Generative face inpainting hashing for occluded face retrieval.

COVID-19 has resulted in a significant impact on individual lives, bringing a unique challenge for face retrieval under occlusion. In this paper, an occluded face retrieval method which consists of generator, discriminator, and deep hashing retrieval network is proposed for face retrieval in a large-scale face image dataset under variety of occlusion situations. In the proposed method, occluded face images are firstly reconstructed using a face inpainting model, in which the adversarial loss, reconstruction loss and hash bits loss are combined for training. With the trained model, hash codes of real face images and corresponding reconstructed face images are aimed to be as similar as possible. Then, a deep hashing retrieval network is used to generate compact similarity-preserving hashing codes using reconstructed face images for a better retrieval performance. Experimental results show that the proposed method can successfully generate the reconstructed face images under occlusion. Meanwhile, the proposed deep hashing retrieval network achieves better retrieval performance for occluded face retrieval than existing state-of-the-art deep hashing retrieval methods.

Bioinformatics analysis to identify intersection genes, associated pathways and therapeutic drugs between COVID-19 and Oral candidiasis.

BACKGROUND: Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has a serious threat to human health. Oral candidiasis (OC) may be one of the causes of morbidity in severe COVID-19 patients. However, there is currently no treatment for oral candidiasis and COVID-19 (OC/COVID-19). The purpose of this study was to use text mining and data analysis to investigate the target genes for treatment and explore potential therapeutic drugs for OC/COVID-19. METHODS: We used the text mining tool pubmed2ensembl to detect genes associated with OC, and the dataset GSE164805 was used for the data analysis. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed on the two intersection genes using the Database of Annotation, Visualization and Integrated Discovery (DAVID) platform. The protein-protein interaction (PPI) networks were constructed by STRING software, and gene module analysis was performed using Molecular Complex Detection (MCODE), a plug-in in Cytoscape. The most significant genes were selected as hub genes and their functions and pathways were analyzed using Metascape. We revealed the upstream pathway activity of the hub genes. The drug-gene interaction database (DGIdb) and the traditional Chinese medicines integrated database (TCMID) were used to discover potential drugs for the treatment of OC/COVID-19. RESULTS: The analysis indicated that there were 2869 differentially expressed genes (DEGs) in GSE164805. We identified 161 unique genes associated with oral candidiasis through text mining. A total of 20 intersection genes were identified as the therapeutic targets for OC/COVID-19. Based on the bioinformatics analysis, nine genes (TNF, IL1B, IFNG, CSF2, ELANE, CCL2, MMP9, CXCR4, and IL1A) were identified as hub genes that were mainly enriched in the IL-17 signaling pathway, TNF signaling pathway, AGE-RAGE signaling pathway in diabetic complications and NOD-like receptor signaling pathway. We identified four of the nine genes that target five existing drugs, including BKT140, mavorixafor, sivelestat, canakinumab, and rilonacept. Furthermore, twenty herb ingredients were also screened as potential drugs. CONCLUSION: In this study, TNF, IL1B, IFNG, CSF2, ELANE, CCL2, MMP9, CXCR4, and IL1A were potentially key genes involved in the treatment of OC/COVID-19. Taken together five drugs and twenty herb ingredients were identified as potential therapeutic agents for OC/COVID-19 treatment and management.

Sarcopenia and COVID-19 Outcomes.

Coronavirus disease 2019 (COVID-19) spread rapidly and became a severe global public health threat. Older adults have a high risk of COVID-19 and its associated mortality. Sarcopenia has emerged as a predictor of poor outcomes in COVID-19 patients, including lengthy hospital stays, mortality, intensive care unit admission, need for invasive mechanical ventilation, and poor rehabilitation outcomes. Chronic inflammation, immune dysfunction, respiratory muscle dysfunction, and swallowing dysfunction may underlie the association between sarcopenia and the poor outcomes of COVID-19 patients. Interleukin 6 receptor blockers (tocilizumab or sarilumab) are recommended for treating patients with severe COVID-19, and their therapeutic effects on sarcopenia are of great interest. This review aimed to analyze the current reports on the association between sarcopenia and COVID-19 and provide an update on the contribution of sarcopenia to the severity and adverse outcomes of COVID-19 and its underlying mechanisms. We also aimed to explore the different screening tools for sarcopenia concurrent with COVID-19, and advocate for early diagnosis and treatment of sarcopenia. Given that the fight against the COVID-19 pandemic may be long-term, further research into understanding the effects of sarcopenia in patients infected with the Omicron variant is necessary.

The top 100 most cited articles on COVID-19 vaccine: a bibliometric analysis.

This study aimed to uncover the current major topics regarding COVID-19 vaccine, and systematically evaluate the development trends for future research. The top 100 most cited original articles on COVID-19 vaccine from January 2020 to October 2022 were identified from Web of Science Core Collection database. CiteSpace (v6.1.R3) was adopted for bibliometric analysis with statistical and visual analysis. The number of citations ranged from 206 to 5881, with a median of 349.5. The USA (n = 56), England (n = 33), and China (n = 16) ranked the top three countries/regions in terms of the number of publications. Harvard Medical School (centrality = 0.71), Boston Children's Hospital (centrality = 0.67), and Public Health England (centrality = 0.57) were the top three institutions leading the way on COVID-19 vaccine research. The New England of medicine journal dominated with 22 articles in the 32 high-quality journals. The three most frequent keywords were immunization (centrality = 0.25), influenza vaccination (centrality = 0.21), and coronavirus (centrality = 0.18). Cluster analysis of keywords showed that the top four categories were protection efficacy, vaccine hesitancy, spike protein, and second vaccine dose (Q value = 0.535, S value = 0.879). Cluster analysis of cited references showed that top eight largest categories were Cov-2 variant, clinical trial, large integrated health system, COV-2 rhesus macaque, mRNA vaccine, vaccination intent, phase II study, and Cov-2 omicron variant (Q value = 0.672, S value = 0.794). The research on COVID-19 vaccine is currently the hottest topic in academic community. At present, COVID-19 vaccines researches have focused on vaccine efficacy, vaccine hesitancy, and the efficacy of current vaccines on omicron variants. However, how to increase vaccine uptake, focus on mutations in the spike protein, evaluate of the efficacy of booster vaccine, and how effective new vaccines under pre- and clinical development against omicron will be spotlight in the future.

Human migration-based graph convolutional network for PM2.5 forecasting in post-COVID-19 pandemic age.

Due to the coronavirus disease 2019 pandemic, local authorities always implanted non-pharmaceutical interventions, such as maintaining social distance to reduce human migration. Besides, previous studies have proved that human migration highly influenced air pollution concentration in an area. Therefore, this study aims to explore whether human migration can work as a significant factor in the post-pandemic age to help PM2.5 concentration forecasting. In this work, we first analyze the variations of PM2.5 in 11 cities of Hubei from 2015 to 2020 and further compare PM2.5 trends with the migration trends of Hubei province in 2020. Experimental results indicate that the human migration indirectly affected the urban PM2.5 concentration. Then, we established a graph data structure based on the migration network describing the migration flow size between any two areas in the Hubei province and proposed a migration attentive graph convolutional network (MAGCN) for forecasting PM2.5. Combined with the migration data. The proposed model can attentively aggregate the information of neighbor nodes through migration weights. Experimental results indicate that the proposed MAGCN can forecast PM2.5 concentration accurately.

Edge-enhanced instance segmentation by grid regions of interest.

This paper focuses on the instance segmentation task. The purpose of instance segmentation is to jointly detect, classify and segment individual instances in images, so it is used to solve a large number of industrial tasks such as novel coronavirus diagnosis and autonomous driving. However, it is not easy for instance models to achieve good results in terms of both efficiency of prediction classes and segmentation results of instance edges. We propose a single-stage instance segmentation model EEMask (edge-enhanced mask), which generates grid ROIs (regions of interest) instead of proposal boxes. EEMask divides the image uniformly according to the grid and then calculates the relevance between the grids based on the distance and grayscale values. Finally, EEMask uses the grid relevance to generate grid ROIs and grid classes. In addition, we design an edge-enhanced layer, which enhances the model's ability to perceive instance edges by increasing the number of channels with higher contrast at the instance edges. There is not any additional convolutional layer overhead, so the whole process is efficient. We evaluate EEMask on a public benchmark. On average, EEMask is 17.8% faster than BlendMask with the same training schedule. EEMask achieves a mask AP score of 39.9 on the MS COCO dataset, which outperforms Mask RCNN by 7.5% and BlendMask by 3.9%.

The COVID-19 vaccination hesitancy among Chinese individuals with diabetes and the impact on glycemic control of vaccination: a questionnaire study.

OBJECTIVE: The study aimed to investigate the attitudes of people with diabetes mellitus (DM) on COVID-19 vaccination and its influence on the glycemic control. METHODS: Data were collected from a consecutive series of adults (age > 18 years) with type 2 diabetes under regular follow-ups in the Integrated Care Diabetes Outpatient Clinic of Peking University First Hospital from December 1st to December 31st 2021. An online interview questionnaire was conducted, and demographic data including age, sex category, history of drug allergy, history of hypertension, the duration of diabetes, reasons for vaccine hesitancy (VH) and adverse reactions after each injection of vaccines was collected. Glucose levels were collected from medical records. RESULTS: Thirty-nine (22.9%) subjects experienced VH and 131 (77.1%) people living with diabetes received inactivated vaccine against COVID-19. Hesitant individuals had a higher proportion of female gender (vaccinated group vs. VH group, 62/131 vs. 26/39, p = 0.044), higher baseline glycosylated hemoglobin A1c (HbA1c) (vaccinated group vs. VH group, 6.56 ± 0.95% vs. 7.54 ± 2.01%, p < 0.001) and elevated baseline postprandial blood glucose (PBG) (vaccinated group vs. VH group, 8.32 ± 1.97 mmol/L vs. 9.44 ± 2.94 mmol/L, p = 0.015). Subjects of male gender (p = 0.025) and history of hypertension (p = 0.021) were likely to get vaccinated, while higher HbA1c was negatively associated with an elevated propensity to receive anti-COVID-19 vaccine (p = 0.003). Most common reasons for hesitating to receive COVID-19 vaccination were worrying about the possibility of leading to other diseases (30.8%), followed by fearing of glucose variation (17.9%). Systemic adverse reactions were reported in 30.5% individuals after the first injection of inactivated vaccines, and resolved within 3 days in medium. Fasting blood glucose (FBG) decreased significantly after the third injection compared with FBG after the second dose (second vs. third, 6.78 ± 1.24 mmol/L vs. 6.41 ± 1.30 mmol/L, p = 0.027). HbA1c reduced significantly from 6.56% before vaccination to 6.35% after the second injection (p = 0.012). CONCLUSIONS: Our study demonstrated that vaccine hesitancy was lower among male subjects and people with hypertension, while vaccine confidence was reduced in people with poor glycemic control. HbA1c level was lower along with vaccination.

Virus inactivation by sequential ultraviolet-chlorine disinfection: Synergistic effect and mechanism.

The COVID-19 outbreak has raised concerns about the efficacy of the disinfection process followed in water treatment plants in preventing the spread of viruses. Ultraviolet (UV) and chlorine multi-barrier disinfection processes are commonly used in water treatment plants; however, their effects on virus inactivation are still unclear. In this study, the effects of different disinfection processes (i.e., UV, free chlorine, and their combination) on waterborne viruses were analyzed using bacteriophage surrogates (i.e., MS2 and PR772) as alternative indicators. The results showed that the inactivation rates of PR772 by either UV or free chlorine disinfection were higher than those of MS2. PR772 was approximately 1.5 times more sensitive to UV disinfection and 8.4 times more sensitive to chlorine disinfection than MS2. Sequential UV-chlorine disinfection had a synergistic effect on virus inactivation, which was enhanced by an increase in the UV dose. As compared with single free chlorine disinfection, UV irradiation at 40 mJ cm-2 enhanced MS2 and PR772 inactivation significantly with a 2.7-fold (MS2) and a 1.7-fold (PR772) increase in the inactivation rate constants on subsequent chlorination in phosphate buffered saline. The synergistic effect was also observed in real wastewater samples, in which the MS2 inactivation rate increased 1.4-fold on subsequent chlorination following UV irradiation at 40 mJ cm-2. The mechanism of the synergistic effect of sequential UV-chlorine disinfection was determined via sodium dodecyl sulfate-polyacrylamide gel electrophoresis, using MS2 as an indicator. The results showed that the synergistic effect was due to damage to MS2 surface proteins caused by previous UV disinfection, which enhanced the sensitivity of MS2 to chlorination. This study provides a feasible approach for the efficient inactivation of viruses in water supply and drainage.

The Expression Levels of SARS-CoV-2 Infection-Mediating Molecules Promoted by Interferon-γ and Tumor Necrosis Factor-α Are Downregulated by Hydrogen Sulfide.

Autoimmune thyroid diseases (AITDs), which include Hashimoto's thyroiditis (HT) and Graves' disease (GD), have a higher prevalence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in the literature. The effects of AITD-associated cytokines on SARS-CoV-2 infection-mediating molecule levels might be involved in the pathogenesis of susceptibility. We speculated that hydrogen sulfide (H2S) might attenuate this process since H2S has antiviral effects. Using immunohistochemistry, we found that angiotensin-converting enzyme-II (ACE2) expression was higher in the HT group and neuropilin 1 (NRP1) expression was higher in HT and GD groups than in the normal group, while transmembrane protease serine type 2 (TMPRSS2) expression was lower in HT and GD groups. When culturing primary thyrocytes with cytokines or sodium hydrosulfide (NaHS) plus cytokines, we found that ACE2 and NRP1 mRNA levels were upregulated while TMPRSS2 levels were downregulated by interferon-γ (IFN-γ) and tumor necrosis factor-α (TNF-α). After pretreatment with NaHS in thyrocytes, ACE2 and NRP1 expression were downregulated compared to IFN-γ or TNF-α treatment, and NaHS had no effect on TMPRSS2 expression. Our findings suggested that IFN-γ and TNF-α, which are elevated in AITDs, promoted ACE2 and NRP1 expression and inhibited TMPRSS2 expression. H2S might protect against SARS-CoV-2 infection by downregulating ACE2 and NRP1 levels.

Estimating unconfirmed COVID-19 infection cases and multiple waves of pandemic progression with consideration of testing capacity and non-pharmaceutical interventions: A dynamic spreading model.

The novel coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has unique epidemiological characteristics that include presymptomatic and asymptomatic infections, resulting in a large proportion of infected cases being unconfirmed, including patients with clinical symptoms who have not been identified by screening. These unconfirmed infected individuals move and spread the virus freely, presenting difficult challenges to the control of the pandemic. To reveal the actual pandemic situation in a given region, a simple dynamic susceptible-unconfirmed-confirmed-removed (D-SUCR) model is developed taking into account the influence of unconfirmed cases, the testing capacity, the multiple waves of the pandemic, and the use of non-pharmaceutical interventions. Using this model, the total numbers of infected cases in 51 regions of the USA and 116 countries worldwide are estimated, and the results indicate that only about 40% of the true number of infections have been confirmed. In addition, it is found that if local authorities could enhance their testing capacities and implement a timely strict quarantine strategy after identifying the first infection case, the total number of infected cases could be reduced by more than 90%. Delay in implementing quarantine measures would drastically reduce their effectiveness.

Molecular transmission networks and pre-treatment drug resistance among individuals with acute HIV-1 infection in Baoding, China.

BACKGROUND: Human immunodeficiency virus type 1 (HIV-1) genetic diversity and pre-treatment drug resistance (PDR) are major barriers to successful antiretroviral therapy (ART). In China, sexual intercourse is the most frequent route of HIV-1 transmission. However, few studies have analyzed PDR and transmission networks in detail among individuals in China with acute HIV-1 infection and their sexual contacts. METHODS: A cross-sectional study was conducted in Baoding City, Hebei Province, China from 2019-2020. CD4 T cell counts and viral loads were assessed and a HIV-1 genotypic PDR assay was developed in-house. Transmission networks were visualized using Cytoscape with a threshold genetic distance of 0.015 among HIV-1 subtypes. RESULTS: From 139 newly diagnosed and drug-naïve individuals with HIV-1, 132 pol gene sequences were obtained and revealed eight HIV-1 subtypes. Circulating recombinant form (CRF)01_AE was the most frequent subtype (53.0%, 70/132) followed by CRF07_BC (26.5%, 35/132), B (13.6%, 18/132), unique recombinant forms (2.3%, 3/132), CRF55_01B (1.5%, 2/132), CRF103_01B (1.5%, 2/132), CRF65_cpx (0.8%, 1/132), and C (0.8%, 1/132). A total of 47 pol gene sequences were used to generate 10 molecular transmission networks. The overall prevalence of PDR was 7.6% and that of PDR to non-nucleotide reverse transcriptase inhibitors was 6.1%. Of three transmission networks for PDR, two were closely associated with Beijing and Tianjin, while another was restricted to sequences determined in this study. CONCLUSIONS: These results demonstrate that during acute HIV-1 infection, PDR is transmitted in dynamic networks. This suggests that early detection, diagnosis, surveillance, and treatment are critical to effectively control HIV-1 spread.

Health Literacy and Disparities in Knowledge, Attitude and Practice Regarding COVID-19 Among College Students During the COVID-19 Outbreak in China: A Cross-Sectional Study.

PURPOSE: This study aimed to assess the knowledge, attitude and practice (KAP) towards COVID-19 of Chinese college students during the COVID-19 outbreak and identify the determinants considerably associated with KAP. METHODS: An online cross-sectional questionnaire survey was conducted in February 2020 in China to recruit participants on the Wen-Juan-Xing online platform. A self-made questionnaire was used to assess the KAP towards COVID-19. RESULTS: The average scores of COVID-19-related KAP among college students were 32.16 ± 4.09, 16.84 ± 3.18 and 15.36 ± 1.83, respectively. The average correct response rate of COVID-19-related knowledge was 84.62%. 94.59% of the respondents believed China could control and conquer the pandemic; 98.92% of the respondents wore a mask when they went out. The multiple linear regression showed that the female students had higher scores of COVID-19-related knowledge than male students (ß = 0.79, P = 0.01), and the students from 985 or 211 universities (ß = -2.22, P < 0.01) were significantly associated with higher knowledge scores. Furthermore, a significantly positive correlation was found between the scores of COVID-19-related KAP among students. CONCLUSION: The college students had a good KAP towards the COVID-19 pandemic. The present study was able to access the COVID-19-related KAP among college students during the pandemic and identify their weaknesses in pandemic prevention. Consequently, the results of this study provide theoretical basis for implementing precision health education among college students and reference for campuses to conduct scientific prevention and control during a pandemic. Results also provide beneficial experience to curb similar major public health emergencies in the future.

Practical considerations for Ultraviolet-C radiation mediated decontamination of N95 respirator against SARS-CoV-2 virus.

Decontaminating N95 respirators for reuse could mitigate shortages during the COVID-19 pandemic. Although the United States Center for Disease Control has identified Ultraviolet-C irradiation as one of the most promising methods for N95 decontamination, very few studies have evaluated the efficacy of Ultraviolet-C for SARS-CoV-2 inactivation. In addition, most decontamination studies are performed using mask coupons that do not recapitulate the complexity of whole masks. We sought to directly evaluate the efficacy of Ultraviolet-C mediated inactivation of SARS-CoV-2 on N95 respirators. To that end we created a portable UV-C light-emitting diode disinfection chamber and tested decontamination of SARS-CoV-2 at different sites on two models of N95 respirator. We found that decontamination efficacy depends on mask model, material and location of the contamination on the mask. Our results emphasize the need for caution when interpreting efficacy data of UV-C decontamination methods.

Validation of the analytical performance of nine commercial RT-qPCR kits for SARS-CoV-2 detection using certified reference material.

The ongoing coronavirus disease 2019 (COVID-19) pandemic has become a public health emergency. Although many reverse-transcription PCR (RT-PCR) assays have been developed, their performance, especially sensitivity assessment, has been insufficiently tested. In this study, a preliminary comparison of the analytical sensitivity of nine RT-qPCR kits from different manufacturers was first conducted using a certified reference material derived from the genomic RNA of SARS-CoV-2 as the template. Subsequently, three of the nine kits, comprising two highly sensitive kits (DAAN, Huirui) and one less sensitive kit (Geneodx), were selected for further sensitivity and specificity validation. The results revealed variations in the performance between kits of the two groups. For the two highly sensitive kits, the limits of detection at 95 % probability (LOD95%) were 5.6 copies of the N gene and 3.5 copies of the ORF 1ab per reaction (DAAN), and 6.4 (N) and 4.6 (ORF 1ab) copies per reaction (Huirui). These LOD95% values were approximately 3 to 4-fold better than those of the Geneodx Kit. However, none of these three Kits showed cross-reactivity against 6 other types of human coronaviruses or respiratory viruses. Because most of these commercial kits are approved as in vitro diagnostics (testing specimens without direct human contact), it would be beneficial for their manufacturers to improve the diagnostic capability of these kits and thus reduce the clinical risks associated with false-negative results.

Comparative Study of COVID-19 Pandemic Progressions in 175 Regions in Australia, Canada, Italy, Japan, Spain, U.K. and USA Using a Novel Model That Considers Testing Capacity and Deficiency in Confirming Infected Cases.

Not identified as being exposed or infected, the group of asymptomatic and presymptomatic patients has become the key source of infectious hosts for the COVID-19 pandemic, triggering the re-emergence of outbreaks. Acknowledging the impacts of movement of unidentified patients and the limited testing capacity on understanding the spread of the virus, an augmented Susceptible-Exposed-Infectious-Confirmed-Recovered (SEICR) model integrating intercity migration data and testing capacity is developed to probe into the number of unidentified COVID-19 infected patients. This model allows evaluation of the effectiveness of active interventions, and more accurate prediction of the pandemic progression in a country, region or city. A pseudo-coevolutionary algorithm is adopted in the model fitting to provide an effective estimation of high-dimensional unknown parameter sets using a limited amount of historical data. The model is applied to 175 regions in Australia, Canada, Italy, Japan, Spain, the UK and USA to estimate the number of unconfirmed cases using limited historical data. Results showed that the actual number of infected cases could be 4.309 times as many as the official confirmed number. By implementing mass COVID-19 testing, the number of infected cases could be reduced by about 50%.