ONLINE BEHAVIOR PREDICTION BASED ON DEEP LEARNING IN HEALTHCARE

In recent years, with the rapid development of Internet and computer technology, network education has developed rapidly. With the rapid development of learning technology, online education has been widely popularized. Especially in 2020, novel coronavirus pneumonia suddenly came into being. Online education based on Internet technology has played a great role in the crisis control period. It has also enriched teaching forms and teaching methods. The blended teaching under online and offline integration has increased the availability of students' learning data. Therefore, more and more scholars begin to pay attention to the research of learning early warning based on educational data mining or learning analysis. However, most early warning studies use traditional machine learning algorithms, and there are still deficiencies in the granularity of data collection, technical implementation mechanism, early warning state recognition and so on. With the success of deep learning in artificial intelligence and other fields, scholars began to study the application of deep learning to solve the problems in the field of learning early warning. Combining variational self-coding (LVAE) and deep neural network, this paper proposes a scheme (LVAEpre) which can solve the problem of unbalanced distribution of educational data sets. This paper determines the weight value of each dimension and index by adjusting the weight parameters of the model, and obtains the threshold value of the early warning line, and empirically tests its effectiveness. Finally, the paper designs a learning early warning model and builds a learning early warning platform based on process data. The results show that the early warning effect is good. The proposal of the learning early warning model based on process data and the application of the learning early warning platform have greatly improved the teaching quality, reduced the risk of students failing to attend the course, and effectively realized the early warning function. The experimental results show that the framework improves the prediction ability of identifying risk learners as soon as possible, timely intervene and guide risk learners, improves learning efficiency, and provides effective guidance strategies for the development of network education. [ FROM AUTHOR] Copyright of Journal of Mechanics in Medicine & Biology is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Clinical diagnosis and treatment of Epstein-Barr virus infection initially manifested as ocular diseases in children

Objective: To investigate the clinical. features and treatment of ophthalmopathy secondary to EB virus(EBV) infection in children.

Social media mining under the COVID-19 context: Progress, challenges, and opportunities.

Social media platforms allow users worldwide to create and share information, forging vast sensing networks that allow information on certain topics to be collected, stored, mined, and analyzed in a rapid manner. During the COVID-19 pandemic, extensive social media mining efforts have been undertaken to tackle COVID-19 challenges from various perspectives. This review summarizes the progress of social media data mining studies in the COVID-19 contexts and categorizes them into six major domains, including early warning and detection, human mobility monitoring, communication and information conveying, public attitudes and emotions, infodemic and misinformation, and hatred and violence. We further document essential features of publicly available COVID-19 related social media data archives that will benefit research communities in conducting replicable and reproducible studies. In addition, we discuss seven challenges in social media analytics associated with their potential impacts on derived COVID-19 findings, followed by our visions for the possible paths forward in regard to social media-based COVID-19 investigations. This review serves as a valuable reference that recaps social media mining efforts in COVID-19 related studies and provides future directions along which the information harnessed from social media can be used to address public health emergencies.

Sentiment analysis and topic modeling for COVID-19 vaccine discussions.

The outbreak of the novel coronavirus disease (COVID-19) has been ongoing for almost two years and has had an unprecedented impact on the daily lives of people around the world. More recently, the emergence of the Delta variant of COVID-19 has once again put the world at risk. Fortunately, many countries and companies have developed vaccines for the coronavirus. As of 23 August 2021, more than 20 vaccines have been approved by the World Health Organization (WHO), bringing light to people besieged by the pandemic. The global rollout of the COVID-19 vaccine has sparked much discussion on social media platforms, such as the effectiveness and safety of the vaccine. However, there has not been much systematic analysis of public opinion on the COVID-19 vaccine. In this study, we conduct an in-depth analysis of the discussions related to the COVID-19 vaccine on Twitter. We analyze the hot topics discussed by people and the corresponding emotional polarity from the perspective of countries and vaccine brands. The results show that most people trust the effectiveness of vaccines and are willing to get vaccinated. In contrast, negative tweets tended to be associated with news reports of post-vaccination deaths, vaccine shortages, and post-injection side effects. Overall, this study uses popular Natural Language Processing (NLP) technologies to mine people's opinions on the COVID-19 vaccine on social media and objectively analyze and visualize them. Our findings can improve the readability of the confusing information on social media platforms and provide effective data support for the government and policy makers.

Sentiment analysis and topic modeling for COVID-19 vaccine discussions

The outbreak of the novel coronavirus disease (COVID-19) has been ongoing for almost two years and has had an unprecedented impact on the daily lives of people around the world. More recently, the emergence of the Delta variant of COVID-19 has once again put the world at risk. Fortunately, many countries and companies have developed vaccines for the coronavirus. As of 23 August 2021, more than 20 vaccines have been approved by the World Health Organization (WHO), bringing light to people besieged by the pandemic. The global rollout of the COVID-19 vaccine has sparked much discussion on social media platforms, such as the effectiveness and safety of the vaccine. However, there has not been much systematic analysis of public opinion on the COVID-19 vaccine. In this study, we conduct an in-depth analysis of the discussions related to the COVID-19 vaccine on Twitter. We analyze the hot topics discussed by people and the corresponding emotional polarity from the perspective of countries and vaccine brands. The results show that most people trust the effectiveness of vaccines and are willing to get vaccinated. In contrast, negative tweets tended to be associated with news reports of post-vaccination deaths, vaccine shortages, and post-injection side effects. Overall, this study uses popular Natural Language Processing (NLP) technologies to mine people’s opinions on the COVID-19 vaccine on social media and objectively analyze and visualize them. Our findings can improve the readability of the confusing information on social media platforms and provide effective data support for the government and policy makers.

Early immune responses and prognostic factors in children with COVID-19: a single-center retrospective analysis.

BACKGROUND: Early diagnostic indicators and the identification of possible progression to severe or critical COVID-19 in children are unknown. To investigate the immune characteristics of early SARS-CoV-2 infection in children and possible key prognostic factors for early identification of critical COVID-19, a retrospective study including 121 children with COVID-19 was conducted. Peripheral blood lymphocyte subset counts, T cell-derived cytokine concentrations, inflammatory factor concentrations, and routine blood counts were analyzed statistically at the initial presentation. RESULTS: The T lymphocyte subset and natural killer cell counts decreased with increasing disease severity. Group III (critical cases) had a higher Th/Tc ratio than groups I and II (common and severe cases); group I had a higher B cell count than groups II and III. IL-6, IL-10, IFN-γ, SAA, and procalcitonin levels increased with increasing disease severity. Hemoglobin concentration, and RBC and eosinophil counts decreased with increasing disease severity. Groups II and III had significantly lower lymphocyte counts than group I. T, Th, Tc, IL-6, IL-10, RBC, and hemoglobin had relatively high contribution and area under the curve values. CONCLUSIONS: Decreased T, Th, Tc, RBC, hemoglobin and increased IL-6 and IL-10 in early SARS-CoV-2 infection in children are valuable indices for early diagnosis of severe disease. The significantly reduced Th and Tc cells and significantly increased IL-6, IL-10, ferritin, procalcitonin, and SAA at this stage in children with critical COVID-19 may be closely associated with the systemic cytokine storm caused by immune dysregulation.

Application of polyamide 6 microfiber non-woven fabrics in the large-scale production of all-solid-state lithium metal batteries.

All-solid-state electrolytes have received extensive attention due to their excellent safety and good electrochemical performance. However, due to the harsh conditions of the preparation process, the commercial production of all-solid-state electrolytes remains a challenge. The outbreak of the novel coronavirus pneumonia (COVID-19) has caused great inconvenience to people, while also allowing soft, lightweight and mass-producible non-woven fabrics in masks come into sight. Here, a polymer/polymer solid composite electrolyte is obtained by introducing the polyamide 6 (PA6) microfiber non-woven fabric into PEO polymer through the hot-pressing method. The addition of the PA6 non-woven fabric with lithium-philic properties can not only reduce the crystallinity of the polymer, but also provide more functional transmission sites and then promote the migration of lithium ions at the molecular level. Moreover, due to the sufficient mechanical strength and flexibility of the PA6 non-woven fabric, the composite electrolyte shows excellent inhibition ability of lithium dendrite growth and high electrochemical stability. The novel design concept of introducing low-cost and large-scale production of non-woven fabrics into all-solid-state composite electrolytes to develop high-performance lithium metal batteries is attractive, and can also be broadened to the combination of different types of polymers to meet the needs of various batteries.

Screening of SARS-CoV-2 in 299 Hospitalized Children with Hemato-oncological Diseases: A Multicenter Survey in Hubei, China.

The SARS-CoV-2 infection status of hospitalized children was surveyed in the department of pediatric hematology and oncology in three different hospitals of epidemic areas in Hubei, China. A cross-sectional study was performed to investigate the clinical characteristics, lung CT scan, SARS-CoV-2 nucleic acid test and serum antibodies of hospitalized children with hemato-oncological diseases from January 23 to April 24, 2020. 299 children were enrolled in this study, including 176 males (58.9%) and 123 females (41.1%), aged from 2 months to 16 years. 255 cases (85.3%) received chemotherapy or other immunosuppressive therapies, and there were 44 cases (14.7%) of other benign diseases. Nucleic acid test was performed on 258 children (86.3%) and one case was positive. 163 cases (54.5%) were tested for serum antibodies, and all of them were negative. Lung CT scan was performed on 247 children (82.6%), and 107 of them showed infectious changes. Only one case (0.33%) of COVID-19 was diagnosed in the group. The prevalence rate of COVID-19 in enrolled children with hemato-oncological diseases in Hubei was 0.33%. Immunosuppressed patients are not prone to produce related antibodies. Comprehensive protective measures and ward management can reduce the risk of SARS-CoV-2 infection in the group patients.

Immune-related factors associated with pneumonia in 127 children with coronavirus disease 2019 in Wuhan.

OBJECTIVE: Information regarding the association of immune-related factors with pneumonia in children with coronavirus disease 2019 (COVID-19) is scarce. This study aims to summarize the immune-related factors and their association with pneumonia in children with COVID-19. METHODS: Children with COVID-19 at Wuhan Children's Hospital from 28 January to 12 March 2020 were enrolled. Pneumonia due to causes other than COVID-19 were excluded. The clinical and laboratory information including routine blood tests, blood biochemistry, lymphocyte subsets, immunoglobulins, cytokines, and inflammatory factors were analyzed retrospectively in 127 patients. Normal ranges and mean values of laboratory markers were applied as parameters for logistic regression analyses of their association with pneumonia. RESULTS: In nonintensive care unit patients, 48.8% and 22.4% of patients had increased levels of procalcitonin and hypersensitive C-reactive protein (hs-CRP) respectively. A total 12.6% and 18.1% of patients had decreased levels of immunoglobulin A (IgA) and interleukin 10 (IL-10), respectively. Approximately 65.8% of patients had pneumonia. These patients had decreased levels of globulin (odds ratio [OR], 3.13; 95% confidence interval [CI] 1.41-6.93; P = .005), IgA (OR, 4.00; 95% CI, 1.13-14.18; P = .032), and increased levels of hs-CRP (OR, 3.14; 95% CI, 1.34-7.36; P = .008), procalcitonin (OR, 3.83; 95% CI, 2.03-7.24; P < .001), IL-10 (OR, 7.0; 95% CI, 1.59-30.80; P = .010), and CD4+ CD25+ T lymphocyte less than 5.0% (OR, 1.93; 95% CI, 1.04-3.61; P = 0.038). CONCLUSION: Decreased IgA and CD4+ CD25+ T lymphocyte percentage, and increased hs-CRP, procalcitonin, and IL-10 were associated with pneumonia, suggesting that the immune-related factors may participate in the pathogenesis of pneumonia in children with COVID-19.

Symptomatic Infection is Associated with Prolonged Duration of Viral Shedding in Mild Coronavirus Disease 2019: A Retrospective Study of 110 Children in Wuhan.

BACKGROUND: Information regarding viral shedding in children with coronavirus disease 2019 (COVID-19) was limited. This study aims to investigate the clinical and laboratory characteristics associated with viral shedding in children with mild COVID-19. METHODS: The clinical and laboratory information of 110 children with COVID-19 at Wuhan Children's Hospital, Wuhan, China, from January 30 to March 10, 2020, were analyzed retrospectively. RESULTS: The median age was 6 years old. The median period of viral shedding of COVID-19 was 15 days (interquartile range [IQR], 11-20 days) as measured from illness onset to discharge. This period was shorter in asymptomatic patients (26.4%) compared with symptomatic patients (73.6%) (11 days vs. 17 days). Multivariable regression analysis showed increased odds of symptomatic infection was associated with age <6 years (odds ratio [OR] 8.94, 95% confidence interval [CI]: 2.55-31.35; P = 0.001), hypersensitive C-reactive protein >3.0 mg/L (OR 4.89; 95% CI: 1.10-21.75; P = 0.037) and presenting pneumonia in chest radiologic findings (OR 8.45; 95% CI: 2.69-26.61; P < 0.001). Kaplan-Meier analysis displayed symptomatic infection (P < 0.001), fever (P = 0.006), pneumonia (P = 0.003) and lymphocyte counts <2.0 × 10/L (P = 0.008) in children with COVID-19 were associated with prolonged duration of viral shedding in children with COVID-19. CONCLUSION: Prolonged duration of viral shedding in children with COVID-19 was associated with symptomatic infection, fever, pneumonia and lymphocyte count less than 2.0 × 10/L. Monitoring of symptoms could help to know the viral shedding in children with COVID-19.