Scaffolding Junior Middle School Students' Engagement in Online Project-based Learning During the COVID-19 Pandemic: A Case Study from East China.

During the COVID-19 pandemic, online learning has experienced increasing utilization and poses new challenges for schoolteachers to engage students. Project-based Learning (PBL) is widely acknowledged as an effective pedagogy for motivating and involving students. However, few studies have examined scaffolds that facilitate student engagement in the context of distance PBL. This mixed-method study was conducted with grade 7 teachers and students in a junior middle school in East China from March 2020 to April 2020. Qualitative analysis was employed in interviews with 2 teachers and 21 students. Quantitative analysis was used to visualize the self-reflection reports of 39 students. The findings suggest that the scaffolds of teacher direction, technology support, peer collaboration, and parental assistance play a significant role. In addition, specific scaffolding within the above categories was revealed. The results highlight the problem-oriented, methodological, and synthesized application of various scaffold(ing)s in engaging students and emphasize that scaffolding students emotionally is the core issue to support engagement for remote learning.

Cancer and COVID-19 Susceptibility and Severity: A Two-Sample Mendelian Randomization and Bioinformatic Analysis.

The clinical management of patients with COVID-19 and cancer is a Gordian knot that has been discussed widely but has not reached a consensus. We introduced two-sample Mendelian randomization to investigate the causal association between a genetic predisposition to cancers and COVID-19 susceptibility and severity. Moreover, we also explored the mutation landscape, expression pattern, and prognostic implications of genes involved with COVID-19 in distinct cancers. Among all of the cancer types we analyzed, only the genetic predisposition to lung adenocarcinoma was causally associated with increased COVID-19 severity (OR = 2.93, ß = 1.074, se = 0.411, p = 0.009) with no obvious heterogeneity (Q = 17.29, p = 0.24) or symmetry of the funnel plot. In addition, the results of the pleiotropy test demonstrated that instrument SNPs were less likely to affect COVID-19 severity via approaches other than lung adenocarcinoma cancer susceptibility (p = 0.96). Leave-one-out analysis showed no outliers in instrument SNPs, whose elimination rendered alterations in statistical significance, which further supported the reliability of the MR results. Broad mutation and differential expression of these genes were also found in cancers, which may provide valuable information for developing new treatment modalities for patients with both cancer and COVID-19. For example, ERAP2, a risk factor for COVID-19-associated death, is upregulated in lung squamous cancer and negatively associated with patient prognosis. Hence, ERAP2-targeted treatment may simultaneously reduce COVID-19 disease severity and restrain cancer progression. Our results highlighted the importance of strengthening medical surveillance for COVID-19 deterioration in patients with lung adenocarcinoma by showing their causal genetic association. For these patients, a delay in anticancer treatment, such as chemotherapy and surgery, should be considered.

Does non-COVID-19 lung lesion help? investigating transferability in COVID-19 CT image segmentation.

BACKGROUND AND OBJECTIVE: Coronavirus disease 2019 (COVID-19) is a highly contagious virus spreading all around the world. Deep learning has been adopted as an effective technique to aid COVID-19 detection and segmentation from computed tomography (CT) images. The major challenge lies in the inadequate public COVID-19 datasets. Recently, transfer learning has become a widely used technique that leverages the knowledge gained while solving one problem and applying it to a different but related problem. However, it remains unclear whether various non-COVID19 lung lesions could contribute to segmenting COVID-19 infection areas and how to better conduct this transfer procedure. This paper provides a way to understand the transferability of non-COVID19 lung lesions and a better strategy to train a robust deep learning model for COVID-19 infection segmentation. METHODS: Based on a publicly available COVID-19 CT dataset and three public non-COVID19 datasets, we evaluate four transfer learning methods using 3D U-Net as a standard encoder-decoder method. i) We introduce the multi-task learning method to get a multi-lesion pre-trained model for COVID-19 infection. ii) We propose and compare four transfer learning strategies with various performance gains and training time costs. Our proposed Hybrid-encoder Learning strategy introduces a Dedicated-encoder and an Adapted-encoder to extract COVID-19 infection features and general lung lesion features, respectively. An attention-based Selective Fusion unit is designed for dynamic feature selection and aggregation. RESULTS: Experiments show that trained with limited data, proposed Hybrid-encoder strategy based on multi-lesion pre-trained model achieves a mean DSC, NSD, Sensitivity, F1-score, Accuracy and MCC of 0.704, 0.735, 0.682, 0.707, 0.994 and 0.716, respectively, with better genetalization and lower over-fitting risks for segmenting COVID-19 infection. CONCLUSIONS: The results reveal the benefits of transferring knowledge from non-COVID19 lung lesions, and learning from multiple lung lesion datasets can extract more general features, leading to accurate and robust pre-trained models. We further show the capability of the encoder to learn feature representations of lung lesions, which improves segmentation accuracy and facilitates training convergence. In addition, our proposed Hybrid-encoder learning method incorporates transferred lung lesion features from non-COVID19 datasets effectively and achieves significant improvement. These findings promote new insights into transfer learning for COVID-19 CT image segmentation, which can also be further generalized to other medical tasks.

Clinical Features of Fatalities in Patients With COVID-19.

OBJECTIVES: The novel coronavirus disease 2019 (COVID-19) pandemic has spread to over 213 countries and territories. We sought to describe the clinical features of fatalities in patients with severe COVID-19. METHODS: We conducted an Internet-based retrospective cohort study through retrieving the clinical information of 100 COVID-19 deaths from nonduplicating incidental reports in Chinese provincial and other governmental websites between January 23 and March 10, 2020. RESULTS: Approximately 6 of 10 COVID-19 deaths were males (64.0%). The average age was 70.7 ± 13.5 y, and 84% of patients were elderly (over age 60 y). The mean duration from admission to diagnosis was 2.2 ± 3.8 d (median: 1 d). The mean duration from diagnosis to death was 9.9 ± 7.0 d (median: 9 d). Approximately 3 of 4 cases (76.0%) were complicated by 1 or more chronic diseases, including hypertension (41.0%), diabetes (29.0%) and coronary heart disease (27.0%), respiratory disorders (23.0%), and cerebrovascular disease (12.0%). Fever (46.0%), cough (33.0%), and shortness of breath (9.0%) were the most common first symptoms. Multiple organ failure (67.9%), circulatory failure (20.2%), and respiratory failure (11.9%) are the top 3 direct causes of death. CONCLUSIONS: COVID-19 deaths are mainly elderly and patients with chronic diseases especially cardiovascular disorders and diabetes. Multiple organ failure is the most common direct cause of death.

Clinical Characteristics of 2019 Novel Coronavirus Pneumonia in China: A Systematic Review and Meta-analysis (preprint)

Background Although novel pneumonia associated with the Corona Virus Disease 2019 (COVID-19) suddenly broke out in China, China has controlled this epidemic effectively. Therefore, evidence-based descriptions of medical and clinical characteristics in China are necessary.Methods Literatures have been systematically performed a search on PubMed, Embase, Web of Science, GreyNet International, and The Cochrane Library from inception up to March 15, 2020. Quality of evidence was evaluated according to the STROBE checklist, and publication bias was analyzed by Egger’s test. In the single-arm meta-analysis, A random-effects model was used to obtain a pooled incidence rate. We conducted subgroup analysis according to geographic region and research scale.Results A total of 30 Chinese studies and 1969 patients were included in this meta-analysis. The valid pooled incidence rates of symptoms were as follows: rhinorrhea 5.1% (95% CI: 3.7–6.8, I2 = 31.90), diarrhea 11.0% (95% CI: 9.3–12.9, I2 = 16.58), pharyngalgia 9.4% (95% CI: 7.5–11.7, I2 = 36.40), headache 9.5% (95% CI: 8.5–11.1, I2 = 5.7), and lymphocytopenia 36.7% (95% CI: 33.8–39.8 I2 = 28.73). Meanwhile, 4.3% (95% CI: 3.5–5.4, I2 = 0.00) of patients were found without any symptoms, although they were diagnosed by RT-PCR. In terms of lung CT imaging, most of the patients showed bilateral mottling or ground-glass opacity, and 7.7% (95% CI: 4.4–12.9, I2 = 35.64) of patients had a crazy-paving pattern. In subgroup analysis, the pooled incidence rate of normal CT presentations in the Wuhan area and outside Wuhan area was 2.3% (95% CI: 1.4–3.6, I2 = 24.78) and 5.8% (95% CI: 4.4–7.7, I2 = 32.76) respectively (P = 0.001).Conclusions The findings suggest that although most of the COVID-19 patients have symptoms or abnormal CT imaging presentations, a few of them accompany with no symptoms or abnormal CT imaging results should also be noticed. The digestive symptoms and lymphocytopenia may be the potential clinical characteristics, especially for patients with a history of contact with COVID-19. Additionally, the incidence rate of ARDS in the Wuhan area and outside Wuhan area was different; however, the reasons for this phenomenon are unclear.