Nursing workforce allocation in the intensive care units of COVID-19-designated hospitals: A nationwide cross-sectional survey in China.

AIM: To explore the nursing workforce allocation in intensive care units (ICUs) of COVID-19-designated hospitals during the epidemic peak in China. DESIGN: A nationwide cross-sectional online survey. METHODS: A total of 37 head nurses and 262 frontline nurses in 37 ICUs of COVID-19-designated tertiary hospitals located in 22 cities of China were surveyed. The self-reported human resource allocation questionnaire was used to assess the nursing workforce allocation. RESULTS: The average patient-to-nurse ratio was 1.89 ± 1.14, and the median working hours per shift was 5 h. The top four majors of front-line nurses in ICUs were respiratory (31.30%), lemology (27.86%), intensive care (21.76%) and emergency (17.18%). We also found that a smaller average patient-to-nurse ratio (odds ratio [OR]: 0.328, 95% CI: 0.108, 1.000), longer average weekly rest time per person (OR: 0.193, 95% CI: 0.051, 0.729) and larger proportion of 6-9 working years (OR: 0.002, 95% CI: 0.001, 1.121) decreased the occurrence of nursing adverse events.

The Global Prevalence of Anxiety Symptoms Among Healthcare Workers Before and During COVID-19: A Systematic Review and Meta-analysis (preprint)

Importance: The COVID-19 pandemic’s effect on the prevalence of anxiety symptoms among healthcare workers remains unclear. Hence, an investigation into their prevalence and the changes before and during the pandemic is needed. Objective: To provide a comprehensive summary of the prevalence of anxiety symptoms among healthcare workers internationally and to compare this before and during the COVID-19 pandemic. Data Sources: A literature search was conducted using Medline, PubMed databases, and Embase for observational studies from their establishment in January 1963, January 1963, and January 1989, respectively, to August 2022. Study Selection: A standardized search strategy was applied to the databases with keywords assembled into three categories, including “healthcare workers”, “anxiety symptoms”, and “miscellaneous”. The inclusion criteria were: (I) evaluation of the prevalence of anxiety symptoms among healthcare workers employed in hospitals; (II) measured through validation tools or questionnaires; (III) published in English. The exclusion criteria were: (I) no overall prevalence of anxiety provided; (II) case series, case reports, intervention research, and commentaries. Data Extraction and Synthesis: The protocol was developed based on the PRISMA guidelines. The pooled prevalence and 95% confidence interval (CI) were estimated using a random effects model. Main Outcome(s) and Measure(s): The articles were analyzed according to subgroups divided based on the time of the studies conducted, participant occupations, locations, screening instruments, and publication years. Results: A total of 358 cross-sectional articles with 373 estimates from 65 countries were included. The overall prevalence of anxiety symptoms among healthcare workers was 41.4% (95% CI, 39.3-43.4). The prevalence of anxiety symptoms before and during the COVID-19 pandemic were 40.7% (95% CI: 34.1-47.3) and 41.2% (95% CI: 39.1-43.3), respectively. Compared with studies before the pandemic, a higher prevalence of anxiety symptoms was identified among nurses, residents, and physicians during the pandemic. Conclusions and Relevance: A considerable proportion of healthcare workers have experienced anxiety symptoms, with their global prevalence increasing during the COVID-19 pandemic. Further research is needed to determine effective prevention and treatment strategies, particularly during the COVID-19 pandemic. Systematic review registration: CRD42022370819.

DeepMPF: deep learning framework for predicting drug-target interactions based on multi-modal representation with meta-path semantic analysis.

BACKGROUND: Drug-target interaction (DTI) prediction has become a crucial prerequisite in drug design and drug discovery. However, the traditional biological experiment is time-consuming and expensive, as there are abundant complex interactions present in the large size of genomic and chemical spaces. For alleviating this phenomenon, plenty of computational methods are conducted to effectively complement biological experiments and narrow the search spaces into a preferred candidate domain. Whereas, most of the previous approaches cannot fully consider association behavior semantic information based on several schemas to represent complex the structure of heterogeneous biological networks. Additionally, the prediction of DTI based on single modalities cannot satisfy the demand for prediction accuracy. METHODS: We propose a multi-modal representation framework of 'DeepMPF' based on meta-path semantic analysis, which effectively utilizes heterogeneous information to predict DTI. Specifically, we first construct protein-drug-disease heterogeneous networks composed of three entities. Then the feature information is obtained under three views, containing sequence modality, heterogeneous structure modality and similarity modality. We proposed six representative schemas of meta-path to preserve the high-order nonlinear structure and catch hidden structural information of the heterogeneous network. Finally, DeepMPF generates highly representative comprehensive feature descriptors and calculates the probability of interaction through joint learning. RESULTS: To evaluate the predictive performance of DeepMPF, comparison experiments are conducted on four gold datasets. Our method can obtain competitive performance in all datasets. We also explore the influence of the different feature embedding dimensions, learning strategies and classification methods. Meaningfully, the drug repositioning experiments on COVID-19 and HIV demonstrate DeepMPF can be applied to solve problems in reality and help drug discovery. The further analysis of molecular docking experiments enhances the credibility of the drug candidates predicted by DeepMPF. CONCLUSIONS: All the results demonstrate the effectively predictive capability of DeepMPF for drug-target interactions. It can be utilized as a useful tool to prescreen the most potential drug candidates for the protein. The web server of the DeepMPF predictor is freely available at http://120.77.11.78/DeepMPF/ , which can help relevant researchers to further study.

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

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

Impact of the COVID-19 Pandemic on Meal Gathering in China.

During the COVID-19 pandemic, the Chinese government adopted a series of preventative measures to control the spread of the virus. This paper studies the impact of the COVID-19 pandemic and its associated prevention methods on meal sharing in China. Meal gathering during multiple periods before and after the outbreak of COVID-19 is captured through two waves of online survey across China between March and June 2020, collecting a total of 1847 observations. We employ the difference-in-difference (DID) method to identify the causal effects of COVID-19 severity on meal sharing. The results show that relative to the same period in 2019, the frequency of meal gathering decreased sharply after the initial outbreak of the coronavirus in 2020 in both epicenters and non-epicenters. Furthermore, the impact of COVID-19 differed across different types of meal sharing. Our findings have implications for consumers, food service operators, as well as policymakers to understand the social and community impact of the pandemic and to adjust their coping strategies.

Abnormal myocardial enzymes in the prediction of mortality and hypertension in COVID-19 patients: a retrospective study.

This study aims to determine the differences in myocardial enzymes in COVID-19 patients with and without hypertension. A total of 130 patients with COVID-19 in Yunmeng County People's Hospital were analyzed. The clinical manifestations and laboratory indicators were collected and analyzed. We found that COVID-19 patients with hypertension had higher mortality rate, greater age, and higher rates of basic disease such as diabetes than patients without hypertension. The γ-glutamyl transpeptidase (GGT), blood urea nitrogen (BUN), albumin/globulin (A/G), Ca, Mg, lactate dehydrogenase (LDH), and α-hydroxybutyric-dehydrogenase (α-HBD) levels in COVID-19 patients with hypertension were higher than in COVID-19 patients without hypertension. We found that the predictive effect of the creatine kinase isoenzyme (CK-MB), LDH-L, and α-HBD levels in the COVID-19 patients without hypertension were higher than in COVID-19 patients with hypertension. We used the ROC curve model to predict whether patients would have hypertension, and we found that CK-MB, LDH-L and HBD parameters could distinguish the COVID-19 patients with hypertension and non-hypertension, and could predict the mortality of COVID-19 patients.

A biomedical knowledge graph-based method for drug-drug interactions prediction through combining local and global features with deep neural networks.

Drug-drug interactions (DDIs) prediction is a challenging task in drug development and clinical application. Due to the extremely large complete set of all possible DDIs, computer-aided DDIs prediction methods are getting lots of attention in the pharmaceutical industry and academia. However, most existing computational methods only use single perspective information and few of them conduct the task based on the biomedical knowledge graph (BKG), which can provide more detailed and comprehensive drug lateral side information flow. To this end, a deep learning framework, namely DeepLGF, is proposed to fully exploit BKG fusing local-global information to improve the performance of DDIs prediction. More specifically, DeepLGF first obtains chemical local information on drug sequence semantics through a natural language processing algorithm. Then a model of BFGNN based on graph neural network is proposed to extract biological local information on drug through learning embedding vector from different biological functional spaces. The global feature information is extracted from the BKG by our knowledge graph embedding method. In DeepLGF, for fusing local-global features well, we designed four aggregating methods to explore the most suitable ones. Finally, the advanced fusing feature vectors are fed into deep neural network to train and predict. To evaluate the prediction performance of DeepLGF, we tested our method in three prediction tasks and compared it with state-of-the-art models. In addition, case studies of three cancer-related and COVID-19-related drugs further demonstrated DeepLGF's superior ability for potential DDIs prediction. The webserver of the DeepLGF predictor is freely available at http://120.77.11.78/DeepLGF/.

Association Between Clinical Competencies and Mental Health Symptoms Among Frontline Medical Staff During the COVID-19 Outbreak: A Cross-Sectional Study.

Background: In China, mental health of frontline medical staff might be influenced by clinicians' ability to handle the outbreak of coronavirus disease 2019 (COVID-19). Few studies to-date have addressed the association between clinicians' competencies and mental health in this context. This cross-sectional study was to examine the prevalence of mental health symptoms among frontline medical staff that fought against the COVID-19 outbreak, and explore the associations between their competencies, and separate and concurrent depressive and anxiety symptoms. Methods: A total of 623 frontline medical staff was included in this study. Competencies, depressive symptoms, and anxiety symptoms were assessed using a self-reported short form of the Chinese clinical physicians' competency model, Patient Health Questionnaire-9, and Generalized Anxiety Disorder-7 questionnaire, respectively. Logistic regression models were used to evaluate the associations between one SD increase in competency scores and the prevalence of mental health problems. Results: The prevalence of depressive, anxiety, and comorbid depressive and anxiety symptoms was 40.93, 31.78, and 26.00%, respectively. Among the medical staff with higher total competency scores, the prevalence of depressive [odds ratios (ORs) = 0.67, 95% confidence intervals (CIs): 0.55-0.81], anxiety (OR = 0.68, 95% CI: 0.56-0.83), and comorbid anxiety and depressive symptoms (OR = 0.69, 95% CI: 0.55-0.83) was lower than among their lower-scoring counterparts. Subgroup analyses stratified by core competency scores revealed similar associations as the main analyses. Conclusion: The present findings highlight the association between high core competency scores and lower prevalence of depressive, anxiety, and comorbid anxiety and depressive symptoms.

Longitudinal Dynamics of Cellular Responses in Recovered COVID-19 Patients.

Safe and effective vaccines and therapeutics based on the understanding of antiviral immunity are urgently needed to end the COVID-19 pandemic. However, the understanding of these immune responses, especially cellular immune responses to SARS-CoV-2 infection, is limited. Here, we conducted a cohort study of COVID-19 patients who were followed and had blood collected to characterize the longitudinal dynamics of their cellular immune responses. Compared with healthy controls, the percentage of activation of SARS-CoV-2 S/N-specific T cells in recovered patients was significantly higher. And the activation percentage of S/N-specific CD8+ T cells in recovered patients was significantly higher than that of CD4+ T cells. Notably, SARS-CoV-2 specific T-cell responses were strongly biased toward the expression of Th1 cytokines, included the cytokines IFNγ, TNFα and IL2. Moreover, the secreted IFNγ and IL2 level in severe patients was higher than that in mild patients. Additionally, the number of IFNγ-secreting S-specific T cells in recovered patients were higher than that of N-specific T cells. Overall, the SARS-CoV-2 S/N-specific T-cell responses in recovered patients were strong, and virus-specific immunity was present until 14-16 weeks after symptom onset. Our work provides a basis for understanding the immune responses and pathogenesis of COVID-19. It also has implications for vaccine development and optimization and speeding up the licensing of the next generation of COVID-19 vaccines.

Probable Vogt-Koyanagi-Harada Disease after COVID-19 Vaccination: Case Report and Literature Review.

COVID-19 vaccination is considered the most effective and promising approach for the elimination of the SARS-CoV-2 pandemic globally. Although the vaccine has been proven to be safe, as evidenced by the promotion of mass vaccination, new side effects, including several ocular complications that were not described during the experimental stage, are now emerging. In the present study, we report a 33-year-old Chinese man who developed probable Vogt-Koyanagi-Harada (VKH) disease only one day after his first dose of an inactivated COVID-19 vaccine, without any systemic symptoms. His medical history was unremarkable, except for hypertension. Although successfully relieved by oral prednisone, the patient progressed to the chronic stage of VKH disease with ocular depigmentation 4 months after onset. By reviewing similar cases previously reported, we discuss and summarize the common characteristics of VKH disease associated with vaccines against SARS-CoV-2, as well as the possible mechanisms behind this phenomenon. Although the causality is unclear, ophthalmologists and generalists should be aware of this possible ocular adverse effect after COVID-19 vaccination.

T-Cell Expression of Angiotensin-Converting Enzyme 2 and Binding of Severe Acute Respiratory Coronavirus 2.

The pathogenesis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is not completely understood. SARS-CoV-2 infection frequently causes significant immune function consequences including reduced T cell numbers and enhanced T cell exhaustion that contribute to disease severity. The extent to which T cell effects are directly mediated through infection or indirectly result from infection of respiratory-associated cells is unclear. We show that primary human T cells express sufficient levels of angiotensin converting enzyme 2 (ACE-2), the SARS-CoV-2 receptor, to mediate viral binding and entry into T cells. We further show that T cells exposed to SARS-CoV-2 particles demonstrate reduced proliferation and apoptosis compared to uninfected controls, indicating that direct interaction of SARS-CoV-2 with T cells may alter T cell growth, activation, and survival. Regulation of T cell activation and/or turnover by SARS-CoV-2 may contribute to impaired T cell function observed in patients with severe disease.

Nursing allocation in isolation wards of COVID-19 designated hospitals: a nationwide study in China.

BACKGROUND: Appropriate allocation of nursing staff is key to ensuring efficient nursing in hospitals, and is significantly correlated with patient safety, nursing quality, and nurse job satisfaction. However, there are few studies on nursing workforce allocation in the isolation wards of COVID-19 designated hospitals globally. This study aims to better understand the nursing workforce allocation in the isolation wards of COVID-19 designated hospitals in China, and provide a theoretical basis for efficiently deploying first-line nurses in China and across the world in the future. METHODS: An online survey was conducted among the head nurses (n = 229) and nurses (n = 1378) in the isolation wards of 117 hospitals (selected by stratified sampling), using a self-reported human resource allocation questionnaire. RESULTS: The average bed-to-nurse ratios of different isolation wards were different (Z = 36.742, P = 0.000). The bed-to-nurse ratios of the ICU, suspected COVID-19 cases ward, and confirmed COVID-19 cases ward, were 1:1.88, 1:0.56, and 1:0.45, respectively. The nurse work hours per shift in different isolation wards were also different (Z = 8.468, P = 0.014), with the specific values of the ICU, suspected COVID-19 cases ward, and confirmed COVID-19 cases ward, being 5, 6, and 6 h, respectively. A correlation analysis showed that the average work hours per shift was proportional to the overtime work of nurses (rs = 0.146), the proportion of nurse practitioners was proportional to the overall utilization rate of nursing human resources in the wards (rs = 0.136), and the proportion of nurses with college degrees was proportional to teamwork (rs = 0.142). The proportion of nurses above grade 10 was inversely proportional to teamwork and psychological problems (rs = 0.135, rs = 0.203). The results of multiple stepwise regression analyses showed that the work hours of nurses per shift was the main factor affecting nurse satisfaction and that the proportion of nurses and the work hours of nurses per shift were both independent factors affecting the length of stay (LOS) of patients. CONCLUSION: Hospitals in China have made good nursing workforce allocations during the COVID-19 pandemic, but there are certain shortcomings. Therefore, scientific and efficient nursing workforce allocation practice plans should be established to improve the ability of hospitals to deal with public health emergencies and are urgent problems that need to be addressed soon.

Chinese Consensus Report on Family-Based Helicobacter pylori Infection Control and Management (2021 Edition).

OBJECTIVE: Helicobacter pylori infection is mostly a family-based infectious disease. To facilitate its prevention and management, a national consensus meeting was held to review current evidence and propose strategies for population-wide and family-based H. pylori infection control and management to reduce the related disease burden. METHODS: Fifty-seven experts from 41 major universities and institutions in 20 provinces/regions of mainland China were invited to review evidence and modify statements using Delphi process and grading of recommendations assessment, development and evaluation system. The consensus level was defined as ≥80% for agreement on the proposed statements. RESULTS: Experts discussed and modified the original 23 statements on family-based H. pylori infection transmission, control and management, and reached consensus on 16 statements. The final report consists of three parts: (1) H. pylori infection and transmission among family members, (2) prevention and management of H. pylori infection in children and elderly people within households, and (3) strategies for prevention and management of H. pylori infection for family members. In addition to the 'test-and-treat' and 'screen-and-treat' strategies, this consensus also introduced a novel third 'family-based H. pylori infection control and management' strategy to prevent its intrafamilial transmission and development of related diseases. CONCLUSION: H. pylori is transmissible from person to person, and among family members. A family-based H. pylori prevention and eradication strategy would be a suitable approach to prevent its intra-familial transmission and related diseases. The notion and practice would be beneficial not only for Chinese residents but also valuable as a reference for other highly infected areas.

Intratumoral OH2, an oncolytic herpes simplex virus 2, in patients with advanced solid tumors: a multicenter, phase I/II clinical trial.

BACKGROUND: OH2 is a genetically engineered oncolytic herpes simplex virus type 2 designed to selectively amplify in tumor cells and express granulocyte-macrophage colony-stimulating factor to enhance antitumor immune responses. We investigated the safety, tolerability and antitumor activity of OH2 as single agent or in combination with HX008, an anti-programmed cell death protein 1 antibody, in patients with advanced solid tumors. METHODS: In this multicenter, phase I/II trial, we enrolled patients with standard treatment-refractory advanced solid tumors who have injectable lesions. In phase I, patients received intratumoral injection of OH2 at escalating doses (106, 107 and 108CCID50/mL) as single agent or with fixed-dose HX008. The recommended doses were then expanded in phase II. Primary endpoints were safety and tolerability defined by the maximum-tolerated dose and dose-limiting toxicities (DLTs) in phase I, and antitumor activity assessed per Response Evaluation Criteria in Solid Tumors (RECIST version 1.1) and immune-RECIST in phase II. RESULTS: Between April 17, 2019 and September 22, 2020, 54 patients with metastatic cancers were enrolled. Forty patients were treated with single agent OH2, and 14 with OH2 plus HX008. No DLTs were reported with single agent OH2 in phase I. Four patients, having metastatic mismatch repair-proficient rectal cancer or metastatic esophageal cancer, achieved immune-partial response, with two from the single agent cohort and two from the combination cohort. The duration of response were 11.25+ and 14.03+ months for the two responders treated with single agent OH2, and 1.38+ and 2.56+ months for the two responders in the combination cohort. The most common treatment-related adverse event (TRAE) with single agent OH2 was fever (n=18, 45.0%). All TRAEs were of grade 1-2, except one case of grade 3 fever in the 108CCID50/mL group. No treatment-related serious AEs occurred. Single agent OH2 induced alterations in the tumor microenvironment, with clear increases in CD3+ and CD8+ cell density and programmed death-ligand 1 expression in the patients' post-treatment biopsies relative to baseline. CONCLUSIONS: Intratumoral injection of OH2 was well-tolerated, and demonstrated durable antitumor activity in patients with metastatic esophageal and rectal cancer. Further clinical development of OH2 as single agent or with immune checkpoint inhibitors in selected tumor types is warranted.

Intracellular receptor EPAC regulates von Willebrand factor secretion from endothelial cells in a PI3K-/eNOS-dependent manner during inflammation.

Coagulopathy is associated with both inflammation and infection, including infections with novel severe acute respiratory syndrome coronavirus-2, the causative agent Coagulopathy is associated with both inflammation and infection, including infection with novel severe acute respiratory syndrome coronavirus-2, the causative agent of COVID-19. Clot formation is promoted via cAMP-mediated secretion of von Willebrand factor (vWF), which fine-tunes the process of hemostasis. The exchange protein directly activated by cAMP (EPAC) is a ubiquitously expressed intracellular cAMP receptor that plays a regulatory role in suppressing inflammation. To assess whether EPAC could regulate vWF release during inflammation, we utilized our EPAC1-null mouse model and revealed increased secretion of vWF in endotoxemic mice in the absence of the EPAC1 gene. Pharmacological inhibition of EPAC1 in vitro mimicked the EPAC1-/- phenotype. In addition, EPAC1 regulated tumor necrosis factor-α-triggered vWF secretion from human umbilical vein endothelial cells in a manner dependent upon inflammatory effector molecules PI3K and endothelial nitric oxide synthase. Furthermore, EPAC1 activation reduced inflammation-triggered vWF release, both in vivo and in vitro. Our data delineate a novel regulatory role for EPAC1 in vWF secretion and shed light on the potential development of new strategies to control thrombosis during inflammation.

Integrated Microfluidic-Based Platforms for On-Site Detection and Quantification of Infectious Pathogens: Towards On-Site Medical Translation of SARS-CoV-2 Diagnostic Platforms.

The rapid detection and quantification of infectious pathogens is an essential component to the control of potentially lethal outbreaks among human populations worldwide. Several of these highly infectious pathogens, such as Middle East respiratory syndrome (MERS) and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), have been cemented in human history as causing epidemics or pandemics due to their lethality and contagiousness. SARS-CoV-2 is an example of these highly infectious pathogens that have recently become one of the leading causes of globally reported deaths, creating one of the worst economic downturns and health crises in the last century. As a result, the necessity for highly accurate and increasingly rapid on-site diagnostic platforms for highly infectious pathogens, such as SARS-CoV-2, has grown dramatically over the last two years. Current conventional non-microfluidic diagnostic techniques have limitations in their effectiveness as on-site devices due to their large turnaround times, operational costs and the need for laboratory equipment. In this review, we first present criteria, both novel and previously determined, as a foundation for the development of effective and viable on-site microfluidic diagnostic platforms for several notable pathogens, including SARS-CoV-2. This list of criteria includes standards that were set out by the WHO, as well as our own "seven pillars" for effective microfluidic integration. We then evaluate the use of microfluidic integration to improve upon currently, and previously, existing platforms for the detection of infectious pathogens. Finally, we discuss a stage-wise means to translate our findings into a fundamental framework towards the development of more effective on-site SARS-CoV-2 microfluidic-integrated platforms that may facilitate future pandemic diagnostic and research endeavors. Through microfluidic integration, many limitations in currently existing infectious pathogen diagnostic platforms can be eliminated or improved upon.

Ambient air pollution and COVID-19 risk: Evidence from 35 observational studies.

BACKGROUND AND AIMS: The coronavirus disease 2019 (COVID-19) pandemic is severely threatening and challenging public health worldwide. Epidemiological studies focused on the influence of outdoor air pollution (AP) on COVID-19 risk have produced inconsistent conclusions. We aimed to quantitatively explore this association using a meta-analysis. METHODS: We searched for studies related to outdoor AP and COVID-19 risk in the Embase, PubMed, and Web of Science databases. No language restriction was utilized. The search date entries were up to August 13, 2021. Pooled estimates and 95% confidence intervals (CIs) were obtained with random-/fixed-effects models. PROSPERO registration number: CRD42021244656. RESULTS: A total of 35 articles were eligible for the meta-analysis. For long-term exposure to AP, COVID-19 incidence was positively associated with 1 µg/m3 increase in nitrogen dioxide (NO2; effect size = 1.042, 95% CI 1.017-1.068), particulate matter with diameter <2.5 µm (PM2.5; effect size = 1.056, 95% CI 1.039-1.072), and sulfur dioxide (SO2; effect size = 1.071, 95% CI 1.002-1.145). The COVID-19 mortality was positively associated with 1 µg/m3 increase in nitrogen dioxide (NO2; effect size = 1.034, 95% CI 1.006-1.063), PM2.5 (effect size = 1.047, 95% CI 1.025-1.1071). For short-term exposure to air pollutants, COVID-19 incidence was positively associated with 1 unit increase in air quality index (effect size = 1.001, 95% CI 1.001-1.002), 1 µg/m3 increase NO2 (effect size = 1.014, 95% CI 1.011-1.016), particulate matter with diameter <10 µm (PM10; effect size = 1.005, 95% CI 1.003-1.008), PM2.5 (effect size = 1.003, 95% CI 1.002-1.004), and SO2 (effect size = 1.015, 95% CI 1.007-1.023). CONCLUSIONS: Outdoor air pollutants are detrimental factors to COVID-19 outcomes. Measurements beneficial to reducing pollutant levels might also reduce the burden of the pandemic.

Antibody response in COVID-19 patients with and without re-positive RT-PCR results during the convalescent phase.

Nucleic acid testing and antibody testing data from 143 recovered COVID-19 patients during the convalescent phase were retrospectively analyzed. A total of 23 (16.1%) recovered patients re-tested positive for SARS-CoV-2 RNA by RT-PCR. Three months after symptom onset, 100% and 99.3% of the patients remained positive for total and IgG antibodies, and the antibody levels remained high. IgM antibodies declined rapidly, with a median time to seroconversion of 67 (95% CI: 59, 75) days after onset. Approximately 25% of patients were seronegative for IgA antibodies at three months after onset. There was no statistically significant difference in antibody kinetics between patients with and without re-positive RT-PCR results during the convalescent phase.

Characterization of performance and disinfection resilience of nonwoven filter materials for use in 3D-printed N95 respirators.

The COVID-19 pandemic has caused a high demand for respiratory protection among health care workers in hospitals, especially surgical N95 filtering facepiece respirators (FFRs). To aid in alleviating that demand, a survey of commercially available filter media was conducted to determine whether any could serve as a substitute for an N95 FFR while held in a 3D-printed mask (Stopgap Surgical Face Mask from the NIH 3D Print Exchange). Fourteen filter media types and eight combinations were evaluated for filtration efficiency, breathing resistance (pressure drop), and liquid penetration. Additional testing was conducted to evaluate two filter media disinfection methods in the event that the filters were reused in a hospital setting. Efficiency testing was conducted in accordance with the procedures established for approving an N95 FFR. One apparatus used a filter-holding device and another apparatus employed a manikin head to which the 3D-printed mask could be sealed. The filter media and combinations exhibited collection efficiencies varied between 3.9% and 98.8% when tested with a face velocity comparable to that of a standard N95 FFR at the 85 L min-1 used in the approval procedure. Breathing resistance varied between 10.8 to >637 Pa (1.1 to > 65 mm H2O). When applied to the 3D-printed mask efficiency decreased by an average of 13% and breathing resistance increased 4-fold as a result of the smaller surface area of the filter media when held in that mask compared to that of an N95 FFR. Disinfection by dry heat, even after 25 cycles, did not significantly affect filter efficiency and reduced viral infectivity by > 99.9%. However, 10 cycles of 59% vaporized H2O2 significantly (p < 0.001) reduced filter efficiency of the media tested. Several commercially available filter media were found to be potential replacements for the media used to construct the typical cup-like N95 FFR. However, their use in the 3D-printed mask demonstrated reduced efficiency and increased breathing resistance at 85 L min-1.