Learner' Online Self-regulated Learning Skills: A Comparison Between Chinese Undergraduates and International African Undergraduates

The outbreak of the COVID-19 pandemic forced students to move from face-to-face learning to online learning. Online learning has high demands on students' Self-regulated Learning (SRL) skills. In this study, a questionnaire that used five-point Likert scale was administrated between international African undergraduates and Chinese undergraduate students to investigate their online learning behaviors. The questionnaire was composed of six categories: environment structuring, goal setting, time management, help-seeking, task strategies, and self-evaluation. 441 valid responses were received, 89 from international African students and 352 from Chinese undergraduates. The collected data were analyzed with SPSS Version 24.0. The results showed that there was no significant difference between Chinese student' and international African students' SRL skills in the six sub-scales. This may be due to the small sample size of African students and the similar learning environment. Larger samples are needed in future research to further verify the conclusion. The research results can be used as a reference for the future online learning design to strengthen learners' SRL skills. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Probit-Based Discrete-Continuous Choice Model to Explore the Relationship Between Car Ownership and Commuters' Non-Work Activity Durations in Xiaoshan District of Hangzhou, China

In China, a developing country, the car ownership level is much lower than that in developed countries, but transportation policies have been implemented to discourage car ownership and mitigate traffic congestion. However, car ownership (considered as car availability in this paper, meaning that an individual has access to a household private car) may influence travelers' well-being. To highlight the interrelation between car ownership and travelers' well-being, this paper develops a probit-based discrete-continuous model to analyze the relationship between car ownership and the duration of commuters' three major non-work outdoor activities (Act1: shopping and dining;Act2: leisure and entertainment;and Act3: visiting relatives or friends) in Xiaoshan District, Hangzhou, China. Empirical results indicate strong effects of individual and household socio-demographics, built environment attributes, and work-related characteristics on the car ownership decision and the duration of three non-work activities. The analysis shows positive correlations in unobserved factors between the car ownership decision and the duration of Acts1-3, indicating a mutually promotive relationship. Similarly, negative correlations among the duration of Acts1-3 show that non-work activities' duration is mutually substitutive. These findings will help to better understand commuters' car ownership decisions and non-work outdoor activity behavior restricted by fixed work schedules in developing countries, which can, in turn, better evaluate the impact of transportation policies (such as car ownership restriction) on travel demand as well as well-being, and provide decision support for the formulation of transportation policies.

COVID-ResNet: COVID-19 Recognition Based on Improved Attention ResNet

COVID-19 is the most widespread infectious disease in the world. There is an incubation period in the early stage of infection. At present, there are some difficulties in the diagnosis of COVID-19. Medical image analysis based on computed tomography (CT) images is an important tool for clinical diagnosis. However, the lesion size of COVID-19 is smaller, and the lesion shape of COVID-19 is more complex. The effect of the aided diagnosis model is not good. To solve this problem, an aided diagnostic model of COVID-ResNet was proposed based on CT images. Firstly, an improved attention ResNet model was designed based on CT images to focus on the focal lesion area. Secondly, the SE-Res block was constructed. The squeeze excitation mechanism with the residual connection was introduced into the ResNet. The SE-Res block can enhance the correlation degree among different channels and improve the overall accuracy of the model. Thirdly, MFCA (multi-layer feature converge attention) blocks were proposed, which extract multi-layer features. In this model, coordinated attention was used to focus on the direction information of the lesion area. Different layer features were concatenated so that the shallow layer and deep layer features were fused. The experimental results showed that the model could significantly improve the recognition accuracy of COVID-19. Compared with similar models, COVID-ResNet has better performance. On the COVID-19 CT dataset, the accuracy, recall rate, F1 score, and AUC value could reach 96.89%, 98.15%,96.96%, and 99.04%, respectively. Compared with the ResNet model, the accuracy, recall rate, F1 score, and AUC value were higher by 3.1%, 2.46%, 3.0%, and 1.16%, respectively. In ablation experiments, the experimental results showed that the SE-Res block and MFCA model proposed by us were effective. COVID-ResNet transfers the shallow features to the deep, gathers the features, and makes the information complementary. COVID-ResNet can improve the work efficiency of doctors and reduce the misdiagnosis rate. It has a positive significance for the computer-aided diagnosis of COVID-19. © 2023 by the authors.

Imaging features of COVID-19: a series of 56 cases.

Objective: To evaluate the imaging features of CT scan in patients with COVID-19. Method(s): Clinical data of 56 patients with COVID-19 from January 17 to 19, 2020 admitted to Wenzhou Central Hospital and the Second Affiliated Hospital of Wenzhou Medical University were retrospectively reviewed. The clinical manifestations, lung imaging characteristics and treatment outcomes of patients with different severity were analyzed with SAS software. Result(s): Fever (92.8%, 52/56), dry cough (75.0%, 42/56) and asthenia (58.9%, 33/56) were the first symptoms in most of the patients;some patients also had shortness of breath (25.0%, 14/56) and pharyngeal pain (10.7%, 6/56). Chest high-resolution CT scan showed that in 42 moderate patients, ground glass-like high-density shadows in the lung were observed in 30 cases (71.4%);localized plaque consolidation shadows and bronchial inflation signs were observed in 10 cases (23.8%). In 12 severe patients, 11 had high-density patches involving multiple lung lobes (>=3). In 2 critically ill patients the patches and stripes involving the entire lung were observed;and cord-like high-density shadow, local consolidation and fibrosis were also shown. Conclusion(s): The multiple ground-glass changes outside the lungs are early imaging manifestations of COVID-19 patients. The increase in pulmonary lobe consolidation and fibrosis may indicate the disease progression, and the degree of lung consolidation and fibrosis is closely related to the disease severity.Copyright © 2020 by the Chinese Medical Association.

A Review of Deep Learning Imaging Diagnostic Methods for COVID-19

COVID-19 (coronavirus disease 2019) is a new viral infection disease that is widely spread worldwide. Deep learning plays an important role in COVID-19 images diagnosis. This paper reviews the recent progress of deep learning in COVID-19 images applications from five aspects;Firstly, 33 COVID-19 datasets and data enhancement methods are introduced;Secondly, COVID-19 classification methods based on supervised learning are summarized from four aspects of VGG, ResNet, DenseNet and Lightweight Networks. The COVID-19 segmentation methods based on supervised learning are summarized from four aspects of attention mechanism, multiscale mechanism, residual connectivity mechanism, and dense connectivity mechanism;Thirdly, the application of deep learning in semi-supervised COVID-19 images diagnosis in terms of consistency regularization methods and self-training methods. Fourthly, the application of deep learning in unsupervised COVID-19 diagnosis in terms of autoencoder methods and unsupervised generative adversarial methods. Moreover, the challenges and future work of COVID-19 images diagnostic methods in the field of deep learning are summarized. This paper reviews the latest research status of COVID-19 images diagnosis in deep learning, which is of positive significance to the detection of COVID-19. © 2023 by the authors.

Imaging features of COVID-19: a series of 56 cases.

Objective: To evaluate the imaging features of CT scan in patients with COVID-19. Method(s): Clinical data of 56 patients with COVID-19 from January 17 to 19, 2020 admitted to Wenzhou Central Hospital and the Second Affiliated Hospital of Wenzhou Medical University were retrospectively reviewed. The clinical manifestations, lung imaging characteristics and treatment outcomes of patients with different severity were analyzed with SAS software. Result(s): Fever (92.8%, 52/56), dry cough (75.0%, 42/56) and asthenia (58.9%, 33/56) were the first symptoms in most of the patients;some patients also had shortness of breath (25.0%, 14/56) and pharyngeal pain (10.7%, 6/56). Chest high-resolution CT scan showed that in 42 moderate patients, ground glass-like high-density shadows in the lung were observed in 30 cases (71.4%);localized plaque consolidation shadows and bronchial inflation signs were observed in 10 cases (23.8%). In 12 severe patients, 11 had high-density patches involving multiple lung lobes (>=3). In 2 critically ill patients the patches and stripes involving the entire lung were observed;and cord-like high-density shadow, local consolidation and fibrosis were also shown. Conclusion(s): The multiple ground-glass changes outside the lungs are early imaging manifestations of COVID-19 patients. The increase in pulmonary lobe consolidation and fibrosis may indicate the disease progression, and the degree of lung consolidation and fibrosis is closely related to the disease severity.Copyright © 2020 by the Chinese Medical Association.

Synthesis and enzymatic inhibition effects of thiazolidinedione 3C-like protease inhibitors

The 3C-like protease (also known as Mpro) plays a key role in SARS-CoV-2 replication and has similar substrates across mutant coronaviruses, making it an ideal drug target. We synthesized 19 thiazolidinedione derivatives via the Knoevenagel condensations and Mitsunobu reactions as potential 3C-like protease inhibitors. The activity of these inhibitors is screened in vitro by employing the enzymatic screening model of 3C-like protease using fluorescence resonance energy transfer. Dithiothreitol is included in the enzymatic reaction system to avoid non-specific enzymatic inhibition. Active inhibitors with diverse activity are found in this series of compounds, and two representative inhibitors with potent inhibitory activity are highlighted. © The Author(s) 2023.

A portable, low-cost and high-throughput electrochemical impedance spectroscopy device for point-of-care biomarker detection

This paper presents a portable, fast and accurate electrochemical impedance spectroscopy (EIS) device with 8-well interdigitated electrode chips for biomarker detection. The design adopts low crest factor multisine signal synthesis at low frequencies (<1 kHz) and single-tone signals at high frequencies (>1 kHz), which significantly increases measurement speed without sacrificing accuracy. In addition, the low excitation amplitude of 10 mV preserves impedance linearity and protects the biosamples. The system achieved an average magnitude accuracy error of 0.30% in the frequency range of interest and it requires only 0.46 s to scan 28 frequency points from 10 Hz to 1 MHz. Experiments were conducted to test the capability to detect antibodies against SARS-CoV-2. Gold nanoparticles bound with protein G (GNP-G) were employed as the conjugated secondary antibody probe to detect anti-SARS-CoV-2 IgG in serum. A highly statistical significance (p = 7×10−6) could be found in the impedance data at 10 kHz. The impedance magnitude alteration caused by the GNP-G of the positive and negative groups were 27.2%±13.6% and 4.1%±1.7%, respectively. The results imply that the proposed system enables rapid COVID-19 antibody biomarker detection. Moreover, the EIS system and GNPs have the potential to be modified to detect other biomarkers. © 2022 The Author(s)

Safety of BNT162b2 or CoronaVac COVID-19 vaccines in patients with heart failure: A self-controlled case series study

Background: COVID-19 vaccines are important for patients with heart failure (HF) to prevent severe outcomes but the safety concerns could lead to vaccine hesitancy. This study aimed to investigate the safety of two COVID-19 vaccines, BNT162b2 and CoronaVac, in patients with HF. Methods: We conducted a self-controlled case series analysis using the data from the Hong Kong Hospital Authority and the Department of Health. The primary outcome was hospitalization for HF and the secondary outcomes were major adverse cardiovascular events (MACE) and all hospitalization. We identified patients with a history of HF before February 23, 2021 and developed the outcome event between February 23, 2021 and March 31, 2022 in Hong Kong. Incidence rate ratios (IRR) were estimated using conditional Poisson regression to evaluate the risks following the first three doses of BNT162b2 or CoronaVac. Findings: We identified 32,490 patients with HF, of which 3035 were vaccinated and had a hospitalization for HF during the observation period (BNT162b2 = 755;CoronaVac = 2280). There were no increased risks during the 0–13 days (IRR 0.64 [95% confidence interval 0.33–1.26];0.94 [0.50–1.78];0.82 [0.17–3.98]) and 14–27 days (0.73 [0.35–1.52];0.95 [0.49–1.84];0.60 [0.06–5.76]) after the first, second and third doses of BNT162b2. No increased risks were observed for CoronaVac during the 0–13 days (IRR 0.60 [0.41–0.88];0.71 [0.45–1.12];1.64 [0.40–6.77]) and 14–27 days (0.91 [0.63–1.32];0.79 [0.46–1.35];1.71 [0.44–6.62]) after the first, second and third doses. We also found no increased risk of MACE or all hospitalization after vaccination. Interpretation: Our results showed no increased risk of hospitalization for HF, MACE or all hospitalization after receiving BNT162b2 or CoronaVac vaccines in patients with HF. Funding: The project was funded by a Research Grant from the Food and Health Bureau, The Government of the Hong Kong Special Administrative Region (Ref. No. COVID19F01). F.T.T.L. (Francisco T.T. Lai) and I.C.K.W. (Ian C.K. Wong)'s posts were partly funded by the D24H;hence this work was partly supported by AIR@InnoHK administered by Innovation and Technology Commission. © 2022 The Authors

Analysis of People's Dissatisfaction on COVID-19 using Discontent Questionnaire Data

The COVID-19 outbreak and accompanying policies for prevention and control, such as lockdown and movement constraints, have influenced many areas of society and every aspect of everyone's life and work. To find out factors that have the biggest influences on people's lives, we use the BERT model to classify 'Discontent Questionnaire Data on COVID-19' and apply machine learning methods to evaluate the accuracy of the results. The results show the top three influencing factors are work, stress, and worry about the future, and the classification results show a high degree of consistency and correlation. © 2022 IEEE.

Leveraging Human Mobility Data for Efficient Parameter Estimation in Epidemic Models of COVID-19

Effectively predicting the evolution of COVID-19 is of great significance to contain the pandemic. Extensive previous studies proposed a great number of SIR variants, which are efficient to capture the transmission characteristics of COVID-19. However, the parameter estimation methods in previous studies are based on data from epidemiological investigations, which inevitably have caused a large delay. The popularity of digital trajectory data world-wide makes it possible to understand epidemic spreading from human mobility perspective. The major advantage of digital trajectory data lies in that the co-location level of a population is reflected at every moment, making it possible to forecast the evolution in advance. We showed that the mobility data contributed by mobile phone users could be exploited to estimate the contact probability between individuals, thus revealing the dynamic transmission of COVID-19. Specifically, we developed an estimation method to obtain human co-location levels and quantified the variations of human mobility during the epidemic. Then, we extended the infection rate with a real-time co-location level to further forecast the transmission of an epidemic, predicting the epidemic size much more accurately than conventional methods. Finally, the proposed method was applied to evaluate the quantitative effect of different non-pharmacological interventions by predicting the epidemic situations with various mobility characteristics. The empirical results and simulations corroborated our theoretical analysis, providing effective guidance to contain the pandemic. IEEE

Predicting next 7-day Discharges of Hospitalized COVID-19 Patients Using Ensemble Learning

Background. COVID-19 pandemic, especially during resurgences of cases in hard-hit areas, led to significant shortage of hospital beds. Such shortages may be alleviated through timely and effective forecasting of hospital discharges. The objective of this study is to predict next 7-day discharges of hospitalized COVID-19 patients using daily-based electronic health records (EHR) data. Methods. Using EHR data of hospitalized COVID-19 patients from 03/2020-08/ 2021, we employed ensemble learning to predict next 7-day discharges of individual patients. We used both baseline and daily inpatient features for model training, validation, and test. Baseline features include demographic and clinical characteristics, and comorbidities. The daily inpatient features were vital signs, laboratory tests, medications administered, acute physiological scores, use of ventilator, and use of intensive care unit. 1832 hospitalized patients were identified (12,397 hospital days). Samples were randomly split at patient level (7:2:1) into training set (N=1,283 patients with 8,704 hospital days), validation set (N=366 patients with 2,524 days), and test/ holdout set (patient N=183, and 1,169 days). Prediction models were trained on the training set and the validation set. We conducted the model training separately on the samples of admission day and the samples of days after admission day. The predictions were based on the ensemble learning from decision tree, XGBoost, logistic regression, and multilayer perceptron, long short-term memory (LSTM), bi-directional LSTM, and convolutional neural network. The combination of ensemble learning on the test/holdout set was used for final next 7-day predictions based on 'hard' voting (by majority). Where there was a tie, we used 'soft' voting (sum of probabilities) to break the tie. (Figure Presented) Results. The overall average hospital length of stay was 8.7 (SD=10.5) days. The ensemble learning accuracies for admission-day samples and after-admission-day samples were 0.781 and 0.793, and the F1-scores for were 0.761 and 0.789, respectively. Conclusion. EHR data of hospitalized COVID-19 patients can be used to predict next 7-day hospital discharges. Additional inpatient features and more advanced machine learning techniques are needed for prediction accuracy improvement.

Association Between Sleep and COVID-19 Vaccination Response in Hemodialysis Patients

Background: Hemodialysis (HD) patients are less likely to mount a response to the COVID-19 vaccination (CoVac). Poor sleep is associated with blunted vaccination response in the general population. We aim to explore the association between CoVac and sleep quality (SQ) in HD patients. Method(s): Patients from 3 HD clinics were enrolled if they were >=18 years and able to give written consent. Patients were administered the Insomnia Severity Index (ISI) and the Pittsburg Sleep Quality Index (PSQI). Blood specimen were collected after the primary series of COVID-19 vaccination. SARS-CoV-2 neutralization antibodies (nAB) were assayed using the GenScript SARS-CoV-2 Surrogate Virus Neutralization Test Kit (Cat#L00847-A). nAB titers are presented as Unit/ml on a natural log scale. PSQI scores of >5 were categorized as poor SQ and <=5 as good SQ. ISI scores were grouped as no clinically significant insomnia (NI;score 0-7), subthreshold insomnia (SI;score 8-14), and clinical insomnia (CI;score 14-28). T-test and ANOVA analysis were performed on PSQI and ISI scores, respectively, to determine the statistical association between SQ and nAB levels Results: 58 patients were included (60+/-9 years old, HD vintage 4.7+/-4.5 years, 62% male, 66% Black, 21% Hispanic). In the PSQI, 72% (n=42) had poor SQ. In the ISI, 52% = NI, 31% = SI, and 17% CI. Box plots of nAB levels with median and IQR are shown in Fig. 1. There is no association between SQ and nAB levels. Conclusion(s): There is no association between SQ and CoVac response. Given the immune dysfunction in this population, any modifying effect SQ has on CoVac, as observed in the general population, is unlikely. Other methods of improving CoVac response in this vulnerable population should be explored. (Figure Presented).

Research progress of small molecular drugs targeting RNA

"At present, majority of the small molecular drugs used in clinics target proteins, they exert the efficacy through the binding to specific sites on the target protein. However, the ""druggable"" protein targets account for a small portion of the total number of proteins, and ""non-druggable"" proteins account for 80%, because of not having suitable drug binding sites. In the central rule, RNA is located in the upstream of proteins and controls the transcription of proteins. The research of small molecule drugs targeting RNA can solve the problem of protein ""undruggable proteins"" in some extent. This review summarizes the representative research achievements of small molecular drugs targeting RNA in recent years, and the screening methods applied to this field, with the focuses on the latest progress of small molecular drugs targeting novel coronavirus RNA. © 2022, Chinese Pharmaceutical Association. All rights reserved."

Information asymmetry evaluation in hotel E-commerce market: Dynamics and pricing strategy under pandemic

Information asymmetry in different service products is lacking research. Tourism e-commerce as a dynamic financial market driven by advanced information technology deepens the concern for asymmetry brought to hotel consumers. COVID-19 provides a natural intervention on market asymmetry in the hotel price information, which effects are worth evaluating. This study therefore evaluates the degree of information asymmetry in terms of the lodging price of international tourist hotels (ITHs). Through applying the concept of stochastic frontier approach (SFA), this study estimates the degree of information asymmetry and the inefficiencies. The estimation results indicate that the hotel location, traveler type, operation type may cause different information asymmetry. By comparing with the asymmetry before the pandemic, it is found that the asymmetry in lodging price information decreased since the outbreak of the COVID-19 pandemic. Among the six regions studied, the mean range of information equity degree increased from the original 0.634-0.832 to 0.7633-0.866. It indicates that the outbreak of COVID-19 changed the structure of the consumer groups in hotel operations and then affect the hotelier's pricing strategy. © 2022

ASSOCIATION BETWEEN IMMUNE SYSTEM FUNCTION AS DETERMINED BY HEPATITIS B INFECTION/VACCINATION STATUS AND RISK OF COVID-19 INFECTION

BACKGROUND AND AIMS: Since the beginning of the COVID-19 pandemic in early 2020, >290 million people were infected by SARS-CoV-2 and >5.4 million have died from or with COVID-19 (https://coronavirus.jhu.edu/). Patients with chronic health conditions such as end-stage kidney disease (ESKD) experience particularly high morbidity and mortality because of COVID-19. ESKD patients on hemodialysis are widely vaccinated for hepatitis B (HBV) and seroconversion is routinely measured. This practice presents a rare opportunity to study immune function on a wide scale. It can be reasonably assumed that patients who are able to produce a vaccinal or post-HBV antibodies titers have a better immune function than those who are unable to mount such a serological response. We aim to jointly analyze results of SARS-CoV-2 RT-PCR and hepatitis B serology to determine if presence of vaccinal or post-HBV antibodies is associated with likelihood of developing COVID-19 infection. METHOD: Patients who were tested for COVID-19 at Fresenius Medical Care North America dialysis clinics from May 2020 to September 2020 were included in this analysis. HBV infection/vaccination status, demographic parameters and clinical parameters were obtained from the medical record. Nasopharyngeal swab specimen was tested via RT-PCR to detect presence of SARS-CoV-2. Patients were categorized as having good immune function or poor immune function based on vaccinal and post-HBV sero-status. Patients who were vaccinated against HBV but did not seroconvert were considered to have poor immune function. On the other hand, patients who mounted vaccinal or post-HBV antibodies were considered to have good immune function. Univariate and multivariate logistic regression were utilized to study the association between immune function and other demographic, anthropometric and clinical parameters on the likelihood of not being diagnosed with COVID-19. Four models were constructed: Model 1: unadjusted;Model 2: adjusted for age. Model 3: adjusted for age, gender, race, ethnicity, body mass index (BMI). Model 4: adjusted for parameters in model 3 and dialysis vintage (in years), diabetes and congestive heart failure (CHF). RESULTS: 11 870 patients were included in this analysis. 54% patients were male, 33% were Black, 24% of the patients were Hispanic, 69% had diabetes and 22% had CHF. Patients were 61.2 ± 14.4 years old with dialysis vintage of 3.9 ± 3.9 years, BMI of 29.6 ± 9.7 kg/m2 and eKt/V 1.5 ± 0.3. Of these patients, 21% had poor immune function and 79% had good immune function. Results of the logistic regression models are shown in Table 1. In the unadjusted model, poor immune function was associated with an increased likelihood of being diagnosed with COVID-19. In models, 2, 3 and 4 age, vintage and presence of diabetes were all significantly associated with a higher likelihood of being diagnosed with COVID-19. However, poor immune function was not a significant predictor of COVID-19 diagnosis in the adjusted models. CONCLUSION: Patients who have vaccinal or post-HBV antibodies did not have a lower likelihood of COVID-19 compared with patients who were unable to mount an adequate vaccinal or post-HBV antibody response. Response to HBV vaccination or infection may not be adequate to characterize a patient as having good immune response. Other factors that are routinely measured in hemodialysis patients, which may allow us to make inferences about a patient's immune function should be explored.

EFFECT OF COVID-19 PANDEMIC ON LIVER ABSCESS ASSOCIATED WITH TYPE 2 DIABETES MELLITUS

Objective: To investigate the effect of the coronavirus disease 2019 (COVID-19) pandemic on patients with liver abscess associated with type 2 diabetes mellitus (T2DM). Methods: Data about consecutive cases of T2DM-associated liver abscess diagnosed and treated during the pandemic (January-April 2020) or earlier (January-April in 2017-2019) were compared. Results: A total of 177 patients (122 men;median age, 66 years;124 treated in 2017-2019 and 53 treated in 2020) were included in the study. Antibiotic therapy alone led to abscess resolution in 75 patients;the remaining 102 patients underwent successful abscess aspiration (n=56) or drain placement (n = 46). The mean random plasma glucose (15.9±2.7 vs 12.7±2.7 mmol/L;P<0.001), fasting plasma glucose (11.4±2.0 vs 10.6±2.0 mmol/L;P=0.017), and glycosylated hemoglobin A1c (9.1%±1.5% vs 7.8%±0.9%;P<0.001) levels at the presentation were higher among patients treated in 2020 than among those treated earlier. The mean interval between symptom onset and presentation was shorter for patients treated in 2020 (36.5±7.2 hours) than for those treated earlier (50.4±17.4 hours;P<0.001). The mean interval between presentation and diagnosis was longer among patients treated in 2020 (18.4±9.9 hours) than among those treated earlier (11.3±4.9 hours;P<0.001). Conclusions: The COVID-19 pandemic may have promoted the occurrence of liver abscess among patients with poorly controlled T2DM, and control measures for the pandemic may have led to delays in diagnosis. © 2022 A. CARBONE Editore. All rights reserved.