CMM: A CNN-MLP Model for COVID-19 Lesion Segmentation and Severity Grading.

In this paper, a CNN-MLP model (CMM) is proposed for COVID-19 lesion segmentation and severity grading in CT images. The CMM starts by lung segmentation using UNet, and then segmenting the lesion from the lung region using a multi-scale deep supervised UNet (MDS-UNet), finally implementing the severity grading by a multi-layer preceptor (MLP). In MDS-UNet, shape prior information is fused with the input CT image to reduce the searching space of the potential segmentation outputs. The multi-scale input compensates for the loss of edge contour information in convolution operations. In order to enhance the learning of multiscale features, the multi-scale deep supervision extracts supervision signals from different upsampling points on the network. In addition, it is empirical that the lesion which has a whiter and denser appearance tends to be more severe in the COVID-19 CT image. So, the weighted mean gray-scale value (WMG) is proposed to depict this appearance, and together with the lung and lesion area to serve as input features for the severity grading in MLP. To improve the precision of lesion segmentation, a label refinement method based on the Frangi vessel filter is also proposed. Comparative experiments on COVID-19 public datasets show that our proposed CMM achieves high accuracy on COVID-19 lesion segmentation and severity grading. Source codes and datasets are available at our GitHub repository (https://github.com/RobotvisionLab/COVID-19-severity-grading.git).

Prediction Model of hospitalization time of COVID-19 patients based on Gradient Boosted Regression Trees.

When an outbreak of COVID-19 occurs, it will cause a shortage of medical resources and the surge of demand for hospital beds. Predicting the length of stay (LOS) of COVID-19 patients is helpful to the overall coordination of hospital management and improves the utilization rate of medical resources. The purpose of this paper is to predict LOS for patients with COVID-19, so as to provide hospital management with auxiliary decision-making of medical resource scheduling. We collected the data of 166 COVID-19 patients in a hospital in Xinjiang from July 19, 2020, to August 26, 2020, and carried out a retrospective study. The results showed that the median LOS was 17.0 days, and the average of LOS was 18.06 days. Demographic data and clinical indicators were included as predictive variables to construct a model for predicting the LOS using gradient boosted regression trees (GBRT). The MSE, MAE and MAPE of the model are 23.84, 4.12 and 0.76 respectively. The importance of all the variables involved in the prediction of the model was analyzed, and the clinical indexes creatine kinase-MB (CK-MB), C-reactive protein (CRP), creatine kinase (CK), white blood cell count (WBC) and the age of patients had a higher contribution to the LOS. We found our GBRT model can accurately predict the LOS of COVID-19 patients, which will provide good assistant decision-making for medical management.

PF-D-Trimer, a protective SARS-CoV-2 subunit vaccine: immunogenicity and application.

The COVID-19 pandemic, caused by the SARS-CoV-2 virus, has had and continues to have a significant impact on global public health. One of the characteristics of SARS-CoV-2 is a surface homotrimeric spike protein, which is primarily responsible for the host immune response upon infection. Here we present the preclinical studies of a broadly protective SARS-CoV-2 subunit vaccine developed from our trimer domain platform using the Delta spike protein, from antigen design through purification, vaccine evaluation and manufacturability. The pre-fusion trimerized Delta spike protein, PF-D-Trimer, was highly expressed in Chinese hamster ovary (CHO) cells, purified by a rapid one-step anti-Trimer Domain monoclonal antibody immunoaffinity process and prepared as a vaccine formulation with an adjuvant. Immunogenicity studies have shown that this vaccine candidate induces robust immune responses in mouse, rat and Syrian hamster models. It also protects K18-hACE2 transgenic mice in a homologous viral challenge. Neutralizing antibodies induced by this vaccine show cross-reactivity against the ancestral WA1, Delta and several Omicrons, including BA.5.2. The formulated PF-D Trimer is stable for up to six months without refrigeration. The Trimer Domain platform was proven to be a key technology in the rapid production of PF-D-Trimer vaccine and may be crucial to accelerate the development and accessibility of updated versions of SARS-CoV-2 vaccines.

Multi-Attention Segmentation Networks Combined with the Sobel Operator for Medical Images.

Medical images are used as an important basis for diagnosing diseases, among which CT images are seen as an important tool for diagnosing lung lesions. However, manual segmentation of infected areas in CT images is time-consuming and laborious. With its excellent feature extraction capabilities, a deep learning-based method has been widely used for automatic lesion segmentation of COVID-19 CT images. However, the segmentation accuracy of these methods is still limited. To effectively quantify the severity of lung infections, we propose a Sobel operator combined with multi-attention networks for COVID-19 lesion segmentation (SMA-Net). In our SMA-Net method, an edge feature fusion module uses the Sobel operator to add edge detail information to the input image. To guide the network to focus on key regions, SMA-Net introduces a self-attentive channel attention mechanism and a spatial linear attention mechanism. In addition, the Tversky loss function is adopted for the segmentation network for small lesions. Comparative experiments on COVID-19 public datasets show that the average Dice similarity coefficient (DSC) and joint intersection over union (IOU) of the proposed SMA-Net model are 86.1% and 77.8%, respectively, which are better than those in most existing segmentation networks.

Research on the Application of Artificial Intelligence in Public Health Management: Leveraging Artificial Intelligence to Improve COVID-19 CT Image Diagnosis.

Since the start of 2020, the outbreak of the Coronavirus disease (COVID-19) has been a global public health emergency, and it has caused unprecedented economic and social disaster. In order to improve the diagnosis efficiency of COVID-19 patients, a number of researchers have conducted extensive studies on applying artificial intelligence techniques to the analysis of COVID-19-related medical images. The automatic segmentation of lesions from computed tomography (CT) images using deep learning provides an important basis for the quantification and diagnosis of COVID-19 cases. For a deep learning-based CT diagnostic method, a few of accurate pixel-level labels are essential for the training process of a model. However, the translucent ground-glass area of the lesion usually leads to mislabeling while performing the manual labeling operation, which weakens the accuracy of the model. In this work, we propose a method for correcting rough labels; that is, to hierarchize these rough labels into precise ones by performing an analysis on the pixel distribution of the infected and normal areas in the lung. The proposed method corrects the incorrectly labeled pixels and enables the deep learning model to learn the infected degree of each infected pixel, with which an aiding system (named DLShelper) for COVID-19 CT image diagnosis using the hierarchical labels is also proposed. The DLShelper targets lesion segmentation from CT images, as well as the severity grading. The DLShelper assists medical staff in efficient diagnosis by providing rich auxiliary diagnostic information (including the severity grade, the proportions of the lesion and the visualization of the lesion area). A comprehensive experiment based on a public COVID-19 CT image dataset is also conducted, and the experimental results show that the DLShelper significantly improves the accuracy of segmentation for the lesion areas and also achieves a promising accuracy for the severity grading task.

Relationship between behavioral inhibition/activation system and Internet addiction among Chinese college students: The mediating effects of intolerance of uncertainty and self-control and gender differences.

Background: Internet addiction is a global public health issue among college students that is associated with a range of negative outcomes. Especially the COVID-19 pandemic has forced them to shift most of their studies and life activities from offline to online, leading to a growing problem of Internet dependence and even Internet addiction. Although previous studies have indicated that the Behavioral Inhibition/Activation System (BIS/BAS) have important effects on college students' Internet addiction, the mechanisms underlying these associations and gender differences are still unclear. Aims: The present study investigated the mediating roles of intolerance of uncertainty and self-control in the association between BIS/BAS and Internet addiction following the Interaction of Person-Affect-Cognition-Execution model. Gender differences in such associations between variables were also tested. Method: A total of 747 Chinese college students were surveyed by using Young's Diagnostic Questionnaire for Internet Addiction, BIS/BAS Scales, the Intolerance of Uncertainty Scale and the Brief Self-Control Scale. Results: The results from the structural equation modeling analysis showed that BIS was positively related to Internet addiction and that BAS had a negative association with Internet addiction. Moreover, intolerance of uncertainty and self-control mediated the relationships between BIS/BAS and Internet addiction. Multi-group analysis further revealed that the associations between BAS and Internet addiction and between intolerance of uncertainty and Internet addiction were stronger among the male students than among female students. The relationship between self-control and Internet addiction was greater in the female sample than in the male sample. Conclusions: These findings extend our understanding of how BIS/BAS influence Internet addiction among college students and suggest that not only should training approaches based on intolerance of uncertainty and self-control be fully considered, but different intervention programs should be focused on gender sensitivity to maximize the intervention effect.

Causal associations between thyroid dysfunction and COVID-19 susceptibility and severity: A bidirectional Mendelian randomization study.

Background: Observational studies have reported an association between coronavirus disease 2019 (COVID-19) risk and thyroid dysfunction, but without a clear causal relationship. We attempted to evaluate the association between thyroid function and COVID-19 risk using a bidirectional two-sample Mendelian randomization (MR) analysis. Methods: Summary statistics on the characteristics of thyroid dysfunction (hypothyroidism and hyperthyroidism) were obtained from the ThyroidOmics Consortium. Genome-wide association study statistics for COVID-19 susceptibility and its severity were obtained from the COVID-19 Host Genetics Initiative, and severity phenotypes included hospitalization and very severe disease in COVID-19 participants. The inverse variance-weighted (IVW) method was used as the primary analysis method, supplemented by the weighted-median (WM), MR-Egger, and MR-PRESSO methods. Results were adjusted for Bonferroni correction thresholds. Results: The forward MR estimates show no effect of thyroid dysfunction on COVID-19 susceptibility and severity. The reverse MR found that COVID-19 susceptibility was the suggestive risk factor for hypothyroidism (IVW: OR = 1.577, 95% CI = 1.065-2.333, P = 0.022; WM: OR = 1.527, 95% CI = 1.042-2.240, P = 0.029), and there was lightly association between COVID-19 hospitalized and hypothyroidism (IVW: OR = 1.151, 95% CI = 1.004-1.319, P = 0.042; WM: OR = 1.197, 95% CI = 1.023-1.401, P = 0.023). There was no evidence supporting the association between any phenotype of COVID-19 and hyperthyroidism. Conclusion: Our results identified that COVID-19 might be the potential risk factor for hypothyroidism. Therefore, patients infected with SARS-CoV-2 should strengthen the monitoring of thyroid function.

Associations between sedentary behavior and negative emotions in adolescents during home confinement: Mediating role of social support and sleep quality

Background Prolonged periods of sedentary behaviour, for instance, engendered by home confinement in Shenzhen city, has led to negative mental health consequences, especially in adolescents. Previous research suggests, in general, that sedentary behavior can increase negative emotions. However, the specific mechanism driving the relationship between sedentary behavior and negative emotions is still relatively unclear. Social support and sleep quality might partly explain the effect of sedentary behavior on negative emotions. Thus, the current study aimed to examine the associations between sedentary behavior and negative emotions, and to investigate if social support and sleep quality mediate such a relationship. Method During home confinement due to the COVID-19 Omicron variant outbreak, 1179 middle and high school students in Shenzhen were invited to voluntarily complete an e-questionnaire, including the 21-item Depression Anxiety Stress Scale (DASS-21), the short form of the International Physical Activity Questionnaire (IPAQ-SF), the Social Support Rating Scale (SSRS) and the Pittsburgh Sleep Quality Index (PSQI). Data from 1065 participants were included in the analysis. Results We observed significant sex-related and demografic-related differences in emotional (e.g., anxiety, stress and social support) and other outcome variables (e.g., sitting duration and PSQI score). Furthermore, sedentary behavior, social support, and sleep quality were associated with negative emotions (p < .01), even after controlling for sex, age, only-child case, body mass index, and metabolic equivalent level. In addition, social support and sleep quality partially mediated the association between sedentary behavior and negative emotions. Conclusion The findings of the current study suggest that social support and sleep quality partially mediate the relationship between sedentary behavior and negative emotions in middle and high school students during home confinement in Shenzhen city.

Causal associations between thyroid dysfunction and COVID-19 susceptibility and severity: A bidirectional Mendelian randomization study

Background Observational studies have reported an association between coronavirus disease 2019 (COVID-19) risk and thyroid dysfunction, but without a clear causal relationship. We attempted to evaluate the association between thyroid function and COVID-19 risk using a bidirectional two-sample Mendelian randomization (MR) analysis. Methods Summary statistics on the characteristics of thyroid dysfunction (hypothyroidism and hyperthyroidism) were obtained from the ThyroidOmics Consortium. Genome-wide association study statistics for COVID-19 susceptibility and its severity were obtained from the COVID-19 Host Genetics Initiative, and severity phenotypes included hospitalization and very severe disease in COVID-19 participants. The inverse variance-weighted (IVW) method was used as the primary analysis method, supplemented by the weighted-median (WM), MR-Egger, and MR-PRESSO methods. Results were adjusted for Bonferroni correction thresholds. Results The forward MR estimates show no effect of thyroid dysfunction on COVID-19 susceptibility and severity. The reverse MR found that COVID-19 susceptibility was the suggestive risk factor for hypothyroidism (IVW: OR = 1.577, 95% CI = 1.065–2.333, P = 0.022;WM: OR = 1.527, 95% CI = 1.042–2.240, P = 0.029), and there was lightly association between COVID-19 hospitalized and hypothyroidism (IVW: OR = 1.151, 95% CI = 1.004–1.319, P = 0.042;WM: OR = 1.197, 95% CI = 1.023-1.401, P = 0.023). There was no evidence supporting the association between any phenotype of COVID-19 and hyperthyroidism. Conclusion Our results identified that COVID-19 might be the potential risk factor for hypothyroidism. Therefore, patients infected with SARS-CoV-2 should strengthen the monitoring of thyroid function.

Municipal and neighbourhood level wastewater surveillance and subtyping of an influenza virus outbreak.

Recurrent influenza epidemics and pandemic potential are significant risks to global health. Public health authorities use clinical surveillance to locate and monitor influenza and influenza-like cases and outbreaks to mitigate hospitalizations and deaths. Currently, global integration of clinical surveillance is the only reliable method for reporting influenza types and subtypes to warn of emergent pandemic strains. The utility of wastewater surveillance (WWS) during the COVID-19 pandemic as a less resource intensive replacement or complement for clinical surveillance has been predicated on analyzing viral fragments in wastewater. We show here that influenza virus targets are stable in wastewater and partitions favorably to the solids fraction. By quantifying, typing, and subtyping the virus in municipal wastewater and primary sludge during a community outbreak, we forecasted a citywide flu outbreak with a 17-day lead time and provided population-level viral subtyping in near real-time to show the feasibility of influenza virus WWS at the municipal and neighbourhood levels in near real time using minimal resources and infrastructure.

Thyroid Function and COVID-19 Susceptibility and Its Severity: A Two-Sample Mendelian Randomization Study.

Several observational studies have confirmed the relationship between thyroid hormones and coronavirus disease 2019 (COVID-19), but this correlation remains controversial. We performed a two-sample Mendelian randomization (MR) analysis based on the largest publicly available summary datasets. Summary statistics with 49 269 individuals for free thyroxine (FT4) and 54 288 for thyroid stimulating hormone (TSH) were used as exposure instruments. Genome-wide association studies of susceptibility (cases = 38 984; controls = 1 644 784), hospitalization (cases: 9986 = controls = 1 877 672), and very severe disease (cases = 5101; controls = 1 383 241) of COVID-19 were used as the outcome. We used the inverse-variance weighted (IVW) method as the primary analysis, and utilized MR-Egger regression, weighted median, and robust adjusted profile score (RAPS) for sensitivity analysis. Genetic predisposition to higher serum levels of FT4 within the normal range was negatively associated with the risk of COVID-19 hospitalization (odds ratio [OR] = 0.818; 95% CI, 0.718-0.932; P = 2.6 × 10-3) and very severe disease (OR = 0.758; 95% CI, 0.626-0.923; P = 5.8 × 10-3), but not susceptibility. There is no evidence that genetically predicted circulating TSH levels are associated with COVID-19 susceptibility and severity risk. Neither apparent pleiotropy nor heterogeneity were detected in the sensitivity analysis. In summary, we found that higher FT4 levels may reduce the risk of COVID-19 severity, suggesting that thyroid function testing may be required for patients with COVID-19.

Association of social support with negative emotions among Chinese adolescents during Omicron-related lockdown of Shenzhen City: The roles of rumination and sleep quality.

Objective: Adolescents are likely to suffer from negative emotions such as depression and anxiety due to the rapid development of biological, cognitive and social changes. Previous studies have indicated possible risk (rumination) and protective (good social support and high sleep quality) factors for depression and anxiety among this age group. The present study is the first to investigate the association between social support and negative emotions during the Outbreak of Omicron variant, on this basis, to further determine the mediating role of rumination and sleep quality on this link. Method: A total of 1,065 Chinese middle- and high-school students (51.5% female, M age = 13.80, SD = 1.20) completed a psychosocial battery, including the Social Support Rating Scale (SSRS), the Pittsburgh Sleep Quality Index (PSQI), the Ruminative Responses Scale (RRS), the Depression Anxiety Stress Scale (DASS). Serial multiple mediation analysis was conducted using PROCESS macro based on SPSS. Results: Social support, rumination, and sleep quality were significantly negatively correlated with negative emotional states (Ps < 0.05). Further, rumination and sleep quality were found to partially mediate the relationship between social support and negative emotional states. Conclusions: For early detection and prevention of depression and anxiety, providing sufficient social support is necessary for adolescents, because rumination and sleep problems are reported during stressful periods, such as the COVID-19 pandemic.

Wastewater to clinical case (WC) ratio of COVID-19 identifies insufficient clinical testing, onset of new variants of concern and population immunity in urban communities.

Clinical testing has been the cornerstone of public health monitoring and infection control efforts in communities throughout the COVID-19 pandemic. With the anticipated reduction of clinical testing as the disease moves into an endemic state, SARS-CoV-2 wastewater surveillance (WWS) will have greater value as an important diagnostic tool. An in-depth analysis and understanding of the metrics derived from WWS is required to interpret and utilize WWS-acquired data effectively (McClary-Gutierrez et al., 2021; O'Keeffe, 2021). In this study, the SARS-CoV-2 wastewater signal to clinical cases (WC) ratio was investigated across seven cities in Canada over periods ranging from 8 to 21 months. This work demonstrates that significant increases in the WC ratio occurred when clinical testing eligibility was modified to appointment-only testing, identifying a period of insufficient clinical testing (resulting in a reduction to testing access and a reduction in the number of daily tests) in these communities, despite increases in the wastewater signal. Furthermore, the WC ratio decreased significantly in 6 of the 7 studied locations, serving as a potential signal of the emergence of the Alpha variant of concern (VOC) in a relatively non-immunized community (40-60 % allelic proportion), while a more muted decrease in the WC ratio signaled the emergence of the Delta VOC in a relatively well-immunized community (40-60 % allelic proportion). Finally, a significant decrease in the WC ratio signaled the emergence of the Omicron VOC, likely because of the variant's greater effectiveness at evading immunity, leading to a significant number of new reported clinical cases, even when community immunity was high. The WC ratio, used as an additional monitoring metric, could complement clinical case counts and wastewater signals as individual metrics in its potential ability to identify important epidemiological occurrences, adding value to WWS as a diagnostic technology during the COVID-19 pandemic and likely for future pandemics.

Understanding the dynamic relation between wastewater SARS-CoV-2 signal and clinical metrics throughout the pandemic.

Wastewater surveillance (WWS) of SARS-CoV-2 was proven to be a reliable and complementary tool for population-wide monitoring of COVID-19 disease incidence but was not as rigorously explored as an indicator for disease burden throughout the pandemic. Prior to global mass immunization campaigns and during the spread of the wildtype COVID-19 and the Alpha variant of concern (VOC), viral measurement of SARS-CoV-2 in wastewater was a leading indicator for both COVID-19 incidence and disease burden in communities. As the two-dose vaccination rates escalated during the spread of the Delta VOC in Jul. 2021 through Dec. 2021, relations weakened between wastewater signal and community COVID-19 disease incidence and maintained a strong relationship with clinical metrics indicative of disease burden (new hospital admissions, ICU admissions, and deaths). Further, with the onset of the vaccine-resistant Omicron BA.1 VOC in Dec. 2021 through Mar. 2022, wastewater again became a strong indicator of both disease incidence and burden during a period of limited natural immunization (no recent infection), vaccine escape, and waned vaccine effectiveness. Lastly, with the populations regaining enhanced natural and vaccination immunization shortly prior to the onset of the Omicron BA.2 VOC in mid-Mar 2022, wastewater is shown to be a strong indicator for both disease incidence and burden. Hospitalization-to-wastewater ratio is further shown to be a good indicator of VOC virulence when widespread clinical testing is limited. In the future, WWS is expected to show moderate indication of incidence and strong indication of disease burden in the community during future potential seasonal vaccination campaigns.

FAM: focal attention module for lesion segmentation of COVID-19 CT images.

The novel coronavirus pneumonia (COVID-19) is the world's most serious public health crisis, posing a serious threat to public health. In clinical practice, automatic segmentation of the lesion from computed tomography (CT) images using deep learning methods provides an promising tool for identifying and diagnosing COVID-19. To improve the accuracy of image segmentation, an attention mechanism is adopted to highlight important features. However, existing attention methods are of weak performance or negative impact to the accuracy of convolutional neural networks (CNNs) due to various reasons (e.g. low contrast of the boundary between the lesion and the surrounding, the image noise). To address this issue, we propose a novel focal attention module (FAM) for lesion segmentation of CT images. FAM contains a channel attention module and a spatial attention module. In the spatial attention module, it first generates rough spatial attention, a shape prior of the lesion region obtained from the CT image using median filtering and distance transformation. The rough spatial attention is then input into two 7 × 7 convolution layers for correction, achieving refined spatial attention on the lesion region. FAM is individually integrated with six state-of-the-art segmentation networks (e.g. UNet, DeepLabV3+, etc.), and then we validated these six combinations on the public dataset including COVID-19 CT images. The results show that FAM improve the Dice Similarity Coefficient (DSC) of CNNs by 2%, and reduced the number of false negatives (FN) and false positives (FP) up to 17.6%, which are significantly higher than that using other attention modules such as CBAM and SENet. Furthermore, FAM significantly improve the convergence speed of the model training and achieve better real-time performance. The codes are available at GitHub (https://github.com/RobotvisionLab/FAM.git).

Study on Invulnerability of Material Emergency Transportation System Based on Three-Layer Interdependent Network

The material transportation capacity under emergency conditions is an important guarantee for the country to deal with war, epidemic outbreak, and other crisis situations. Under emergency conditions, some nodes of the material transportation system may fail to work normally, which may lead to the collapse of the whole system. Based on analyzing the characteristics of the material emergency transportation system, this article builds a three-layer interdependent network model and uses the improved M-L model to describe the failure propagation mechanism of node damage in the three-layer network. Then, the network model is attacked randomly, and the relationship between invulnerability of the three-layer network and the three main indexes of network flow, average degree, and probability of interdependence are studied. Afterwards, the propagation of cascading failure among three subnetworks in the interdependent network is analyzed and compared. This article provides a theoretical basis for building an efficient and robust material emergency transportation system.

Release and Demand of Public Health Information in Social Media During the Outbreak of COVID-19 in China.

Information release is a key to the macro-economy during the outbreak of the Coronavirus Diosease-2019 (COVID-19). To explore the relationship between information supply by the government and public information demand in the pandemic, this study collected over 4,000 posts published on the most popular social media platform, i.e., WeChat. Many approaches, such as text mining, are employed to explore the information at different stages during the pandemic. According to the results, the government attached great importance to the information related to the pandemic. The main topics of information released by the government included the latest situation of the pandemic, announcements by the State Council, and prevention policies for COVID-19. Information mismatch between the public and Chinese governments contributed to the economic depression caused by the pandemic. Specifically, the topics of "the latest situation" and "popular scientific knowledge regarding the pandemic" have gained the most attention of the public. The information demand of the public has changed from the pandemic itself to the recovery of social life and industrial activities after the authority announced the control of the pandemic. However, during the recession phase, the information demand has shifted to asymptomatic infections and global pandemic trends. By contrast, some of the main topics provided by the government, such as "How beautiful you are," were excessive because the public demand is insufficient. Therefore, severe mismatches existed between information release of the government and public information demand during the pandemic, which impeded the recovery of the economy. The results in this study provide strategical suggestions of information release and opinion guidance for the authorities.

Study on the Relationship between Thermal Comfort and Learning Efficiency of Different Classroom-Types in Transitional Seasons in the Hot Summer and Cold Winter Zone of China

The physical environment of classrooms has a strong relationship with student learning performance and health. Since the outbreak of COVID-19 in 2019, almost all universities have begun implementing closed instructional management, which has forced students to spend a much longer amount of time inside the classroom. This has also led to an increasing problem of thermal comfort in classroom indoor environments. In this paper, classrooms evolved from three dominant teaching modes at Zhejiang Sci-Tech University (ZSTU), located in the Hot Summer and Cold Winter (HSCW) zone of China, were selected as experimental spaces. Meanwhile, 12 learning groups with 60 students (30 of each sex) were selected as the tested samples. The relationship between thermal comfort and learning efficiency of the tested students was established through thermal comfort questionnaires and learning efficiency tests under the typical natural conditions in transition seasons. Based on this, improvement strategies were proposed for the current state of the classroom environment, providing a database for optimizing the environmental conditions of university classrooms in HSCW zone on the basis of improving students’ learning efficiency.

COVID-19 wastewater surveillance in rural communities: Comparison of lagoon and pumping station samples.

Wastewater-based epidemiology/wastewater surveillance has been a topic of significant interest over the last year due to its application in SARS-CoV-2 surveillance to track prevalence of COVID-19 in communities. Although SARS-CoV-2 surveillance has been applied in more than 50 countries to date, the application of this surveillance has been largely focused on relatively affluent urban and peri-urban communities. As such, there is a knowledge gap regarding the implementation of reliable wastewater surveillance in small and rural communities for the purpose of tracking rates of incidence of COVID-19 and other pathogens or biomarkers. This study examines the relationships existing between SARS-CoV-2 viral signal from wastewater samples harvested from an upstream pumping station and from an access port at a downstream wastewater treatment lagoon with the community's COVID-19 rate of incidence (measured as percent test positivity) in a small, rural community in Canada. Real-time quantitative polymerase chain reaction (RT-qPCR) targeting the N1 and N2 genes of SARS-CoV-2 demonstrate that all 24-h composite samples harvested from the pumping station over a period of 5.5 weeks had strong viral signal, while all samples 24-h composite samples harvested from the lagoon over the same period were below the limit of quantification. RNA concentrations and integrity of samples harvested from the lagoon were both lower and more variable than from samples from the upstream pumping station collected on the same date, indicating a higher overall stability of SARS-CoV-2 RNA upstream of the lagoon. Additionally, measurements of PMMoV signal in wastewater allowed normalizing SARS-CoV-2 viral signal for fecal matter content, permitting the detection of actual changes in community prevalence with a high level of granularity. As a result, in sewered small and rural communities or low-income regions operating wastewater lagoons, samples for wastewater surveillance should be harvested from pumping stations or the sewershed as opposed to lagoons.

Effect of Convalescent Plasma Therapy on Viral Shedding and Survival in Patients With Coronavirus Disease 2019.

Currently, coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has been reported in almost all countries globally. No effective therapy has been documented for COVID-19, and the role of convalescent plasma therapy is unknown. In the current study, 6 patients with COVID-19 and respiratory failure received convalescent plasma a median of 21.5 days after viral shedding was first detected, all tested negative for SARS-CoV-2 RNA within 3 days after infusion, and 5 eventually died. In conclusion, convalescent plasma treatment can end SARS-CoV-2 shedding but cannot reduce the mortality rate in critically ill patients with end-stage COVID-19, and treatment should be initiated earlier.