High-breathable, antimicrobial and water-repellent face mask for breath monitoring

Graphic Face masks with multiple functionalities and exceptional durability have attracted increasing interests during the COVID-19 pandemic. How to integrate the antibacterial property, comfortability during long-time wearing, and breath monitoring capability together on a face mask is still challenging. Here we developed a kind of face mask that assembles the particles-free water-repellent fabric, antibacterial fabric, and hidden breath monitoring device together, resulting in the highly breathable, water-repellent, and antibacterial face mask with breath monitoring capability. Based on the rational design of the functional layers, the mask shows exceptional repellency to micro-fogs generated during breathing while maintaining high air permeability and inhibiting the passage of bacteria-containing aerogel. More importantly, the multi-functional mask can also monitor the breath condition in a wireless and real-time fashion, and collect the breath information for epidemiological analysis. The resultant mask paves the way to develop multi-functional breath-monitoring masks that can aid the prevention of the secondary transmission of bacteria and viruses while preventing potential discomfort and face skin allergy during long-period wearing.

VR-Enhanced Cognitive Learning: Method, Framework, and Application

Both constructivist learning and situation-cognitive learning believe that learning outcomes are significantly affected by the context or learning environments. However, since 2019, the world has been ravaged by COVID-19. Under the threat of the virus, many offline activities, such as some practical or engineering courses, have been subjected to certain restrictions. Virtual Reality (VR) is an emerging, promising, and rapidly developing technology that enables users to obtain a near-real immersion experience by combining technologies such as computer science, communication, vision, etc. In the context of COVID-19, the advantages of VR immersive experiences are highlighted. By constructing a virtual learning environment, VR technology can greatly compensate for the shortage of traditional teaching conditions and help learners to carry out cognitive learning better. However, currently, VR-enhanced cognitive learning is still in its infancy, along with numerous problems and limitations. Therefore, this paper first conducted an in-depth study of some related concepts, such as constructivist learning and situated cognition learning. Then it proposes a general VR-enhanced cognitive learning framework and designs the general steps for constructing learning situations with VR technology. Based on the proposed model and framework, it developed a campus knowledge-learning APP using VR tools. Through a case study, it verified the validity and performance of the model and strategies. Questionnaire survey and experimental results show that the new model achieves a good learning effect and improves the efficiency of learning by at least 20% compared to the traditional learning methods.

Multidimensional evaluation of offline and online education in dermatology teaching during the COVID-19 pandemic in a chinese teaching hospital: a cross-sectional study.

BACKGROUND: The global spread of Coronavirus disease (COVID-19) has led to the use of online teaching methods in universities, but the effect of online education on dermatology teaching remains unclear. METHODS: We designed a multi-dimensional teaching evaluation form for data collection, student teaching feedback evaluation, and assessed the scores of final theoretical and clinical skill tests, to compare the effective difference between online and offline teaching of dermatology. RESULTS: A total of 311 valid questionnaires of medical undergraduates were collected, 116 of which were enrolled for offline learning, and 195 for online learning. The average score of final theoretical test in the online teaching group had no significant difference compared with that in the offline teaching group (75.33 ± 7.37 vs.75.63 ± 7.51, P = 0.734). However, both scores of skin lesion recognition test and medical history collection test in the online teaching group were significantly lower than that in the offline teaching group (6.53 ± 0.86 vs. 7.10 ± 1.11, P < 0.001; 6.70 ± 1.16 vs. 7.62 ± 0.85, P < 0.001). Additionally, the scores of understanding skin lesions in the online teaching group were significantly lower than that in the offline group (P < 0.001), and the scores of overall understanding of skin diseases and evaluating their learning mode in the online teaching group also decreased (P < 0.05). Among the 195 students enrolled in the online learning group, 156 students (80.0%) recognized that the time of offline teaching should be increased. CONCLUSIONS: Both online and offline education can be used in dermatology theory teaching, but online education is less efficient in skin lesion and practical skills learning. More online teaching software with skin diseases characteristic should be developed to improve the online teaching effect.

A multistage multimodal deep learning model for disease severity assessment and early warnings of high-risk patients of COVID-19.

The outbreak of coronavirus disease 2019 (COVID-19) has caused massive infections and large death tolls worldwide. Despite many studies on the clinical characteristics and the treatment plans of COVID-19, they rarely conduct in-depth prognostic research on leveraging consecutive rounds of multimodal clinical examination and laboratory test data to facilitate clinical decision-making for the treatment of COVID-19. To address this issue, we propose a multistage multimodal deep learning (MMDL) model to (1) first assess the patient's current condition (i.e., the mild and severe symptoms), then (2) give early warnings to patients with mild symptoms who are at high risk to develop severe illness. In MMDL, we build a sequential stage-wise learning architecture whose design philosophy embodies the model's predicted outcome and does not only depend on the current situation but also the history. Concretely, we meticulously combine the latest round of multimodal clinical data and the decayed past information to make assessments and predictions. In each round (stage), we design a two-layer multimodal feature extractor to extract the latent feature representation across different modalities of clinical data, including patient demographics, clinical manifestation, and 11 modalities of laboratory test results. We conduct experiments on a clinical dataset consisting of 216 COVID-19 patients that have passed the ethical review of the medical ethics committee. Experimental results validate our assumption that sequential stage-wise learning outperforms single-stage learning, but history long ago has little influence on the learning outcome. Also, comparison tests show the advantage of multimodal learning. MMDL with multimodal inputs can beat any reduced model with single-modal inputs only. In addition, we have deployed the prototype of MMDL in a hospital for clinical comparison tests and to assist doctors in clinical diagnosis.

Longitudinal efficacy and toxicity of SARS-CoV-2 vaccination in cancer patients treated with immunotherapy.

Despite more than 2 years having elapsed since the onset of SARS-CoV-2 pandemic, a level of hesitation around increased SARS-CoV-2 vaccine toxicity in cancer patients receiving immunotherapy (IO) remains. This hesitation stems from the idea that IO agents could elicit an overwhelming immune stimulation post vaccination and therefore increase the risk of vaccine-related toxicity. The aim of our study was to explore serological responses to SARS-CoV-2 vaccination in patients treated with IO and describe the level of immune stimulation using parameters such as blood cytokines, autoantibody levels and immune related adverse events (irAEs) post vaccination. Fifty-one evaluable patients were enrolled in this longitudinal study. Absolute levels and neutralization potential of anti-SARS-CoV-2 antibodies were not significantly different in the IO group compared to non-IO. Chemotherapy adversely affected seroconversion when compared to IO and/or targeted treatment. Following vaccination, the prevalence of grade ≥2 irAEs in patients treated with IO was not higher than the usual reported IO toxicity. We report, for the first time, that anti-SARS-CoV-2 vaccination, elicited the generation of five autoantibodies. The significantly increased autoantibodies were IgM autoantibodies against beta-2 glycoprotein (p = 0.02), myeloperoxidase (p = 0.03), nucleosome (p = 0.041), SPLUNC2 (p < 0.001) and IgG autoantibody against Myosin Heavy Chain 6 (MYH6) (p < 0.001). Overall, comprehensive analysis of a small cohort showed that co-administration of SARS-CoV-2 vaccine and IO is not associated with increased irAEs. Nevertheless, the detection of autoantibodies post anti-SARS-CoV-2 vaccination warrants further investigation (NCT03702309).

A multistage multimodal deep learning model for disease severity assessment and early warnings of high-risk patients of COVID-19

The outbreak of coronavirus disease 2019 (COVID-19) has caused massive infections and large death tolls worldwide. Despite many studies on the clinical characteristics and the treatment plans of COVID-19, they rarely conduct in-depth prognostic research on leveraging consecutive rounds of multimodal clinical examination and laboratory test data to facilitate clinical decision-making for the treatment of COVID-19. To address this issue, we propose a multistage multimodal deep learning (MMDL) model to (1) first assess the patient's current condition (i.e., the mild and severe symptoms), then (2) give early warnings to patients with mild symptoms who are at high risk to develop severe illness. In MMDL, we build a sequential stage-wise learning architecture whose design philosophy embodies the model's predicted outcome and does not only depend on the current situation but also the history. Concretely, we meticulously combine the latest round of multimodal clinical data and the decayed past information to make assessments and predictions. In each round (stage), we design a two-layer multimodal feature extractor to extract the latent feature representation across different modalities of clinical data, including patient demographics, clinical manifestation, and 11 modalities of laboratory test results. We conduct experiments on a clinical dataset consisting of 216 COVID-19 patients that have passed the ethical review of the medical ethics committee. Experimental results validate our assumption that sequential stage-wise learning outperforms single-stage learning, but history long ago has little influence on the learning outcome. Also, comparison tests show the advantage of multimodal learning. MMDL with multimodal inputs can beat any reduced model with single-modal inputs only. In addition, we have deployed the prototype of MMDL in a hospital for clinical comparison tests and to assist doctors in clinical diagnosis.

Disrupted education, underdogs and the propensity for entrepreneurship: Evidence from China’s sent-down youth program

Societal crises, such as those presented by COVID-19, disrupt the education experiences of youth and deprive them of human capital. Studies show that more human capital gained through education increases the propensity for new business formation. We suggest and find consistent results for the complementary perspective that disrupted education increases such inclination, contributing to theory on human capital. We deploy Mao Zedong’s decision to remove 17 million Chinese use from their schools to the countryside as a natural experiment to test hypotheses. Results from difference-in-differences estimation procedures show that disrupted education increases the propensity for entrepreneurship by over seven per cent, which is stronger for those with parents from elite backgrounds.

[Effect of noninvasive positive pressure ventilation and high-flow nasal cannula oxygen therapy on the clinical efficacy of coronavirus disease 2019 patients with acute respiratory distress syndrome].

OBJECTIVE: To observe the effect of noninvasive positive pressure ventilation (NIPPV) and high-flow nasal cannula oxygen therapy (HFNC) on the prognosis of patients with coronavirus disease 2019 (COVID-19) accompanied with acute respiratory distress syndrome (ARDS). METHODS: A retrospective study was conducted in Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology when authors worked as medical team members for treating COVID-19. COVID-19 patients with pulse oxygen saturation/fraction of inspiration oxygen (SpO2/FiO2, S/F) ratio < 235, managed by medical teams [using S/F ratio instead of oxygenation index (PaO2/FiO2) to diagnose ARDS] from February to April 2020 were included. The patients were divided into NIPPV group and HFNC group according to their oxygen therapy modes. Clinical data of patients were collected, including general characteristics, respiratory rate (RR), fraction of FiO2, SpO2, heart rate (HR), mean arterial pressure (MAP), S/F ratio in the first 72 hours, lymphocyte count (LYM), percentage of lymphocyte (LYM%) and white blood cell count (WBC) at admission and discharge or death, the duration of dyspnea before NIPPV and HFNC, and the length from onset to admission. The differences of intubation rate, all-cause mortality, S/F ratio and RR were analyzed, and single factor analysis and generalized estimation equation (GEE) were used to analyze the risk factors affecting S/F ratio. RESULTS: Among the 41 patients, the proportion of males was high (68.3%, 28 cases), the median age was 68 (58-74) years old, 28 cases had complications (68.3%), and 34 cases had multiple organ dysfunction syndrome (MODS, 82.9%). Compared with HFNC group, the proportion of complications in NIPPV group was higher [87.5% (21/24) vs. 41.2% (7/17), P < 0.05], and the value of LYM% was lower [5.3% (3.4%-7.8%) vs. 10.0% (3.9%-19.7%), P < 0.05], the need of blood purification was also significantly lower [0% (0/24) vs. 29.4% (5/17), P < 0.05]. The S/F ratio of NIPPV group gradually increased after 2 hours treatment and RR gradually decreased with over time, S/F ratio decreased and RR increased in HFNC group compared with baseline, but there was no significant difference in S/F ratio between the two groups at each time point. RR in NIPPV group was significantly higher than that in HFNC group after 2 hours treatment [time/min: 30 (27-33) vs. 24 (21-27), P < 0.05]. There was no significant difference in rate need intubation and hospital mortality between NIPPV group and HFNC group [66.7% (16/24) vs. 70.6% (12/17), 58.3% (14/24) vs. 52.9% (9/17), both P > 0.05]. Analysis of the factors affecting the S/Fratio in the course of oxygen therapy showed that the oxygen therapy mode and the course of illness at admission were the factors affecting the S/F ratio of patients [ßvalues were -15.827, 1.202, 95% confidence interval (95%CI) were -29.102 to -2.552 and 0.247-2.156, P values were 0.019 and 0.014, respectively]. CONCLUSIONS: Compared with HFNC, NIPPV doesn't significantly reduce the intubation rate and mortality of patients with COVID-19 accompanied with ARDS, but it significantly increases the S/F ratio of those patients.

Regional coal power overcapacity assessment in China from 2020 to 2025

Abstact Since the beginning of 2020, the weak domestic power demand caused by the COVID-19 pandemic and the large-scale advancement of coal power construction projects may have further aggravated the coal power overcapacity in China. Given the new situation, this study collected the data on China’s installed electricity capacity and electricity demand during the 13th Five-Year Plan period. Moreover, a reasonable capacity evaluation model of coal power was established based on the energy and electric power balance to analyze China’s coal power overcapacity in 2019 and determine the reasonable capacity for 2025. The internal reasons of unreasonable energy structure and regional difference of energy structure are systematically discussed. Results show that the overcapacity in 2019 was approximately 170 GW, and the overcapacity situation in North, Northwest, and South China was particularly serious. The reasonable coal power capacity for 2025 under the basic situation is 950 GW, indicating that if all the coal power units under construction and planning are operated, the overcapacity in 2025 will be 300 GW. Sensitivity and comprehensive scenario analyses show that under different scenarios, the upper and lower limits of the reasonable coal power capacity in 2025 are 1083 and 794 GW, respectively. Finally, this paper proposes relevant policy recommendations to cope with China’s serious coal power overcapacity problem.

Predictive value of chest CT scoring in COVID-19 patients in Wuhan, China: A retrospective cohort study.

BACKGROUND: Computed tomography (CT) findings of COVID-19 patients were demonstrated by cases series and descriptive studies, but quantitative analysis performed by clinical doctors and studies on its predictive value were rarely seen. The aim of the study is to analyze CT score in COVID-19 patients and explore its predictive value. MATERIALS AND METHODS: We conducted a retrospective cohort study among confirmed COVID -19 patients with available CT images between February 8, 2020 and March 7, 2020. The lung was divided into six zones by the level of tracheal carina and the level of inferior pulmonary vein bilaterally on CT. Ground-glass opacity (GGO), consolidation, crazy-paving pattern and overall lung involvement were rated by Likert scale of 0-4 or binary as 0 or 1. Global severity score for each targeted pattern was calculated as total score of six zones. RESULTS: There were 53 patients and 137 CT scans included in the study. There were 18(34%) of the patients classified as moderate cases while 35(66%) patients were severe/critical cases. Severe/critical patients had higher CT scores in several types of abnormalities than moderate patients from the second week to the fourth week post symptom onset. Overall lung involvement score in the second week demonstrated predictive value for severity with a sensitivity of 81.0% and specificity of 69.2%. CONCLUSIONS: Our modified semi-quantitative CT scoring system for COVID-19 patients demonstrated feasibility. Overall lung involvement score on the second week had predictive value for clinical severity and could be indicator for further treatment.

An investigation of transmission control measures during the first 50 days of the COVID-19 epidemic in China.

Responding to an outbreak of a novel coronavirus [agent of coronavirus disease 2019 (COVID-19)] in December 2019, China banned travel to and from Wuhan city on 23 January 2020 and implemented a national emergency response. We investigated the spread and control of COVID-19 using a data set that included case reports, human movement, and public health interventions. The Wuhan shutdown was associated with the delayed arrival of COVID-19 in other cities by 2.91 days. Cities that implemented control measures preemptively reported fewer cases on average (13.0) in the first week of their outbreaks compared with cities that started control later (20.6). Suspending intracity public transport, closing entertainment venues, and banning public gatherings were associated with reductions in case incidence. The national emergency response appears to have delayed the growth and limited the size of the COVID-19 epidemic in China, averting hundreds of thousands of cases by 19 February (day 50).