vaccines have been shown to induce robust immune responses. Recent studies demonstrated that the immune responses of people who received one dose of ChAdOx1 and one dose of BNT were better than those of people who received vaccines with two homologous ChAdOx1 or two BNT doses. However, how heterologous vaccines function has not been extensively investigated. In this study, single-cell RNA sequencing data from three classes of samples: volunteers vaccinated with heterologous ChAdOx1–BNT and volunteers vaccinated with homologous ChAd–ChAd and BNT–BNT vaccinations after 7 days were divided into three types of immune cells (3654 B, 8212 CD4+ T, and 5608 CD8+ T cells). To identify differences in gene expression in various cell types induced by vaccines administered through different vaccination strategies, multiple advanced feature selection methods (max-relevance and min-redundancy, Monte Carlo feature selection, least absolute shrinkage and selection operator, light gradient boosting machine, and permutation feature importance) and classification algorithms (decision tree and random forest) were integrated into a computational framework. Feature selection methods were in charge of analyzing the importance of gene features, yielding multiple gene lists. These lists were fed into incremental feature selection, incorporating decision tree and random forest, to extract essential genes, classification rules and build efficient classifiers. Highly ranked genes include PLCG2, whose differential expression is important to the B cell immune pathway and is positively correlated with immune cells, such as CD8+ T cells, and B2M, which is associated with thymic T cell differentiation. This study gave an important contribution to the mechanistic explanation of results showing the stronger immune response of a heterologous ChAdOx1–BNT vaccination schedule than two doses of either BNT or ChAdOx1, offering a theoretical foundation for vaccine modification.

Identification of dynamic gene expression profiles during sequential vaccination with ChAdOx1/BNT162b2 using machine learning methods.

To date, COVID-19 remains a serious global public health problem. Vaccination against SARS-CoV-2 has been adopted by many countries as an effective coping strategy. The strength of the body's immune response in the face of viral infection correlates with the number of vaccinations and the duration of vaccination. In this study, we aimed to identify specific genes that may trigger and control the immune response to COVID-19 under different vaccination scenarios. A machine learning-based approach was designed to analyze the blood transcriptomes of 161 individuals who were classified into six groups according to the dose and timing of inoculations, including I-D0, I-D2-4, I-D7 (day 0, days 2-4, and day 7 after the first dose of ChAdOx1, respectively) and II-D0, II-D1-4, II-D7-10 (day 0, days 1-4, and days 7-10 after the second dose of BNT162b2, respectively). Each sample was represented by the expression levels of 26,364 genes. The first dose was ChAdOx1, whereas the second dose was mainly BNT162b2 (Only four individuals received a second dose of ChAdOx1). The groups were deemed as labels and genes were considered as features. Several machine learning algorithms were employed to analyze such classification problem. In detail, five feature ranking algorithms (Lasso, LightGBM, MCFS, mRMR, and PFI) were first applied to evaluate the importance of each gene feature, resulting in five feature lists. Then, the lists were put into incremental feature selection method with four classification algorithms to extract essential genes, classification rules and build optimal classifiers. The essential genes, namely, NRF2, RPRD1B, NEU3, SMC5, and TPX2, have been previously associated with immune response. This study also summarized expression rules that describe different vaccination scenarios to help determine the molecular mechanism of vaccine-induced antiviral immunity.

Identification of genes related to immune enhancement caused by heterologous ChAdOx1-BNT162b2 vaccines in lymphocytes at single-cell resolution with machine learning methods.

The widely used ChAdOx1 nCoV-19 (ChAd) vector and BNT162b2 (BNT) mRNA vaccines have been shown to induce robust immune responses. Recent studies demonstrated that the immune responses of people who received one dose of ChAdOx1 and one dose of BNT were better than those of people who received vaccines with two homologous ChAdOx1 or two BNT doses. However, how heterologous vaccines function has not been extensively investigated. In this study, single-cell RNA sequencing data from three classes of samples: volunteers vaccinated with heterologous ChAdOx1-BNT and volunteers vaccinated with homologous ChAd-ChAd and BNT-BNT vaccinations after 7 days were divided into three types of immune cells (3654 B, 8212 CD4+ T, and 5608 CD8+ T cells). To identify differences in gene expression in various cell types induced by vaccines administered through different vaccination strategies, multiple advanced feature selection methods (max-relevance and min-redundancy, Monte Carlo feature selection, least absolute shrinkage and selection operator, light gradient boosting machine, and permutation feature importance) and classification algorithms (decision tree and random forest) were integrated into a computational framework. Feature selection methods were in charge of analyzing the importance of gene features, yielding multiple gene lists. These lists were fed into incremental feature selection, incorporating decision tree and random forest, to extract essential genes, classification rules and build efficient classifiers. Highly ranked genes include PLCG2, whose differential expression is important to the B cell immune pathway and is positively correlated with immune cells, such as CD8+ T cells, and B2M, which is associated with thymic T cell differentiation. This study gave an important contribution to the mechanistic explanation of results showing the stronger immune response of a heterologous ChAdOx1-BNT vaccination schedule than two doses of either BNT or ChAdOx1, offering a theoretical foundation for vaccine modification.

Immune responses of different COVID-19 vaccination strategies by analyzing single-cell RNA sequencing data from multiple tissues using machine learning methods.

Multiple types of COVID-19 vaccines have been shown to be highly effective in preventing SARS-CoV-2 infection and in reducing post-infection symptoms. Almost all of these vaccines induce systemic immune responses, but differences in immune responses induced by different vaccination regimens are evident. This study aimed to reveal the differences in immune gene expression levels of different target cells under different vaccine strategies after SARS-CoV-2 infection in hamsters. A machine learning based process was designed to analyze single-cell transcriptomic data of different cell types from the blood, lung, and nasal mucosa of hamsters infected with SARS-CoV-2, including B and T cells from the blood and nasal cavity, macrophages from the lung and nasal cavity, alveolar epithelial and lung endothelial cells. The cohort was divided into five groups: non-vaccinated (control), 2*adenovirus (two doses of adenovirus vaccine), 2*attenuated (two doses of attenuated virus vaccine), 2*mRNA (two doses of mRNA vaccine), and mRNA/attenuated (primed by mRNA vaccine, boosted by attenuated vaccine). All genes were ranked using five signature ranking methods (LASSO, LightGBM, Monte Carlo feature selection, mRMR, and permutation feature importance). Some key genes that contributed to the analysis of immune changes, such as RPS23, DDX5, PFN1 in immune cells, and IRF9 and MX1 in tissue cells, were screened. Afterward, the five feature sorting lists were fed into the feature incremental selection framework, which contained two classification algorithms (decision tree [DT] and random forest [RF]), to construct optimal classifiers and generate quantitative rules. Results showed that random forest classifiers could provide relative higher performance than decision tree classifiers, whereas the DT classifiers provided quantitative rules that indicated special gene expression levels under different vaccine strategies. These findings may help us to develop better protective vaccination programs and new vaccines.

Identification of Genes Associated with the Impairment of Olfactory and Gustatory Functions in COVID-19 via Machine-Learning Methods.

The coronavirus disease 2019 (COVID-19), as a severe respiratory disease, affects many parts of the body, and approximately 20-85% of patients exhibit functional impairment of the senses of smell and taste, some of whom even experience the permanent loss of these senses. These symptoms are not life-threatening but severely affect patients' quality of life and increase the risk of depression and anxiety. The pathological mechanisms of these symptoms have not been fully identified. In the current study, we aimed to identify the important biomarkers at the expression level associated with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection-mediated loss of taste or olfactory ability, and we have suggested the potential pathogenetic mechanisms of COVID-19 complications. We designed a machine-learning-based approach to analyze the transcriptome of 577 COVID-19 patient samples, including 84 COVID-19 samples with a decreased ability to taste or smell and 493 COVID-19 samples without impairment. Each sample was represented by 58,929 gene expression levels. The features were analyzed and sorted by three feature selection methods (least absolute shrinkage and selection operator, light gradient boosting machine, and Monte Carlo feature selection). The optimal feature sets were obtained through incremental feature selection using two classification algorithms: decision tree (DT) and random forest (RF). The top genes identified by these multiple methods (H3-5, NUDT5, and AOC1) are involved in olfactory and gustatory impairments. Meanwhile, a high-performance RF classifier was developed in this study, and three sets of quantitative rules that describe the impairment of olfactory and gustatory functions were obtained based on the optimal DT classifiers. In summary, this study provides a new computation analysis and suggests the latent biomarkers (genes and rules) for predicting olfactory and gustatory impairment caused by COVID-19 complications.

Safety and immunogenicity of a protein subunit COVID-19 vaccine (ZF2001) in healthy children and adolescents aged 3-17 years in China: a randomised, double-blind, placebo-controlled, phase 1 trial and an open-label, non-randomised, non-inferiority, phase 2 trial.

BACKGROUND: ZF2001 is a recombinant protein subunit vaccine against SARS-CoV-2 that has been approved for use in China, Colombia, Indonesia, and Uzbekistan in adults aged 18 years or older, but not yet in children and adolescents younger than 18 years. We aimed to evaluate the safety and immunogenicity of ZF2001 in children and adolescents aged 3-17 years in China. METHODS: The randomised, double-blind, placebo-controlled, phase 1 trial and the open-label, non-randomised, non-inferiority, phase 2 trial were done at the Xiangtan Center for Disease Control and Prevention (Hunan Province, China). Healthy children and adolescents aged 3-17 years, without a history of SARS-CoV-2 vaccination, without a history of COVID-19, without COVID-19 at the time of the study, and without contact with patients with confirmed or suspected COVID-19 were included in the phase 1 and phase 2 trials. In the phase 1 trial, participants were divided into three groups according to age (3-5 years, 6-11 years, and 12-17 years). Each group was randomly assigned (4:1), using block randomisation with five blocks, each with a block size of five, to receive three 25 µg doses of the vaccine, ZF2001, or placebo intramuscularly in the arm 30 days apart. The participants and investigators were masked to treatment allocation. In the phase 2 trial, participants received three 25 µg doses of ZF2001 30 days apart and remained stratified by age group. For phase 1, the primary endpoint was safety and the secondary endpoint was immunogenicity (humoral immune response on day 30 after the third vaccine dose: geometric mean titre [GMT] of prototype SARS-CoV-2 neutralising antibodies and seroconversion rate, and geometric mean concentration [GMC] of prototype SARS-CoV-2 receptor-binding domain [RBD]-binding IgG antibodies and seroconversion rate). For phase 2, the primary endpoint was the GMT of SARS-CoV-2 neutralising antibodies with seroconversion rate on day 14 after the third vaccine dose, and the secondary endpoints included the GMT of RBD-binding antibodies and seroconversion rate on day 14 after the third vaccine dose, the GMT of neutralising antibodies against the omicron BA.2 subvariant and seroconversion rate on day 14 after the third vaccine dose, and safety. Safety was analysed in participants who received at least one dose of the vaccine or placebo. Immunogenicity was analysed in the full-analysis set (ie, participants who received at least one dose and had antibody results) by intention to treat and in the per-protocol set (ie, participants who completed the whole vaccination course and had antibody results). Non-inferiority in the phase 2 trial (neutralising antibody titre of participants from this trial aged 3-17 years vs that of participants aged 18-59 years from a separate phase 3 trial) for clinical outcome assessment was based on the geometric mean ratio (GMR) and was considered met if the lower bound of the 95% CI for the GMR was 0·67 or greater. These trials are registered with ClinicalTrials.gov, NCT04961359 (phase 1) and NCT05109598 (phase 2). FINDINGS: Between July 10 and Sept 4, 2021, 75 children and adolescents were randomly assigned to receive ZF2001 (n=60) or placebo (n=15) in the phase 1 trial and were included in safety and immunogenicity analyses. Between Nov 5, 2021, and Feb 14, 2022, 400 participants (130 aged 3-7 years, 210 aged 6-11 years, and 60 aged 12-17 years) were included in the phase 2 trial and were included in the safety analysis; six participants were excluded from the immunogenicity analyses. 25 (42%) of 60 participants in the ZF2001 group and seven (47%) of 15 participants in the placebo group in phase 1, and 179 (45%) of 400 participants in phase 2, had adverse events within 30 days after the third vaccination, without a significant difference between groups in phase 1. Most adverse events were grade 1 or 2 (73 [97%] of 75 in the phase 1 trial, and 391 [98%] of 400 in the phase 2 trial). One participant in the phase 1 trial and three in the phase 2 trial who received ZF2001 had serious adverse events. One serious adverse event (acute allergic dermatitis) in the phase 2 trial was possibly related to the vaccine. In the phase 1 trial, on day 30 after the third dose, in the ZF2001 group, seroconversion of neutralising antibodies against SARS-CoV-2 was observed in 56 (93%; 95% CI 84-98) of 60 participants, with a GMT of 176·5 (95% CI 118·6-262·8), and seroconversion of RBD-binding antibodies was observed in all 60 (100%; 95% CI 94-100) participants, with a GMC of 47·7 IU/mL (95% CI 40·1-56·6). In the phase 2 trial, on day 14 after the third dose, seroconversion of neutralising antibodies against SARS-CoV-2 was seen in 392 (99%; 95% CI 98-100) participants, with a GMT of 245·4 (95% CI 220·0-273·7), and seroconversion of RBD-binding antibodies was observed in all 394 (100%; 99-100) participants, with a GMT of 8021 (7366-8734). On day 14 after the third dose, seroconversion of neutralising antibodies against the omicron subvariant BA.2 was observed in 375 (95%; 95% CI 93-97) of 394 participants, with a GMT of 42·9 (95% CI 37·9-48·5). For the non-inferiority comparison of participants aged 3-17 years with those aged 18-59 years for SARS-CoV-2 neutralising antibodies, the adjusted GMR was 8·6 (95% CI 7·0-10·4), with the lower bound of the GMR greater than 0·67. INTERPRETATION: ZF2001 is safe, well tolerated, and immunogenic in children and adolescents aged 3-17 years. Vaccine-elicited sera can neutralise the omicron BA.2 subvariant, but with reduced activity. The results support further studies of ZF2001 in children and adolescents. FUNDING: Anhui Zhifei Longcom Biopharmaceutical and the Excellent Young Scientist Program from National Natural Science Foundation of China. TRANSLATION: For the Chinese translation of the abstract see Supplementary Materials section.

Predicting ozone formation in petrochemical industrialized Lanzhou city by interpretable ensemble machine learning.

Ground-level ozone (O3) formation depends on meteorology, precursor emissions, and atmospheric chemistry. Understanding the key drivers behind the O3 formation and developing an accurate and efficient method for timely assessing the O3-VOCs-NOx relationships applicable in different O3 pollution events are essential. Here, we developed a novel machine learning ensemble model coupled with a Shapley additive explanation algorithm to predict the O3 formation regime and derive O3 formation sensitivity curves. The algorithm was tested for O3 events during the COVID-19 lockdown, a sandstorm event, and a heavy O3 pollution episode (maximum hourly O3 concentration >200 µg/m3) from 2019 to 2021. We show that increasing O3 concentrations during the COVID-19 lockdown and the heavy O3 pollution event were mainly caused by the photochemistry subject to local air quality and meteorological conditions. Influenced by the sandstorm weather, low O3 levels were mainly attributable to weak sunlight and low precursor levels. O3 formation sensitivity curves demonstrate that O3 formation in the study area was in a VOCs-sensitive regime. The VOCs-specific O3 sensitivity curves can also help make hybrid and timely strategies for O3 abatement. The results demonstrate that machine learning driven by observational data has the potential to be a very useful tool in predicting and interpreting O3 formation.

Keep it real: Assessing destination image congruence and its impact on tourist experience evaluations

Inaccurate promotional information about tourist destinations may result in tourists' negative evaluations. This study proposes a new approach to measure the congruence between projected and received images of a destination's attractions. Based on online textual data, this study investigates how image congruence influences tourists' evaluations of their destination experiences. Using promotional messages and reviews of attractions in Hainan, China obtained from a leading Chinese online travel agency (Ctrip) and a three-way fixed-effects regression model, this study demonstrates that image congruence positively affects tourists' appraisal of their destination experiences. External crises (e.g., the COVID-19 pandemic), the readability of promotional messages, and tourists' expertise moderate this relationship, reducing the positive impact of image congruence on tourist experience evaluation. This study bridges theoretical and empirical gaps in destination image (in)congruence research, informing tourism marketing agencies of effective promotional strategies in different contexts.

Nucleolin mediates SARS-CoV-2 replication and viral-induced apoptosis of host cells.

Host-oriented antiviral therapeutics are promising treatment options to combat COVID-19 and its emerging variants. However, relatively little is known about the cellular proteins hijacked by SARS-CoV-2 for its replication. Here we show that SARS-CoV-2 induces expression and cytoplasmic translocation of the nucleolar protein, nucleolin (NCL). NCL interacts with SARS-CoV-2 viral proteins and co-localizes with N-protein in the nucleolus and in stress granules. Knockdown of NCL decreases the stress granule component G3BP1, viral replication and improved survival of infected host cells. NCL mediates viral-induced apoptosis and stress response via p53. SARS-CoV-2 increases NCL expression and nucleolar size and number in lungs of infected hamsters. Inhibition of NCL with the aptamer AS-1411 decreases viral replication and apoptosis of infected cells. These results suggest nucleolin as a suitable target for anti-COVID therapies.

Identification of COVID-19-Specific Immune Markers Using a Machine Learning Method.

Notably, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has a tight relationship with the immune system. Human resistance to COVID-19 infection comprises two stages. The first stage is immune defense, while the second stage is extensive inflammation. This process is further divided into innate and adaptive immunity during the immune defense phase. These two stages involve various immune cells, including CD4+ T cells, CD8+ T cells, monocytes, dendritic cells, B cells, and natural killer cells. Various immune cells are involved and make up the complex and unique immune system response to COVID-19, providing characteristics that set it apart from other respiratory infectious diseases. In the present study, we identified cell markers for differentiating COVID-19 from common inflammatory responses, non-COVID-19 severe respiratory diseases, and healthy populations based on single-cell profiling of the gene expression of six immune cell types by using Boruta and mRMR feature selection methods. Some features such as IFI44L in B cells, S100A8 in monocytes, and NCR2 in natural killer cells are involved in the innate immune response of COVID-19. Other features such as ZFP36L2 in CD4+ T cells can regulate the inflammatory process of COVID-19. Subsequently, the IFS method was used to determine the best feature subsets and classifiers in the six immune cell types for two classification algorithms. Furthermore, we established the quantitative rules used to distinguish the disease status. The results of this study can provide theoretical support for a more in-depth investigation of COVID-19 pathogenesis and intervention strategies.

Identification of COVID-19 severity biomarkers based on feature selection on single-cell RNA-Seq data of CD8+ T cells.

The global outbreak of the COVID-19 epidemic has become a major public health problem. COVID-19 virus infection triggers a complex immune response. CD8+ T cells, in particular, play an essential role in controlling the severity of the disease. However, the mechanism of the regulatory role of CD8+ T cells on COVID-19 remains poorly investigated. In this study, single-cell gene expression profiles from three CD8+ T cell subtypes (effector, memory, and naive T cells) were downloaded. Each cell subtype included three disease states, namely, acute COVID-19, convalescent COVID-19, and unexposed individuals. The profiles on each cell subtype were individually analyzed in the same way. Irrelevant features in the profiles were first excluded by the Boruta method. The remaining features for each CD8+ T cells subtype were further analyzed by Max-Relevance and Min-Redundancy, Monte Carlo feature selection, and light gradient boosting machine methods to obtain three feature lists. These lists were then brought into the incremental feature selection method to determine the optimal features for each cell subtype. Their corresponding genes may be latent biomarkers to determine COVID-19 severity. Genes, such as ZFP36, DUSP1, TCR, and IL7R, can be confirmed to play an immune regulatory role in COVID-19 infection and recovery. The results of functional enrichment analysis revealed that these important genes may be associated with immune functions, such as response to cAMP, response to virus, T cell receptor complex, T cell activation, and T cell differentiation. This study further set up different gene expression pattens, represented by classification rules, on three states of COVID-19 and constructed several efficient classifiers to distinguish COVID-19 severity. The findings of this study provided new insights into the biological processes of CD8+ T cells in regulating the immune response.

Identifying MicroRNA Markers That Predict COVID-19 Severity Using Machine Learning Methods.

Individuals with the SARS-CoV-2 infection may experience a wide range of symptoms, from being asymptomatic to having a mild fever and cough to a severe respiratory impairment that results in death. MicroRNA (miRNA), which plays a role in the antiviral effects of SARS-CoV-2 infection, has the potential to be used as a novel marker to distinguish between patients who have various COVID-19 clinical severities. In the current study, the existing blood expression profiles reported in two previous studies were combined for deep analyses. The final profiles contained 1444 miRNAs in 375 patients from six categories, which were as follows: 30 patients with mild COVID-19 symptoms, 81 patients with moderate COVID-19 symptoms, 30 non-COVID-19 patients with mild symptoms, 137 patients with severe COVID-19 symptoms, 31 non-COVID-19 patients with severe symptoms, and 66 healthy controls. An efficient computational framework containing four feature selection methods (LASSO, LightGBM, MCFS, and mRMR) and four classification algorithms (DT, KNN, RF, and SVM) was designed to screen clinical miRNA markers, and a high-precision RF model with a 0.780 weighted F1 was constructed. Some miRNAs, including miR-24-3p, whose differential expression was discovered in patients with acute lung injury complications brought on by severe COVID-19, and miR-148a-3p, differentially expressed against SARS-CoV-2 structural proteins, were identified, thereby suggesting the effectiveness and accuracy of our framework. Meanwhile, we extracted classification rules based on the DT model for the quantitative representation of the role of miRNA expression in differentiating COVID-19 patients with different severities. The search for novel biomarkers that could predict the severity of the disease could aid in the clinical diagnosis of COVID-19 and in exploring the specific mechanisms of the complications caused by SARS-CoV-2 infection. Moreover, new therapeutic targets for the disease may be found.

Identification of Transcriptome Biomarkers for Severe COVID-19 with Machine Learning Methods.

The rapid spread of COVID-19 has become a major concern for people's lives and health all around the world. COVID-19 patients in various phases and severity require individualized treatment given that different patients may develop different symptoms. We employed machine learning methods to discover biomarkers that may accurately classify COVID-19 in various disease states and severities in this study. The blood gene expression profiles from 50 COVID-19 patients without intensive care, 50 COVID-19 patients with intensive care, 10 non-COVID-19 individuals without intensive care, and 16 non-COVID-19 individuals with intensive care were analyzed. Boruta was first used to remove irrelevant gene features in the expression profiles, and then, the minimum redundancy maximum relevance was applied to sort the remaining features. The generated feature-ranked list was fed into the incremental feature selection method to discover the essential genes and build powerful classifiers. The molecular mechanism of some biomarker genes was addressed using recent studies, and biological functions enriched by essential genes were examined. Our findings imply that genes including UBE2C, PCLAF, CDK1, CCNB1, MND1, APOBEC3G, TRAF3IP3, CD48, and GZMA play key roles in defining the different states and severity of COVID-19. Thus, a new point of reference is provided for understanding the disease's etiology and facilitating a precise therapy.

Associations between the use of aspirin or other antiplatelet drugs and all-cause mortality among patients with COVID-19: A meta-analysis

Introduction: Whether aspirin or other antiplatelet drugs can reduce mortality among patients with coronavirus disease (COVID-19) remains controversial. Methods: We identified randomized controlled trials, prospective cohort studies, and retrospective studies on associations between aspirin or other antiplatelet drug use and all-cause mortality among patients with COVID-19 in the PubMed database between March 2019 and September 2021. Newcastle–Ottawa Scale and Cochrane Risk of Bias Assessment Tool were used to assess the risk of bias. The I2 statistic was used to assess inconsistency among trial results. The summary risk ratio (RR) and odds ratio (OR) were obtained through the meta-analysis. Results: The 34 included studies comprised three randomized controlled trials, 27 retrospective studies, and 4 prospective cohort studies. The retrospective and prospective cohort studies showed low-to-moderate risks of bias per the Newcastle–Ottawa Scale score, while the randomized controlled trials showed low-to-high risks of bias per the Cochrane Risk of Bias Assessment Tool. The randomized controlled trials showed no significant effect of aspirin use on all-cause mortality in patients with COVID-19 {risk ratio (RR), 0.96 [95% confidence interval (CI) 0.90–1.03]}. In retrospective studies, aspirin reduced all-cause mortality in patients with COVID-19 by 20% [odds ratio (OR), 0.80 (95% CI 0.70–0.93)], while other antiplatelet drugs had no significant effects. In prospective cohort studies, aspirin decreased all-cause mortality in patients with COVID-19 by 15% [OR, 0.85 (95% CI 0.80–0.90)]. Conclusion: The administration of aspirin may reduce all-cause mortality in patients with COVID-19.

Identification of methylation signatures and rules for predicting the severity of SARS-CoV-2 infection with machine learning methods.

Patients infected with SARS-CoV-2 at various severities have different clinical manifestations and treatments. Mild or moderate patients usually recover with conventional medical treatment, but severe patients require prompt professional treatment. Thus, stratifying infected patients for targeted treatment is meaningful. A computational workflow was designed in this study to identify key blood methylation features and rules that can distinguish the severity of SARS-CoV-2 infection. First, the methylation features in the expression profile were deeply analyzed by a Monte Carlo feature selection method. A feature list was generated. Next, this ranked feature list was fed into the incremental feature selection method to determine the optimal features for different classification algorithms, thereby further building optimal classifiers. These selected key features were analyzed by functional enrichment to detect their biofunctional information. Furthermore, a set of rules were set up by a white-box algorithm, decision tree, to uncover different methylation patterns on various severity of SARS-CoV-2 infection. Some genes (PARP9, MX1, IRF7), corresponding to essential methylation sites, and rules were validated by published academic literature. Overall, this study contributes to revealing potential expression features and provides a reference for patient stratification. The physicians can prioritize and allocate health and medical resources for COVID-19 patients based on their predicted severe clinical outcomes.

Research on Ecological Landscape Design and Healing Effect Based on 3D Roaming Technology.

Impacted by the COVID-19 epidemic, the human sub-health in national high-tech zones (hereinafter referred to as high-tech zones) has become more prominent. It is critical for the mental sub-health group in the high-tech zone to relieve the anxiety and tension caused by the pressure of life and work. This paper uses SketchUp virtual engine (Unity 2019) software, and 3D roaming technology to carry out the ecological landscape transformation design of the Baotzixi ecological corridor in the East Lake High-tech Zone, to construct a 3D roaming landscape scene and measure its therapeutic effect by inviting subjects to participate in an interactive experience experiment on the ErgoLAB platform. The results illustrate that: (1) the thermogram trend shows that the more attractive the 3D roaming landscape scene is, the stronger the subjects' interest is; (2) the participants have a positive emotional arousal state in the immersive experience of the 3D roaming landscape scene after the modification design; and (3) the mean skin conductance (SC) fluctuation variance of the subjects is 5.819%, indicating that the healing effect is significant in the state of positive emotional arousal. The research results show that there is a connection between the subjects and the 3D roaming landscape scene after the transformation design of "high interest, emotional arousal and significant healing".