Predicting Demands of COVID-19 Prevention and Control Materials via Co-Evolutionary Transfer Learning.

The novel coronavirus pneumonia (COVID-19) has created great demands for medical resources. Determining these demands timely and accurately is critically important for the prevention and control of the pandemic. However, even if the infection rate has been estimated, the demands of many medical materials are still difficult to estimate due to their complex relationships with the infection rate and insufficient historical data. To alleviate the difficulties, we propose a co-evolutionary transfer learning (CETL) method for predicting the demands of a set of medical materials, which is important in COVID-19 prevention and control. CETL reuses material demand knowledge not only from other epidemics, such as severe acute respiratory syndrome (SARS) and bird flu but also from natural and manmade disasters. The knowledge or data of these related tasks can also be relatively few and imbalanced. In CETL, each prediction task is implemented by a fuzzy deep contractive autoencoder (CAE), and all prediction networks are cooperatively evolved, simultaneously using intrapopulation evolution to learn task-specific knowledge in each domain and using interpopulation evolution to learn common knowledge shared across the domains. Experimental results show that CETL achieves high prediction accuracies compared to selected state-of-the-art transfer learning and multitask learning models on datasets during two stages of COVID-19 spreading in China.

Comprehensive analysis of the novel omicron receptor AXL in cancers.

The SARS-CoV-2 is constantly mutating, and the new coronavirus such as Omicron has spread to many countries around the world. Anexelekto (AXL) is a transmembrane protein with biological functions such as promoting cell growth, migration, aggregation, metastasis and adhesion, and plays an important role in cancers and coronavirus disease 2019 (COVID-19). Unlike angiotensin-converting enzyme 2 (ACE2), AXL was highly expressed in respiratory system cells. In this study, we verified the AXL expression in cancer and normal tissues and found AXL expression was strongly correlated with cancer prognosis, tumor mutation burden (TMB), the microsatellite instability (MSI) in most tumor types. Immune infiltration analysis also demonstrated that there was an inextricable link between AXL expression and immune scores in cancer patients, especially in BLCA, BRCA and CESC. The NK-cells, plasmacytoid dendritic cells, myeloid dendritic cells, as one of the important components of the tumor microenvironment, were highly expressed AXL. In addition, AXL-related tumor neoantigens were identified and might provide the novel potential targets for tumor vaccines or SARS-Cov-2 vaccines research in cancer patients.

Foe and friend in the COVID-19-associated acute kidney injury: an insight on intrarenal renin-angiotensin system.

Since the first reported case in December of 2019, the coronavirus disease 2019 (COVID-19) has became an international public health emergency. So far, there are more than 228,206,384 confirmed cases including 4,687,066 deaths. Kidney with high expression of angiotensin-converting enzyme 2 (ACE2) is one of the extrapulmonary target organs affected in patients with COVID-19. Acute kidney injury (AKI) is one of the independent risk factors for the death of COVID-19 patients. The imbalance between ACE2-Ang(1-7)-MasR and ACE-Ang II-AT1R axis in the kidney may contribute to COVID-19-associated AKI. Although series of research have shown the inconsistent effects of multiple common RAS inhibitors on ACE2 expression and enzyme activity, most of the retrospective cohort studies indicated the safety and protective effects of ACEI/ARB in COVID-19 patients. This review article highlights the current knowledge on the possible involvement of intrarenal RAS in COVID-19-associated AKI with a primary focus on the opposing effects of ACE2-Ang(1-7)-MasR and ACE-Ang II-AT1R signaling in the kidney. Human recombinant soluble ACE2 or ACE2 variants with preserved ACE2-enzymatic activity may be the best options to improve COVID-19-associated AKI.

Longitudinal neutralization activities on authentic Omicron variant provided by three doses of BBIBP-CorV vaccination during one year.

The majority of people in China have been immunized with the inactivated viral vaccine BBIBP-CorV. The emergence of the Omicron variant raised the concerns about protection efficacy of the inactivated viral vaccine in China. However, longitudinal neutralization data describing protection efficacy against Omicron variant is still lacking. Here we present one-year longitudinal neutralization data of BBIBP-CorV on authentic Omicron, Delta, and wild-type strains using 224 sera collected from 14 volunteers who have finished three doses BBIBP-CorV. The sera were also subjected for monitoring the SARS-CoV-2 specific IgG, IgA, and IgM responses on protein and peptide microarrays. The neutralization titers showed different protection efficacies against the three strains. By incorporating IgG and IgA signals of proteins and Spike protein derived peptide on microarray, panels as potential surrogate biomarkers for rapid estimation of neutralization titers were established. These data support the necessity of the 3rd dose of BBIBP-CorV vaccination. After further validation and assay development, the panels could be used for reliable, convenient and fast evaluation of the efficacy of vaccination.

A Computer Simulation of SARS-CoV-2 Mutation Spectra for Empirical Data Characterization and Analysis.

It is very important to compute the mutation spectra, and simulate the intra-host mutation processes by sequencing data, which is not only for the understanding of SARS-CoV-2 genetic mechanism, but also for epidemic prediction, vaccine, and drug design. However, the current intra-host mutation analysis algorithms are not only inaccurate, but also the simulation methods are unable to quickly and precisely predict new SARS-CoV-2 variants generated from the accumulation of mutations. Therefore, this study proposes a novel accurate strand-specific SARS-CoV-2 intra-host mutation spectra computation method, develops an efficient and fast SARS-CoV-2 intra-host mutation simulation method based on mutation spectra, and establishes an online analysis and visualization platform. Our main results include: (1) There is a significant variability in the SARS-CoV-2 intra-host mutation spectra across different lineages, with the major mutations from G- > A, G- > C, G- > U on the positive-sense strand and C- > U, C- > G, C- > A on the negative-sense strand; (2) our mutation simulation reveals the simulation sequence starts to deviate from the base content percentage of Alpha-CoV/Delta-CoV after approximately 620 mutation steps; (3) 2019-NCSS provides an easy-to-use and visualized online platform for SARS-Cov-2 online analysis and mutation simulation.

Identification and semisynthesis of (-)-anisomelic acid as oral agent against SARS-CoV-2 in mice.

(-)-Anisomelic acid, isolated from Anisomeles indica (L.) Kuntze (Labiatae) leaves, is a macrocyclic cembranolide with a trans-fused α-methylene-γ-lactone motif. Anisomelic acid effectively inhibits SARS-CoV-2 replication and viral-induced cytopathic effects with an EC50 of 1.1 and 4.3 µM, respectively. Challenge studies of SARS-CoV-2-infected K18-hACE2 mice showed that oral administration of anisomelic acid and subcutaneous dosing of remdesivir can both reduce the viral titers in the lung tissue at the same level. To facilitate drug discovery, we used a semisynthetic approach to shorten the project timelines. The enantioselective semisynthesis of anisomelic acid from the naturally enriched and commercially available starting material (+)-costunolide was achieved in five steps with a 27% overall yield. The developed chemistry provides opportunities for developing anisomelic-acid-based novel ligands for selectively targeting proteins involved in viral infections.

Chinese expert consensus on intestinal microecology and management of digestive tract complications related to tumor treatment (version 2022).

The human gut microbiota represents a complex ecosystem that is composed of bacteria, fungi, viruses, and archaea. It affects many physiological functions including metabolism, inflammation, and the immune response. The gut microbiota also plays a role in preventing infection. Chemotherapy disrupts an organism's microbiome, increasing the risk of microbial invasive infection; therefore, restoring the gut microbiota composition is one potential strategy to reduce this risk. The gut microbiome can develop colonization resistance, in which pathogenic bacteria and other competing microorganisms are destroyed through attacks on bacterial cell walls by bacteriocins, antimicrobial peptides, and other proteins produced by symbiotic bacteria. There is also a direct way. For example, Escherichia coli colonized in the human body competes with pathogenic Escherichia coli 0157 for proline, which shows that symbiotic bacteria compete with pathogens for resources and niches, thus improving the host's ability to resist pathogenic bacteria. Increased attention has been given to the impact of microecological changes in the digestive tract on tumor treatment. After 2019, the global pandemic of novel coronavirus disease 2019 (COVID-19), the development of novel tumor-targeting drugs, immune checkpoint inhibitors, and the increased prevalence of antimicrobial resistance have posed serious challenges and threats to public health. Currently, it is becoming increasingly important to manage the adverse effects and complications after chemotherapy. Gastrointestinal reactions are a common clinical presentation in patients with solid and hematologic tumors after chemotherapy, which increases the treatment risks of patients and affects treatment efficacy and prognosis. Gastrointestinal symptoms after chemotherapy range from nausea, vomiting, and anorexia to severe oral and intestinal mucositis, abdominal pain, diarrhea, and constipation, which are often closely associated with the dose and toxicity of chemotherapeutic drugs. It is particularly important to profile the gastrointestinal microecological flora and monitor the impact of antibiotics in older patients, low immune function, neutropenia, and bone marrow suppression, especially in complex clinical situations involving special pathogenic microbial infections (such as clostridioides difficile, multidrug-resistant Escherichia coli, carbapenem-resistant bacteria, and norovirus).

Guest Editorial Introduction to the Special Issue on Intelligent Transportation Systems in Epidemic Areas

The COVID-19 pandemic has posed significant challenges to transportation systems in various aspects, such as transferring patients and medical resources, enforcing physical distancing in public transportation, and controlling virus transmission through transportation networks. To address these challenges, a variety of artificial intelligence technologies, such as autonomous driving, big data analytics, intelligent vehicle routing and scheduling, and intelligent traffic control, have been employed in the design of intelligent transportation systems. This Special Issue provides a forum for researchers and practitioners to present the most recent advances in presenting and applying intelligent technologies to promote transportation systems in large-scale epidemics.

Faecal microbiome-based machine learning for multi-class disease diagnosis.

Systemic characterisation of the human faecal microbiome provides the opportunity to develop non-invasive approaches in the diagnosis of a major human disease. However, shared microbial signatures across different diseases make accurate diagnosis challenging in single-disease models. Herein, we present a machine-learning multi-class model using faecal metagenomic dataset of 2,320 individuals with nine well-characterised phenotypes, including colorectal cancer, colorectal adenomas, Crohn's disease, ulcerative colitis, irritable bowel syndrome, obesity, cardiovascular disease, post-acute COVID-19 syndrome and healthy individuals. Our processed data covers 325 microbial species derived from 14.3 terabytes of sequence. The trained model achieves an area under the receiver operating characteristic curve (AUROC) of 0.90 to 0.99 (Interquartile range, IQR, 0.91-0.94) in predicting different diseases in the independent test set, with a sensitivity of 0.81 to 0.95 (IQR, 0.87-0.93) at a specificity of 0.76 to 0.98 (IQR 0.83-0.95). Metagenomic analysis from public datasets of 1,597 samples across different populations observes comparable predictions with AUROC of 0.69 to 0.91 (IQR 0.79-0.87). Correlation of the top 50 microbial species with disease phenotypes identifies 363 significant associations (FDR < 0.05). This microbiome-based multi-disease model has potential clinical application in disease diagnostics and treatment response monitoring and warrants further exploration.

Integration of machine learning prediction and heuristic optimization for mask delivery in COVID-19.

The novel coronavirus pneumonia (COVID-19) has created huge demands for medical masks that need to be delivered to a lot of demand points to protect citizens. The efficiency of delivery is critical to the prevention and control of the epidemic. However, the huge demands for masks and massive number of demand points scattered make the problem highly complex. Moreover, the actual demands are often obtained late, and hence the time duration for solution calculation and mask delivery is often very limited. Based on our practical experience of medical mask delivery in response to COVID-19 in China, we present a hybrid machine learning and heuristic optimization method, which uses a deep learning model to predict the demand of each region, schedules first-echelon vehicles to pre-distribute the predicted number of masks from depot(s) to regional facilities in advance, reassigns demand points among different regions to balance the deviations of predicted demands from actual demands, and finally routes second-echelon vehicles to efficiently deliver masks to the demand points in each region. For the subproblems of demand point reassignment and two-batch routing whose complexities are significantly lower, we propose variable neighborhood tabu search heuristics to efficiently solve them. Application of the proposed method in emergency mask delivery in three megacities in China during the peak of COVID-19 demonstrated its significant performance advantages over other methods without pre-distribution or reassignment. We also discuss key success factors and lessons learned to facilitate the extension of our method to a wider range of problems.

Novel coronavirus mutations: Vaccine development and challenges.

The ongoing global pandemic of novel coronavirus pneumonia (COVID-19) caused by the SARS-CoV-2 has a significant impact on global health and economy system. In this context, there have been some landmark advances in vaccine development. Over 100 new coronavirus vaccine candidates have been approved for clinical trials, with ten WHO-approved vaccines including four inactivated virus vaccines, two mRNA vaccines, three recombinant viral vectored vaccines and one protein subunit vaccine on the "Emergency Use Listing". Although the SARS-CoV-2 has an internal proofreading mechanism, there have been a number of mutations emerged in the pandemic affecting its transmissibility, pathogenicity and immunogenicity. Of these, mutations in the spike (S) protein and the resultant mutant variants have posed new challenges for vaccine development and application. In this review article, we present an overview of vaccine development, the prevalence of new coronavirus variants and their impact on protective efficacy of existing vaccines and possible immunization strategies coping with the viral mutation and diversity.

Impact of Coronavirus Disease 2019 Pandemic on Pediatric Out-of-Hospital Cardiac Arrest in the Emergency Department.

Background: Out-of-hospital cardiac arrest (OHCA) in children is a critical condition with a poor prognosis. After the coronavirus disease 2019 (COVID-19) pandemic developed, the epidemiology and clinical characteristics of the pediatric emergency department (PED) visits have changed. This study aimed to analyze the impact of the COVID-19 pandemic on pediatric OHCA in the PED. Methods: From January 2018 to September 2021, we retrospectively collected data of children (18 years or younger) with a definite diagnosis of OHCA admitted to the PED. Patient data studied included demographics, pre-/in-hospital information, treatment modalities; and outcomes of interest included sustained return of spontaneous circulation (SROSC) and survival to hospital-discharge (STHD). These were analyzed and compared between the periods before and after the COVID-19 pandemic. Results: A total of 97 patients with OHCA (68 boys and 29 girls) sent to the PED were enrolled in our study. Sixty cases (61.9%) occurred in the pre-pandemic period and 37 during the pandemic. The most common age group was infants (40.2%) (p = 0.018). Asystole was the most predominant cardiac rhythm (72.2%, P = 0.048). Eighty patients (82.5%) were transferred by the emergency medical services, 62 (63.9%) gained SROSC, and 25 (25.8%) were STHD. During the COVID-19 pandemic, children with non-trauma OHCA had significantly shorter survival duration and prolonged EMS scene intervals (both p < 0.05). Conclusion: During the COVID-19 pandemic, children with OHCA had a significantly lower rate of SROSC and STHD than that in the pre-pandemic period. The COVID-19 pandemic has changed the nature of PED visits and has affected factors related to ROSC and STHD in pediatric OHCA.

A robust multi-objective model for healthcare resource management and location planning during pandemics.

In this study, we consider the problem of healthcare resource management and location planning problem during the early stages of a pandemic/epidemic under demand uncertainty. Our main ambition is to improve the preparedness level and response effectiveness of healthcare authorities in fighting pandemics/epidemics by implementing analytical techniques. Building on lessons from the Chinese experience in the COVID-19 outbreak, we first develop a deterministic multi-objective mixed integer linear program (MILP) which determines the location and size of new pandemic hospitals (strategic level planning), periodic regional health resource re-allocations (tactical level planning) and daily patient-hospital assignments (operational level planning). Taking the forecasted number of cases along a planning horizon as an input, the model minimizes the weighted sum of the number of rejected patients, total travel distance, and installation cost of hospitals subject to real-world constraints and organizational rules. Next, accounting for the uncertainty in the spread speed of the disease, we employ an across scenario robust (ASR) model and reformulate the robust counterpart of the deterministic MILP. The ASR attains relatively more realistic solutions by considering multiple scenarios simultaneously while ensuring a predefined threshold of relative regret for the individual scenarios. Finally, we demonstrate the performance of proposed models on the case of Wuhan, China. Taking the 51 days worth of confirmed COVID-19 case data as an input, we solve both deterministic and robust models and discuss the impact of all three level decisions to the quality and performance of healthcare services during the pandemic. Our case study results show that although it is a challenging task to make strategic level decisions based on uncertain forecasted data, an immediate action can considerably improve the response effectiveness of healthcare authorities. Another important observation is that, the installation times of pandemic hospitals have significant impact on the system performance in fighting with the shortage of beds and facilities.

Characterization of Altered Oropharyngeal Microbiota in Hospitalized Patients With Mild SARS-CoV-2 Infection.

Coronavirus disease 2019 (COVID-19) remains a serious emerging global health problem, and little is known about the role of oropharynx commensal microbes in infection susceptibility and severity. Here, we present the oropharyngeal microbiota characteristics identified by full-length 16S rRNA gene sequencing through the NANOPORE platform of oropharynx swab specimens from 10 mild COVID-19 patients and 10 healthy controls. Our results revealed a distinct oropharyngeal microbiota composition in mild COVID-19 patients, characterized by enrichment of opportunistic pathogens such as Peptostreptococcus anaerobius and Pseudomonas stutzeri and depletion of Sphingomonas yabuuchiae, Agrobacterium sullae, and Pseudomonas veronii. Based on the relative abundance of the oropharyngeal microbiota at the species level, we built a microbial classifier to distinguish COVID-19 patients from healthy controls, in which P. veronii, Pseudomonas fragi, and S. yabuuchiae were identified as the most prominent signatures for their depletion in the COVID-19 group. Several members of the genus Campylobacter, especially Campylobacter fetus and Campylobacter rectus, which were highly enriched in COVID-19 patients with higher severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) viral load and showed a significant correlation with disease status and several routine clinical blood indicators, indicate that several bacteria may transform into opportunistic pathogen in COVID-19 patients when facing the challenges of viral infection. We also found the diver taxa Streptococcus anginosus and Streptococcus alactolyticus in the network of disease patients, suggesting that these oropharynx microbiota alterations may impact COVID-19 severity by influencing the microbial association patterns. In conclusion, the low sample size of SARS-CoV-2 infection patients (n = 10) here makes these results tentative; however, we have provided the overall characterization that oropharyngeal microbiota alterations and microbial correlation patterns were associated with COVID-19 severity in Anhui Province.

Aorta in Pathologies May Function as an Immune Organ by Upregulating Secretomes for Immune and Vascular Cell Activation, Differentiation and Trans-Differentiation-Early Secretomes may Serve as Drivers for Trained Immunity.

To determine whether aorta becomes immune organ in pathologies, we performed transcriptomic analyses of six types of secretomic genes (SGs) in aorta and vascular cells and made the following findings: 1) 53.7% out of 21,306 human protein genes are classified into six secretomes, namely, canonical, caspase 1, caspase 4, exosome, Weibel-Palade body, and autophagy; 2) Atherosclerosis (AS), chronic kidney disease (CKD) and abdominal aortic aneurysm (AAA) modulate six secretomes in aortas; and Middle East Respiratory Syndrome Coronavirus (MERS-CoV, COVID-19 homologous) infected endothelial cells (ECs) and angiotensin-II (Ang-II) treated vascular smooth muscle cells (VSMCs) modulate six secretomes; 3) AS aortas upregulate T and B cell immune SGs; CKD aortas upregulate SGs for cardiac hypertrophy, and hepatic fibrosis; and AAA aorta upregulate SGs for neuromuscular signaling and protein catabolism; 4) Ang-II induced AAA, canonical, caspase 4, and exosome SGs have two expression peaks of high (day 7)-low (day 14)-high (day 28) patterns; 5) Elastase induced AAA aortas have more inflammatory/immune pathways than that of Ang-II induced AAA aortas; 6) Most disease-upregulated cytokines in aorta may be secreted via canonical and exosome secretomes; 7) Canonical and caspase 1 SGs play roles at early MERS-CoV infected ECs whereas caspase 4 and exosome SGs play roles in late/chronic phases; and the early upregulated canonical and caspase 1 SGs may function as drivers for trained immunity (innate immune memory); 8) Venous ECs from arteriovenous fistula (AVF) upregulate SGs in five secretomes; and 9) Increased some of 101 trained immunity genes and decreased trained tolerance regulator IRG1 participate in upregulations of SGs in atherosclerotic, Ang-II induced AAA and CKD aortas, and MERS-CoV infected ECs, but less in SGs upregulated in AVF ECs. IL-1 family cytokines, HIF1α, SET7 and mTOR, ROS regulators NRF2 and NOX2 partially regulate trained immunity genes; and NRF2 plays roles in downregulating SGs more than that of NOX2 in upregulating SGs. These results provide novel insights on the roles of aorta as immune organ in upregulating secretomes and driving immune and vascular cell differentiations in COVID-19, cardiovascular diseases, inflammations, transplantations, autoimmune diseases and cancers.

Changes in Serum Neutralizing Antibodies Levels During Convalescence of COVID-19 Patients.

Detection of serum-specific SARS-CoV-2 antibody has become a complementary means for the identification of coronavirus disease 2019 (COVID-19). As we already know, the neutralizing antibody titers in patients with COVID-19 decrease during the course of time after convalescence, whereas the duration of antibody responses in the convalescent patients has not been defined clearly. In the current study, we collected 148 serum samples from 37 confirmed COVID-19 cases with different disease severities. The neutralizing antibodies (Nabs), IgM and IgG against COVID-19 were determined by CLIA Microparticle and microneutralization assay, respectively. The time duration of serum titers of SARS-CoV-2 antibodies were recorded. Our results indicate that IgG (94.44%) and Nabs (89.19%) can be detected at low levels within 190-266 days of disease onset. The findings can advance knowledge regarding the antibody detection results for COVID-19 patients and provide a method for evaluating the immune response after vaccination.

Mycoplasma pneumoniae among Chinese Outpatient Children with Mild Respiratory Tract Infections during the Coronavirus Disease 2019 Pandemic.

Mycoplasma pneumoniae is a common pathogen causing respiratory disease in children. We sought to investigate the epidemiology of M. pneumoniae among outpatient children with mild respiratory tract infections (RTIs) during the coronavirus disease 2019 (COVID-19) pandemic. Eligible patients were prospectively enrolled from January 2020 to June 2021. Throat swabs were tested for M. pneumoniae RNA. M. pneumoniae IgM was tested by a colloidal gold assay. Macrolide resistance and the effect of the COVID-19 countermeasures on M. pneumoniae prevalence were assessed. Symptom scores, treatments, and outcomes were evaluated. Eight hundred sixty-two eligible children at 15 centers in China were enrolled. M. pneumoniae was detected in 78 (9.0%) patients. Seasonally, M. pneumoniae peaked in the first spring and dropped dramatically to extremely low levels over time until the next summer. Decreases in COVID-19 prevalence were significantly associated with decreases in M. pneumoniae prevalence (r = 0.76, P = 0.001). The macrolide resistance rate was 7.7%. The overall sensitivity and specificity of the colloidal gold assay used in determining M. pneumoniae infection were 32.1% and 77.9%, respectively. No more benefits for improving the severity of symptoms and outcomes were observed in M. pneumoniae-infected patients treated with a macrolide than in those not treated with a macrolide during follow-up. The prevalences of M. pneumoniae and macrolide resistance in outpatient children with mild RTIs were at low levels in the early stage of the COVID-19 pandemic but may have rebounded recently. The colloidal gold assay for M. pneumoniae IgM may be not appropriate for diagnosis of M. pneumoniae infection. Macrolides should be used with caution among outpatients with mild RTIs. IMPORTANCE This is the first and largest prospective, multicenter, active, population-based surveillance study of the epidemiology of Mycoplasma pneumoniae among outpatient children with mild respiratory tract infections (RTIs) during the COVID-19 pandemic. Nationwide measures like strict face mask wearing and restrictions on population movement implemented to prevent the spread of COVID-19 might also effectively prevent the spread of M. pneumoniae. The prevalence of M. pneumoniae and the proportion of drug-resistant M. pneumoniae isolates in outpatient children with mild RTIs were at low levels in the early stage of the COVID-19 pandemic but may have rebounded recently. The colloidal gold assay for M. pneumoniae IgM may be not appropriate for screening and diagnosis of M. pneumoniae infection. Macrolides should be used with caution among outpatients with mild RTIs.