Advances in the study of nervous system infections in COVID‐19

Shortly after its outbreak, coronavirus disease 2019 (COVID‐19) has very rapidly spread to become a global epidemic. Early clinical findings mainly included typical symptoms such as fever and cough with a very high transmission rate. Recent findings have demonstrated neurological manifestations of atypical symptoms, which is associated with poor prognosis. In this paper, we describe the neurological aspects of COVID‐19 pneumonia in terms of relevant neurons, virus‐associated receptors, and olfactory and neurological clinical manifestations and offer insights on treatment.

Automatic detection of A-line in lung ultrasound images using deep learning and image processing.

BACKGROUND: Auxiliary diagnosis and monitoring of lung diseases based on lung ultrasound (LUS) images is important clinical research. A-line is one of the most common indicators of LUS that can offer support for the assessment of lung diseases. A traditional A-line detection method mainly relies on experienced clinicians, which is inefficient and cannot meet the needs of these areas with backward medical level. Therefore, how to realize the automatic detection of A-line in LUS image is important. PURPOSE: In order to solve the disadvantages of traditional A-line detection methods, realize automatic and accurate detection, and provide theoretical support for clinical application, we proposed a novel A-line detection method for LUS images with different probe types in this paper. METHODS: First, the improved Faster R-CNN model with a selection strategy of localization box was designed to accurately locate the pleural line. Then, the LUS image below the pleural line was segmented for independent analysis excluding the influence of other similar structures. Next, image-processing methods based on total variation, matched filter, and gray difference were applied to achieve the automatic A-line detection. Finally, the "depth" index was designed to verify the accuracy by judging whether the automatic measurement results belong to corresponding manual results (±5%). In experiments, 3000 convex array LUS images were used for training and validating the improved pleural line localization model by five-fold cross validation. 850 convex array LUS images and 1080 linear array LUS images were used for testing the trained pleural line localization model and the proposed image-processing-based A-line detection method. The accuracy analysis, error statistics, and Harsdorff distance were employed to evaluate the experimental results. RESULTS: After 100 epochs, the mean loss value of training and validation set of improved Faster R-CNN model reached 0.6540 and 0.7882, with the validation accuracy of 98.70%. The trained pleural line localization model was applied in the testing set of convex and linear probes and reached the accuracy of 97.88% and 97.11%, respectively, which were 3.83% and 8.70% higher than the original Faster R-CNN model. The accuracy, sensitivity, and specificity of A-line detection reached 95.41%, 0.9244%, 0.9875%, and 94.63%, 0.9230%, and 0.9766% for convex and linear probes, respectively. Compared to the experienced clinicians' results, the mean value and p value of depth error were 1.5342 ± 1.2097 and 0.9021, respectively, and the Harsdorff distance was 5.7305 ± 1.8311. In addition, the accumulated accuracy of the two-stage experiment (pleural line localization and A-line detection) was calculated as the final accuracy of the whole A-line detection system. They were 93.39% and 91.90% for convex and linear probes, respectively, which were higher than these previous methods. CONCLUSIONS: The proposed method combining image processing and deep learning can automatically and accurately detect A-line in LUS images with different probe types, which has important application value for clinical diagnosis.

Mental health and psychosocial problems among laboratory technicians in response to the COVID-19 pandemic in Hebei, China.

Objective: During the coronavirus disease 2019 (COVID-19) pandemic, an increased mental burden has been widely reported among medical health workers such as physicians and nurses. However, data on laboratory technicians exposed to COVID-19 have rarely been published. The aim of this study was to assess the magnitude of psychological symptoms among laboratory technicians and analyze potential risk factors associated with these symptoms. Methods: A cross-sectional online survey was performed via the Wenjuanxing platform (a professional online questionnaire platform) (https://www.wjx.cn/mobile/statnew.aspx) to investigate the mental health of laboratory technicians during the COVID-19 pandemic in Hebei, China from October 4, 2021, to November 3, 2021. The online questionnaire included demographic and occupational characteristics data of responders, and the Symptom Check List-90-Revised (SCL90-R)was used to quantify the magnitude of psychological symptoms among laboratory technicians. Participants' demographic and occupational characteristics were analyzed using descriptive statistical analyses. Chi-square tests were applied to compare the severity of each symptom between two or more groups. A binary logistic regression model was developed to identify the predictors of laboratory technicians' mental health in response to the COVID-19 pandemic, and outcomes are presented as odds ratios and 95% confidence interval. Statistical analysis was performed using SPSS version 21 (SPSS, New Orchard Road, Armonk, New York, USA). Results: A total of 3081 valid questionnaires were collected. Of these 3081 participants, 338 (11.0%) reported a total SCL90-R score >160, which indicated positive psychological symptoms. Among the 338 participants who reported psychological problems, most of them were mild symptoms. Several factors associated with mental health problems in laboratory technicians during COVID-19 were found, which include a history of physical and/or psychological problems (all 10 symptoms p < 0.001), more than 10 years of work experience (depression symptoms: OR = 2.350, p = 0.024; anxiety symptoms: OR = 2.642, p = 0.038), frontline work (depression symptoms: OR = 1.761, p = 0.001; anxiety symptoms: OR = 2.619, p < 0.001; hostility symptoms: OR = 1.913, p = 0.001), participant in more than 3 times large-scale SARS-CoV-2 screenings and more than 36 h per week in SARS-CoV-2 nucleic acid testing. Conclusion: A portion of laboratory technicians reported experiencing varying levels of psychological burden. During the COVID-19 pandemic, multiple interventions should be developed and implemented to address existing psychosocial challenges and promote the mental health of laboratory technicians.

An integrated dual-layer microfluidic platform for multiple respiratory viruses screening.

Currently, the coronavirus disease 2019 (COVID-19) caused by the outbreak of a novel coronavirus (SARS-CoV-2) is spreading rapidly worldwide. Due to the high incidence of influenza coinciding with SARS-CoV-2, rapid detection is crucial to prevent spreading. Here, we present an integrated dual-layer microfluidic platform for specific and highly sensitive SARS-CoV-2, influenza viruses A (FluA) H1N1, H3N2, and influenza virus B (FluB) simultaneous detection. The platform includes a dual microchip (Dµchip) and a portable detection device for real-time fluorescence detection, temperature control and online communication. The Reverse Transcription Loop-mediated Isothermal Amplification (RT-LAMP) and Cas12a cleavage were performed on the Dµchip. The limit of detection (LoD) of the Dµchip assay was 10 copies for SARS-CoV-2, FluA H1N1, H3N2, and FluB RNAs. The Dµchip assay yielded no cross-reactivity against other coronaviruses, so it was suitable for the screening of multiple viruses. Moreover, the positive percentage agreement (PPA) and negative percentage agreement (NPA) of the assay were 97.9% and 100%, respectively, in 75 clinical samples compared to data from RT-PCR-based assays. Furthermore, the assay allowed the detection SARS-CoV-2 and influenza viruses in spiked samples. Overall, the present platform would provide a rapid method for the screening of multiple viruses in hospital emergency, community and primary care settings and facilitate the remote diagnosis and outbreak control of the COVID-19.

Attachable Inertial Device with Machine Learning toward Head Posture Monitoring in Attention Assessment.

The monitoring of head posture is crucial for interactive learning, in order to build feedback with learners' attention, especially in the explosion of digital teaching that occurred during the current COVID-19 pandemic. However, conventional monitoring based on computer vision remains a great challenge in the multi-freedom estimation of head posture, owing to low-angle annotation and limited training accuracy. Here, we report a fully integrated attachable inertial device (AID) that comfortably monitors in situ head posture at the neck, and provides a machine learning-based assessment of attention. The device consists of a stretchable inertial sensing unit and a fully integrated circuit-based system, as well as mechanically compliant encapsulation. Due to the mechanical flexibility, the device can be seamlessly attach to a human neck's epidermis without frequent user interactions, and wirelessly supports six-axial inertial measurements, thereby obtaining multidimensional tracking of individual posture. These head postures (40 types) are then divided into 10 rotation actions which correspond to diverse situations that usually occur in daily activities of teaching. Benefiting from a 2D convolutional neural network (CNN)-based machine learning model, their classification and prediction of head postures can be used to analyze and infer attention behavior. The results show that the proposed 2D CNN-based machine learning method can effectively distinguish the head motion posture, with a high accuracy of 98.00%, and three actual postures were successfully verified and evaluated in a predefined attention model. The inertial monitoring and attention evaluation based on attachable devices and machine learning will have potential in terms of learning feedback and planning for learners.

East Asian summer monsoon enhanced by COVID-19.

Anthropogenic emissions decreased dramatically during the COVID-19 pandemic, but its possible effect on monsoon is unclear. Based on coupled models participating in the COVID Model Intercomparison Project (COVID-MIP), we show modeling evidence that the East Asian summer monsoon (EASM) is enhanced by 2.2% in terms of precipitation and by 5.4% in terms of the southerly wind at lower troposphere, and the amplitude of the forced response reaches about 1/3 of the standard deviation for interannual variability. The enhanced EASM during COVID-19 pandemic is a fast response to reduced aerosols, which is confirmed by the simulated response to the removal of all anthropogenic aerosols. The observational evidence, i.e., the anomalously strong EASM observed in 2020 and 2021, also supports the simulated enhancement of EASM. The essential mechanism for the enhanced EASM in response to COVID-19 is the enhanced zonal thermal contrast between Asian continent and the western North Pacific in the troposphere, due to the reduced aerosol concentration over Asian continent and the associated latent heating feedback. As the enhancement of EASM is a fast response to the reduction in aerosols, the effect of COVID-19 on EASM dampens soon after the rebound of emissions based on the models participating in COVID-MIP. Supplementary Information: The online version contains supplementary material available at 10.1007/s00382-022-06247-8.

Variations in Sulfur and Nitrogen Oxidation Rates in Summer Aerosols from 2014 to 2020 in Wuhan, China

To date, research regarding the changes of the sulfur and nitrogen rates in Wuhan during the summer is limited. In this study, we analyzed the air quality in Wuhan, China, using water-soluble ion, gaseous precursor, and weather data. A Spearman correlation analysis was then performed to investigate the temporal changes in air quality characteristics and their driving factors to provide a reference for air pollution control in Wuhan. The results indicate that SO2 in the atmosphere at Wuhan undergoes secondary conversion and photo-oxidation, and the conversion degree of SO2 is higher than that of NO2. During the summers of 2016 and 2017, secondary inorganic atmospheric pollution was more severe than during other years. The fewest oxidation days occurred in summer 2020 (11 days), followed by the summers of 2017 and 2014 (25 and 27 days, respectively). During the study period, ion neutralization was the strongest in summer 2015 and the weakest in August 2020. The aerosols in Wuhan were mostly acidic and NH4+ was an important neutralizing component. The neutralization factors of all cations showed little change in 2015. K+, Mg2+, and Ca2+ level changes were the highest in 2017 and 2020. At low temperature, high humidity, and low wind speed conditions, SO2 and NO2 were more easily converted into SO42− and NO3−.

Analysis of the meteorological factors affecting the short-term increase in O3 concentrations in nine global cities during COVID-19.

Surface ozone (O3) is a major air pollutant around the world. This study investigated O3 concentrations in nine cities during the Coronavirus disease 2019 (COVID-19) lockdown phases. A statistical model, named Generalized Additive Model (GAM), was also developed to assess different meteorological factors, estimate daily O3 release during COVID-19 lockdown and determine the relationship between the two. We found that: (1) Daily O3 significantly increased in all selected cities during the COVID-19 lockdown, presenting relative increases from -5.7% (in São Paulo) to 58.9% (in Guangzhou), with respect to the average value for the same period in the previous five years. (2) In the GAM model, the adjusted coefficient of determination (R2) ranged from 0.48 (Sao Paulo) to 0.84 (Rome), and it captured 51-85% of daily O3 variations. (3) Analyzing the expected O3 concentrations during the lockdown, using GAM fed by meteorological data, showed that O3 anomalies were dominantly controlled by meteorology. (4) The relevance of different meteorological variables depended on the cities. The positive O3 anomalies in Beijing, Wuhan, Guangzhou, and Delhi were mostly associated with low relative humidity and elevated maximum temperature. Low wind speed, elevated maximum temperature, and low relative humidity were the leading meteorological factors for O3 anomalies in London, Paris, and Rome. The two other cities had different leading factor combinations.

Structures, properties, and challenges of emerging 2D materials in bioelectronics and biosensors

Bioelectronics are powerful tools for monitoring and stimulating biological and biochemical processes, with applications ranging from neural interface simulation to biosensing. The increasing demand for bioelectronics has greatly promoted the development of new nanomaterials as detection platforms. Recently, owing to their ultrathin structures and excellent physicochemical properties, emerging two‐dimensional (2D) materials have become one of the most researched areas in the fields of bioelectronics and biosensors. In this timely review, the physicochemical structures of the most representative emerging 2D materials and the design of their nanostructures for engineering high‐performance bioelectronic and biosensing devices are presented. We focus on the structural optimization of emerging 2D material‐based composites to achieve better regulation for enhancing the performance of bioelectronics. Subsequently, the recent developments of emerging 2D materials in bioelectronics, such as neural interface simulation, biomolecular/biomarker detection, and skin sensors are discussed thoroughly. Finally, we provide conclusive views on the current challenges and future perspectives on utilizing emerging 2D materials and their composites for bioelectronics and biosensors. This review will offer important guidance in designing and applying emerging 2D materials in bioelectronics, thus further promoting their prospects in a wide biomedical field.

Spatio-Temporal Heterogeneity of the Relationships Between PM2.5 and Its Determinants: A Case Study of Chinese Cities in Winter of 2020.

Fine particulate matter (PM2.5) poses threat to human health in China, particularly in winter. The pandemic of coronavirus disease 2019 (COVID-19) led to a series of strict control measures in Chinese cities, resulting in a short-term significant improvement in air quality. This is a perfect case to explore driving factors affecting the PM2.5 distributions in Chinese cities, thus helping form better policies for future PM2.5 mitigation. Based on panel data of 332 cities, we analyzed the function of natural and anthropogenic factors to PM2.5 pollution by applying the geographically and temporally weighted regression (GTWR) model. We found that the PM2.5 concentration of 84.3% of cities decreased after lockdown. Spatially, in the winter of 2020, cities with high PM2.5 concentrations were mainly distributed in Northeast China, the North China Plain and the Tarim Basin. Higher temperature, wind speed and relative humidity were easier to promote haze pollution in northwest of the country, where enhanced surface pressure decreased PM2.5 concentrations. Furthermore, the intensity of trip activities (ITAs) had a significant positive effect on PM2.5 pollution in Northwest and Central China. The number of daily pollutant operating vents of key polluting enterprises in the industrial sector (VOI) in northern cities was positively correlated with the PM2.5 concentration; inversely, the number of daily pollutant operating vents of key polluting enterprises in the power sector (VOP) imposed a negative effect on the PM2.5 concentration in these regions. This work provides some implications for regional air quality improvement policies of Chinese cities in wintertime.

Evaluation of Pulmonary Edema Using Ultrasound Imaging in Patients With COVID-19 Pneumonia Based on a Non-local Channel Attention ResNet.

Recent research has revealed that COVID-19 pneumonia is often accompanied by pulmonary edema. Pulmonary edema is a manifestation of acute lung injury (ALI), and may progress to hypoxemia and potentially acute respiratory distress syndrome (ARDS), which have higher mortality. Precise classification of the degree of pulmonary edema in patients is of great significance in choosing a treatment plan and improving the chance of survival. Here we propose a deep learning neural network named Non-local Channel Attention ResNet to analyze the lung ultrasound images and automatically score the degree of pulmonary edema of patients with COVID-19 pneumonia. The proposed method was designed by combining the ResNet with the non-local module and the channel attention mechanism. The non-local module was used to extract the information on characteristics of A-lines and B-lines, on the basis of which the degree of pulmonary edema could be defined. The channel attention mechanism was used to assign weights to decisive channels. The data set contains 2220 lung ultrasound images provided by Huoshenshan Hospital, Wuhan, China, of which 2062 effective images with accurate scores assigned by two experienced clinicians were used in the experiment. The experimental results indicated that our method achieved high accuracy in classifying the degree of pulmonary edema in patients with COVID-19 pneumonia by comparison with previous deep learning methods, indicating its potential to monitor patients with COVID-19 pneumonia.

Automated lung ultrasound scoring for evaluation of coronavirus disease 2019 pneumonia using two-stage cascaded deep learning model.

Coronavirus disease 2019 (COVID-19) pneumonia has erupted worldwide, causing massive population deaths and huge economic losses. In clinic, lung ultrasound (LUS) plays an important role in the auxiliary diagnosis of COVID-19 pneumonia. However, the lack of medical resources leads to the low using efficiency of the LUS, to address this problem, a novel automated LUS scoring system for evaluating COVID-19 pneumonia based on the two-stage cascaded deep learning model was proposed in this paper. 18,330 LUS images collected from 26 COVID-19 pneumonia patients were successfully assigned scores by two experienced doctors according to the designed four-level scoring standard for training the model. At the first stage, we made a secondary selection of these scored images through five ResNet-50 models and five-fold cross validation to obtain the available 12,949 LUS images which were highly relevant to the initial scoring results. At the second stage, three deep learning models including ResNet-50, Vgg-19, and GoogLeNet were formed the cascaded scored model and trained using the new dataset, whose predictive result was obtained by the voting mechanism. In addition, 1000 LUS images collected another 5 COVID-19 pneumonia patients were employed to test the model. Experiments results showed that the automated LUS scoring model was evaluated in terms of accuracy, sensitivity, specificity, and F1-score, being 96.1%, 96.3%, 98.8%, and 96.1%, respectively. They proved the proposed two-stage cascaded deep learning model could automatically score an LUS image, which has great potential for application to the clinics on various occasions.

Exposure Risk of Global Surface O3 During the Boreal Spring Season.

Surface ozone (O3) is an oxidizing gaseous pollutant; long-term exposure to high O3 concentrations adversely affects human health. Based on daily surface O3 concentration data, the spatiotemporal characteristics of O3 concentration, exposure risks, and driving meteorological factors in 347 cities and 10 major countries (China, Japan, India, South Korea, the United States, Poland, Spain, Germany, France, and the United Kingdom) worldwide were analyzed using the MAKESENS model, Moran' I analysis, and Generalized additive model (GAM). The results indicated that: in the boreal spring season from 2015 to 2020, the global O3 concentration exhibited an increasing trend at a rate of 0.6 µg/m3/year because of the volatile organic compounds (VOCs) and NOx changes caused by human activities. Due to the lockdown policies after the outbreak of COVID-19, the average O3 concentration worldwide showed an inverted U-shaped growth during the study period, increasing from 21.9 µg/m3 in 2015 to 27.3 µg/m3 in 2019, and finally decreasing to 25.9 µg/m3 in 2020. According to exposure analytical methods, approximately 6.32% of the population (31.73 million people) in the major countries analyzed reside in rapidly increasing O3 concentrations. 6.53% of the population (32.75 million people) in the major countries were exposed to a low O3 concentration growth environment. Thus, the continuous increase of O3 concentration worldwide is an important factor leading to increasing threats to human health. Further we found that mean wind speed, maximum temperature, and relative humidity are the main factors that determine the change of O3 concentration. Our research results are of great significance to the continued implementation of strict air quality policies and prevention of population hazards. However, due to data limitations, this research can only provide general trends in O3 and human health, and more detailed research will be carried out in the follow-up. Supplementary Information: The online version contains supplementary material available at 10.1007/s12403-022-00463-7.

Impact of chemotherapy and immunotherapy on the composition and function of immune cells in COVID-19 convalescent with gynecological tumors.

Ongoing pandemic and potential resurgence of Coronavirus disease 2019 (COVID-19) has prompted urgent efforts to investigate the immunological memory of convalescent patients, especially in patients with active cancers. Here we performed single-cell RNA sequencing in peripheral blood samples of 3 healthy donors (HDs), 4 COVID-19 patients (Covs) and 4 COVID-19 patients with active gynecological tumor (TCs) pre- and post- anti-tumor treatment. All Covs patients had recovered from their acute infection. Interestingly, the molecular features of PBMCs in TCs are similar to that in Covs, suggesting that convalescent COVID-19 with gynecologic tumors do not have major immunological changes and may be protected against reinfection similar to COVID-19 patients without tumors. Moreover, the chemotherapy given to these patients mainly caused neutropenia, while having little effect on the proportion and functional phenotype of T and B cells, and T cell clonal expansion. Notably, anti-PD-L1 treatment massively increased cytotoxic scores of NK cells, and T cells, and facilitated clonal expansion of T cells in these patients. It is likely that T cells could protect patients from SARS-CoV-2 virus reinfection and anti-PD-L1 treatment can enhance the anti-viral activity of the T cells.

Quantitative Analysis and Automated Lung Ultrasound Scoring for Evaluating COVID-19 Pneumonia With Neural Networks.

As being radiation-free, portable, and capable of repetitive use, ultrasonography is playing an important role in diagnosing and evaluating the COVID-19 Pneumonia (PN) in this epidemic. By virtue of lung ultrasound scores (LUSS), lung ultrasound (LUS) was used to estimate the excessive lung fluid that is an important clinical manifestation of COVID-19 PN, with high sensitivity and specificity. However, as a qualitative method, LUSS suffered from large interobserver variations and requirement for experienced clinicians. Considering this limitation, we developed a quantitative and automatic lung ultrasound scoring system for evaluating the COVID-19 PN. A total of 1527 ultrasound images prospectively collected from 31 COVID-19 PN patients with different clinical conditions were evaluated and scored with LUSS by experienced clinicians. All images were processed via a series of computer-aided analysis, including curve-to-linear conversion, pleural line detection, region-of-interest (ROI) selection, and feature extraction. A collection of 28 features extracted from the ROI was specifically defined for mimicking the LUSS. Multilayer fully connected neural networks, support vector machines, and decision trees were developed for scoring LUS images using the fivefold cross validation. The model with 128×256 two fully connected layers gave the best accuracy of 87%. It is concluded that the proposed method could assess the ultrasound images by assigning LUSS automatically with high accuracy, potentially applicable to the clinics.

Clinical and molecular characteristics of COVID-19 patients with persistent SARS-CoV-2 infection.

The characteristics of COVID-19 patients with persistent SARS-CoV-2 infection are not yet well described. Here, we compare the clinical and molecular features of patients with long duration of viral shedding (LDs) with those from patients with short duration patients (SDs), and healthy donors (HDs). We find that several cytokines and chemokines, such as interleukin (IL)-2, tumor necrosis factor (TNF) and lymphotoxin α (LT-α) are present at lower levels in LDs than SDs. Single-cell RNA sequencing shows that natural killer (NK) cells and CD14+ monocytes are reduced, while regulatory T cells are increased in LDs; moreover, T and NK cells in LDs are less activated than in SDs. Importantly, most cells in LDs show reduced expression of ribosomal protein (RP) genes and related pathways, with this inversed correlation between RP levels and infection duration further validated in 103 independent patients. Our results thus indicate that immunosuppression and low RP expression may be related to the persistence of the viral infection in COVID-19 patients.

Epidemiological Traceability Investigation of a New Wave of COVID-19 Outbreaks in Beijing (preprint)

Background: To investigate a new wave of COVID-19 outbreaks in Beijing at the end of 2020, and to analyze the infection situation and transmission chain of each generation.Methods: Using a unified questionnaire, through the combination of field investigation and telephone survey, and with the help of trajectory big data technology to track the infected persons and close contacts for clues and case investigation. All nasal/pharyngeal swabs were collected and detected by real-time fluorescence quantitative RT-PCR method. The propagation relationship was analyzed by using the schematic diagram of transmission chain.Results: The outbreak involved 21 infected persons (15 confirmed cases and 6 asymptomatic). The first case was only an index case rather than the source of infection. The infected persons were spread through family exposure, workplace exposure, public premises exposure, vehicle exposure, diet exposure and so on. The average incubation period was 4 days, and those who had contact during both the virus incubation period and the symptom period had a higher risk of infection (c2=30.688, P<0.001). Unprotected family exposure, diet exposure, and exposure to small spaces, such as within transport, presented a higher risk of infection (c2=33.461, P<0.001). Conclusion: Against the background of global integration, the COVID-19 situation is still grim, still can not relax. While speeding up the recovery of the economy, "external defense input and internal defense rebound" is still the top priority in China's epidemic prevention and control work.

Self-reported Taste and Smell Disorders in Patients with COVID-19: Distinct Features in China.

Last December 2019, a cluster of viral pneumonia cases identified as coronavirus disease 2019 (COVID-19) was reported in Wuhan, China. We aimed to explore the frequencies of nasal symptoms in patients with COVID-19, including loss of smell and taste, as well as their presentation as the first symptom of the disease and their association with the severity of COVID-19. In this retrospective study, 1206 laboratory-confirmed COVID-19 patients were included and followed up by telephone one month after discharged from Tongji Hospital, Wuhan. Demographic data, laboratory values, comorbidities, symptoms, and numerical rating scale scores (0-10) of nasal symptoms were extracted from the hospital medical records, and confirmed or reevaluated by the telephone follow-up. From patients (n=1172) completing follow-up, 199 (17%) subjects had severe COVID-19 and 342 (29.2%) reported nasal symptoms. 20.6% COVID-19 patients had loss of taste (median score=6), while 11.4% had loss of smell (median score=5). Loss of taste scores, but not loss of smell scores, were significantly increased in severe vs. non-severe COVID-19 patients. Interleukin (IL)-6 and lactose dehydrogenase (LDH) serum levels were positively correlated with loss of taste scores. About 80% of COVID-19 patients recovered from smell and taste dysfunction in 2 weeks. In this cohort, only 1 out of 10 hospital admitted patients had loss of smell while 1 out of 5 reported loss of taste which was associated to severity of COVID-19. Most patients recovered smell and taste dysfunctions in 2 weeks.

Global, continental, and national variation in PM2.5, O3, and NO2 concentrations during the early 2020 COVID-19 lockdown.

Lockdowns implemented in response to COVID-19 have caused an unprecedented reduction in global economic and transport activity. In this study, variation in the concentration of health-threatening air pollutants (PM2.5, NO2, and O3) pre- and post-lockdown was investigated at global, continental, and national scales. We analyzed ground-based data from >10,000 monitoring stations in 380 cities across the globe. Global-scale results during lockdown (March to May 2020) showed that concentrations of PM2.5 and NO2 decreased by 16.1% and 45.8%, respectively, compared to the baseline period (2015-2019). However, O3 concentration increased by 5.4%. At the continental scale, concentrations of PM2.5 and NO2 substantially dropped in 2020 across all continents during lockdown compared to the baseline, with a maximum reduction of 20.4% for PM2.5 in East Asia and 42.5% for NO2 in Europe. The maximum reduction in O3 was observed in North America (7.8%), followed by Asia (0.7%), while small increases were found in other continents. At the national scale, PM2.5 and NO2 concentrations decreased significantly during lockdown, but O3 concentration showed varying patterns among countries. We found maximum reductions of 50.8% for PM2.5 in India and 103.5% for NO2 in Spain. The maximum reduction in O3 (22.5%) was found in India. Improvements in air quality were temporary as pollution levels increased in cities since lockdowns were lifted. We posit that these unprecedented changes in air pollutants were mainly attributable to reductions in traffic and industrial activities. Column reductions could also be explained by meteorological variability and a decline in emissions caused by environmental policy regulations. Our results have implications for the continued implementation of strict air quality policies and emission control strategies to improve environmental and human health.

Bioinspired Spiky Peroxidase-Mimics for Localized Bacterial Capture and Synergistic Catalytic Sterilization.

Besides the pandemic caused by the coronavirus outbreak, many other pathogenic microbes also pose a devastating threat to human health, for instance, pathogenic bacteria. Due to the lack of broad-spectrum antibiotics, it is urgent to develop nonantibiotic strategies to fight bacteria. Herein, inspired by the localized "capture and killing" action of bacteriophages, a virus-like peroxidase-mimic (V-POD-M) is synthesized for efficient bacterial capture (mesoporous spiky structures) and synergistic catalytic sterilization (metal-organic-framework-derived catalytic core). Experimental and theoretical calculations show that the active compound, MoO3 , can serve as a peroxo-complex-intermediate to reduce the free energy for catalyzing H2 O2 , which mainly benefits the generation of •OH radicals. The unique virus-like spikes endow the V-POD-M with fast bacterial capture and killing abilities (nearly 100% at 16 µg mL-1 ). Furthermore, the in vivo experiments show that V-POD-M possesses similar disinfection treatment and wound skin recovery efficiencies to vancomycin. It is suggested that this inexpensive, durable, and highly reactive oxygen species (ROS) catalytic active V-POD-M provides a promising broad-spectrum therapy for nonantibiotic disinfection.