Olfactory impairment in COVID-19: Two methods for the assessment of olfactory function

Background Olfactory impairment is a major symptom of COVID-19. Is it necessary for COVID-19 patients to perform the detection of olfactory function, even how to select the olfactory psychophysical assessment tool. Methods Patients infected with SARS-CoV-2 Delta variant were firstly taken into three categories (mild, moderate, and severe) according to the clinical classification. The Odor Stick Identification Test for the Japanese (OSIT-J) and the Simple Olfactory Test were used to assess olfactory function. Moreover, these patients were divided into three groups based on the results of the olfactory degree (euosmia, hyposmia, and dysosmia), too. The statistical analysis of the correlations between olfaction and clinical characteristics of patients were performed. Results Our study demonstrated that the elderly men of Han were more susceptible to infected SARS-CoV-2, the clinical symptoms of the COVID-19 patients showed a clear correspondence with the disease type and the degree of olfactory disturbance. Whether or not to vaccinate and whether to complete the whole course of vaccination was closely related to the patient's condition. OSIT-J Test and Simple Test were consistent in our work, indicating that olfactory grading would worsen with the aggravation of symptoms. Furthermore, the OSIT-J method maybe better than Simple Olfactory Test. Conclusion The vaccination has an important protective effect on the general population, and vaccination should be vigorously promoted. Moreover, it is necessary for COVID-19 patients to perform the detection of olfactory function, and the easier, faster and less expensive method for determination of olfactory function should be utilized to COVID-19 patients as the vital physical examination.

Olfactory impairment in COVID-19: Two methods for the assessment of olfactory function.

Background: Olfactory impairment is a major symptom of COVID-19. Is it necessary for COVID-19 patients to perform the detection of olfactory function, even how to select the olfactory psychophysical assessment tool. Methods: Patients infected with SARS-CoV-2 Delta variant were firstly taken into three categories (mild, moderate, and severe) according to the clinical classification. The Odor Stick Identification Test for the Japanese (OSIT-J) and the Simple Olfactory Test were used to assess olfactory function. Moreover, these patients were divided into three groups based on the results of the olfactory degree (euosmia, hyposmia, and dysosmia), too. The statistical analysis of the correlations between olfaction and clinical characteristics of patients were performed. Results: Our study demonstrated that the elderly men of Han were more susceptible to infected SARS-CoV-2, the clinical symptoms of the COVID-19 patients showed a clear correspondence with the disease type and the degree of olfactory disturbance. Whether or not to vaccinate and whether to complete the whole course of vaccination was closely related to the patient's condition. OSIT-J Test and Simple Test were consistent in our work, indicating that olfactory grading would worsen with the aggravation of symptoms. Furthermore, the OSIT-J method maybe better than Simple Olfactory Test. Conclusion: The vaccination has an important protective effect on the general population, and vaccination should be vigorously promoted. Moreover, it is necessary for COVID-19 patients to perform the detection of olfactory function, and the easier, faster and less expensive method for determination of olfactory function should be utilized to COVID-19 patients as the vital physical examination.

BiT-MAC: Mortality prediction by bidirectional time and multi-feature attention coupled network on multivariate irregular time series.

Mortality prediction is crucial to evaluate the severity of illness and assist in improving the prognosis of patients. In clinical settings, one way is to analyze the multivariate time series (MTSs) of patients based on their medical data, such as heart rates and invasive mean arterial blood pressure. However, this suffers from sparse, irregularly sampled, and incomplete data issues. These issues can compromise the performance of follow-up MTS-based analytic applications. Plenty of existing methods try to deal with such irregular MTSs with missing values by capturing the temporal dependencies within a time series, yet in-depth research on modeling inter-MTS couplings remains rare and lacks model interpretability. To this end, we propose a bidirectional time and multi-feature attention coupled network (BiT-MAC) to capture the temporal dependencies (i.e., intra-time series coupling) and the hidden relationships among variables (i.e., inter-time series coupling) with a bidirectional recurrent neural network and multi-head attention, respectively. The resulting intra- and inter-time series coupling representations are then fused to estimate the missing values for a more robust MTS-based prediction. We evaluate BiT-MAC by applying it to the missing-data corrupted mortality prediction on two real-world clinical datasets, i.e., PhysioNet'2012 and COVID-19. Extensive experiments demonstrate the superiority of BiT-MAC over cutting-edge models, verifying the great value of the deep and hidden relations captured by MTSs. The interpretability of features is further demonstrated through a case study.

Utility of Epstein-Barr Virus DNA in Nasopharynx Swabs as a Reflex Test to Triage Seropositive Individuals in Nasopharyngeal Carcinoma Screening Programs.

BACKGROUND: Epstein-Barr virus (EBV) DNA detection in the nasopharynx is considered a biomarker for nasopharyngeal carcinoma (NPC). We evaluated its performance as a reflex test to triage EBV seropositives within an NPC screening program in China. METHODS: The study population was embedded within an ongoing NPC screening trial and included 1111 participants who screened positive for anti-EBV VCA (antibodies against EBV capsid antigens)/EBNA1 (EBV nuclear antigen1)-IgA antibodies (of 18 237 screened). Nasopharynx swabs were collected/tested for EBNA1 gene EBV DNA load. We evaluated performance of EBV DNA in the nasopharynx swab as a reflex test to triage EBV serological high-risk (those referred to endoscopy/MRI) and medium-risk (those referred to accelerated screening) individuals. RESULTS: By the end of 2019, we detected 20 NPC cases from 317 serological high-risk individuals and 4 NPC cases from 794 medium-risk individuals. When used to triage serological high-risk individuals, nasopharynx swab EBV DNA was detected in 19/20 cases (positivity rate among cases: 95.0%; 95% CI, 75.1%-99.9%), with a referral rate of 63.4% (201/317, 95% CI, 57.8%-68.7%) and NPC detection rate among positives of 9.5% (19/201, 95% CI, 5.8%-14.4%). The performance of an algorithm that combined serology with triage of serology high-risk individuals using EBV DNA testing yielded a sensitivity of 72.4% (95% CI, 3.0%-81.4%) and specificity of 97.6% (95% CI, 97.2%-97.9%). When used to triage EBV serological medium-risk individuals, the positivity rate among cases was 75.0% (95% CI, 19.4%-99.4%), with a referral rate of 61.8% (95% CI, 58.4%-65.2%) and NPC detection rate among positives of 0.6% (95% CI, 0.1%-1.8%). CONCLUSIONS: Nasopharynx swab EBV DNA showed promise as a reflex test to triage serology high-risk individuals, reducing referral by ca. 40% with little reduction in sensitivity compared to a serology-only screening program.

Strength Characteristics and Failure Mode of Solidified Soil Reinforced by Waste Masks

COVID-19 prevention and control consumed a large number of disposable masks,which had a large amount of waste disposal and high environmental protection requirements. It is one of the important methods to solve the problems of difficult source and high price of high-quality subgrade fillers. This paper proposes the solution of used waste masks to reinforce sludge solidified soil with high moisture contents,which will be used as subgrade fillers,with cement as curing agents and waste masks as reinforcing materials,the unconfined compressive resistance of waste mask reinforced cement solidified soil under different waste mask contents,mask sizes and ages is measured,additionally,the influences of the mask sizes and ages on the reinforcement effect of cement solidified soil are discussed,and this study also analyze the influence of reinforcement of waste mask on the failure modes of cement solidified soil. The results show that,under the experimental conditions,the optimum content of waste mask is about 0.5%,and the unconfined compressive strength is increased by about 87.5%.The stress-strain curve of unconfined compressive strength of cement-solidified soil reinforced with waste masks shows a softening pattern,and the reinforcement of waste mask improves the deformation resistance of the sample. The cement-solidified soil reinforced with waste masks presents a certain plastic failure characteristic.

Gut microbiota, inflammation, and molecular signatures of host response to infection.

Gut microbial dysbiosis has been linked to many noncommunicable diseases. However, little is known about specific gut microbiota composition and its correlated metabolites associated with molecular signatures underlying host response to infection. Here, we describe the construction of a proteomic risk score based on 20 blood proteomic biomarkers, which have recently been identified as molecular signatures predicting the progression of the COVID-19. We demonstrate that in our cohort of 990 healthy individuals without infection, this proteomic risk score is positively associated with proinflammatory cytokines mainly among older, but not younger, individuals. We further discover that a core set of gut microbiota can accurately predict the above proteomic biomarkers among 301 individuals using a machine learning model and that these gut microbiota features are highly correlated with proinflammatory cytokines in another independent set of 366 individuals. Fecal metabolomics analysis suggests potential amino acid-related pathways linking gut microbiota to host metabolism and inflammation. Overall, our multi-omics analyses suggest that gut microbiota composition and function are closely related to inflammation and molecular signatures of host response to infection among healthy individuals. These results may provide novel insights into the cross-talk between gut microbiota and host immune system.

Predicting Mutational Effects on Receptor Binding of the Spike Protein of SARS-CoV-2 Variants.

The pandemic caused by SARS-CoV-2 has cost millions of lives and tremendous social/financial loss. The virus continues to evolve and mutate. In particular, the recently emerged "UK", "South Africa", and Delta variants show higher infectivity and spreading speed. Thus, the relationship between the mutations of certain amino acids and the spreading speed of the virus is a problem of great importance. In this respect, understanding the mutational mechanism is crucial for surveillance and prediction of future mutations as well as antibody/vaccine development. In this work, we used a coarse-grained model (that was used previously in predicting the importance of mutations of N501) to calculate the free energy change of various types of single-site or combined-site mutations. This was done for the UK, South Africa, and Delta mutants. We investigated the underlying mechanisms of the binding affinity changes for mutations at different spike protein domains of SARS-CoV-2 and provided the energy basis for the resistance of the E484 mutant to the antibody m396. Other potential mutation sites were also predicted. Furthermore, the in silico predictions were assessed by functional experiments. The results establish that the faster spreading of recently observed mutants is strongly correlated with the binding-affinity enhancement between virus and human receptor as well as with the reduction of the binding to the m396 antibody. Significantly, the current approach offers a way to predict new variants and to assess the effectiveness of different antibodies toward such variants.

A proof of concept for neutralizing antibody-guided vaccine design against SARS-CoV-2.

Mutations and transient conformational movements of the receptor binding domain (RBD) that make neutralizing epitopes momentarily unavailable present immune escape routes for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). To mitigate viral escape, we developed a cocktail of neutralizing antibodies (NAbs) targeting epitopes located on different domains of spike (S) protein. Screening of a library of monoclonal antibodies generated from peripheral blood mononuclear cells of COVID-19 convalescent patients yielded potent NAbs, targeting the N-terminal domain (NTD) and RBD domain of S, effective at nM concentrations. Remarkably, a combination of RBD-targeting NAbs and NTD-binding NAbs, FC05, enhanced the neutralization potency in cell-based assays and an animal model. Results of competitive surface plasmon resonance assays and cryo-electron microscopy (cryo-EM) structures of antigen-binding fragments bound to S unveil determinants of immunogenicity. Combinations of immunogens, identified in the NTD and RBD of S, when immunized in rabbits and macaques, elicited potent protective immune responses against SARS-CoV-2. More importantly, two immunizations of this combination of NTD and RBD immunogens provided complete protection in macaques against a SARS-CoV-2 challenge, without observable antibody-dependent enhancement of infection. These results provide a proof of concept for neutralization-based immunogen design targeting SARS-CoV-2 NTD and RBD.

COVID-19 lung infection segmentation with a novel two-stage cross-domain transfer learning framework.

With the global outbreak of COVID-19 in early 2020, rapid diagnosis of COVID-19 has become the urgent need to control the spread of the epidemic. In clinical settings, lung infection segmentation from computed tomography (CT) images can provide vital information for the quantification and diagnosis of COVID-19. However, accurate infection segmentation is a challenging task due to (i) the low boundary contrast between infections and the surroundings, (ii) large variations of infection regions, and, most importantly, (iii) the shortage of large-scale annotated data. To address these issues, we propose a novel two-stage cross-domain transfer learning framework for the accurate segmentation of COVID-19 lung infections from CT images. Our framework consists of two major technical innovations, including an effective infection segmentation deep learning model, called nCoVSegNet, and a novel two-stage transfer learning strategy. Specifically, our nCoVSegNet conducts effective infection segmentation by taking advantage of attention-aware feature fusion and large receptive fields, aiming to resolve the issues related to low boundary contrast and large infection variations. To alleviate the shortage of the data, the nCoVSegNet is pre-trained using a two-stage cross-domain transfer learning strategy, which makes full use of the knowledge from natural images (i.e., ImageNet) and medical images (i.e., LIDC-IDRI) to boost the final training on CT images with COVID-19 infections. Extensive experiments demonstrate that our framework achieves superior segmentation accuracy and outperforms the cutting-edge models, both quantitatively and qualitatively.

Inf-Net: Automatic COVID-19 Lung Infection Segmentation From CT Images.

Coronavirus Disease 2019 (COVID-19) spread globally in early 2020, causing the world to face an existential health crisis. Automated detection of lung infections from computed tomography (CT) images offers a great potential to augment the traditional healthcare strategy for tackling COVID-19. However, segmenting infected regions from CT slices faces several challenges, including high variation in infection characteristics, and low intensity contrast between infections and normal tissues. Further, collecting a large amount of data is impractical within a short time period, inhibiting the training of a deep model. To address these challenges, a novel COVID-19 Lung Infection Segmentation Deep Network (Inf-Net) is proposed to automatically identify infected regions from chest CT slices. In our Inf-Net, a parallel partial decoder is used to aggregate the high-level features and generate a global map. Then, the implicit reverse attention and explicit edge-attention are utilized to model the boundaries and enhance the representations. Moreover, to alleviate the shortage of labeled data, we present a semi-supervised segmentation framework based on a randomly selected propagation strategy, which only requires a few labeled images and leverages primarily unlabeled data. Our semi-supervised framework can improve the learning ability and achieve a higher performance. Extensive experiments on our COVID-SemiSeg and real CT volumes demonstrate that the proposed Inf-Net outperforms most cutting-edge segmentation models and advances the state-of-the-art performance.

Systematic profiling of ACE2 expression in diverse physiological and pathological conditions for COVID-19/SARS-CoV-2.

Recent retrospective studies of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) disease (COVID-19) revealed that the patients with common comorbidities of cancers and chronic diseases face significantly poorer clinical outcomes than those without. Since the expression profile of ACE2, a crucial cell entry receptor for SARS-CoV-2, could indicate the susceptibility to SARS-CoV-2 infection, here we systematically dissected ACE2 expression using large-scale multi-omics data from 30 organs/tissues, 33 cancer types and some common chronic diseases involving >28 000 samples. It was found that sex and age could be correlated with the susceptibility of SARS-CoV-2 infection for certain tissues. Strikingly, ACE2 was up-regulated in cervical squamous cell carcinoma and endocervical adenocarcinoma, colon adenocarcinoma, oesophageal carcinoma, kidney renal papillary cell carcinoma, lung adenocarcinoma and uterine corpus endometrial carcinoma compared to controls. Furthermore, the patients with common chronic diseases regarding angiocardiopathy, type 2 diabetes, liver, pneumonia and hypertension were also with higher ACE2 expression compared to related controls, which were validated using independent data sets. Collectively, our study may reveal a novel important mechanism that the patients with certain cancers and chronic diseases may express higher ACE2 expression compared to the individuals without diseases, which could lead to their higher susceptibility to multi-organ injury of SARS-CoV-2 infection.