ABSTRACT
Densely occupied spaces (e.g., classrooms) are generally over-crowded and pose a high risk of cross-infection during the pandemic of COVID-19. Among various ventilation systems, impinging jet ventilation (IJV) system might be promising for such spaces. However, the exhaust location of the IJV system used for densely occupied classrooms is unclear. This study aims to investigate the effects of exhaust location on the removal of exhaled contaminants in a classroom (15 × 7 × 5 m3) occupied by 50 students. Exhaled contaminants are modeled by a tracer gas released at the top of each manikin. The reference case has three exhausts evenly distributed in the ceiling. The results indicate that: a) a recirculation airflow entraining exhaled contaminants exists above the occupied zone;b) this recirculation air flow entrains contaminants and accumulates them at the upper part of the room near the diffuser;c) locating merely one exhaust on the same side of the supply diffuser leads to the best indoor air quality, i.e., it reduces the mean age of air from 278 s to 243 s, the mass fraction of CO2 from 753 ppm to 726 ppm, and the concentration of tracer gas from 305 ppm to 266 ppm;d) this layout still performs the best when the supply velocity drops to 0.5 m/s. It is worth noting that the proposed layout has fewer exhausts than the reference case but performs better. These results conclude that the exhaust for large spaces is not evenly distributed but depends on the indoor airflow pattern: the key is locating the exhaust near the region with high contaminant concentration. Factors determining the recirculation airflow are suggested to be further studied. Graphical abstract Image 1
ABSTRACT
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.
ABSTRACT
Airborne black carbon is a strong warming component of the atmosphere. Therefore, curbing black carbon emissions should slow down global warming. The 2019 coronavirus pandemic (COVID-19) is a unique opportunity for studying the response of black carbon to the varied human activities, in particular due to lockdown policies. Actually, there is few knowledge on the variations of black carbon in China during lockdowns. Here, we studied the concentrations of particulate matter (PM2.5) and black carbon before, during, and after the lockdown in nine sites of the Yangtze River Delta in Eastern China. Results show 40-60% reduction of PM2.5 and 40-50% reduction of black carbon during the lockdown. The classical bimodal peaks of black carbon in the morning and evening rush hours were highly weakened, indicating the substantial decrease of traffic activities. Contributions from fossil fuels combustion to black carbon decreased about 5-10% during the lockdown. Spatial correlation analysis indicated the clustering of the multi-site black carbon concentrations in the Yangtze River Delta during the lockdown. Overall, control of emissions from traffic and industrial activities should be efficient to curb black carbon levels in the frame of a 'green public transit system' for mega-city clusters such as the Yangtze River Delta. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10311-021-01327-3.
ABSTRACT
The coronavirus disease 2019 (COVID-19) is a highly contagious and fatal disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In general, the diagnostic tests for COVID-19 are based on the detection of nucleic acid, antibodies, and protein. Among different analytes, the gold standard of the COVID-19 test is the viral nucleic acid detection performed by the quantitative reverse transcription polymerase chain reaction (qRT-PCR) method. However, the gold standard test is time-consuming and requires expensive instrumentation, as well as trained personnel. Herein, we report an ultrasensitive electrochemical biosensor based on zinc sulfide/graphene (ZnS/graphene) nanocomposite for rapid and direct nucleic acid detection of SARS-CoV-2. We demonstrated a simple one-step route for manufacturing ZnS/graphene by employing an ultrafast (90 s) microwave-based non-equilibrium heating approach. The biosensor assay involves the hybridization of target DNA or RNA samples with probes that are immersed into a redox active electrolyte, which are detectable by electrochemical measurements. In this study, we have performed the tests for synthetic DNA samples and, SARS-CoV-2 standard samples. Experimental results revealed that the proposed biosensor could detect low concentrations of all different SARS-CoV-2 samples, using such as S, ORF 1a, and ORF 1b gene sequences as targets. This microwave-synthesized ZnS/graphene-based biosensor could be reliably used as an on-site, real-time, and rapid diagnostic test for COVID-19. Supplementary Information: The online version contains supplementary material available at 10.1007/s42114-023-00630-7.
ABSTRACT
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
ABSTRACT
In late 2019, the coronavirus began to spread around the world and impact international politics and economies significantly. In the face of the pandemic, stock markets around the world fluctuated sharply. The study aims to investigate the impact of the pandemic on the predictive variables of a stock prediction model, formed using chip-based variables and sentiment variables derived from comments posted on a social media platform. This study first performs feature engineering analysis to identify the indicators suitable for constructing the prediction model. The analysis then establishes a set of phrase rules to assign sentiment scores to the opinions expressed in replies and evaluates the effect on the accuracy of predictions. The results show that the major chip-based indicators affecting changes in the stock market differ before and after the pandemic. Hence, prediction models should be established separately for analysis in either period. In addition, the results indicate that the model relying on reply-based sentiment scores as a predictive variable provides more accurate predictions of stock price change.
ABSTRACT
Recent advances in epitranscriptomics have unveiled functional associations between RNA modifications (RMs) and multiple human diseases, but distinguishing the functional or disease-related single nucleotide variants (SNVs) from the majority of 'silent' variants remains a major challenge. We previously developed the RMDisease database for unveiling the association between genetic variants and RMs concerning human disease pathogenesis. In this work, we present RMDisease v2.0, an updated database with expanded coverage. Using deep learning models and from 873 819 experimentally validated RM sites, we identified a total of 1 366 252 RM-associated variants that may affect (add or remove an RM site) 16 different types of RNA modifications (m6A, m5C, m1A, m5U, Ψ, m6Am, m7G, A-to-I, ac4C, Am, Cm, Um, Gm, hm5C, D and f5C) in 20 organisms (human, mouse, rat, zebrafish, maize, fruit fly, yeast, fission yeast, Arabidopsis, rice, chicken, goat, sheep, pig, cow, rhesus monkey, tomato, chimpanzee, green monkey and SARS-CoV-2). Among them, 14 749 disease- and 2441 trait-associated genetic variants may function via the perturbation of epitranscriptomic markers. RMDisease v2.0 should serve as a useful resource for studying the genetic drivers of phenotypes that lie within the epitranscriptome layer circuitry, and is freely accessible at: www.rnamd.org/rmdisease2.
ABSTRACT
The coronavirus disease 2019 (COVID-19) is a highly contagious and fatal disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In general, the diagnostic tests for COVID-19 are based on the detection of nucleic acid, antibodies, and protein. Among different analytes, the gold standard of the COVID-19 test is the viral nucleic acid detection performed by the quantitative reverse transcription polymerase chain reaction (qRT-PCR) method. However, the gold standard test is time-consuming and requires expensive instrumentation, as well as trained personnel. Herein, we report an ultrasensitive electrochemical biosensor based on zinc sulfide/graphene (ZnS/graphene) nanocomposite for rapid and direct nucleic acid detection of SARS-CoV-2. We demonstrated a simple one-step route for manufacturing ZnS/graphene by employing an ultrafast (90 s) microwave-based non-equilibrium heating approach. The biosensor assay involves the hybridization of target DNA or RNA samples with probes that are immersed into a redox active electrolyte, which are detectable by electrochemical measurements. In this study, we have performed the tests for synthetic DNA samples and, SARS-CoV-2 standard samples. Experimental results revealed that the proposed biosensor could detect low concentrations of all different SARS-CoV-2 samples, using such as S, ORF 1a, and ORF 1b gene sequences as targets. This microwave-synthesized ZnS/graphene-based biosensor could be reliably used as an on-site, real-time, and rapid diagnostic test for COVID-19. Supplementary Information The online version contains supplementary material available at 10.1007/s42114-023-00630-7.
ABSTRACT
In late 2019, the coronavirus began to spread around the world and impact international politics and economies significantly. In the face of the pandemic, stock markets around the world fluctuated sharply. The study aims to investigate the impact of the pandemic on the predictive variables of a stock prediction model, formed using chip-based variables and sentiment variables derived from comments posted on a social media platform. This study first performs feature engineering analysis to identify the indicators suitable for constructing the prediction model. The analysis then establishes a set of phrase rules to assign sentiment scores to the opinions expressed in replies and evaluates the effect on the accuracy of predictions. The results show that the major chip-based indicators affecting changes in the stock market differ before and after the pandemic. Hence, prediction models should be established separately for analysis in either period. In addition, the results indicate that the model relying on reply-based sentiment scores as a predictive variable provides more accurate predictions of stock price change.
ABSTRACT
The spread of fake news increased dramatically during the COVID-19 pandemic worldwide. This study aims to synthesize the extant literature to understand the magnitude of this phenomenon in the wake of the pandemic in 2021, focusing on the motives and sociodemographic profiles, Artificial Intelligence (AI)-based tools developed, and the top trending topics related to fake news. A scoping review was adopted targeting articles published in five academic databases (January 2021-November 2021), resulting in 97 papers. Most of the studies were empirical in nature (N = 69) targeting the general population (N = 26) and social media users (N = 13), followed by AI-based detection tools (N = 27). Top motives for fake news sharing include low awareness, knowledge, and health/media literacy, Entertainment/Pass Time/Socialization, Altruism, and low trust in government/news media, whilst the phenomenon was more prominent among those with low education, males and younger. Machine and deep learning emerged to be the widely explored techniques in detecting fake news, whereas top topics were related to vaccine, virus, cures/remedies, treatment, and prevention. Immediate intervention and prevention efforts are needed to curb this anti-social behavior considering the world is still struggling to contain the spread of the COVID-19 virus.
ABSTRACT
The study purpose is to analyze changes in the mental health structure and its disorders among students who receive online education. The research involved 23 students (6 boys and 17 girls, average age 18 years). The study covers the period from October 2019 to September 2020. The participating students underwent the assessment using a Telegram before and after quarantine when they studied under the conventional full-time program. Additionally, they were tested during the lockdown due to the COVID-19 when they received online training. The results showed that study participants did not experience stress, depression, or anxiety before switching to online training. However, shifting to online learning mode, students under study experienced an increase in the stress level by 55.4%. Moreover, the study also showed that the stress level among students increased after returning from online to full-time training. However, the indicators had a lesser extent (by 40.2%, p = 0.0031). During online training, the number of students who did not experience stress and students with a mild stress level decreased. Meantime, students who faced stress reactions increased four times (p = 0.029). However, the study did not indicate any significant changes in anxiety levels among students. [ FROM AUTHOR] Copyright of Interactive Learning Environments is the property of Routledge and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
ABSTRACT
Emerging in December 2019, coronavirus disease 2019 (COVID-19) eventually became a pandemic and has posed a tremendous threat to global public health. However, the origins of SARS-CoV-2, the causative agent of COVID-19, remain to be determined. It has reported that a certain number of the early case clusters had a contact history with Huanan Seafood Market. Therefore, surveillance of SARS-CoV-2 within the market is of vital importance. Herein, we presented the SARS-CoV-2 detection results of 1380 samples collected from the environment and the animals within the market in early 2020. By SARS-CoV-2-specific RT-qPCR, 73 environmental samples tested positive for SARS-CoV-2 and three live viruses were successfully isolated. The viruses from the market shared nucleotide identity of 99.980% to 99.993% with the human isolate HCoV/Wuhan/IVDC-HB-01. In contrast, no virus was detected in the animal swabs covering 18 species of animals in the market. The SARS-COV-2 nucleic acids in the positive environmental samples showed significant correlation of abundance of Homo sapiens with SARS-CoV-2. In summary, this study provided convincing evidence of the prevalence of SARS-CoV-2 in the Huanan Seafood Market during the early stage of COVID-19 outbreak.
Subject(s)
COVID-19ABSTRACT
COVID-19 is a severe disease in humans, as highlighted by the current global pandemic. Several studies about the metabolome of COVID-19 patients have revealed metabolic disorders and some potential diagnostic markers during disease progression. However, the longitudinal changes of metabolomics in COVID-19 patients, especially their association with disease progression, are still unclear. Here, we systematically analyzed the dynamic changes of the serum metabolome of COVID-19 patients, demonstrating that most of the metabolites did not recover by 1-3 days before discharge. A prominent signature in COVID-19 patients comprised metabolites of amino acids, peptides, and analogs, involving nine essential amino acids, 10 dipeptides, and four N-acetylated amino acids. The levels of 12 metabolites in amino acid metabolism, especially three metabolites of the ornithine cycle, were significantly higher in severe patients than in mild ones, mainly on days 1-3 or 4-6 since onset. Integrating blood metabolomic, biochemical, and cytokine data, we uncovered a highly correlated network, including 6 cytokines, 13 biochemical parameters, and 49 metabolites. Significantly, five ornithine cycle-related metabolites (ornithine, N-acetylornithine, 3-amino-2-piperidone, aspartic acid, and asparagine) highly correlated with "cytokine storms" and coagulation index. We discovered that the ornithine cycle dysregulation significantly correlated with inflammation and coagulation in severe patients, which may be a potential mechanism of COVID-19 pathogenicity. Our study provided a valuable resource for detailed exploration of metabolic factors in COVID-19 patients, guiding metabolic recovery, understanding the pathogenic mechanisms, and creating drugs against SARS-CoV-2 infection.
ABSTRACT
OBJECTIVE: To understand racial bias in clinical settings from the perspectives of minority patients and healthcare providers to inspire changes in the way healthcare providers interact with their patients. METHODS: Articles on racial bias were searched on Medline, CINAHL, PsycINFO, Web of Science. Full text review and quality appraisal was conducted, before data was synthesized and analytically themed using the Thomas and Harden methodology. RESULTS: 23 articles were included, involving 1,006 participants. From minority patients' perspectives, two themes were generated: 1) alienation of minorities due to racial supremacism and lack of empathy, resulting in inadequate medical treatment; 2) labelling of minority patients who were stereotyped as belonging to a lower socio-economic class and having negative behaviors. From providers' perspectives, one theme recurred: the perpetuation of racial fault lines by providers. However, some patients and providers denied racism in the healthcare setting. CONCLUSION: Implicit racial bias is pervasive and manifests in patient-provider interactions, exacerbating health disparities in minorities. Beyond targeted anti-racism measures in healthcare settings, wider national measures to reduce housing, education and income inequality may mitigate racism in healthcare and improve minority patient care.
Subject(s)
Racism , Health Personnel , Healthcare Disparities , Humans , Minority GroupsABSTRACT
The spike (S) protein receptor-binding domain (RBD) of SARS-CoV-2 is an attractive target for COVID-19 vaccine developments, which naturally exists in a trimeric form. Here, guided by structural and computational analyses, we present a mutation-integrated trimeric form of RBD (mutI tri-RBD) as a broadly protective vaccine candidate, in which three RBDs were individually grafted from three different circulating SARS-CoV-2 strains including the prototype, Beta (B.1.351) and Kappa (B.1.617). The three RBDs were then connected end-to-end and co-assembled to possibly mimic the native trimeric arrangements in the natural S protein trimer. The recombinant expression of the mutI tri-RBD, as well as the homo-tri-RBD where the three RBDs were all truncated from the prototype strain, by mammalian cell exhibited correct folding, strong bio-activities, and high stability. The immunization of both the mutI tri-RBD and homo-tri-RBD plus aluminum adjuvant induced high levels of specific IgG and neutralizing antibodies against the SARS-CoV-2 prototype strain in mice. Notably, regarding to the immune-escape Beta (B.1.351) variant, mutI tri-RBD elicited significantly higher neutralizing antibody titers than homo-tri-RBD. Furthermore, due to harboring the immune-resistant mutations as well as the evolutionarily convergent hotspots, the designed mutI tri-RBD also induced strong broadly neutralizing activities against various SARS-CoV-2 variants, especially the variants partially resistant to homo-tri-RBD. Homo-tri-RBD has been approved by the China National Medical Products Administration to enter clinical trial (No. NCT04869592), and the superior broad neutralization performances against SARS-CoV-2 support the mutI tri-RBD as a more promising vaccine candidate for further clinical developments.
Subject(s)
COVID-19ABSTRACT
BACKGROUND AND OBJECTIVE: The global COVID-19 pandemic creates an obvious acute health care resourcing and response problem. The different timing of pandemic peak in geographically distinct locations creates a short window of response opportunity. Rapid dissemination of medical information from early affected areas to later ones is therefore crucial to optimise planning. Formulating the best system response for at-risk patient populations is especially complex. People with multiple sclerosis (pwMS) are exposed to long-term immunosuppressive disease modifying treatments (DMTs) and, in theory, could be at increased risk of contracting the virus and developing complications. Social media, such as Twitter, can provide a global platform to rapidly share information and individual experiences. METHODS AND RESULTS: This report summarizes the case experience of pwMS with COVID-19 infection in the first month of the pandemic as reported on Twitter using the #MSCOVID19 hashtag. 26 individual cases of COVID-19 in pwMS were reported from Europe and the United States of America. The cases involved a combination of relapsing and progressive MS phenotypes treated with a range of DMT (5 anti CD20 therapy, 4 cladribine, 4 fingolimod, 4 injectables, 3 alemtuzumab, 2 dimethyl fumarate, 2 untreated, 1 teriflunomide, 1 natalizumab). The cases shared present the earliest reported data on outcomes of COVID-19 infection in pwMS. Whilst limited, the cautiously reassuring nature of these early cases assisted in clinical management by allowing neurologists to continuously reassess their approach to DMT management.