Pan-Cancer Profiling of Intron Retention and Its Clinical Significance in Diagnosis and Prognosis (preprint)

Alternative splicing can produce transcripts that affect cancer development and shows potential for cancer diagnosis and treatment. However, intron retention (IR), a type of alternative splicing, has been less systematically studied in cancer biology research. Here, we generated a pan-cancer IR landscape for more than 10,000 samples across 33 cancer types from The Cancer Genome Atlas (TCGA). We characterized differentially retained introns between tumor and normal samples and identified retained introns associated with survival. We discovered 988 differentially retained introns in 14 cancers, some of which demonstrated diagnostic potential in multiple cancer types. We also inferred a large number of prognosis-related introns in 33 cancer types, and the associated genes included well-known cancer hallmarks such as angiogenesis, metastasis, and DNA mutations. Notably, we discovered a novel intron retention event inside 5′UTR of STN1 that is associated with the survival of lung cancer patients. The retained intron reduces translation efficiency by producing upstream open reading frames (uORFs) and thereby inhibits colony formation and cell migration of lung cancer cells. Besides, the IR-based prognostic model achieved good stratification on certain cancers, as illustrated in acute myeloid leukemia. Taken together, we performed a comprehensive IR survey at a pan-cancer level, and the results implied that IR has the potential to be diagnostic and prognostic cancer biomarkers, as well as new drug targets.

Deep Learning-Based Intrusion Detection for Rare Class Network Attacks (preprint)

With the continuous development of network technology, complex network systems generate massive unbalanced attack traffic. Due to the severe imbalance in the quantities of normal samples and attack samples, as well as among different types of attack samples, intrusion detection systems suffer from low detection rates for rare class attack data. In this paper, we propose a geometric synthetic minority oversampling technique based on optimized kernel density estimation algorithm. This method can generate diverse rare class attack data by learning the distribution of rare class attack data while maintaining similarity with the original sample features. Meanwhile, the balanced data is input to a feature extraction module built upon multiple denoising autoencoders, reducing information redundancy in high-dimensional data and improving the detection performance for unknown attacks. Subsequently, a soft voting ensemble learning technique is utilized for multi-class anomaly detection on the balanced and dimensionally reduced data. Finally, an intrusion detection system is constructed based on data preprocessing, imbalance handling, feature extraction, and anomaly detection modules, and validated on the NSL-KDD and N-BaIoT datasets. Comparative experiments with baseline models and other state-of-the-art methods demonstrate that the proposed system improves the detection rate of rare class attack data. Furthermore, it achieves a good overall detection rate on the Internet of Things dataset (N-BaIoT), indicating its strong applicability.

SARS-CoV-2 N protein enhances the anti-apoptotic activity of MCL-1 to promote viral replication.

Viral infection in respiratory tract usually leads to cell death, impairing respiratory function to cause severe disease. However, the diversity of clinical manifestations of SARS-CoV-2 infection increases the complexity and difficulty of viral infection prevention, and especially the high-frequency asymptomatic infection increases the risk of virus transmission. Studying how SARS-CoV-2 affects apoptotic pathway may help to understand the pathological process of its infection. Here, we uncovered SARS-CoV-2 imployed a distinct anti-apoptotic mechanism via its N protein. We found SARS-CoV-2 virus-like particles (trVLP) suppressed cell apoptosis, but the trVLP lacking N protein didn't. Further study verified that N protein repressed cell apoptosis in cultured cells, human lung organoids and mice. Mechanistically, N protein specifically interacted with anti-apoptotic protein MCL-1, and recruited a deubiquitinating enzyme USP15 to remove the K63-linked ubiquitination of MCL-1, which stabilized this protein and promoted it to hijack Bak in mitochondria. Importantly, N protein promoted the replications of IAV, DENV and ZIKV, and exacerbated death of IAV-infected mice, all of which could be blocked by a MCL-1 specific inhibitor, S63845. Altogether, we identifed a distinct anti-apoptotic function of the N protein, through which it promoted viral replication. These may explain how SARS-CoV-2 effectively replicates in asymptomatic individuals without cuasing respiratory dysfunction, and indicate a risk of enhanced coinfection with other viruses. We anticipate that abrogating the N/MCL-1-dominated apoptosis repression is conducive to the treatments of SARS-CoV-2 infection as well as coinfections with other viruses.

Facilitated virtual learning for advanced geriatric education among nursing students during the COVID pandemic in Taiwan

PurposeThis study explored the students' perception of their adoption and acceptance of virtual learning (VL), the factors affecting the adoption of educational technologies and the correlation between their intention, perceived behavioral control and care competence in caring for older adults.Design/methodology/approachA cross-sectional survey was conducted. Surveys were administered to evaluate the participants who were involved in VL on geriatric care during coronavirus disease 2019 (COVID-19) pandemic. A total of 315 nursing students participated in the survey, and 287 valid questionnaires were collected (response rate: 91.11%).FindingsA total of 287 participants (mean age 21.09, SD 1.44 years;242/287, 84.3% female) were included in the study. The variables of intention to use technologies were positively correlated with care competence (r = 0.59, p < 0.001). The results revealed that the major predictors were perceived ease-of-use (PEOU) (β = 0.28, 95% confidence interval (CI) 0.16–0.40) and perceived usefulness (PU) (β = 0.22, CI 0.09–0.35) which were significantly positive predictors of competence in geriatric care.Research limitations/implicationsNursing students lack in clinical knowledge and situational experience in geriatric care;therefore, their perceptiveness, expressions and reflection on the process of providing care to hospitalized older patients should be increased. These results indicated that students improved in geriatric healthcare after/during the VL program during COVID-19 pandemic.Originality/valueIt is hoped that the present study would make an invaluable contribution to existing research on education in general and on the quality of care in geriatric nursing as limited studies have been published so far.

Microneedle-Mediated Immunization Promotes Lung CD8+ T-Cell Immunity.

Microneedle array has proven more efficient in stimulating humoral immunity than intramuscular vaccination. However, its effectiveness in inducing pulmonary CD8+ T cells remains elusive, which is essential to the frontline defense against pulmonary viral infections such as influenza and COVID-19 viruses. The current investigation reveals that superior CD8+ T-cell responses are elicited by immunization with a microneedle array over intradermal or intramuscular immunization using the model antigen ovalbumin, irrespective of whether or not the antigen is provided in the lung. Mechanistically, microneedle array-mediated immunization targeted the epidermal layer and stimulated predominantly Langerhans cells, resulting in increased expression of α4ß1 adhesion molecules on the CD8+ T-cell surface, which may play a role in T-cell homing to the lung, whereas CD8+ T cells induced by intramuscular immunization did not express the adhesion molecule sufficiently. CD8+ T cells with a lung-homing propensity were also seen after intradermal vaccination, yet to a much lesser extent. Accordingly, microneedle array immunization provided stronger protection against influenza viral infection than intradermal or intramuscular immunization. The observations offer insights into a strong cross-talk between epidermal immunization and lung immunity and are valuable for designing and delivering vaccines against respiratory viral infections.

Human Pathogenic Monkeypox Disease Recognition Using Q-Learning Approach.

While the world is working quietly to repair the damage caused by COVID-19's widespread transmission, the monkeypox virus threatens to become a global pandemic. There are several nations that report new monkeypox cases daily, despite the virus being less deadly and contagious than COVID-19. Monkeypox disease may be detected using artificial intelligence techniques. This paper suggests two strategies for improving monkeypox image classification precision. Based on reinforcement learning and parameter optimization for multi-layer neural networks, the suggested approaches are based on feature extraction and classification: the Q-learning algorithm determines the rate at which an act occurs in a particular state; Malneural networks are binary hybrid algorithms that improve the parameters of neural networks. The algorithms are evaluated using an openly available dataset. In order to analyze the proposed optimization feature selection for monkeypox classification, interpretation criteria were utilized. In order to evaluate the efficiency, significance, and robustness of the suggested algorithms, a series of numerical tests were conducted. There were 95% precision, 95% recall, and 96% f1 scores for monkeypox disease. As compared to traditional learning methods, this method has a higher accuracy value. The overall macro average was around 0.95, and the overall weighted average was around 0.96. When compared to the benchmark algorithms, DDQN, Policy Gradient, and Actor-Critic, the Malneural network had the highest accuracy (around 0.985). In comparison with traditional methods, the proposed methods were found to be more effective. Clinicians can use this proposal to treat monkeypox patients and administration agencies can use it to observe the origin and current status of the disease.

Effects of Hepatitis B Virus Infection on Patients with COVID-19: A Meta-Analysis.

BACKGROUND: The COVID-19 pandemic has brought new problems to patients infected with hepatitis B virus (HBV). AIM: We aim to know the effects of HBV infection on patients with COVID-19. METHODS: We searched PubMed, Embase, and Web of Science for data and utilized Stata 14.0 software for this meta-analysis with a random-effects model. This paper was conducted in alignment with the preferred reporting items for systematic review and meta-analysis (PRISMA) guideline. RESULTS: In total, 37,696 patients were divided into two groups: 2591 COVID-19 patients infected with HBV in the experimental group and 35,105 COVID-19 patients not infected with HBV in the control group. Our study showed that the in-hospital mortality of the experimental group was significant higher than that of the control group (OR = 2.04, 95% CI 1.49-2.79). We also found that COVID-19 patients infected with HBV were more likely to develop severe disease (OR = 1.90, 95% CI 1.32-2.73) than COVID-19 patients not infected with HBV. Upon measuring alanine aminotransferase (SMD = 0.62, 95% CI 0.25-0.98), aspartate aminotransferase (SMD = 0.60, 95% CI 0.30-0.91), total bilirubin (SMD = 0.45, 95% CI 0.23-0.67), direct bilirubin (SMD = 0.36, 95% CI 0.24-0.47), lactate dehydrogenase (SMD = 0.32, 95% CI 0.18-0.47), we found that HBV infection led to significantly higher laboratory results in COVID-19 patients. CONCLUSION: COVID-19 patients infected with HBV should receive more attention, and special attention should be given to various liver function indices during treatment.

Effectiveness of an "online + in-person" hybrid model for an undergraduate molecular biology lab during COVID-19.

The COVID-19 outbreak has created turbulence and uncertainty into multiple aspects of life in countries around the world. In China, the pandemic continues to pose a great challenge to the nature of traditional in-class education in schools. Chinese education has faced the difficult decision of whether to resume in-person teaching in an unprecedented and time-pressured manner. To ensure the quality of teaching and learning during this time, this study aims to explore the effectiveness of an "online + in-person" hybrid teaching model with a new three-part approach to the hybrid teaching lab, where students prepare for the in-person lab using virtual simulated experiments and learning modules and debrief their learning afterwards online as well. This approach not only enhances the efficiency during the in-person lab but also strongly reinforces concepts and laboratory skills by providing a "practice run" before physically attending the lab. A total of 400 medical undergraduates from Dalian Medical University in China were recruited for this study. In an undergraduate molecular biology laboratory course, we observed 200 students in a hybrid teaching model. We evaluated the learning outcomes from the "online + in-person" hybrid teaching model with a questionnaire survey and assessed the quality of experiment execution, report writing, and group collaboration. Moreover, the 200 students from the hybrid group were evaluated during an annual science competition at the university and compared to 200 students from the competition cohort who had no experience with a hybrid learning model. The comparison data were analyzed using a student's t-test statistical analysis. The students in the hybrid learning group demonstrated a strong enthusiasm for the model, high amount of time utilizing the online system, and high scores on laboratory evaluation assignments. Approximately 98% of the hybrid learning students reported that they preferred mixed teaching to the traditional teaching mode, and all students scored above 96% on the online laboratory report. Teachers of the course observed that the hybrid group had a noticeably higher level of proficiency in lab skills compared to the previous students. At the Dalian Medical University annual science competition, where we compared our hybrid group to a traditional learning group, scores for both the objective and subjective items showed that the students instructed with the hybrid lab model had superior performance (p < 0.05). In the context of the COVID-19 pandemic, we developed a new three-part molecular biology laboratory course that strongly improved students' laboratory skills, knowledge retention, and enthusiasm for the course using online learning to improve their learning efficiency and expedite the in-person laboratory experience. We found that these students performed at a higher level in a combined theoretical/practical science competition compared to the students in traditional in-person lab courses. Additionally, our model subjectively fostered enthusiasm and excellence in both teachers and students. Further, cultivation of the students' independent learning and creative problem solving skills were emphasized. The exploration of an effective teaching model, such as the one described here, not only provides students with a solid foundation for their future medical studies and career development but also promotes more efficient in-person laboratory time.

New observation of perceptive mechanism behind the long-lasting change of people's community mobility: evidence from COVID-19 in China.

COVID-19 pandemic provides an opportunity to investigate how a new and long-lasting threat affects public risk perception and social distancing behavior, which is important for pandemic risk management and recovery of the tertiary industry. We have found that the mechanism that perception decides behavior changes over time. At the beginning of the pandemic, risk directly shapes people's willingness of going out. But under a persistent threat, perception no longer plays the direct role of shape people's willingness. Instead, perception indirectly influences the willingness by shaping people's judgment about the necessity of traveling. Switching from direct to indirect influence, perception's effect is enlarged, which partially prevents people from returning to normal life even if the governmental ban is removed in a zero-COVID community.

Comparative susceptibility of SARS-CoV-2, SARS-CoV, and MERS-CoV across mammals.

Exploring wild reservoirs of pathogenic viruses is critical for their long-term control and for predicting future pandemic scenarios. Here, a comparative in vitro infection analysis was first performed on 83 cell cultures derived from 55 mammalian species using pseudotyped viruses bearing S proteins from SARS-CoV-2, SARS-CoV, and MERS-CoV. Cell cultures from Thomas's horseshoe bats, king horseshoe bats, green monkeys, and ferrets were found to be highly susceptible to SARS-CoV-2, SARS-CoV, and MERS-CoV pseudotyped viruses. Moreover, five variants (del69-70, D80Y, S98F, T572I, and Q675H), that beside spike receptor-binding domain can significantly alter the host tropism of SARS-CoV-2. An examination of phylogenetic signals of transduction rates revealed that closely related taxa generally have similar susceptibility to MERS-CoV but not to SARS-CoV and SARS-CoV-2 pseudotyped viruses. Additionally, we discovered that the expression of 95 genes, e.g., PZDK1 and APOBEC3, were commonly associated with the transduction rates of SARS-CoV, MERS-CoV, and SARS-CoV-2 pseudotyped viruses. This study provides basic documentation of the susceptibility, variants, and molecules that underlie the cross-species transmission of these coronaviruses.

Care patterns and Traditional Chinese Medicine constitution as factors of depression and anxiety in patients with systemic sclerosis: A cross-sectional study during the COVID-19 pandemic.

Objective: Care patterns and Traditional Chinese Medicine (TCM) constitution affects the emotion and health of patients with systemic sclerosis (SSc) while the prevalence of COVID-19 may aggravate such patients' emotion and health. We investigated the depression and anxiety levels of patients with SSc during the pandemic to identify the correlation between care patterns, TCM constitution, and patients' emotion. Materials and methods: This was a cross-sectional study. Patients with SSc and healthy individuals were surveyed using the patient health questionnaire-9, generalized anxiety disorder-7, and constitution in Chinese medicine questionnaire and a modified care pattern questionnaire. Factors correlated with depression and anxiety were screened using univariate and multivariate logistic regression analyses. Results: A total of 273 patients with SSc and 111 healthy individuals were included in the analysis. The proportion of patients with SSc who were depressed was 74.36%, who had anxiety was 51.65%, and who experienced disease progression during the pandemic was 36.99%. The proportion of income reduction in the online group (56.19%) was higher than that in the hospital group (33.33%) (P = 0.001). Qi-deficiency [adjusted odds ratio (OR) = 2.250] and Qi-stagnation (adjusted OR = 3.824) constitutions were significantly associated with depression. Remote work during the outbreak (adjusted OR = 1.920), decrease in income (adjusted OR = 3.556), and disease progression (P = 0.030) were associated with the occurrence of depression. Conclusion: Chinese patients with SSc have a high prevalence of depression and anxiety. The COVID-19 pandemic has changed the care patterns of Chinese patients with SSc, and work, income, disease progression, and change of medications were correlates of depression or anxiety in patients with SSc. Qi-stagnation and Qi-deficiency constitutions were associated with depression, and Qi-stagnation constitution was associated with anxiety in patients with SSc. Trial registration: http://www.chictr.org.cn/showproj.aspx?proj=62301, identifier ChiCTR2000038796.

Opinion and Sentiment Analysis of Palliative Care in the Era of COVID-19.

During the COVID-19 pandemic, the value of palliative care has become more evident than ever. The current study quantitatively investigated the perceptions of palliative care emerging from the pandemic experience by analyzing a total of 26,494 English Tweets collected between 1 January 2020 and 1 January 2022. Such an investigation was considered invaluable in the era of more people sharing and seeking healthcare information on social media, as well as the emerging roles of palliative care. Using a web scraping method, we reviewed 6000 randomly selected Tweets and identified four themes in the extracted Tweets: (1) Negative Impact of the Pandemic on Palliative Care; (2) Positive Impact of the Pandemic on Palliative Care; (3) Recognized Benefits of Palliative Care; (4) Myth of Palliative Care. Although a large volume of Tweets focused on the negative impact of COVID-19 on palliative care as expected, we found almost the same volume of Tweets that were focused on the positive impact of COVID-19 on palliative care. We also found a smaller volume of Tweets associated with myths about palliative care. Using these manually classified Tweets, we trained machine learning (ML) algorithms to automatically classify the remaining tweets. The automatic classification of Tweets was found to be effective in classifying the negative impact of the COVID-19.

Association between sleep duration and myopia among Chinese children during the COVID-19 pandemic: A cross-sectional study.

Background: The studies on the association between sleep duration and myopia are limited, and the evidence is inconsistent. This study aimed to evaluate the association between sleep duration and myopia, cycloplegic spherical equivalent (SE) and axial length (AL) among Chinese children during the Corona Virus Disease 2019 (COVID-19) pandemic. Methods: The study was a cross-sectional study on Chinese children aged 6-18 years. The comprehensive ophthalmic examinations for children included cycloplegic SE, AL, and standardized questionnaires. The questionnaire included sleep duration, parental myopia, outdoor time, and continuous near work duration without breaks. Myopia was defined as SE ≤-0.50 diopters (D). Results: A total of 1,140 children were included in the analyses, with 84.7% of myopic children and 74.4% of children's daily sleep duration being more than 8 h/d. In univariate regression analysis, compared with sleep duration < 8 h/d, children with sleep duration of 8-9 and >9 h/d were less myopia (p < 0.01 for all), and had less myopic SE (p < 0.01 for all), and shorter AL (p < 0.01 for all). After adjusting for age, gender, parental myopia, outdoor time, and continuous near work duration without breaks, sleep duration was not associated with myopia, cycloplegic SE, and AL (p > 0.05 for all). Conclusions: This study showed sleep duration was related to myopia, cycloplegic SE, and AL among Chinese children during the COVID-19 pandemic-related lifestyles, but no independent association.

Association between sleep duration and myopia among Chinese children during the COVID-19 pandemic: A cross-sectional study

Background The studies on the association between sleep duration and myopia are limited, and the evidence is inconsistent. This study aimed to evaluate the association between sleep duration and myopia, cycloplegic spherical equivalent (SE) and axial length (AL) among Chinese children during the Corona Virus Disease 2019 (COVID-19) pandemic. Methods The study was a cross-sectional study on Chinese children aged 6–18 years. The comprehensive ophthalmic examinations for children included cycloplegic SE, AL, and standardized questionnaires. The questionnaire included sleep duration, parental myopia, outdoor time, and continuous near work duration without breaks. Myopia was defined as SE ≤-0.50 diopters (D). Results A total of 1,140 children were included in the analyses, with 84.7% of myopic children and 74.4% of children's daily sleep duration being more than 8 h/d. In univariate regression analysis, compared with sleep duration < 8 h/d, children with sleep duration of 8–9 and >9 h/d were less myopia (p < 0.01 for all), and had less myopic SE (p < 0.01 for all), and shorter AL (p < 0.01 for all). After adjusting for age, gender, parental myopia, outdoor time, and continuous near work duration without breaks, sleep duration was not associated with myopia, cycloplegic SE, and AL (p > 0.05 for all). Conclusions This study showed sleep duration was related to myopia, cycloplegic SE, and AL among Chinese children during the COVID-19 pandemic-related lifestyles, but no independent association.

Emergency methods, moral dilemma and coping styles among frontline nurses: an explorative study during the COVID-19 pandemic

Background: Coronavirus disease 2019 (COVID-19) pandemic has greatly impacted China, especially the emergency services since 2020. For many, it raises unique ethical dilemmas, including psychological, moral, social, and economic issues, especially among frontline health workers.

Evaluation of cardiopulmonary resuscitation quality during the pandemic of COVID-19.

BACKGROUND: Cardiopulmonary resuscitation (CPR) is an important technique of first aid. It is necessary to be popularized. Large-scale offline training has been affected after the outbreak of Coronavirus disease 2019 (COVID-19). Online training will be the future trend, but the quality of online assessment is unclear. This study aims to compare online and offline evaluations of CPR quality using digital simulator and specialist scoring methods. METHODS: Forty-eight out of 108 contestants who participated in the second Chinese National CPR Skill Competition held in 2020 were included in this study. The competition comprised two stages. In the preliminary online competition, the contestants practiced on the digital simulator while the specialist teams scored live videos. The final competition was held offline, and consisted of live simulator scoring and specialist scoring. The grades of the simulator and specialists in different stages were compared. RESULTS: There was no statistical significance for simulator grades between online and offline competition(37.7 ± 2.0 vs. 36.4 ± 3.4, p = 0.169). For specialists' grades, the video scores were lower than live scores (55.0 ± 1.4 vs. 57.2 ± 1.7, p < 0.001). CONCLUSION: Simulator scoring provided better reliability than specialist scoring in the online evaluation of CPR quality. However, the simulator could only collect quantified data. Specialist scoring is necessary in conjunction with online tests to provide a comprehensive evaluation. A complete and standardized CPR quality evaluation system can be established by combining simulator and specialist contributions.

Emotion Analysis of COVID-19 Vaccines Based on a Fuzzy Convolutional Neural Network.

COVID-19 created immense global challenges in 2020, and the world will live under its threat indefinitely. Much of the information on social media supported the government in addressing this major public health event. On January 9, to control the virus, the Chinese government announced universal vaccinations. However, due to a range of varied interpretations, people held different attitudes towards vaccination. Therefore, the success of the mass immunization strategy greatly depended on the public perception of the COVID-19 vaccine. This article explores the changes in people's emotional attitudes towards vaccines and the reasons behind them in the context of the global pandemic in an effort to help mankind overcome this ongoing crisis. For this article, microblogs from January to September containing Chinese people's responses to the COVID-19 vaccines were collected. Based on fuzzy logic and deep learning, we advance the hypothesis that fuzzy vector adaptive improvements will make it possible to better express language emotion and that fuzzy emotion vectors can be integrated into deep learning models, thus making these models more interpretable. Based on this assumption, we design a deep learning model with a fuzzy emotion vector. The experimental results show the positive effect of this model. By applying the model in analyses of people's attitudes towards vaccines, we can obtain people's attitudes towards vaccines in different time periods. We discovered that the most negative emotions about the vaccine appeared in April and that the most positive emotions about the vaccine appeared in February. Combined with word cloud technology and the LDA model, we can effectively explore the reasons for the changes in vaccine attitudes. Our findings show that people's negative emotions about the vaccine are always higher than their positive emotions about the vaccine and that people's attitudes towards the vaccine are closely related to the progress of the epidemic. There is also a certain relationship between people's attitudes towards the vaccine and those towards the vaccination.

Nanofiber-Based Odor-Free Medical Mask Fabrication Using Polyvinyl Butyral and Eucalyptus Anti Odor Agent.

Following the 2020 COVID-19 worldwide outbreak, many countries adopted sanitary and safety measures to safeguard public health such as wearing medical face mask. While face masks became a necessity for people, disadvantages impede their long period wearing such as uncomfortable breathability and odor. The intermediate layer of the medical face mask is composed of porous non-woven fabric to block external particles while maintaining breathability. To overcome aforementioned limitation, this study uses electrospinning to design and fabricate odorless face masks via the use of aromatic oil. Eucalyptus essential oil is encapsulated through mixing and layer-by-layer by hydrophobic polyvinyl butyral and further used to fabricate the medical mask intermediate layer. We found that adding 0.2 g of eucalyptus into polyvinyl butyral fabric through mixing results in the deodorization rate of 80% after 2 h, with fabric thickness of 440.9 µm, and melt-blown non-woven fabric thickness of 981.7 µm. The Particle Filtration Efficiency of 98.3%, Bacterial Filtration Efficiency above 99.9%, and the differential pressure of 4.7 mm H2O/cm2 meet the CNS 14774 standard on medical face masks. Therefore, this study successfully proved that this type of masks' middle layer not only effectively protects against coronavirus, but also provides better scents and makes it more comfortable for consumers.

Oncological big data platforms for promoting digital competencies and professionalism in Chinese medical students: a cross-sectional study.

OBJECTIVES: Advancements in big data technology are reshaping the healthcare system in China. This study aims to explore the role of medical big data in promoting digital competencies and professionalism among Chinese medical students. DESIGN, SETTING AND PARTICIPANTS: This study was conducted among 274 medical students who attended a workshop on medical big data conducted on 8 July 2021 in Tongji Hospital. The workshop was based on the first nationwide multifunction gynecologic oncology medical big data platform in China, at the National Union of Real-World Gynecologic Oncology Research & Patient Management Platform (NUWA platform). OUTCOME MEASURES: Data on knowledge, attitudes towards big data technology and professionalism were collected before and after the workshop. We have measured the four skill categories: doctor‒patient relationship skills, reflective skills, time management and interprofessional relationship skills using the Professionalism Mini-Evaluation Exercise (P-MEX) as a reflection for professionalism. RESULTS: A total of 274 students participated in this workshop and completed all the surveys. Before the workshop, only 27% of them knew the detailed content of medical big data platforms, and 64% knew the potential application of medical big data. The majority of the students believed that big data technology is practical in their clinical practice (77%), medical education (85%) and scientific research (82%). Over 80% of the participants showed positive attitudes toward big data platforms. They also exhibited sufficient professionalism before the workshop. Meanwhile, the workshop significantly promoted students' knowledge of medical big data (p<0.05), and led to more positive attitudes towards big data platforms and higher levels of professionalism. CONCLUSIONS: Chinese medical students have primitive acquaintance and positive attitudes toward big data technology. The NUWA platform-based workshop may potentially promote their understanding of big data and enhance professionalism, according to the self-measured P-MEX scale.

An empirical study on correlation among poverty, inclusive finance, and CO2 emissions in China.

This paper explores the nonlinear relationship between poverty and CO2 emissions based on the panel data of 30 provinces in China from 2005 to 2019. In this study, the autoregressive distributed lag (ARDL) model is first used. Findings confirm that poverty has a negative impact on CO2 emissions in the short run and a positive impact in the long run, while both effects of inclusive finance on CO2 emissions are negative. In order to explore the reasons for the change in the coefficient of poverty, we introduce a moderating effect (ME) model and a dynamic panel threshold (DPT) model. The result shows that the negative effect of poverty on CO2 emissions diminishes with the moderation of inclusive finance. When inclusive finance crosses the threshold value (IFI = 0.2696), the impact of poverty on CO2 emissions will change from negative to positive gradually, which verifies the applicability of the "Poverty-CO2 Paradox" in China and provides an empirical basis for breaking the "Poverty-CO2 Paradox." Consequently, deepening poverty reduction and pushing the region's inclusive finance to the threshold level are proposed as effective ways to promote CO2 emission reduction.