Influence of health literacy of rural junior middle school students on knowledge, attitudes and practices of COVID-19 protection in Jiangxi Province

Objective: To understand the influence of junior middle school students' health literacy on knowledge, belief and behavior of COVID-19 in rural areas of Jiangxi Province, and to enhance junior middle school students' ability to deal with public health emergencies. Methods: Stratified cluster random sampling was used to investigate the health literacy, knowledge level and behavior of COVID-19 protection of 4 311 grade 7 to grade 8 students in rural areas of Jiangxi Province;Chi-square test and Logistic regression analysis were used to analyze the correlation between junior high school students' health literacy and COVID-19 protection knowledge, belief and behavior. Results: The rate of health literacy of junior middle school students in rural areas was 18.21%(n=785), the reported rate of intermediate level was high (n=2 454, 56.92%), and the reported rate of junior high school students at a low level of health literacy was 24.87%(n=1 072). The rate of junior middle school students in rural areas with good COVID-19 protection knowledge was 63.49%, the rate of positive protection attitude was 74.25%, and the rate of good protection behavior was 85.36%;Rate of COVID-19 protection knowledge (OR=4.85, 95%CI=3.80-6.18) and positive rate of protection attitude of high-level health literacy (OR=44.07, 95%CI=24.57-79.05), protective behavior possession rate (OR=25.99, 95%CI=19.67-34.35) were higher than those with low level of health literacy(P < 0.01). Conclusion: Health literacy is associated with COVID-19 protection knowledge, belief and behavior in rural junior high school students of Jiangxi Province, the findings provide direction for junior middle school students to improve their ability to deal with public health emergencies.

Single-cell analysis of adult human heart across healthy and cardiovascular disease patients reveals the cellular landscape underlying SARS-CoV-2 invasion of myocardial tissue through ACE2.

BACKGROUND: The distribution of ACE2 and accessory proteases (ANAD17 and CTSL) in cardiovascular tissue and the host cell receptor binding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are crucial to understanding the virus's cell invasion, which may play a significant role in determining the viral tropism and its clinical manifestations. METHODS: We conducted a comprehensive analysis of the cell type-specific expression of ACE2, ADAM17, and CTSL in myocardial tissue from 10 patients using RNA sequencing. Our study included a meta-analysis of 2 heart single-cell RNA-sequencing studies with a total of 90,024 cells from 250 heart samples of 10 individuals. We used co-expression analysis to locate specific cell types that SARS-CoV-2 may invade. RESULTS: Our results revealed cell-type specific associations between male gender and the expression levels of ACE2, ADAM17, and CTSL, including pericytes and fibroblasts. AGT, CALM3, PCSK5, NRP1, and LMAN were identified as potential accessory proteases that might facilitate viral invasion. Enrichment analysis highlighted the extracellular matrix interaction pathway, adherent plaque pathway, vascular smooth muscle contraction inflammatory response, and oxidative stress as potential immune pathways involved in viral infection, providing potential molecular targets for therapeutic intervention. We also found specific high expression of IFITM3 and AGT in pericytes and differences in the IFN-II signaling pathway and PAR signaling pathway in fibroblasts from different cardiovascular comorbidities. CONCLUSIONS: Our data indicated possible high-risk groups for COVID-19 and provided emerging avenues for future investigations of its pathogenesis. TRIAL REGISTRATION: (Not applicable).

Exploring lemology teaching with "internet plus" flipped classroom pedagogy.

BACKGROUND: To investigate the use of flipped classroom pedagogy based on "Internet plus" in teaching viral hepatitis in the lemology course during the COVID-19 epidemic. METHODS: This study included students from the clinical medicine general practitioner class at Nanjing Medical University's Kangda College, with the observation group consisting of 67 students from the 2020-2021 school year and the control group consisting of 70 students from the 2019-2020 school year. The observation group used "Internet plus" flipped classroom pedagogy, while the control group used conventional offline instruction. The theory course and case analysis ability scores from the two groups were compared and analyzed, and questionnaire surveys were administered to the observation group. RESULT: After the flipped classroom, the observation group had significantly higher theoretical test scores (38.62 ± 4.52) and case analysis ability scores (21.08 ± 3.58) than the control group (37.37 ± 2.43) (t = 2.024, P = 0.045) and (19.16 ± 1.15) (t = 4.254, P < 0.001), respectively. The questionnaire survey in the observation group revealed that the "Internet plus" flipped classroom pedagogy approach can help enhance students' enthusiasm to learn, clinical thinking ability, practical application ability, and learning efficiency, with satisfaction rates of 81.7%, 85.0%, 83.3%, and 78.8%, respectively; 89.4% of students expressed hope that whenever physical classes resumed, the offline courses could be combined with this pedagogy approach. CONCLUSION: The use of the "Internet plus" flipped classroom pedagogy technique for teaching viral hepatitis in a lemology course boosted students' theory learning ability as well as their case analysis ability. The majority of students were pleased with this type of instruction and hoped that whenever physical classes resumed, the offline courses may be integrated with the "Internet plus" flipped classroom pedagogical approach.

Comparing T- and B-cell responses to COVID-19 vaccines across varied immune backgrounds.

The emergence of adapted variants of the SARS-CoV-2 virus has led to a surge in breakthrough infections worldwide. A recent analysis of immune responses in people who received inactivated vaccines has revealed that individuals with no prior infection have limited resistance to Omicron and its sub-lineages, while those with previous infections exhibit a significant amount of neutralizing antibodies and memory B cells. However, specific T-cell responses remain largely unaffected by the mutations, indicating that T-cell-mediated cellular immunity can still provide protection. Moreover, the administration of a third dose of vaccine has resulted in a marked increase in the spectrum and duration of neutralizing antibodies and memory B cells in vivo, which has enhanced resistance to emerging variants such as BA.2.75 and BA.2.12.1. These results highlight the need to consider booster immunization for previously infected individuals and the development of novel vaccination strategies. The rapid spread of adapted variants of the SARS-CoV-2 virus presents a significant challenge to global health. The findings from this study underscore the importance of tailoring vaccination strategies based on individual immune backgrounds and the potential need for booster shots to combat emerging variants. Continued research and development are crucial to discovering new immunization strategies that will effectively protect public health against the evolving virus.

Detection of the dominant pathogens in diarrheal calves of Ningxia, China in 2021-2022.

Introduction: Calf diarrhea is a complex disease that has long been an unsolved problem in the cattle industry. Ningxia is at the forefront of China in the scale of cattle breeding, and calf diarrhea gravely restricts the development of Ningxia's cattle industry. Methods: From July 2021 to May 2022, we collected diarrhea stool samples from calves aged 1-103 days from 23 farms in five cities in Ningxia, and performed PCR using specific primers for 15 major reported pathogens of calf diarrhea, including bacteria, viruses, and parasites. The effect of different seasons on the occurrence of diarrhea in calves was explored, the respective epidemic pathogens in different seasons were screened, and more detailed epidemiological investigations were carried out in Yinchuan and Wuzhong. In addition, we analyzed the relationship between different ages, river distributions and pathogen prevalence. Results: Eventually, 10 pathogens were detected, of which 9 pathogens were pathogenic and 1 pathogen was non-pathogenic. The pathogens with the highest detection rate were Cryptosporidium (50.46%), Bovine rotavirus (BRV) (23.18%), Escherichia coli (E. coli) K99 (20.00%), and Bovine coronavirus (BCoV) (11.82%). The remaining pathogens such as Coccidia (6.90%), Bovine Astrovirus (BoAstV) (5.46%), Bovine Torovirus (BToV) (4.09%), and Bovine Kobuvirus (BKoV) (3.18%) primarily existed in the form of mixed infection. Discussion: The analysis showed that different cities in Ningxia have different pathogens responsible for diarrhea, with Cryptosporidium and BRV being the most important pathogens responsible for diarrhea in calves in all cities. Control measures against those pathogens should be enforced to effectively prevent diarrhea in calves in China.

Clinical efficacy analysis of paxlovid in children with hematological diseases infected with the omicron SARS-CoV-2 new variant.

Objective: To summarize the clinical characteristics of children with hematological malignancies co-infected with novel coronavirus and explore the safety and effectiveness of Paxlovid treatment. Methods: From December 10, 2022, to January 20, 2023, the clinical data of children with hematological diseases diagnosed with novel coronavirus infection in the outpatient and emergency department of the Seventh Affiliated Hospital of Sun Yat-sen University were retrospectively analyzed. Results: According to whether to give paxlovid or not, it is divided into group A (paxlovid group) and group B (non-paxlovid group). The length of fever was 1-6 days in group A and 0-3 days in group B. The viral clearance time was shorter in group A than in group B. The inflammatory indexes CRP and PCT were significantly higher in group A than in group B (P < 0.05). Twenty patients were followed up for 1 month after leaving the hospital, and there were 5 cases of reappearance of fever, 1 case of increased sleep, 1 case of physical fatigue and 1 case of loss of appetite within 2 weeks. Conclusions: Paxlovid has no apparent adverse reactions in children 12 years old and younger with underlying hematological diseases infected with the new coronavirus. Focusing on the interaction between paxlovid and other drugs is necessary during the treatment.

Deep Neural Network Augments Performance of Junior Residents in Diagnosing COVID-19 Pneumonia on Chest Radiographs.

Chest X-rays (CXRs) are essential in the preliminary radiographic assessment of patients affected by COVID-19. Junior residents, as the first point-of-contact in the diagnostic process, are expected to interpret these CXRs accurately. We aimed to assess the effectiveness of a deep neural network in distinguishing COVID-19 from other types of pneumonia, and to determine its potential contribution to improving the diagnostic precision of less experienced residents. A total of 5051 CXRs were utilized to develop and assess an artificial intelligence (AI) model capable of performing three-class classification, namely non-pneumonia, non-COVID-19 pneumonia, and COVID-19 pneumonia. Additionally, an external dataset comprising 500 distinct CXRs was examined by three junior residents with differing levels of training. The CXRs were evaluated both with and without AI assistance. The AI model demonstrated impressive performance, with an Area under the ROC Curve (AUC) of 0.9518 on the internal test set and 0.8594 on the external test set, which improves the AUC score of the current state-of-the-art algorithms by 1.25% and 4.26%, respectively. When assisted by the AI model, the performance of the junior residents improved in a manner that was inversely proportional to their level of training. Among the three junior residents, two showed significant improvement with the assistance of AI. This research highlights the novel development of an AI model for three-class CXR classification and its potential to augment junior residents' diagnostic accuracy, with validation on external data to demonstrate real-world applicability. In practical use, the AI model effectively supported junior residents in interpreting CXRs, boosting their confidence in diagnosis. While the AI model improved junior residents' performance, a decline in performance was observed on the external test compared to the internal test set. This suggests a domain shift between the patient dataset and the external dataset, highlighting the need for future research on test-time training domain adaptation to address this issue.

Development and testing of a multi-lingual Natural Language Processing-based deep learning system in 10 languages for COVID-19 pandemic crisis: A multi-center study

Purpose The COVID-19 pandemic has drastically disrupted global healthcare systems. With the higher demand for healthcare and misinformation related to COVID-19, there is a need to explore alternative models to improve communication. Artificial Intelligence (AI) and Natural Language Processing (NLP) have emerged as promising solutions to improve healthcare delivery. Chatbots could fill a pivotal role in the dissemination and easy accessibility of accurate information in a pandemic. In this study, we developed a multi-lingual NLP-based AI chatbot, DR-COVID, which responds accurately to open-ended, COVID-19 related questions. This was used to facilitate pandemic education and healthcare delivery. Methods First, we developed DR-COVID with an ensemble NLP model on the Telegram platform (https://t.me/drcovid_nlp_chatbot). Second, we evaluated various performance metrics. Third, we evaluated multi-lingual text-to-text translation to Chinese, Malay, Tamil, Filipino, Thai, Japanese, French, Spanish, and Portuguese. We utilized 2,728 training questions and 821 test questions in English. Primary outcome measurements were (A) overall and top 3 accuracies;(B) Area Under the Curve (AUC), precision, recall, and F1 score. Overall accuracy referred to a correct response for the top answer, whereas top 3 accuracy referred to an appropriate response for any one answer amongst the top 3 answers. AUC and its relevant matrices were obtained from the Receiver Operation Characteristics (ROC) curve. Secondary outcomes were (A) multi-lingual accuracy;(B) comparison to enterprise-grade chatbot systems. The sharing of training and testing datasets on an open-source platform will also contribute to existing data. Results Our NLP model, utilizing the ensemble architecture, achieved overall and top 3 accuracies of 0.838 [95% confidence interval (CI): 0.826–0.851] and 0.922 [95% CI: 0.913–0.932] respectively. For overall and top 3 results, AUC scores of 0.917 [95% CI: 0.911–0.925] and 0.960 [95% CI: 0.955–0.964] were achieved respectively. We achieved multi-linguicism with nine non-English languages, with Portuguese performing the best overall at 0.900. Lastly, DR-COVID generated answers more accurately and quickly than other chatbots, within 1.12–2.15 s across three devices tested. Conclusion DR-COVID is a clinically effective NLP-based conversational AI chatbot, and a promising solution for healthcare delivery in the pandemic era.

Development and testing of a multi-lingual Natural Language Processing-based deep learning system in 10 languages for COVID-19 pandemic crisis: A multi-center study.

Purpose: The COVID-19 pandemic has drastically disrupted global healthcare systems. With the higher demand for healthcare and misinformation related to COVID-19, there is a need to explore alternative models to improve communication. Artificial Intelligence (AI) and Natural Language Processing (NLP) have emerged as promising solutions to improve healthcare delivery. Chatbots could fill a pivotal role in the dissemination and easy accessibility of accurate information in a pandemic. In this study, we developed a multi-lingual NLP-based AI chatbot, DR-COVID, which responds accurately to open-ended, COVID-19 related questions. This was used to facilitate pandemic education and healthcare delivery. Methods: First, we developed DR-COVID with an ensemble NLP model on the Telegram platform (https://t.me/drcovid_nlp_chatbot). Second, we evaluated various performance metrics. Third, we evaluated multi-lingual text-to-text translation to Chinese, Malay, Tamil, Filipino, Thai, Japanese, French, Spanish, and Portuguese. We utilized 2,728 training questions and 821 test questions in English. Primary outcome measurements were (A) overall and top 3 accuracies; (B) Area Under the Curve (AUC), precision, recall, and F1 score. Overall accuracy referred to a correct response for the top answer, whereas top 3 accuracy referred to an appropriate response for any one answer amongst the top 3 answers. AUC and its relevant matrices were obtained from the Receiver Operation Characteristics (ROC) curve. Secondary outcomes were (A) multi-lingual accuracy; (B) comparison to enterprise-grade chatbot systems. The sharing of training and testing datasets on an open-source platform will also contribute to existing data. Results: Our NLP model, utilizing the ensemble architecture, achieved overall and top 3 accuracies of 0.838 [95% confidence interval (CI): 0.826-0.851] and 0.922 [95% CI: 0.913-0.932] respectively. For overall and top 3 results, AUC scores of 0.917 [95% CI: 0.911-0.925] and 0.960 [95% CI: 0.955-0.964] were achieved respectively. We achieved multi-linguicism with nine non-English languages, with Portuguese performing the best overall at 0.900. Lastly, DR-COVID generated answers more accurately and quickly than other chatbots, within 1.12-2.15 s across three devices tested. Conclusion: DR-COVID is a clinically effective NLP-based conversational AI chatbot, and a promising solution for healthcare delivery in the pandemic era.

Multi-omics analysis reveals genomic, clinical and immunological features of SARS-CoV-2 virus target genes in pan-cancer.

The SARS-CoV-2 virus, also known as the severe acute respiratory syndrome coronavirus 2, has raised great threats to humans. The connection between the SARS-CoV-2 virus and cancer is currently unclear. In this study, we thus evaluated the multi-omics data from the Cancer Genome Atlas (TCGA) database utilizing genomic and transcriptomic techniques to fully identify the SARS-CoV-2 target genes (STGs) in tumor samples from 33 types of cancers. The expression of STGs was substantially linked with the immune infiltration and may be used to predict survival in cancer patients. STGs were also substantially associated with immunological infiltration, immune cells, and associated immune pathways. At the molecular level, the genomic changes of STGs were frequently related with carcinogenesis and patient survival. In addition, pathway analysis revealed that STGs were involved in the control of signaling pathways associated with cancer. The prognostic features and nomogram of clinical factors of STGs in cancers have been developed. Lastly, by mining the cancer drug sensitivity genomics database, a list of potential STG-targeting medicines was compiled. Collectively, this work demonstrated comprehensively the genomic alterations and clinical characteristics of STGs, which may offer new clues to explore the mechanisms on a molecular level between SARS-CoV-2 virus and cancers as well as provide new clinical guidance for cancer patients who are threatened by the COVID-19 epidemic.

Immunoinformatics design of B and T-cell epitope-based SARS-CoV-2 peptide vaccination.

SARS-COV-2 is a virulent respiratory virus, first identified in China (Wuhan) at the end of 2019. Scientists and researchers are trying to find any possible solution to this deadly viral disease. Different drug source agents have been identified, including western medicine, natural products, and traditional Chinese medicine. They have the potential to counteract COVID-19. This virus immediately affects the liver and causes a decrease in oxygen levels. In this study, multiple vacciome approaches were employed for designing a multi-epitope subunit vaccine for battling against SARS-COV-2. Vaccine designing, immunogenicity, allergenic, and physico-chemical assessment were performed by using the vacciome approach. The vaccine design is likely to be antigenic and produce potent interactions with ACE2 and NSP3 receptors. The developed vaccine has also been given to in-silico cloning models and immune response predictions. A total number of 12 CTL and 12 HTL antigenic epitopes were predicted from three selected covid-19 virulent proteins (spike protein, nucleocapsid protein, and membrane proteins, respectively) based on C-terminal cleavage and MHC binding scores. These predicted epitopes were amalgamated by AYY and GPGPG linkers, and a ß-defensins adjuvant was inserted into the N-terminus of this vaccine. This analysis shows that the recommended vaccine can produce immune responses against SARS-COV-2. Designing and developing of the mentioned vaccine will require further experimental validation.

Safety and Tolerability of COVID-19 Vaccine in Children With Epilepsy: A Prospective, Multicenter Study.

BACKGROUND: We designed this study to investigate the effects of the coronavirus disease 2019 (COVID-19) vaccine on epileptic seizures, as well as its adverse effects, in children with epilepsy (<18 years). METHODS: This anonymous questionnaire study involved a multicenter prospective survey of outpatients and inpatients with epilepsy (＜18 years) registered in epilepsy clinics in eight hospitals in six cities of Shandong Province. RESULTS: A total of 224 children with epilepsy were included in the study. Fifty of them experienced general adverse events after vaccination. The most common local adverse events were pain or tenderness at the injection site. The most common systemic adverse effects were muscle soreness and headache. No severe adverse events were reported. There were no significant differences in the number of antiseizure medications (P = 0.459), gender (P = 0.336), etiology (P = 0.449), age (P = 0.499), duration of disease (P = 0.546), or seizure type (P = 0.475) between the patients with and without general adverse events. We found that the risk of seizure after vaccination was decreased in children who were seizure free for more than six months before vaccination. There was no significant difference in the number of seizures during the first month before vaccination, the first month after the first dose, and the first month after the second dose (P = 0.091). CONCLUSION: The benefits of vaccination against COVID-19 outweighed the risks of seizures/relapses and severe adverse events after vaccination for children with epilepsy.

Contrastive domain adaptation with consistency match for automated pneumonia diagnosis.

Pneumonia can be difficult to diagnose since its symptoms are too variable, and the radiographic signs are often very similar to those seen in other illnesses such as a cold or influenza. Deep neural networks have shown promising performance in automated pneumonia diagnosis using chest X-ray radiography, allowing mass screening and early intervention to reduce the severe cases and death toll. However, they usually require many well-labelled chest X-ray images for training to achieve high diagnostic accuracy. To reduce the need for training data and annotation resources, we propose a novel method called Contrastive Domain Adaptation with Consistency Match (CDACM). It transfers the knowledge from different but relevant datasets to the unlabelled small-size target dataset and improves the semantic quality of the learnt representations. Specifically, we design a conditional domain adversarial network to exploit discriminative information conveyed in the predictions to mitigate the domain gap between the source and target datasets. Furthermore, due to the small scale of the target dataset, we construct a feature cloud for each target sample and leverage contrastive learning to extract more discriminative features. Lastly, we propose adaptive feature cloud expansion to push the decision boundary to a low-density area. Unlike most existing transfer learning methods that aim only to mitigate the domain gap, our method instead simultaneously considers the domain gap and the data deficiency problem of the target dataset. The conditional domain adaptation and the feature cloud generation of our method are learning jointly to extract discriminative features in an end-to-end manner. Besides, the adaptive feature cloud expansion improves the model's generalisation ability in the target domain. Extensive experiments on pneumonia and COVID-19 diagnosis tasks demonstrate that our method outperforms several state-of-the-art unsupervised domain adaptation approaches, which verifies the effectiveness of CDACM for automated pneumonia diagnosis using chest X-ray imaging.

mHealth App to Facilitate Remote Care for Patients With COVID-19: Rapid Development of the DrCovid+ App.

BACKGROUND: The 2019 novel COVID-19 has severely burdened the health care system through its rapid transmission. Mobile health (mHealth) is a viable solution to facilitate remote monitoring and continuity of care for patients with COVID-19 in a home environment. However, the conceptualization and development of mHealth apps are often time and labor-intensive and are laden with concerns relating to data security and privacy. Implementing mHealth apps is also a challenging feat as language-related barriers limit adoption, whereas its perceived lack of benefits affects sustained use. The rapid development of an mHealth app that is cost-effective, secure, and user-friendly will be a timely enabler. OBJECTIVE: This project aimed to develop an mHealth app, DrCovid+, to facilitate remote monitoring and continuity of care for patients with COVID-19 by using the rapid development approach. It also aimed to address the challenges of mHealth app adoption and sustained use. METHODS: The Rapid Application Development approach was adopted. Stakeholders including decision makers, physicians, nurses, health care administrators, and research engineers were engaged. The process began with requirements gathering to define and finalize the project scope, followed by an iterative process of developing a working prototype, conducting User Acceptance Tests, and improving the prototype before implementation. Co-designing principles were applied to ensure equal collaborative efforts and collective agreement among stakeholders. RESULTS: DrCovid+ was developed on Telegram Messenger and hosted on a cloud server. It features a secure patient enrollment and data interface, a multilingual communication channel, and both automatic and personalized push messaging. A back-end dashboard was also developed to collect patients' vital signs for remote monitoring and continuity of care. To date, 400 patients have been enrolled into the system, amounting to 2822 hospital bed-days saved. CONCLUSIONS: The rapid development and implementation of DrCovid+ allowed for timely clinical care management for patients with COVID-19. It facilitated early patient hospital discharge and continuity of care while addressing issues relating to data security and labor-, time-, and cost-effectiveness. The use case for DrCovid+ may be extended to other medical conditions to advance patient care and empowerment within the community, thereby meeting existing and rising population health challenges.

Effect of inactivated COVID-19 vaccines on seizure frequency in patients with epilepsy: A multicenter, prospective study.

Objectives: Several COVID-19 vaccines list "uncontrolled epilepsy" as a contraindication for vaccination. This consequently restricts vaccination against COVID-19 in patients with epilepsy (PWE). However, there is no strong evidence that COVID-19 vaccination can exacerbate conditions in PWE. This study aims to determine the impact of COVID-19 vaccination on PWE. Methods: PWE were prospectively recruited from 25 epilepsy centers. We recorded the seizure frequency at three time periods (one month before the first vaccination and one month after the first and second vaccinations). A generalized linear mixed-effects model (GLMM) was used for analysis, and the adjusted incidence rate ratio (AIRR) with 95% CI was presented and interpreted accordingly. Results: Overall, 859 PWE were included in the analysis. Thirty-one (3.6%) and 35 (4.1%) patients were found to have increased seizure frequency after the two doses, respectively. Age had an interaction with time. The seizure frequency in adults decreased by 81% after the first dose (AIRR=0.19, 95% CI:0.11-0.34) and 85% after the second dose (AIRR=0.16, 95% CI:0.08-0.30). In juveniles (<18), it was 25% (AIRR=0.75, 95% CI:0.42-1.34) and 51% (AIRR=0.49, 95% CI:0.25-0.95), respectively. Interval between the last seizure before vaccination and the first dose of vaccination (ILSFV) had a significant effect on seizure frequency after vaccination. Seizure frequency in PWE with hereditary epilepsy after vaccination was significantly higher than that in PWE with unknown etiology (AIRR=1.95, 95% CI: 1.17-3.24). Two hundred and seventeen (25.3%) patients experienced non-epileptic but not serious adverse reactions. Discussion: The inactivated COVID-19 vaccine does not significantly increase seizure frequency in PWE. The limitations of vaccination in PWE should focus on aspects other than control status. Juvenile PWE should be of greater concern after vaccination because they have lower safety. Finally, PWE should not reduce the dosage of anti-seizure medication during the peri-vaccination period.

Dual-bionic nano-groove structured nanofibers for breathable and moisture-wicking protective respirators.

Coronavirus disease 2019 (COVID-19) pandemic makes protective respirators highly demanded. The respirator materials should filter out viral fine aerosols effectively, allow airflow to pass through easily, and wick away the exhalant moisture timely. However, the commonly used melt-blown nonwovens perform poorly in meeting these requirements simultaneously. Herein, dual-bionic nano-groove structured (NGS) nanofibers are fabricated to serve as protective, breathable and moisture-wicking respirator materials. The creativity of this design is that the tailoring of dual-bionic nano-groove structure, combined with the strong polarity and hydrophilicity of electrospinning polymer, not only endows the nanofibrous materials with improved particle capture ability but also enable them to wick away and transmit breathing moisture. Benefitting from the synthetic effect of hierarchical structure and the intrinsic property of polymers, the resulting NGS nanofibrous membranes show a high filtration efficiency of 99.96%, a low pressure drop of 110 Pa, and a high moisture transmission rate of 5.67 kg m-2 d-1 at the same time. More importantly, the sharp increase of breathing resistance caused by the condensation of exhaled moisture is avoided, overcoming the bottleneck faced by traditional nonwovens and paving a new way for developing protective respirators with high wear comfortability.

Safety and tolerability of COVID-19 vaccine in children with epilepsy: a prospective, multicenter study

Introduction We designed this study to investigate the effects of the coronavirus disease 2019 (COVID-19) vaccine on epileptic seizures, as well as its adverse effects, in children with epilepsy (< 18 years). Methods This anonymous questionnaire study involved a multicenter prospective survey of outpatients and inpatients with epilepsy (＜18 years) registered in epilepsy clinics in 8 hospitals in six cities of Shandong Province. Results A total of 224 children with epilepsy were included in the study. Fifty of them experienced general adverse events after vaccination. The most common local adverse events were pain or tenderness at the injection site. The most common systemic adverse effects were muscle soreness and headache. No severe adverse events were reported. There were no significant differences in the number of anti-seizure medications (ASMs) (P =0.459), gender (P =0.336), etiology (P =0.449), age (P =0.499), duration of disease (P =0.546) or seizure type (P =0.475) between the patients with and without general adverse events. We found that the risk of seizure after vaccination was decreased in children who were seizure-free for more than 6 months before vaccination. There was no significant difference in the number of seizures during the first month before vaccination, the first month after the first dose and the first month after the second dose (P = 0.091). Conclusion The benefits of vaccination against COVID-19 outweighed the risks of seizures/relapses and severe adverse events after vaccination for children with epilepsy.

The influence of students’ ability on the continuous intention of blended learning

Blended learning has grown in importance in colleges since the COVID-19 pandemic because it is a creative and effective extension of traditional education. The aim of this paper is to characterize how students’ ability affects their desire for continuous learning in a blended learning environment. In order to do this, a proposed and tested integral model of students’ flow in blended learning environments is constructed. This is achieved by a survey resulting in a sample of 344 valid questionnaires. The theoretical model was tested and validated using the standard methodological procedure based on exploratory and confirmatory analyses. According to the results, the students’ web skills and social skills significantly impact the flow experience in a positive way, and metacognitive regulating ability has a significant negative impact on flow experience, which in return significantly impact the continuous learning intention. In conclusion, some suggestions for teachers and the online teaching systems are put forward to improve students’ continuous learning intention.