Antibiotic resistomes in face-mask biofilm along an urban river: Multiple drivers and co-occurrence with human opportunistic pathogens.

Discarded face masks from the global COVID-19 pandemic have contributed significantly to plastic pollution in surface water, whereas their potential as a reservoir for aquatic pollutants is not well understood. Herein, we conducted a field experiment along a human-impacted urban river, investigating the variations of antibiotic resistance genes (ARGs), pathogens, and water-borne contaminants in commonly-used face masks. Results showed that high-biomass biofilms formed on face masks selectively enriched more ARGs than stone biofilm (0.08-0.22 vs 0.07-0.15 copies/16 S rRNA gene copies) from bulk water, which mainly due to unique microbial communities, enhanced horizontal gene transfer, and selective pressure of accumulated contaminants based on redundancy analysis and variation partitioning analysis. Several human opportunistic pathogens (e.g., Acinetobacter, Escherichia-Shigella, Bacillus, and Klebsiella), which are considered potential ARG carriers, were also greatly concentrated in face-mask biofilms, imposing a potential threat to aquatic ecological environment and human health. Moreover, wastewater treatment plant effluents, as an important source of pollutants to urban rivers, further aggravated the abundances of ARGs and opportunistic pathogens in face-mask biofilms. Our findings demonstrated that discarded face masks provide a hotspot for the proliferation and spread of ARGs and pathogens in urban water, highlighting the urgent requirement for implementing stricter regulations in face mask disposal.

Assessing the impacts of pandemic and the increase in minimum down payment rate on Shanghai housing prices.

In 2016, the city of Shanghai increased the minimum down payment rate requirement for purchasing various types of properties. We study the treatment effect of this major policy change on Shanghai's housing market by employing panel data from March 2009 to December 2021. Since the observed data are either in the form of no treatment or under the treatment but before and after the outbreak of COVID-19, we use the panel data approach suggested by Hsiao et al. (J Appl Econ, 27(5):705-740, 2012) to estimate the treatment effects and a time-series approach to disentangle the treatment effects and the effects of the pandemic. The results suggest that the average treatment effect on the housing price index of Shanghai over 36 months after the treatment is -8.17%. For time periods after the outbreak of the pandemic, we find no significant impact of the pandemic on the real estate price indices between 2020 and 2021.

Impact of COVID-19 on perception and preparedness among clinical year Malaysian undergraduate dental students - A cross-sectional study

Objectives: This study aimed to evaluate how quarantine affected final-year dental students' self-perceptions of preparation and assess how online training affected clinical students' education at SEGi University. Methods: Year 3 to 5 students (n=140) were asked to fill up an online questionnaire. The first part included the effects of online education experience between the academic years. The second section assessed the graduating class's self-perceived readiness in cognitive, communication and professional abilities. The Chi-square test was used to analyse the association between the groups regarding academic years, gender, and family income. Results: Year 3 students missed educational experiences during lockdown significantly more than years 4 and 5 (p<0.001). In addition, 86% of year 5 students (p< 0.001) felt that online assessment was not a suitable evaluation method compared to the other clinical years. About two-thirds of the 5th year dental students were unsure of their confidence in their skills before graduation. Around half of the final-year students said they were unsure about starting their practice following graduation. After graduation, 80% of respondents preferred to spend a year in residence with sufficient training. Conclusions: Although students' self-perceived preparation was generally positive, they expressed reservations about the independent practice after graduation. Copyright: © 2021 Rath A, et al. This is an open access article licensed under a Creative Commons Attribution Work 4.0 United States License.

Transmission mitigation of COVID-19: Exhaled contaminants removal and energy saving in densely occupied space by impinging jet ventilation.

The pandemic of COVID-19 and its transmission ability raise much attention to ventilation design as indoor-transmission outstrips outdoor-transmission. Impinging jet ventilation (IJV) systems might be promising to ventilate densely occupied large spaces due to their high jet momentum. However, their performances in densely occupied spaces have rarely been explored. This study proposes a modified IJV system and evaluates its performance numerically in a densely occupied classroom mockup. A new assessment formula is also proposed to evaluate the nonuniformity of target species CO2. The infector is assumed as the manikin with the lowest tracer gas concentration in the head region. The main results include: a) Indoor air quality (IAQ) in the classroom is improved significantly compared with a mixing ventilation system, i.e., averaged CO2 in the occupied zone (OZ) is reduced from 1287 ppm to 1078 ppm, the OZ-averaged mean age of air is reduced from 439 s to 177 s; b) The mean infection probability is reduced from 0.047% to 0.027% with an infector, and from 0.035% to 0.024% with another infector; c) Cooling coil load is reduced by around 21.0%; d) Overall evaluation indices meet the requirements for comfortable environments, i.e., the temperature difference between head and ankle is within 3 °C and the OZ-averaged predictive mean vote is in the range of -0.5 - 0.5; e) Thermal comfort level and uniformity are decreased, e.g., overcooling near diffuser at ankle level. Summarily, the target system effectively improves IAQ, reduces exhaled-contaminant concentration in head regions, and saves energy as well.

Characterizing the cellular and molecular variabilities of peripheral immune cells in healthy recipients of BBIBP-CorV inactivated SARS-CoV-2 vaccine by single-cell RNA sequencing.

Over 3 billion doses of inactivated vaccines for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have been administered globally. However, our understanding of the immune cell functional transcription and T cell receptor (TCR)/B cell receptor (BCR) repertoire dynamics following inactivated SARS-CoV-2 vaccination remains poorly understood. Here, we performed single-cell RNA and TCR/BCR sequencing on peripheral blood mononuclear cells at four time points after immunization with the inactivated SARS-CoV-2 vaccine BBIBP-CorV. Our analysis revealed an enrichment of monocytes, central memory CD4+ T cells, type 2 helper T cells and memory B cells following vaccination. Single-cell TCR-seq and RNA-seq comminating analysis identified a clonal expansion of CD4+ T cells (but not CD8+ T cells) following a booster vaccination that corresponded to a decrease in the TCR diversity of central memory CD4+ T cells and type 2 helper T cells. Importantly, these TCR repertoire changes and CD4+ T cell differentiation were correlated with the biased VJ gene usage of BCR and the antibody-producing function of B cells post-vaccination. Finally, we compared the functional transcription and repertoire dynamics in immune cells elicited by vaccination and SARS-CoV-2 infection to explore the immune responses under different stimuli. Our data provide novel molecular and cellular evidence for the CD4+ T cell-dependent antibody response induced by inactivated vaccine BBIBP-CorV. This information is urgently needed to develop new prevention and control strategies for SARS-CoV-2 infection. (ClinicalTrials.gov Identifier: NCT04871932).

Recent advancements in nucleic acid detection with microfluidic chip for molecular diagnostics.

The coronavirus disease 2019 (COVID-19) has extensively promoted the application of nucleic acid testing technology in the field of clinical testing. The most widely used polymerase chain reaction (PCR)-based nucleic acid testing technology has problems such as complex operation, high requirements of personnel and laboratories, and contamination. The highly miniaturized microfluidic chip provides an essential tool for integrating the complex nucleic acid detection process. Various microfluidic chips have been developed for the rapid detection of nucleic acid, such as amplification-free microfluidics in combination with clustered regularly interspaced short palindromic repeats (CRISPR). In this review, we first summarized the routine process of nucleic acid testing, including sample processing and nucleic acid detection. Then the typical microfluidic chip technologies and new research advances are summarized. We also discuss the main problems of nucleic acid detection and the future developing trend of the microfluidic chip.

Transmission mitigation of COVID-19: Exhaled contaminants removal and energy saving in densely occupied space by impinging jet ventilation

The pandemic of COVID-19 and its transmission ability raise much attention to ventilation design as indoor-transmission outstrips outdoor-transmission. Impinging jet ventilation (IJV) systems might be promising to ventilate densely occupied large spaces due to their high jet momentum. However, their performances in densely occupied spaces have rarely been explored. This study proposes a modified IJV system and evaluates its performance numerically in a densely occupied classroom mockup. A new assessment formula is also proposed to evaluate the nonuniformity of target species CO2. The infector is assumed as the manikin with the lowest tracer gas concentration in the head region. The main results include: a) Indoor air quality (IAQ) in the classroom is improved significantly compared with a mixing ventilation system, i.e., averaged CO2 in the occupied zone (OZ) is reduced from 1287 ppm to 1078 ppm, the OZ-averaged mean age of air is reduced from 439 s to 177 s;b) The mean infection probability is reduced from 0.047% to 0.027% with an infector, and from 0.035% to 0.024% with another infector;c) Cooling coil load is reduced by around 21.0%;d) Overall evaluation indices meet the requirements for comfortable environments, i.e., the temperature difference between head and ankle is within 3 °C and the OZ-averaged predictive mean vote is in the range of −0.5 - 0.5;e) Thermal comfort level and uniformity are decreased, e.g., overcooling near diffuser at ankle level. Summarily, the target system effectively improves IAQ, reduces exhaled-contaminant concentration in head regions, and saves energy as well. Graphical Image 1

Volatility spillover across Chinese carbon markets: Evidence from quantile connectedness method

The paper investigates the volatility spillover across China's carbon emission trading (CET) markets using the connectedness method based on the quantile VAR framework. The non-linear result shows strong volatility spillover effects in upper quantiles, resulting from major economic and political events. This is in accordance with the risk contagion hypothesis that volatility of carbon price returns is affected by the shocks of economic fundamentals and spills over to other pilots. Guangdong and Shanghai are the most significant contributors to volatility transmission because of their high liquidity and active markets. Hubei CET pilot has shifted from transmitter to receiver since the COVID-19 pandemic. Regarding the pairwise directional connectedness, geographical location and similar market attribute also matter in volatility transmission. This provides implications for policymakers and investors to attach importance to risk management given the quantile-based method rather than the average shocks.

How do knowledge diversity and ego-network structures affect firms' sustainable innovation: evidence from alliance innovation networks of China's new energy industries

Purpose>Facing the global public health emergency (GPHE), the conflict of cultural differences and the imbalance of vital resources such as knowledge among different organizations are becoming more severe, which affects the enthusiasm and sustainability of firms' innovation heavily. It is an urgent problem to be solved for firms how to make use of internal knowledge and external power to help firms' sustainable innovation (FSI). Thus, the purpose of this study is to deeply analyze how firms' internal knowledge diversity (KD) and external ego-network structures [ego-network density (ED) and honest brokers (HB)] affect FSI, as well as how the ego-network structures (ED and HB) moderate the relationship between KD and FSI based on the perspective of the ego network.Design/methodology/approach>Based on the data of the alliance innovation networks of China's new energy industries in 2009–2019, this study uses the social network analysis method and negative binomial regression model to explore the effect of KD and ego-network structures (ED and HB) on FSI, as well as the moderating effects of ego-network structures (ED and HB) on the relationship between KD and FSI based on the perspective of ego network.Findings>This study finds that KD, ED and HB can boost FSI. Moreover, ED plays a negative moderating role in the relationship between KD and FSI. However, the negative moderating effect of HB on the relationship between KD and FSI is not significant.Research limitations/implications>This study presents fresh empirical evidence and new insights for firms on how to make full use of firms' internal KD and external ego-network structures to facilitate FSI.Originality/value>First, this study not only enriches the research on the consequences of KD but also expands our understanding of the knowledge-based view to some extent. Second, this study not only enriches the motivation research of the FSI based on the perspective of ego-network in the context of the GPHE but also expands the application scope of social network theory and sustainable innovation' theory in part. Third, this paper is a new attempt to apply social network theory and knowledge-based view at the same time.

Use of radiolabeled hyaluronic acid for preclinical assessment of inflammatory injury and acute respiratory distress syndrome.

Acute respiratory distress syndrome (ARDS) is accompanied by a dramatic increase in lung hyaluronic acid (HA), leading to a dose-dependent reduction of pulmonary oxygenation. This pattern is associated with severe infections, such as COVID-19, and other important lung injury etiologies. HA actively participates in molecular pathways involved in the cytokine storm of COVID-19-induced ARDS. The objective of this study was to evaluate an imaging approach of radiolabeled HA for assessment of dysregulated HA deposition in mouse models with skin inflammation and lipopolysaccharide (LPS)-induced ARDS using a novel portable intensified Quantum Imaging Detector (iQID) gamma camera system. METHODS: HA of 10 kDa molecular weight (HA10) was radiolabeled with 125I and 99mTc respectively to produce [125I]I-HA10 and [99mTc]Tc-HA10, followed by comparative studies on stability, in vivo biodistribution, and uptake at inflammatory skin sites in mice with 12-O-tetradecanoylphorbol-13-acetate (TPA)-inflamed ears. [99mTc]Tc-HA10 was used for iQID in vivo dynamic imaging of mice with ARDS induced by intratracheal instillation of LPS. RESULTS: [99mTc]Tc-HA10 and [125I]I-HA10 had similar biodistribution and localization at inflammatory sites. [99mTc]Tc-HA10 was shown to be feasible in measuring skin injury and monitoring skin wound healing. [99mTc]Tc-HA10 dynamic pulmonary images yielded good visualization of radioactive uptake in the lungs. There was significantly increased lung uptake and slower lung washout in mice with LPS-induced ARDS than in control mice. Postmortem biodistribution measurement of [99mTc]TcHA10 (%ID/g) was 11.0 ± 3.9 vs. 1.3 ± 0.3 in the ARDS mice (n = 6) and controls (n = 6) (P < 0.001), consistent with upregulated HA expression as determined by enzyme-linked immunosorbent assay (ELISA) and immunohistochemistry (IHC) staining. CONCLUSIONS: [99mTc]Tc-HA10 is promising as a biomarker for evaluating HA dysregulation that contributes to pulmonary injury in ARDS. Rapid iQID imaging of [99mTc]Tc-HA10 clearance from injured lungs may provide a functional template for timely assessment and quantitative monitoring of pulmonary pathophysiology and intervention in ARDS.

Predicting willingness to donate blood based on machine learning: two blood donor recruitments during COVID-19 outbreaks.

Machine learning methods are a novel way to predict and rank donors' willingness to donate blood and to achieve precision recruitment, which can improve the recruitment efficiency and meet the challenge of blood shortage. We collected information about experienced blood donors via short message service (SMS) recruitment and developed 7 machine learning-based recruitment models using PyCharm-Python Environment and 13 features which were described as a method for ranking and predicting donors' intentions to donate blood with a floating number between 0 and 1. Performance of the prediction models was assessed by the Area under the receiver operating characteristic curve (AUC), accuracy, precision, recall, and F1 score in the full dataset, and by the accuracy in the four sub-datasets. The developed models were applied to prospective validations of recruiting experienced blood donors during two COVID-19 pandemics, while the routine method was used as a control. Overall, a total of 95,476 recruitments via SMS and their donation results were enrolled in our modelling study. The strongest predictor features for the donation of experienced donors were blood donation interval, age, and donation frequency. Among the seven baseline models, the eXtreme Gradient Boosting (XGBoost) and Support vector machine models (SVM) achieved the best performance: mean (95%CI) with the highest AUC: 0.809 (0.806-0.811), accuracy: 0.815 (0.812-0.818), precision: 0.840 (0.835-0.845), and F1 score of XGBoost: 0.843 (0.840-0.845) and recall of SVM: 0.991 (0.988-0.994). The hit rate of the XGBoost model alone and the combined XGBoost and SVM models were 1.25 and 1.80 times higher than that of the conventional method as a control in 2 recruitments respectively, and the hit rate of the high willingness to donate group was 1.96 times higher than that of the low willingness to donate group. Our results suggested that the machine learning models could predict and determine the experienced donors with a strong willingness to donate blood by a ranking score based on personalized donation data and demographical details, significantly improve the recruitment rate of blood donors and help blood agencies to maintain the blood supply in emergencies.

Effect of COVID-19 lockdown on the characterization and mixing state of carbonaceous particles in the urban atmosphere of Liaocheng, the North China Plain

To investigate the effect of COVID-19 control measures on aerosol chemistry, the chemical compositions, mixing states, and formation mechanisms of carbonaceous particles in the urban atmosphere of Liaocheng in the North China Plain (NCP) were compared before and during the pandemic using a single particle aerosol mass spectrometry (SPAMS). The results showed that the concentrations of five air pollutants including PM2.5, PM10, SO2, NO2, and CO decreased by 41.2%–71.5% during the pandemic compared to those before the pandemic, whereas O3 increased by 1.3 times during the pandemic because of the depressed titration of O3 and more favorable meteorological conditions. The count and percentage contribution of carbonaceous particles in the total detected particles were lower during the pandemic than those before the pandemic. The carbonaceous particles were dominated by elemental and organic carbon (ECOC, 35.9%), followed by elemental carbon-aged (EC-aged, 19.6%) and organic carbon-fresh (OC-fresh, 13.5%) before the pandemic, while EC-aged (25.3%), ECOC (17.9%), and secondary ions-rich (SEC, 17.8%) became the predominant species during the pandemic. The carbonaceous particle sizes during the pandemic showed a broader distribution than that before the pandemic, due to the condensation and coagulation of carbonaceous particles in the aging processes. The relative aerosol acidity (Rra) was smaller before the pandemic than that during the pandemic, indicating the more acidic particle aerosol during the pandemic closely related to the secondary species and relative humidity (RH). More than 95.0% and 86.0% of carbonaceous particles in the whole period were internally mixed with nitrate and sulfate, implying that most of the carbonaceous particles were associated with secondary oxidation during their formation processes. The diurnal variations of oxalate particles and correlation analyses suggested that oxalate particles before the pandemic were derived from aqueous oxidation driven by RH and liquid water content (LWC), while oxalate particles during the pandemic were originated from O3-dominated photochemical oxidation.

The role transition of radiotherapy for the treatment of liver cancer in the COVID-19 era.

The uncontrollable COVID-19 crises in the SARS-CoV-2 high-prevalence areas have greatly disrupted the routine treatment of liver cancer and triggered a role transformation of radiotherapy for liver cancer. The weight of radiotherapy in the treatment algorithm for liver cancer has been enlarged by the COVID-19 pandemic, which is helpful for the optimal risk-benefit profile.

Multiple-Inputs Convolutional Neural Network for COVID-19 Classification and Critical Region Screening From Chest X-ray Radiographs: Model Development and Performance Evaluation.

Background: The COVID-19 pandemic is becoming one of the largest, unprecedented health crises, and chest X-ray radiography (CXR) plays a vital role in diagnosing COVID-19. However, extracting and finding useful image features from CXRs demand a heavy workload for radiologists. Objective: The aim of this study was to design a novel multiple-inputs (MI) convolutional neural network (CNN) for the classification of COVID-19 and extraction of critical regions from CXRs. We also investigated the effect of the number of inputs on the performance of our new MI-CNN model. Methods: A total of 6205 CXR images (including 3021 COVID-19 CXRs and 3184 normal CXRs) were used to test our MI-CNN models. CXRs could be evenly segmented into different numbers (2, 4, and 16) of individual regions. Each region could individually serve as one of the MI-CNN inputs. The CNN features of these MI-CNN inputs would then be fused for COVID-19 classification. More importantly, the contributions of each CXR region could be evaluated through assessing the number of images that were accurately classified by their corresponding regions in the testing data sets. Results: In both the whole-image and left- and right-lung region of interest (LR-ROI) data sets, MI-CNNs demonstrated good efficiency for COVID-19 classification. In particular, MI-CNNs with more inputs (2-, 4-, and 16-input MI-CNNs) had better efficiency in recognizing COVID-19 CXRs than the 1-input CNN. Compared to the whole-image data sets, the efficiency of LR-ROI data sets showed approximately 4% lower accuracy, sensitivity, specificity, and precision (over 91%). In considering the contributions of each region, one of the possible reasons for this reduced performance was that nonlung regions (eg, region 16) provided false-positive contributions to COVID-19 classification. The MI-CNN with the LR-ROI data set could provide a more accurate evaluation of the contribution of each region and COVID-19 classification. Additionally, the right-lung regions had higher contributions to the classification of COVID-19 CXRs, whereas the left-lung regions had higher contributions to identifying normal CXRs. Conclusions: Overall, MI-CNNs could achieve higher accuracy with an increasing number of inputs (eg, 16-input MI-CNN). This approach could assist radiologists in identifying COVID-19 CXRs and in screening the critical regions related to COVID-19 classifications.

Use of radiolabeled hyaluronic acid for preclinical assessment of inflammatory injury and acute respiratory distress syndrome

Acute respiratory distress syndrome (ARDS) is accompanied by a dramatic increase in lung hyaluronic acid (HA), leading to a dose-dependent reduction of pulmonary oxygenation. This pattern is associated with severe infections, such as COVID-19, and other important lung injury etiologies. HA actively participates in molecular pathways involved in the cytokine storm of COVID-19-induced ARDS. The objective of this study was to evaluate an imaging approach of radiolabeled HA for assessment of dysregulated HA deposition in mouse models with skin inflammation and lipopolysaccharide (LPS)-induced ARDS using a novel portable intensified Quantum Imaging Detector (iQID) gamma camera system. Methods HA of 10 kDa molecular weight (HA10) was radiolabeled with 125I and 99mTc respectively to produce [125I]I-HA10 and [99mTc]Tc-HA10, followed by comparative studies on stability, in vivo biodistribution, and uptake at inflammatory skin sites in mice with 12-O-tetradecanoylphorbol-13-acetate (TPA)-inflamed ears. [99mTc]Tc-HA10 was used for iQID in vivo dynamic imaging of mice with ARDS induced by intratracheal instillation of LPS. Results [99mTc]Tc-HA10 and [125I]I-HA10 had similar biodistribution and localization at inflammatory sites. [99mTc]Tc-HA10 was shown to be feasible in measuring skin injury and monitoring skin wound healing. [99mTc]Tc-HA10 dynamic pulmonary images yielded good visualization of radioactive uptake in the lungs. There was significantly increased lung uptake and slower lung washout in mice with LPS-induced ARDS than in control mice. Postmortem biodistribution measurement of [99mTc]TcHA10 (%ID/g) was 11.0 ± 3.9 vs. 1.3 ± 0.3 in the ARDS mice (n = 6) and controls (n = 6) (P < 0.001), consistent with upregulated HA expression as determined by enzyme-linked immunosorbent assay (ELISA) and immunohistochemistry (IHC) staining. Conclusions [99mTc]Tc-HA10 is promising as a biomarker for evaluating HA dysregulation that contributes to pulmonary injury in ARDS. Rapid iQID imaging of [99mTc]Tc-HA10 clearance from injured lungs may provide a functional template for timely assessment and quantitative monitoring of pulmonary pathophysiology and intervention in ARDS. Graphical Unlabelled Image

Multiple-Inputs Convolutional Neural Network for COVID-19 Classification and Critical Region Screening From Chest X-ray Radiographs: Model Development and Performance Evaluation

Background The COVID-19 pandemic is becoming one of the largest, unprecedented health crises, and chest X-ray radiography (CXR) plays a vital role in diagnosing COVID-19. However, extracting and finding useful image features from CXRs demand a heavy workload for radiologists. Objective The aim of this study was to design a novel multiple-inputs (MI) convolutional neural network (CNN) for the classification of COVID-19 and extraction of critical regions from CXRs. We also investigated the effect of the number of inputs on the performance of our new MI-CNN model. Methods A total of 6205 CXR images (including 3021 COVID-19 CXRs and 3184 normal CXRs) were used to test our MI-CNN models. CXRs could be evenly segmented into different numbers (2, 4, and 16) of individual regions. Each region could individually serve as one of the MI-CNN inputs. The CNN features of these MI-CNN inputs would then be fused for COVID-19 classification. More importantly, the contributions of each CXR region could be evaluated through assessing the number of images that were accurately classified by their corresponding regions in the testing data sets. Results In both the whole-image and left- and right-lung region of interest (LR-ROI) data sets, MI-CNNs demonstrated good efficiency for COVID-19 classification. In particular, MI-CNNs with more inputs (2-, 4-, and 16-input MI-CNNs) had better efficiency in recognizing COVID-19 CXRs than the 1-input CNN. Compared to the whole-image data sets, the efficiency of LR-ROI data sets showed approximately 4% lower accuracy, sensitivity, specificity, and precision (over 91%). In considering the contributions of each region, one of the possible reasons for this reduced performance was that nonlung regions (eg, region 16) provided false-positive contributions to COVID-19 classification. The MI-CNN with the LR-ROI data set could provide a more accurate evaluation of the contribution of each region and COVID-19 classification. Additionally, the right-lung regions had higher contributions to the classification of COVID-19 CXRs, whereas the left-lung regions had higher contributions to identifying normal CXRs. Conclusions Overall, MI-CNNs could achieve higher accuracy with an increasing number of inputs (eg, 16-input MI-CNN). This approach could assist radiologists in identifying COVID-19 CXRs and in screening the critical regions related to COVID-19 classifications.