Genomic representation predicts an asymptotic host adaptation of bat coronaviruses using deep learning.

Introduction: Coronaviruses (CoVs) are naturally found in bats and can occasionally cause infection and transmission in humans and other mammals. Our study aimed to build a deep learning (DL) method to predict the adaptation of bat CoVs to other mammals. Methods: The CoV genome was represented with a method of dinucleotide composition representation (DCR) for the two main viral genes, ORF1ab and Spike. DCR features were first analyzed for their distribution among adaptive hosts and then trained with a DL classifier of convolutional neural networks (CNN) to predict the adaptation of bat CoVs. Results and discussion: The results demonstrated inter-host separation and intra-host clustering of DCR-represented CoVs for six host types: Artiodactyla, Carnivora, Chiroptera, Primates, Rodentia/Lagomorpha, and Suiformes. The DCR-based CNN with five host labels (without Chiroptera) predicted a dominant adaptation of bat CoVs to Artiodactyla hosts, then to Carnivora and Rodentia/Lagomorpha mammals, and later to primates. Moreover, a linear asymptotic adaptation of all CoVs (except Suiformes) from Artiodactyla to Carnivora and Rodentia/Lagomorpha and then to Primates indicates an asymptotic bats-other mammals-human adaptation. Conclusion: Genomic dinucleotides represented as DCR indicate a host-specific separation, and clustering predicts a linear asymptotic adaptation shift of bat CoVs from other mammals to humans via deep learning.

Residential mobility predicts COVID-19 and seasonal flu vaccination behaviors in the United States.

Aim: Vaccination is one of the most effective strategies to contain the transmission of infectious diseases; however, people's intentions and behavior for vaccination vary across different regions and countries around the world. It is not clear how socioecological factors such as residential mobility influence people's vaccination behaviors for infectious diseases. Methods: We analyzed public data on residential mobility and vaccination rates for COVID-19 and seasonal flu in the United States and explored how residential mobility in the previous year influenced vaccination rates for COVID-19 and seasonal flu (2011-2018) across 50 states of the US. The data were accessed and analyzed in 2021. Results: Study 1 demonstrated that collective-level residential mobility predicted COVID-19 vaccination rates across the United States (B = -168.162, 95% CI [-307.097, -29.227], adjusted R 2 = 0.091, p = 0.019). Study 2 corroborated this finding by documenting that collective-level residential mobility predicted vaccination rates for seasonal flu from 2011 to 2018 across the United States (B = -0.789, 95% CI = [-1.018, -0.56], adjusted R 2 = 0.222, p < 0.001). The link between residential mobility and vaccination behavior was robust after controlling relevant variables, including collectivism, cultural tightness-looseness, and sociodemographic variables. Conclusions: Our research demonstrated that residential mobility is an important socioecological factor that influences people's vaccination behaviors for COVID-19 and seasonal flu. The results enrich our understanding of the socioecological factors that influence vaccination behaviors and have implications for developing tailored interventions to promote vaccination during pandemics of infectious diseases.

Toward Detecting Previously Undiscovered Interaction Types in Networked Systems.

Studying networked systems in a variety of domains, including biology, social science, and Internet of Things, has recently received a surge of attention. For a networked system, there are usually multiple types of interactions between its components, and such interaction-type information is crucial since it always associated with important features. However, some interaction types that actually exist in the network may not be observed in the metadata collected in practice. This article proposes an approach aiming to detect previously undiscovered interaction types (PUITs) in networked systems. The first step in our proposed PUIT detection approach is to answer the following fundamental question: is it possible to effectively detect PUITs without utilizing metadata other than the existing incomplete interaction-type information and the connection information of the system? Here, we first propose a temporal network model which can be used to mimic any real network and then discover that some special networks which fit the model shall a common topological property. Supported by this discovery, we finally develop a PUIT detection method for networks which fit the proposed model. Both analytical and numerical results show this detection method is more effective than the baseline method, demonstrating that effectively detecting PUITs in networks is achievable. More studies on PUIT detection are of significance and in great need since this approach should be as essential as the previously undiscovered node-type detection which has gained great success in the field of biology.

Preparation of the luciferase-labeled antibody for improving the detection sensitivity of viral antigen.

BACKGROUND: Viral antigen detection test is the most common method used to detect viruses in the field rapidly. However, due to the low sensitivity, it can only be used as an auxiliary diagnosis method for virus infection. Improving sensitivity is crucial for developing more accurate viral antigen tests. Nano luciferase (Nluc) is a sensitive reporter that has not been used in virus detection. RESULTS: In this study, we produced an intracellularly Nluc labeled detection antibody (Nluc-ch2C5) and evaluated its ability to improve the detection sensitivity of respiratory syndrome coronavirus 2 (SARS-CoV-2) antigens. Compared with the traditional horse-radish peroxidase (HRP) labeled antibody (HRP-ch2C5), Nluc-ch2C5 was 41 times more sensitive for inactivated SARS-CoV-2 virus by sandwich chemiluminescence ELISA. Then we applied Nluc-ch2C5 to establish an automatic magnet chemiluminescence immune assay (AMCA) for the SARS-CoV-2 viral spike protein, the limit of detection was 68 pfu/reaction. The clinical sensitivity and specificity reached 75% (24/32) and 100% (48/48) using 32 PCR-positive and 48 PCR-negative swab samples for clinical evaluation, which is more sensitive than the commercial ELSA kit and colloid gold strip kit. CONCLUSIONS: Here, monoclonal antibody ch2C5 served as a model antibody and the SARS-CoV-2 served as a model pathogen. The Nluc labeled detecting antibody (Nluc-ch2C5) significantly improved the detection sensitivity of SARS-CoV-2 antigen. This labeling principle applies to other viral infections, so this labeling and test format could be expected to play an important role in detecting other virus antigens.

Preparation of the luciferase-labeled antibody for improving the detection sensitivity of viral antigen

Viral antigen detection test is the most common method used to detect viruses in the field rapidly. However, due to the low sensitivity, it can only be used as an auxiliary diagnosis method for virus infection. Improving sensitivity is crucial for developing more accurate viral antigen tests. Nano luciferase (Nluc) is a sensitive reporter that has not been used in virus detection. In this study, we produced an intracellularly Nluc labeled detection antibody (Nluc-ch2C5) and evaluated its ability to improve the detection sensitivity of respiratory syndrome coronavirus 2 (SARS-CoV-2) antigens. Compared with the traditional horse-radish peroxidase (HRP) labeled antibody (HRP-ch2C5), Nluc-ch2C5 was 41 times more sensitive for inactivated SARS-CoV-2 virus by sandwich chemiluminescence ELISA. Then we applied Nluc-ch2C5 to establish an automatic magnet chemiluminescence immune assay (AMCA) for the SARS-CoV-2 viral spike protein, the limit of detection was 68 pfu/reaction. The clinical sensitivity and specificity reached 75% (24/32) and 100% (48/48) using 32 PCR-positive and 48 PCR-negative swab samples for clinical evaluation, which is more sensitive than the commercial ELSA kit and colloid gold strip kit. Here, monoclonal antibody ch2C5 served as a model antibody and the SARS-CoV-2 served as a model pathogen. The Nluc labeled detecting antibody (Nluc-ch2C5) significantly improved the detection sensitivity of SARS-CoV-2 antigen. This labeling principle applies to other viral infections, so this labeling and test format could be expected to play an important role in detecting other virus antigens.

Predictive Value of Systemic Immune-Inflammation index and Neutrophil-to-Lymphocyte Ratio in Patients with Severe COVID-19.

Objective: It was initially reported that a novel coronavirus (COVID-19) had been identified in Wuhan, China, in December 2019.To date, COVID-19 is still threatening all humanity and has affected the public healthcare system and the world economic situation. Neutrophil-to-lymphocyte ratio (NLR) has also been demonstrated that associated with severity of COVID-19, but little is known about systemic immune-inflammation index (SII) relation with COVID-19. Methods: One hundred and twenty-five patients with diagnosed COVID-19 including non-severe cases (n = 77) and severe cases (n = 48) were enrolled in this study. Each patient of clinical characteristic information, blood routine parameters, and the haemogram-derived ratios were collected, calculated, and retrospectively analyzed. Receiver operating characteristics (ROC) was performed to investigate whether these parameters could be used to the predictive value of patients with severe COVID-19. Results: White blood cell count (WBC), neutrophil count (NEU), red cell volume distribution width (RDW), NLR, Platelet to lymphocyte ratio (PLR), neutrophil-to-platelet ratio (NPR), and SII were significantly higher in the severe groups than in the non-severe group (p < 0.01).Conversely, the severe group had a markedly decreased lymphocyte count, basophil (Baso#) count, red blood cell count (RBC), Hemoglobin (HGB), hematocrit (HCT), and lymphocyte-to-monocyte ratio (LMR) (P < 0.01).ROC curve analysis showed the AUC, optimal cut-off value, sensitivity, specificity of NLR and SII to early predict severe-patients with COVID-19 were 0.867, 7.25, 70.83%, 92.21% and 0.860, 887.20, 81.25%, 81.82%, respectively. Conclusion The results suggest that the SII and NLR is a potential new diagnosed biomarker in severe-patients with COVID-19.

Convolutional Neural Networks Based on Sequential Spike Predict the High Human Adaptation of SARS-CoV-2 Omicron Variants.

The COVID-19 pandemic has frequently produced more highly transmissible SARS-CoV-2 variants, such as Omicron, which has produced sublineages. It is a challenge to tell apart high-risk Omicron sublineages and other lineages of SARS-CoV-2 variants. We aimed to build a fine-grained deep learning (DL) model to assess SARS-CoV-2 transmissibility, updating our former coarse-grained model, with the training/validating data of early-stage SARS-CoV-2 variants and based on sequential Spike samples. Sequential amino acid (AA) frequency was decomposed into serially and slidingly windowed fragments in Spike. Unsupervised machine learning approaches were performed to observe the distribution in sequential AA frequency and then a supervised Convolutional Neural Network (CNN) was built with three adaptation labels to predict the human adaptation of Omicron variants in sublineages. Results indicated clear inter-lineage separation and intra-lineage clustering for SARS-CoV-2 variants in the decomposed sequential AAs. Accurate classification by the predictor was validated for the variants with different adaptations. Higher adaptation for the BA.2 sublineage and middle-level adaptation for the BA.1/BA.1.1 sublineages were predicted for Omicron variants. Summarily, the Omicron BA.2 sublineage is more adaptive than BA.1/BA.1.1 and has spread more rapidly, particularly in Europe. The fine-grained adaptation DL model works well for the timely assessment of the transmissibility of SARS-CoV-2 variants, facilitating the control of emerging SARS-CoV-2 variants.

Convolutional Neural Networks Based on Sequential Spike Predict the High Human Adaptation of SARS-CoV-2 Omicron Variants

The COVID-19 pandemic has frequently produced more highly transmissible SARS-CoV-2 variants, such as Omicron, which has produced sublineages. It is a challenge to tell apart high-risk Omicron sublineages and other lineages of SARS-CoV-2 variants. We aimed to build a fine-grained deep learning (DL) model to assess SARS-CoV-2 transmissibility, updating our former coarse-grained model, with the training/validating data of early-stage SARS-CoV-2 variants and based on sequential Spike samples. Sequential amino acid (AA) frequency was decomposed into serially and slidingly windowed fragments in Spike. Unsupervised machine learning approaches were performed to observe the distribution in sequential AA frequency and then a supervised Convolutional Neural Network (CNN) was built with three adaptation labels to predict the human adaptation of Omicron variants in sublineages. Results indicated clear inter-lineage separation and intra-lineage clustering for SARS-CoV-2 variants in the decomposed sequential AAs. Accurate classification by the predictor was validated for the variants with different adaptations. Higher adaptation for the BA.2 sublineage and middle-level adaptation for the BA.1/BA.1.1 sublineages were predicted for Omicron variants. Summarily, the Omicron BA.2 sublineage is more adaptive than BA.1/BA.1.1 and has spread more rapidly, particularly in Europe. The fine-grained adaptation DL model works well for the timely assessment of the transmissibility of SARS-CoV-2 variants, facilitating the control of emerging SARS-CoV-2 variants.

A New Joint Approach with Temporal and Profile Information for Social Bot Detection

With the increasing popularity of online social networks (OSNs), a huge number of social bots have emerged. Social bots are involved in various cybercrimes like cyberbullying and rumor dissemination, which have seriously affected the normal order of OSNs. Nowadays, existing studies in this field almost focus on English OSNs like Twitter and Facebook. However, it is difficult to directly apply these detection technologies to Sina Weibo, which is one of the largest Chinese microblogging services in the world. In addition, social bots are evolving rapidly and time-consuming feature engineering may not perform well in detecting newly emerging social bots. In this paper, we propose a new joint approach with Temporal and Profile information for social bot detection (TPBot). The approach includes data collection module, feature extraction module, and detection module. To begin with, data collection module uses a web crawler to obtain user data from Sina Weibo. Next, the feature extraction module regards the user posts as temporal data to extract temporal-semantic and temporal-metadata features. Furthermore, this module extracts features based on users’ profile. Finally, a detection model based on BiGRU and attention mechanism is designed in the detection module. The results show that TPBot performs better than baselines with the F1-score of 0.9837 on the Sina Weibo dataset. Moreover, we have also conducted an experiment on the two datasets collected from Twitter to evaluate the generalization ability of TPBot. It is found that TPBot outperforms baselines on the new datasets and has good generalization ability.

Biocompatible Conductive Hydrogels: Applications in the Field of Biomedicine.

The impact of COVID-19 has rendered medical technology an important factor to maintain social stability and economic increase, where biomedicine has experienced rapid development and played a crucial part in fighting off the pandemic. Conductive hydrogels (CHs) are three-dimensional (3D) structured gels with excellent electrical conductivity and biocompatibility, which are very suitable for biomedical applications. CHs can mimic innate tissue's physical, chemical, and biological properties, which allows them to provide environmental conditions and structural stability for cell growth and serve as efficient delivery substrates for bioactive molecules. The customizability of CHs also allows additional functionality to be designed for different requirements in biomedical applications. This review introduces the basic functional characteristics and materials for preparing CHs and elaborates on their synthetic techniques. The development and applications of CHs in the field of biomedicine are highlighted, including regenerative medicine, artificial organs, biosensors, drug delivery systems, and some other application scenarios. Finally, this review discusses the future applications of CHs in the field of biomedicine. In summary, the current design and development of CHs extend their prospects for functioning as an intelligent and complex system in diverse biomedical applications.

A Review of Recovery of Palladium from the Spent Automobile Catalysts

The spent automobile catalysts (SAC) is the major secondary source of palladium and the production of SAC is increasing rapidly over years. The price of palladium keeps rising over the years, which demonstrates its preciousness and urgent industrial demand. Recovering palladium from the spent automobile catalysts benefits a lot from economic and environmental protection aspects. This review aims to provide some new considerations of recovering palladium from the spent automotive catalysts by summarizing and discussing both hydrometallurgical and pyrometallurgical methods. The processes of pretreatment, leaching/extraction, and separation/recovery of palladium from the spent catalysts are introduced, and related reaction mechanisms and process flows are given, especially detailed for hydrometallurgical methods. Hydrometallurgical methods such as chloride leaching with oxidants possess a high selectivity of palladium and low consumption of energy, and are cost-effective and flexible for different volume feeds compared with pyrometallurgical methods. The recovery ratios of palladium and other platinum-group metals should be the focus of competition since their prices have been rapidly increased over the years, and hence more efficient extractants with high selectivity of palladium even in the complexed leachate should be proposed in the future.

`Development of a rapid neutralizing antibody test for SARS-CoV-2 and its application for neutralizing antibody screening and vaccinated serum testing

Background : Since the outbreak of coronavirus disease (COVID-19), the high infection rate and mutation frequency of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent, have contributed to the ongoing global pandemic. Vaccination has become the most effective means of controlling COVID-19. Traditional neutralizing tests of sera are complex and labor-intensive, therefore, a rapid test for detecting neutralizing antibodies and antibody status post-immunization is needed. Methods : Based on the fact that antibodies exhibit neutralizing activity by blocking the binding of the S protein receptor-binding domain (S-RBD) to ACE2, we developed a rapid neutralizing antibody test, ACE2-Block-ELISA. To evaluate the sensitivity and specificity, we used 54 positive and 84 negative serum samples. We also tested the neutralizing activities of monoclonal antibodies (mAbs) and 214 sera samples from healthy individuals immunized with the inactivated SARS-CoV-2 vaccine. Results : The sensitivity and specificity of the ACE2-Block ELISA were 96.3% and 100%, respectively. For neutralizing mAb screening, ch-2C5 was selected for its ability to block the ACE2–S-RBD interaction. A plaque assay confirmed that ch-2C5 neutralized SARS-CoV-2, with NT50 values of 4.19, 10.63, and 1.074 μg/mL against the SARS-CoV-2 original strain, and the Beta and Delta variants, respectively. For the immunized sera samples, the neutralizing positive rate dropped from 82.14% to 32.16% within 4 months post-vaccination. Conclusions : This study developed and validated an ACE2-Block-ELISA to test the neutralizing activities of antibodies. As a rapid, inexpensive and easy-to-perform method, this ACE2-Block-ELISA has potential applications in rapid neutralizing mAb screening and SARS-CoV-2 vaccine evaluation.

Intestinal Damage in COVID-19: SARS-CoV-2 Infection and Intestinal Thrombosis.

The intestinal tract, with high expression of angiotensin-converting enzyme 2 (ACE2), is a major site of extrapulmonary infection in COVID-19. During pulmonary infection, the virus enters the bloodstream forming viremia, which infects and damages extrapulmonary organs. Uncontrolled viral infection induces cytokine storm and promotes a hypercoagulable state, leading to systemic microthrombi. Both viral infection and microthrombi can damage the gut-blood barrier, resulting in malabsorption, malnutrition, and intestinal flora entering the blood, ultimately increasing disease severity and mortality. Early prophylactic antithrombotic therapy can prevent these damages, thereby reducing mortality. In this review, we discuss the effects of SARS-CoV-2 infection and intestinal thrombosis on intestinal injury and disease severity, as well as corresponding treatment strategies.

Broad Impacts of Coronavirus Disease 2019 (COVID-19) Pandemic on Acute Respiratory Infections in China: An Observational Study.

BACKGROUND: To combat the coronavirus disease 2019 (COVID-19) pandemic, nonpharmaceutical interventions (NPIs) were implemented worldwide, which impacted a broad spectrum of acute respiratory infections (ARIs). METHODS: Etiologically diagnostic data from 142 559 cases with ARIs, who were tested for 8 viral pathogens (influenza virus [IFV], respiratory syncytial virus [RSV], human parainfluenza virus [HPIV], human adenovirus [HAdV], human metapneumovirus [HMPV], human coronavirus [HCoV], human bocavirus [HBoV], and human rhinovirus [HRV]) between 2012 and 2021, were analyzed to assess the changes in respiratory infections in China during the first COVID-19 pandemic year compared with pre-pandemic years. RESULTS: Test-positive rates of all respiratory viruses decreased during 2020, compared to the average levels during 2012-2019, with changes ranging from -17.2% for RSV to -87.6% for IFV. Sharp decreases mostly occurred between February and August when massive NPIs remained active, although HRV rebounded to the historical level during the summer. While IFV and HMPV were consistently suppressed year-round, RSV, HPIV, HCoV, HRV, and HBoV resurged and went beyond historical levels during September 2020-January 2021, after NPIs were largely relaxed and schools reopened. Resurgence was more prominent among children <18 years and in northern China. These observations remain valid after accounting for seasonality and long-term trend of each virus. CONCLUSIONS: Activities of respiratory viral infections were reduced substantially in the early phases of the COVID-19 pandemic, and massive NPIs were likely the main driver. Lifting of NPIs can lead to resurgence of viral infections, particularly in children.

Deep learning based on biologically interpretable genome representation predicts two types of human adaptation of SARS-CoV-2 variants.

Explosively emerging SARS-CoV-2 variants challenge current nomenclature schemes based on genetic diversity and biological significance. Genomic composition-based machine learning methods have recently performed well in identifying phenotype-genotype relationships. We introduced a framework involving dinucleotide (DNT) composition representation (DCR) to parse the general human adaptation of RNA viruses and applied a three-dimensional convolutional neural network (3D CNN) analysis to learn the human adaptation of other existing coronaviruses (CoVs) and predict the adaptation of SARS-CoV-2 variants of concern (VOCs). A markedly separable, linear DCR distribution was observed in two major genes-receptor-binding glycoprotein and RNA-dependent RNA polymerase (RdRp)-of six families of single-stranded (ssRNA) viruses. Additionally, there was a general host-specific distribution of both the spike proteins and RdRps of CoVs. The 3D CNN based on spike DCR predicted a dominant type II adaptation of most Beta, Delta and Omicron VOCs, with high transmissibility and low pathogenicity. Type I adaptation with opposite transmissibility and pathogenicity was predicted for SARS-CoV-2 Alpha VOCs (77%) and Kappa variants of interest (58%). The identified adaptive determinants included D1118H and A570D mutations and local DNTs. Thus, the 3D CNN model based on DCR features predicts SARS-CoV-2, a major type II human adaptation and is qualified to predict variant adaptation in real time, facilitating the risk-assessment of emerging SARS-CoV-2 variants and COVID-19 control.

Exposure to air pollution is associated with congenital anomalies in the population born by in vitro fertilization.

BACKGROUND: Congenital anomalies (CAs) are the leading causes for children's disabilities and mortalities worldwide. The associations between air pollution and CAs are not fully characterized in fetuses born by in vitro fertilization (IVF) who are at high risk of congenital anomalies. METHODS: We conducted a cross-sectional study including 16,971 IVF cycles from three hospitals in Hebei Province, China, 2014-2019. Air quality data was obtained from 149 air monitoring stations. Individual average daily concentrations of PM2.5, PM10, NO2, SO2, CO, and O3 were estimated by spatiotemporal kriging method. Exposure windows were divided into 5: preantral follicle period, antral follicle period, germinal period, embryonic period and early fetal period. Logistic generalized estimating equations were used to estimate the associations between air pollutants and overall or organ-system specific congenital anomalies. Negative control exposure method was used to detect and reduce bias of estimation. RESULTS: We found increasing levels of PM2.5 and PM10 were associated with higher risk of overall congenital anomalies during early fetal period, equating gestation 10-12 weeks (OR: 1.05, 95% CI: 1.02-1.09, p = 0.013 for a 10 µg/m3 increase of PM2.5; OR: 1.03, 95% CI: 1.01-1.06, p = 0.021 for a 10 µg/m3 increase of PM10). Cleft lip and cleft palate were associated with PM10 in germinal period and early fetal period. The CAs of eye, ear, face and neck were related to CO in preantral follicle stage. We did not find an association between chromosome abnormalities and air pollution exposure. CONCLUSIONS: We concluded that ambient air pollution was a risk factor for congenital anomalies in the fetuses conceived through IVF, especially exposure in early fetal period.

Advances in nanoparticle‐based lateral flow immunoassay for point‐of‐care testing

As a representative technology for point‐of‐care testing (POCT), lateral flow immunoassay (LFIA) has been broadly used to detect analytes in many fields. However, its clinical application is severely limited by the unsatisfactory sensitivity, which makes it difficult to obtain accurate results when detecting biomarkers of trace levels, especially in complex matrices. Nanoparticles have been introduced into LFIA for years and become an indispensable part, acting not only as carriers that load and enrich biomolecules, such as antibodies and dyes, but also a miniature platform applied for creative design and construction of nanoprobes. Due to the unique properties at the nanoscale, including the mimetic enzyme activity, the characteristic plasma resonance spectrum and so on, nanomaterials exhibit great potential in the development of novel LFIA and high‐sensitivity detection.

Simultaneous Detection of Seven Human Coronaviruses by Multiplex PCR and MALDI-TOF MS

Human coronaviruses (HCoVs) are associated with a range of respiratory symptoms. The discovery of severe acute respiratory syndrome (SARS)-CoV, Middle East respiratory syndrome, and SARS-CoV-2 pose a significant threat to human health. In this study, we developed a method (HCoV-MS) that combines multiplex PCR with matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS), to detect and differentiate seven HCoVs simultaneously. The HCoV-MS method had high specificity and sensitivity, with a 1–5 copies/reaction detection limit. To validate the HCoV-MS method, we tested 163 clinical samples, and the results showed good concordance with real-time PCR. Additionally, the detection sensitivity of HCoV-MS and real-time PCR was comparable. The HCoV-MS method is a sensitive assay, requiring only 1 μL of a sample. Moreover, it is a high-throughput method, allowing 384 samples to be processed simultaneously in 30 min. We propose that this method be used to complement real-time PCR for large-scale screening studies.