A new classification method for diagnosing COVID-19 pneumonia based on joint CNN features of chest X-ray images and parallel pyramid MLP-mixer module.

During the past three years, the coronavirus disease 2019 (COVID-19) has swept the world. The rapid and accurate recognition of covid-19 pneumonia are ,therefore, of great importance. To handle this problem, we propose a new pipeline of deep learning framework for diagnosing COVID-19 pneumonia via chest X-ray images from normal, COVID-19, and other pneumonia patients. In detail, the self-trained YOLO-v4 network was first used to locate and segment the thoracic region, and the output images were scaled to the same size. Subsequently, the pre-trained convolutional neural network was adopted to extract the features of X-ray images from 13 convolutional layers, which were fused with the original image to form a 14-dimensional image matrix. It was then put into three parallel pyramid multi-layer perceptron (MLP)-Mixer modules for comprehensive feature extraction through spatial fusion and channel fusion based on different scales so as to grasp more extensive feature correlation. Finally, by combining all image features from the 14-channel output, the classification task was achieved using two fully connected layers as well as Softmax classifier for classification. Extensive simulations based on a total of 4099 chest X-ray images were conducted to verify the effectiveness of the proposed method. Experimental results indicated that our proposed method can achieve the best performance in almost all cases, which is good for auxiliary diagnosis of COVID-19 and has great clinical application potential.

A Study on the Learning Early Warning Prediction Based on Homework Habits: Towards Intelligent Sustainable Evaluation for Higher Education

Teachers need a technique to efficiently understand the learning effects of their students. Early warning prediction mechanisms constitute one solution for assisting teachers in changing their teaching strategies by providing a long-term process for assessing each student's learning status. However, current methods of building models necessitate an excessive amount of data, which is not conducive to the final effect of the model, and it is difficult to collect enough information. In this paper, we use educational data mining techniques to analyze students' homework data and propose an algorithm to extract the three main features: Degree of reliability, degree of enthusiasm, and degree of procrastination. Building a predictive model based on homework habits can provide an individualized evaluation of students' sustainability processes and support teachers in adjusting their teaching strategies. This was cross-validated using multiple machine learning algorithms, of which the highest accuracy was 93.34%.

Influence of the COVID-19 pandemic on climate change summit negotiations from the climate governance perspective.

The COVID-19 pandemic has caused significant disruptions to the world since 2020, with over 647 million confirmed cases and 6.7 million reported deaths as of January 2023. Despite its far-reaching impact, the effects of COVID-19 on the progress of global climate change negotiations have yet to be thoroughly evaluated. This discussion paper conducts an examination of COVID-19's impact on climate change actions at global, national, and local levels through a comprehensive review of existing literature. This analysis reveals that the pandemic has resulted in delays in implementing climate policies and altered priorities from climate action to the pandemic response. Despite these setbacks, the pandemic has also presented opportunities for accelerating the transition to a low-carbon economy. The interplay between these outcomes and the different levels of governance will play a crucial role in determining the success or failure of future climate change negotiations.

Online Homework Intelligent Platform Based on Self-Regulated Learning (SRL): Essential for Sustainable Development of Online Higher Education

Online higher education has become a steadily more popular way of learning for university students in the post-pandemic era. It has been emphasized that active learning and interactive communication are key factors in achieving effective performance in online learning. However, due to the lack of learning motivation of students and the lack of feedback data in online learning, there are numerous problems, such as the weak self-discipline of students, unsatisfactory learning experience, a high plagiarism rate of homework, and the low utilization of online teaching resources. In this study, an online homework intelligent platform implemented by information technology (IT) was proposed. It was based on the pedagogical self-regulated learning (SRL) strategy as a theoretical foundation, and information technology as a driver. Through setting online homework assignments, a sustainable means of promoting the four components of the SRL strategy, i.e., self-disciplinary control, independent thinking, reflective learning, and interest development, can be provided to university students. Therefore, this study explained the '4A';functions in the platform and analysed the details of their implementation and value, such as assistance in locating resources, assignment of differentiated homework, assessment of warning learning, and achievement of sharing. After three years of continuous improvements since COVID-19, this online platform has been successfully applied to students and teachers at our university and other pilot universities. A comparison of student teaching data, questionnaire responses and teacher interviews from the Computer Composition Principles course illustrated the sustainability as well as the effectiveness of the method.

Reversible Bronchiectasis in COVID-19 Survivors With Acute Respiratory Distress Syndrome: Pseudobronchiectasis.

To retrospectively analyze whether traction bronchiectasis was reversible in coronavirus disease 2019 (COVID-19) survivors with acute respiratory distress syndrome (ARDS), and whether computed tomography (CT) findings were associated with the reversibility, 41 COVID-19 survivors with ARDS were followed-up for more than 4 months. Demographics, clinical data, and all chest CT images were collected. The follow-up CT images were compared with the previous CT scans. There were 28 (68%) patients with traction bronchiectasis (Group I) and 13 (32%) patients without traction bronchiectasis (Group II) on CT images. Traction bronchiectasis disappeared completely in 21 of the 28 (75%) patients (Group IA), but did not completely disappear in seven of the 28 (25%) patients (Group IB). In the second week after onset, the evaluation score on CT images in Group I was significantly higher than that in Group II (p = 0.001). The proportion of reticulation on the last CT images in Group IB was found higher than that in Group IA (p < 0.05). COVID-19 survivors with ARDS might develop traction bronchiectasis, which can be absorbed completely in most patients. Traction bronchiectasis in a few patients did not disappear completely, but bronchiectasis was significantly relieved. The long-term follow-up is necessary to further assess whether traction bronchiectasis represents irreversible fibrosis.

Estimation of COVID-19 Transmission and Advice on Public Health Interventions

At the end of 2019, an outbreak of the novel coronavirus (COVID-19) made a profound impact on the country’s production and people’s daily lives. Up until now, COVID-19 has not been fully controlled all over the world. Based on the clinical research progress of infectious diseases, combined with epidemiological theories and possible disease control measures, this paper establishes a Susceptible Infected Recovered (SIR) model that meets the characteristics of the transmission of the new coronavirus, using the least square estimation (LSE) method to estimate the model parameters. The simulation results show that quarantine and containment measures as well as vaccine and drug development measures can control the spread of the epidemic effectively. As can be seen from the prediction results of the model, the simulation results of the epidemic development of the whole country and Nanjing are in agreement with the real situation of the epidemic, and the number of confirmed cases is close to the real value. At the same time, the model’s prediction of the prevention effect and control measures have shed new light on epidemic prevention and control.

Predicting the Molecular Mechanism of Shenling Baizhu San in Treating Convalescent Patients With COVID-19 Based on Network Pharmacology and Molecular Docking

Objective: Shenling Baizhu San (SBS) was selected as the regimen for the treatment of COVID-19 in Guangdong Province. It is mainly used for the convalescent treatment of COVID-19 patients with deficiency of both lung and spleen. In this study, we aimed to explore the mechanism of SBS in the treatment of COVID-19 through network pharmacology combined with molecular docking. Methods: The targets of active components of SBS were collected through Traditional Chinese Medicine Systems Pharmacology (TCMSP) and ETCM databases. Using the Genecards, TTD, OMIM and other databases, the targets of COVID-19 were determined. The next step was to use a string database to build a protein?protein interactions (PPI) network between proteins, and use David database to perform gene ontology (GO) function enrichment analysis, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis on core targets. Then we used Cytoscape software to construct the active ingredients-core target-signaling pathway network, and finally the active ingredients of SBS were molecularly docked with the core targets to predict the mechanism of SBS in the treatment of COVID-19. Results: A total of 177 active compounds, 43 core targets and 58 signaling pathways were selected. Molecular docking results showed that the binding energies of the top six active components and the targets were all less than ?5?kcal/MOL. Conclusion: The potential mechanism of action of SBS in the treatment of COVID-19 may be associated with the regulation of genes co-expressed with IL6, DPP4, PTGS2, PTGS1 and TNF.

Expression and Possible Significance of ACE2 in the Human Liver, Esophagus, Stomach, and Colon.

Angiotensin-converting enzyme 2 (ACE2) has been identified as the key receptor of SARS coronavirus that plays a key role in the pathogenesis of SARS. It is known that ACE2 mRNA can be expressed in most organs. However, the protein expression of ACE2 is not clear yet. To explore the role of ACE2 as a precipitating factor in digestive organ damage in COVID-19, this study investigated the expression of ACE2 protein in the human liver, esophagus, stomach, and colon. The result showed that ACE2 can be expressed in the liver, esophagus, stomach, and colon, which suggests SARS-CoV-2 may enter the digestive system through ACE2 and cause liver damage and gastrointestinal damage. It is hoped that the result of the study will provide a new strategy for the prevention and treatment of digestive organ damage under COVID-19.

Admission Rothman Index, Aspirin, and Intermediate Dose Anticoagulation Effects on Outcomes in COVID-19: A Multi-Site Propensity Matched Analysis

Introduction: Venous thromboembolism and in-situ small vessel thrombosis are increased in hospitalized patients with COVID-19 in several patient cohorts. Endotheliopathy and activation of both platelets and coagulation predict critical illness and death. For these reasons the use of anti-platelet agents and increased-intensity anticoagulation in the care of hospitalized patients with COVID-19 is under intense study in several clinical trials. We sought to examine the impact of aspirin and anticoagulation on hospitalization outcomes.Methods: We examined outcomes in a large multi-site cohort of consecutive, hospitalized, COVID-19 laboratory confirmed patients under a risk-stratified treatment algorithm from March 13 through June 18, with a focus on efficacy of aspirin and/or increased-intensity anticoagulation. Out of 4150 identified hospitalized patients with COVID-19, we created 3 study cohorts. The overall cohort (2785 patients) excluded pediatric patients, those with incomplete electronic data, and those with multiple admissions. The aspirin (1956 patients) and anticoagulation (1623 patients) cohorts were nested within the overall cohort;the former excluded patients on any home anti-platelet therapy or those who received non-aspirin anti-platelet therapy in the hospital, while the latter excluded patients who did not receive prophylactic or intermediate dose anticoagulation in the hospital. The primary outcome was in-hospital death. Secondary outcomes were time-to-death with a competing risk (time-to-hospital-discharge), escalation to ICU, length-of-stay and use of mechanical ventilation. Variables examined included age, gender, BMI, race, Rothman Index (RI), D-dimer (DD) and patient co-morbidities including cardiovascular disease, chronic kidney disease, and prior VTE. The aspirin and anticoagulation cohorts underwent propensity score (PS) matching utilizing variables found to be significant in multivariable regression modeling in the overall cohort with 638 and 386 patients, respectively.Results: Univariate followed by multivariable regression modeling in the 2785 patient overall cohort established a novel role for RI, and independent roles for age, BMI, and maximum DD, in predicting severity of illness. In all cohorts the 50th and lower percentile of admission RI was predictive of mortality in multivariable modeling (i.e. aspirin: 3rd and 4th admission RI quartiles with HR = 0.18 for both, p<0.001 for both). In PS matched patients, aspirin was associated with a significant decrease in mortality (OR 0.65 [0.42, 0.98], p=0.044) and a significant increase in mechanical ventilation (OR 1.49 [1.03, 2.18], p=0.037) and ICU status (OR = 1.45 [1.06, 1.98], p=0.021). In PS matched patients in the anticoagulation cohort, intermediate versus prophylactic dose anticoagulation was associated with a marginal decrease in mortality (OR 0.60, p=0.053). In the aspirin cohort examining in-hospital death and discharge as competing risks, the use of aspirin was associated with decreased mortality (p=0.042) and had no effect on discharge (p=0.31). In the anticoagulation cohort a similar competing risk model showed the use of intermediate rather than prophylactic anticoagulation decreased mortality (p=0.046) and had no effect on discharge (p = 0.21).Conclusion: We show in a large cohort of consecutively hospitalized patients with COVID-19 treated under a risk-stratified algorithm the prognostic utility of the admission RI in assessing outcomes in hospitalized patients with COVID-19 and a potential benefit of aspirin therapy on in-hospital death from COVID-19. A potential albeit marginal benefit of intermediate dose anticoagulation over prophylactic dose anticoagulation merits further study with results of clinical trials awaited.Figure

COVID-19 combined with liver injury: Current challenges and management.

Coronavirus disease 2019 (COVID-19) combined with liver injury has become a very prominent clinical problem. Due to the lack of a clear definition of liver injury in patients with COVID-19, the different selection of evaluation parameters and statistical time points, there are the conflicting conclusions about the incidence rate in different studies. The mechanism of COVID-19 combined with liver injury is complicated, including the direct injury of liver cells caused by severe acute respiratory syndrome coronavirus 2 replication and liver injury caused by cytokines, ischemia and hypoxia, and drugs. In addition, underlying diseases, especially chronic liver disease, can aggravate COVID-19 liver injury. In the treatment of COVID-19 combined with liver injury, the primary and basic treatment is to treat the etiology and pathogenesis, followed by support, liver protection, and symptomatic treatment according to the clinical classification and severity of liver injury. This article evaluates the incidence, pathogenesis and prevention and treatment of COVID-19 combined with liver injury, and aims to provide countermeasures for the prevention and treatment of COVID-19 combined with liver injury.

Early fibroproliferative signs on high-resolution CT are associated with mortality in COVID-19 pneumonia patients with ARDS: a retrospective study.

OBJECTIVES: To investigate the chest high-resolution computed tomography (HRCT) findings in coronavirus disease 2019 (COVID-19) pneumonia patients with acute respiratory distress syndrome (ARDS) and to evaluate its relationship with clinical outcome. MATERIALS AND METHODS: In this retrospective study, 79 COVID-19 patients with ARDS were recruited. Clinical data were extracted from electronic medical records and analyzed. HRCT scans, obtained within 3 days before clinical ARDS onset, were evaluated by three independent observers and graded into six findings according to the extent of fibroproliferation. Multivariable Cox proportional hazard regression analysis was used to assess the independent predictive value of the computed tomography (CT) score and radiological fibroproliferation. Patient survival was determined by Kaplan-Meier analysis. RESULTS: Compared with survivors, non-survivors showed higher rates of lung fibroproliferation, whereas there were no significant differences in the area of increased attenuation without traction bronchiolectasis or bronchiectasis. A HRCT score <230 enabled the prediction of survival with 73.5% sensitivity and 93.3% specificity, 100% negative predictive value (NPP), 83.3% positive predictive value (PPV) and 88.6% accuracy (Area Under the Curve [AUC] = 0.9; 95% confidence Interval [CI] 0.831-0.968). A multivariate Cox proportional hazards model showed that the HRCT score is a significant independent risk factor for mortality (Hazard Ratio [HR] 9.94; 95% CI 4.10-24.12). Kaplan-Meier analysis revealed that a HRCT score ⩾230 was associated with a higher fatality rate. Organ injury occurred less frequently in patients with a HRCT score <230 compared to those with a HRCT score ⩾230. CONCLUSION: Early pulmonary fibroproliferative signs on HRCT are associated with increased mortality and susceptibility to organ injury in COVID-19 pneumonia patients with early ARDS.

Intermediate-dose anticoagulation, aspirin, and in-hospital mortality in COVID-19: A propensity score-matched analysis.

Thrombotic complications occur at high rates in hospitalized patients with COVID-19, yet the impact of intensive antithrombotic therapy on mortality is uncertain. We examined in-hospital mortality with intermediate- compared to prophylactic-dose anticoagulation, and separately with in-hospital aspirin compared to no antiplatelet therapy, in a large, retrospective study of 2785 hospitalized adult COVID-19 patients. In this analysis, we established two separate, nested cohorts of patients (a) who received intermediate- or prophylactic-dose anticoagulation ("anticoagulation cohort", N = 1624), or (b) who were not on home antiplatelet therapy and received either in-hospital aspirin or no antiplatelet therapy ("aspirin cohort", N = 1956). To minimize bias and adjust for confounding factors, we incorporated propensity score matching and multivariable regression utilizing various markers of illness severity and other patient-specific covariates, yielding treatment groups with well-balanced covariates in each cohort. The primary outcome was cumulative incidence of in-hospital death. Among propensity score-matched patients in the anticoagulation cohort (N = 382), in a multivariable regression model, intermediate- compared to prophylactic-dose anticoagulation was associated with a significantly lower cumulative incidence of in-hospital death (hazard ratio 0.518 [0.308-0.872]). Among propensity-score matched patients in the aspirin cohort (N = 638), in a multivariable regression model, in-hospital aspirin compared to no antiplatelet therapy was associated with a significantly lower cumulative incidence of in-hospital death (hazard ratio 0.522 [0.336-0.812]). In this propensity score-matched, observational study of COVID-19, intermediate-dose anticoagulation and aspirin were each associated with a lower cumulative incidence of in-hospital death.