Study on positive psychology from 1999 to 2021: A bibliometric analysis

Objective Positive psychology is a revolution in the science of psychology as well as a new milestone in the development of human society. The purpose of the study was to use bibliometrics and visual analysis to assess the current state and trends in positive psychology research. Methods The Web of Science Core Collection was searched for 4,378 papers on positive psychology between 1999 and 2021. The features of positive psychology research were analyzed using Microsoft Excel 2013, VOSviewer (1.6.17), and CiteSpace (5.8.R1). Results The findings demonstrate a steady growth in positive psychology publications from 1999 to 2021. The United States (1,780) and Harvard University (104), respectively, were the most productive nations and organizations in this subject. Frontiers in Psychology was the most productive journal (288), while the Journal of Personality and Social Psychology had the most co-citations (8,469). Seligman was the most influential author, with 3,350 citations and 5,020 co-citations. The top ten co-cited references, in terms of citation explosion, suggesting that these papers provide the foundation for the growth of this discipline. The systematic review, character strengths, positive psychology intervention, language pleasure, and the COVID-19 pandemic are the focal points of research and development developments in this discipline. Conclusion These findings have helped researchers in positive psychology find new ways to collaborate with partners, hot topics, and research frontiers.

A cross-sectional study on burnout and its individual and environmental correlates among hepatological surgery nurses in Hunan Province, China.

BACKGROUND: Burnout is a widespread occupational phenomenon among nurses with significant adverse outcomes for nurses, patients, and society. It is thus important and urgent to understand burnout and its risk factors to guide interventions. This study aimed to examine the level of burnout and explore its individual and environmental correlates. METHODS: This cross-sectional study was conducted in Hunan, China. A total of 623 hepatological surgery nurses completed an online survey (response rate: 72.78%). Burnout was measured using the standard Maslach Burnout Inventory (MBI). Information on individual factors and environmental factors was collected by self-designed questionnaires. RESULTS: The scores of emotional exhaustion, depersonalization, and personal achievement in nurse burnout were 30 (26-34), 11 (8-14), and 23 (20-26) respectively. The prevalence of high burnout ranged from 52.81% for emotional exhaustion to 90.37% for decreased personal achievement. The three dimensions of burnout shared common correlates such as self-rated physical health and working environment, while also having additional unique correlates such as overwork, satisfaction with income, and age. CONCLUSION: Hepatological surgery nurses in Hunan Province are suffering from high levels of burnout, which requires public attention and urgent interventions. Improvement of the physical health and working environment of nurses may be the most beneficial intervention measures to tackle various dimensions of burnout, while other targeted measures are also needed for each specific dimension.

HSPA5 Promotes the Proliferation, Metastasis and Regulates Ferroptosis of Bladder Cancer.

Heat shock protein family A (HSP70) member 5 (HSPA5) is aberrantly expressed in various tumors and closely associated with the progression and prognosis of cancer. Nevertheless, its role in bladder cancer (BCa) remains elusive. The results of our study demonstrated that HSPA5 was upregulated in BCa and correlated with patient prognosis. Cell lines with low expression level of HSPA5 were constructed to explore the role of this protein in BCa. HSPA5 knockdown promoted apoptosis and retarded the proliferation, migration and invasion of BCa cells by regulating the VEGFA/VEGFR2 signaling pathway. In addition, overexpression of VEGFA alleviated the negative effect of HSPA5 downregulation. Moreover, we found that HSPA5 could inhibit the process of ferroptosis through the P53/SLC7A11/GPX4 pathway. Hence, HSPA5 can facilitate the progression of BCa and may be used as a novel biomarker and latent therapeutic target in the clinic.

Traditional Chinese Medicine in Treatment of COVID-19 and Viral Disease: Efficacies and Clinical Evidence.

Coronavirus disease 2019 (COVID-19) remains an uncontained, worldwide pandemic. While battling the disease in China, the Chinese government has actively promoted the use of traditional Chinese medicine, and many studies have been conducted to determine the efficacy of traditional Chinese medicine for treating COVID-19. The present review discusses the effectiveness and safety of traditional Chinese medicine in curing COVID-19 and provides clinical evidence from all confirmed cases in China. Applications of traditional Chinese medicine and specific recipes for treating other viral infections, such as those caused by severe acute respiratory syndrome coronavirus and influenza A viruses (including H1N1), are also discussed. Studies have reported that traditional Chinese medicine treatment plays a significant role in improving clinical symptoms. Therefore, further investigation may be of high translational value in revealing novel targeted therapies for COVID-19.

Research on imbalance machine learning methods for MR[Formula: see text]WI soft tissue sarcoma data.

BACKGROUND: Soft tissue sarcoma is a rare and highly heterogeneous tumor in clinical practice. Pathological grading of the soft tissue sarcoma is a key factor in patient prognosis and treatment planning while the clinical data of soft tissue sarcoma are imbalanced. In this paper, we propose an effective solution to find the optimal imbalance machine learning model for predicting the classification of soft tissue sarcoma data. METHODS: In this paper, a large number of features are first obtained based on [Formula: see text]WI images using the radiomics methods.Then, we explore the methods of feature selection, sampling and classification, get 17 imbalance machine learning models based on the above features and performed extensive experiments to classify imbalanced soft tissue sarcoma data. Meanwhile, we used another dataset splitting method as well, which could improve the classification performance and verify the validity of the models. RESULTS: The experimental results show that the combination of extremely randomized trees (ERT) classification algorithm using SMOTETomek and the recursive feature elimination technique (RFE) performs best compared to other methods. The accuracy of RFE+STT+ERT is 81.57% , which is close to the accuracy of biopsy, and the accuracy is 95.69% when using another dataset splitting method. CONCLUSION: Preoperative predicting pathological grade of soft tissue sarcoma in an accurate and noninvasive manner is essential. Our proposed machine learning method (RFE+STT+ERT) can make a positive contribution to solving the imbalanced data classification problem, which can favorably support the development of personalized treatment plans for soft tissue sarcoma patients.

Role Stress and Psychological Distress Among Chinese Nurses During the COVID-19 Pandemic: A Moderated Mediation Model of Social Support and Burnout.

Objective: Nurses are at high risk of psychological distress including stress, depression, and anxiety due to low personnel density and high work demand. Despite mounting evidence showing that role stress is a risk factor for nurses' psychological distress, the mediating and moderating mechanisms underlying this relationship are less known. This study tests the mediation effect of burnout in the association between role stress and psychological distress, and whether this mediation is moderated by social support. Methods: A sample of 623 Chinese nurses were recruited from all hepatological surgery departments in Hunan Province and filled out an online questionnaire to collect data on socio-demographics, role stress, burnout, psychological distress, and social support. Mediation and moderation analyses were carried out in SPSS macro-PROCESS. Results: Burnout partially mediated the positive association between role stress and psychological distress. Social support moderated the indirect effect of role stress on psychological distress via burnout, with the effect being stronger for nurses with low social support than those with high social support. Conclusions: These findings demonstrated how role stress contributed to nurses' psychological distress both directly and indirectly through burnout, and how this indirect effect was moderated by social support. The results provide important practical implications for future prevention and intervention programs to improve nurses' mental health from multiple aspects such as decreasing role stress and burnout while increasing social support.

Toward an optimized strategy of using various airway mucus clearance techniques to treat critically ill COVID-19 patients

Coronavirus disease 2019 (COVID-19) caused by acute respiratory syndrome coronavirus 2 (SARS-Cov-2) is still threatening the human life and society throughout the world. For those critically ill patients, mechanical ventilation (MV) is essential to provide life support during treatment. However, both the virus infection and MV disrupt the balance between secretion and elimination of airway mucus and lead to mucus accumulation in the lung. Postmortem examination verified that the lungs in patients died of COVID-19 are indeed filled with sticky mucus, suggesting a great need to improve airway mucus clearance in critically ill COVID-19 patients. Therefore, it may be helpful to comprehensively review the current understanding regarding the changes of biochemical and rheological features of airway mucus associated with the disease, as well as the physiological principles and algorithm to decide airway clearance techniques suitable for the critically ill COVID-19 patients. Based on these considerations, optimized strategies may be developed to eliminate the airway mucus accumulated in the airways of critically ill COVID-19 patients.

From COVID-19 Vaccination Intention to Actual Vaccine Uptake: A Longitudinal Study Among Chinese Adults After Six Months of a National Vaccination Campaign.

BACKGROUND: Mass COVID-19 vaccination campaigns have been launched globally, but the translation from vaccination intention to actual vaccine uptake by the public remains unknown, hindering the evaluation of present promotion strategies. METHODS: Six months after the national vaccination campaign in China, a longitudinal study was conducted among the Chinese adult population, whose vaccination intention has been previously surveyed, to examine the vaccine uptake, the relationship between intention and actual vaccination, and factors associated with actual vaccination behaviors with multiplelogistic models. RESULTS: Among the total 1047 participants, 81.8% (834/936) of those who had a prior COVID-19 vaccination intention before the campaign actually received the vaccine, while 61.3% (68/111) of those without a prior intention got vaccinated. Having a prior vaccination intention, believing in vaccine safety and receiving frequent recommendations from community sources were significant predictors of vaccine uptake, while the shortage of vaccine supply would reduce the likelihood of getting vaccinated. CONCLUSIONS: Promotion interventions for vaccination intentions need to be launched well before the availability of the vaccine. Sustaining vaccination attitudes and intentions, reducing barriers (e.g. vaccine safety concerns, accessibility, affordability) and shaping vaccination behavior would be effective in closing the intention-action gap and motivating vaccine uptake.

Public Perceptions and Acceptance of COVID-19 Booster Vaccination in China: A Cross-Sectional Study.

Coronavirus disease 2019 (COVID-19) booster vaccination has been proposed in response to the new challenges of highly contagious variants, yet few studies have examined public acceptance of boosters. This study examined public acceptance of COVID-19 booster vaccination and its influencing factors by using the data from a self-administered online cross-sectional survey conducted in June 2021 in China. Multiple logistic analysis was used to examine the influencing factors of booster acceptance based on the health belief model (HBM). Among 1145 respondents, 84.80% reported to accept COVID-19 booster vaccination. Having COVID-19 vaccination history, perceiving high benefits and low barriers to booster vaccination, being younger (18-30 vs. 41-50), having a lower education level, being employed, and belonging to priority groups for vaccination were associated with increased odds of booster acceptance. The primary reason for refusing booster vaccination was concern about vaccine safety. The vast majority (92.8%) of respondents reported an annual willingness to pay between 0 and 300 CNY (0-46.29 USD) if the booster was not free. Our findings suggest that the acceptance rate of booster vaccination is relatively high in China, and the HBM-based analysis reveals that more efforts are needed to increase perceived benefits and reduce perceived barriers of vaccination to design effective and proper vaccination extension strategies when boosters become widely recommended.

Willingness to accept COVID-19 vaccine among the elderly and the chronic disease population in China.

A cross-sectional field survey was conducted from November 2020 to January 2021 among 7259 participants to investigate the public perception, willingness, and information sources for COVID-19 vaccination, with the focus on the elderly and non-communicable chronic disease (NCD) population. Multiple logistic regressions were performed to identify associated factors of the vaccination willingness. The willingness rate of the elderly to accept the future COVID-19 vaccine (79.08%) was lower than that of the adults aged 18-59 (84.75%). The multiple analysis didn't identify significant relationship between NCD status and the vaccination intention. The main reasons for vaccine hesitancy by the public were: concern for vaccine safety, low infection risk, waiting and seeing others getting vaccinated, concern of vaccine effectiveness and price. Their relative importance differed between adults aged 18-59 and the elderly, and between adults aged 18-59 with or without NCD. Perception for vaccination importance, vaccine confidence, and trust in health workers were significant predictors of the vaccination intention in both age groups. The elderly who perceived high infection risk or had trust in governments were more likely to accept the vaccine. Compared with the adults aged 18-59, the elderly used fewer sources for COVID-19 vaccination information and more trusted in traditional media and family, relatives, and friends for getting vaccination recommendations. To promote vaccine uptake, the vaccination campaigns require comprehensive interventions to improve vaccination attitude, vaccine accessibility and affordability, and tailor strategies to address specific concerns among different population groups and conducted via their trusted sources, especially for the elderly.

The Acceptance of COVID-19 Vaccination Under Different Methods of Investigation: Based on Online and On-Site Surveys in China

As Coronavirus Disease-2019 (COVID-19) vaccines became available in December 2020, increasingly more surveys were organized to examine the acceptance of vaccination, while most of them were conducted online. This study aimed to explore the difference between online and traditional on-site surveys in terms of COVID-19 vaccine acceptance. From November to December 2020, an online survey (n = 2013) and an on-site survey (n = 4,316) were conducted simultaneously in China. Multivariate logistic regression was used to identify influencing factors of acceptance, and propensity score matching (PSM) was adopted to balance the outcomes. As a result, 90.0% of the online respondents accepted COVID-19 vaccination, while it was only 82.1% in the on-site survey. After applying PSM, the acceptance rate of the on-site survey was declined to 78.6%. The age structure, residence location, education, and health status were observed as important factors in addressing vaccination acceptance, which needed to be specifically considered when designing online surveys.

Caregivers' intentions to COVID-19 vaccination for their children in China: a cross-sectional survey.

Chinese caregivers' intentions to allow their children to be vaccinated against coronavirus disease 2019 (COVID-19) is unknown. We explored the intention rate of Chinese caregivers to allow their children to be vaccinated and examined potential influencing factors and underlying reasons for their unwillingness or hesitation. From November 30, 2020, to January 31, 2021, we conducted a cross-sectional survey of 3703 caregivers in six representative provinces in China. We assessed intention rates and correlates of caregivers' intentions to vaccinate children against COVID-19, using descriptive analyses and a multiple logistic regression analysis, respectively. In the study sample, about 84.0% of caregivers intended to get their children vaccinated for COVID-19 if the vaccine was available. In particular, 92.2% of caregivers who were willing to be vaccinated themselves for COVID-19 intended to have their children vaccinated, yet among caregivers who were unwilling (or uncertain) whether to be vaccinated, only 41.1% intended to have their children vaccinated. Older age, lower education level, and perceived safety and effectiveness of the COVID-19 vaccine were associated with increased odds of caregivers intending to have their children vaccinated. Of the six provinces included in the study, residence in a province other than Hubei increased the likelihood that caregivers would choose not to have their children vaccinated. We found a relatively high vaccination rate (84.0%) among caregivers by using a cross-sectional sample in China. Concerted efforts are needed to address caregivers' concerns about vaccine safety, especially among caregivers who do not intend to be vaccinated themselves.

Plain Language Summary: Recently, COVID-19 infection in children has increased. Although most countries have not approved the COVID-19 vaccine for children, it is likely that they will do so. There is a clear need to explore caregivers' intentions and to understand potential hesitancy as means to inform vaccination policies. We found a relatively high caregivers' intention rate to have their children vaccinated against COVID-19, and caregivers' intentions to be vaccinated themselves was associated with their own decision to have their children vaccinated. In this study, older age, lower education level, belief that the COVID-19 vaccine was safe and effective, and residence in Hubei province were associated with increased odds of caregivers intending to have their children vaccinated. Policy makers should address caregivers' concerns about vaccine safety and encourage caregivers themselves to get vaccinated before they decide to have their children vaccinated.

The associations of geographic location and perceived risk of infection with the intentions to get vaccinated against COVID-19 in China.

BACKGROUND: Vaccines are a critical tool against coronavirus disease 2019 (COVID-19) pandemic, yet little is known regarding the associations of geographic location and perceived risk with the intentions to get vaccinated against COVID-19 in China. METHODS: An on-site survey of adults aged 18 or older (n = 7261) was conducted from November to December, 2020 in China, and this survey selected six provinces based on the geographic location. RESULTS: In the total sample, 82.5% reported that they would intend to get vaccinated against COVID-19. Compared with Hubei province, respondents' intentions to get vaccinated decreased by 70% in Zhejiang, 61% in Guangdong, 87% in Gansu, and 71% in Jilin, respectively. However, within Hubei province, compared with Wuhan city, respondents' intentions to get vaccinated in other cities were not significantly different. Respondents with higher perceived risk of infection were associated with increased odds of intentions to get vaccinated against COVID-19. CONCLUSION: Our study identified priority geographic regions that need to pay attention on the vaccination campaign and help design effective immunization strategies to increase the vaccine uptake against COVID-19. More attention should be paid to adults residing farther from the epicenter of the outbreak and having lower perceived risk of infection.

Exploring experiences of COVID-19-positive individuals from social media posts.

AIMS: This study aimed to explore the experience of individuals who claimed to be COVID-19 positive via their Twitter feeds. BACKGROUND: Public social media data are valuable to understanding people's experiences of public health phenomena. To improve care to those with COVID-19, this study explored themes from Twitter feeds, generated by individuals who self-identified as COVID-19 positive. DESIGN: This study utilized a descriptive design for text analysis for social media data. METHODS: This study analysed social media text retrieved by tweets of individuals in the United States who self-reported being COVID-19 positive and posted on Twitter in English between April 2, 2020, and April 24, 2020. In extracting embedded topics from tweets, we applied topic modelling approach based on latent Dirichlet allocation and visualized the results via LDAvis, a related web-based interactive visualization tool. RESULTS: Three themes were mined from 721 eligible tweets: (i) recognizing the seriousness of the condition in COVID-19 pandemic; (ii) having symptoms of being COVID-19 positive; and (iii) sharing the journey of being COVID-19 positive. CONCLUSION: Leveraging the knowledge and context of study themes, we present experiences that may better reflect patient needs while experiencing COVID-19. The findings offer more descriptive support for public health nursing and other translational public health efforts during a global pandemic.

Research Commentary on IS/IT Role in Emergency and Pandemic Management: Current and Future Research

IS/IT plays an important role in our everyday life, especially in today's Internet era. This article discusses the roles of IS/IT in providing services and support on information gathering, analysis, and management during major public emergencies and pandemic crises such as the battle against the new coronavirus. The five selected papers in this special issue introduce advanced methods on data collection and social media user analysis to deal with the challenges brought by the COVID-19 pandemic. This paper also presents future research directions on the use of IS/IT in emergency and pandemic management such as IS control and governance, intelligent health care, enhancing people's lives and mental health, and knowledge management.

A Direct Injection Technique to Improve Biosafety to Analyze Levetiracetam Concentrations in Human Serum and Its Application in Therapeutic Drug Monitoring.

BACKGROUND: With the outbreak of COVID-19, it has become very important to improve biosafety measures taken by medical staff. Fewer pretreatment steps correspond to lower chances of infection. The authors established a direct injection technique to analyze levetiracetam (LEV) concentrations in human serum and studied its application in therapeutic drug monitoring. METHODS: Serum samples were prepared by hollow fiber centrifugal ultrafiltration and the filtrate was directly injected into a ultra-high performance liquid chromatography apparatus (Waters UPLC BEH C18 column: 50 × 2.1 mm, 1.7 µm) for analysis. The mobile phase consisted of acetonitrile and water (8:92) at a flow rate of 1.0 mL/min. The column temperature was maintained at 30°C. The detected wavelength was 210 nm. RESULTS: A linear relationship was obtained for LEV from 0.625 to 80 mcg/mL (r2 = 0.999). The limit of detection for the analysis of LEV was 0.125 mcg/mL. The analysis time was shortened to 4 minutes. The recovery rate of LEV based on the current method was 96.6%-100.1%, whereas the absolute recovery rate was 93.2%-96.8%. The relative SD of intraday and interday precision was <7.3%. Stability was achieved at room temperature for 24 hours after 3 freeze-thaw cycles and at -80°C for 21 days. The method was successfully applied to determine LEV concentrations in the serum of 19 patients. CONCLUSIONS: The present method is simple, accurate, and sensitive, and can improve biosafety with the direct injection technique. It is suitable for the analysis of LEV concentrations in therapeutic drug monitoring.

A deep learning algorithm using CT images to screen for Corona virus disease (COVID-19).

OBJECTIVE: The outbreak of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2) has caused more than 26 million cases of Corona virus disease (COVID-19) in the world so far. To control the spread of the disease, screening large numbers of suspected cases for appropriate quarantine and treatment are a priority. Pathogenic laboratory testing is typically the gold standard, but it bears the burden of significant false negativity, adding to the urgent need of alternative diagnostic methods to combat the disease. Based on COVID-19 radiographic changes in CT images, this study hypothesized that artificial intelligence methods might be able to extract specific graphical features of COVID-19 and provide a clinical diagnosis ahead of the pathogenic test, thus saving critical time for disease control. METHODS: We collected 1065 CT images of pathogen-confirmed COVID-19 cases along with those previously diagnosed with typical viral pneumonia. We modified the inception transfer-learning model to establish the algorithm, followed by internal and external validation. RESULTS: The internal validation achieved a total accuracy of 89.5% with a specificity of 0.88 and sensitivity of 0.87. The external testing dataset showed a total accuracy of 79.3% with a specificity of 0.83 and sensitivity of 0.67. In addition, in 54 COVID-19 images, the first two nucleic acid test results were negative, and 46 were predicted as COVID-19 positive by the algorithm, with an accuracy of 85.2%. CONCLUSION: These results demonstrate the proof-of-principle for using artificial intelligence to extract radiological features for timely and accurate COVID-19 diagnosis. KEY POINTS: • The study evaluated the diagnostic performance of a deep learning algorithm using CT images to screen for COVID-19 during the influenza season. • As a screening method, our model achieved a relatively high sensitivity on internal and external CT image datasets. • The model was used to distinguish between COVID-19 and other typical viral pneumonia, both of which have quite similar radiologic characteristics.

Angiotensin-converting enzyme 2, coronavirus disease 2019, and abdominal aortic aneurysms.

OBJECTIVE: Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is the etiologic agent of the current, world-wide coronavirus disease 2019 (COVID-19) pandemic. Angiotensin-converting enzyme 2 (ACE2) is the SARS-CoV-2 host entry receptor for cellular inoculation and target organ injury. We reviewed ACE2 expression and the role of ACE2-angiotensin 1-7-Mas receptor axis activity in abdominal aortic aneurysm (AAA) pathogenesis to identify potential COVID-19 influences on AAA disease pathogenesis. METHODS: A comprehensive literature search was performed on PubMed, National Library of Medicine. Key words included COVID-19, SARS-CoV-2, AAA, ACE2, ACE or angiotensin II type 1 (AT1) receptor inhibitor, angiotensin 1-7, Mas receptor, age, gender, respiratory diseases, diabetes, and autoimmune diseases. Key publications on the epidemiology and pathogenesis of COVID-19 and AAAs were identified and reviewed. RESULTS: All vascular structural cells, including endothelial and smooth muscle cells, fibroblasts, and pericytes express ACE2. Cigarette smoking, diabetes, chronic obstructive pulmonary disease, lupus, certain types of malignancies, and viral infection promote ACE2 expression and activity, with the magnitude of response varying by sex and age. Genetic deficiency of AT1 receptor, or pharmacologic ACE or AT1 inhibition also increases ACE2 and its catalytic product angiotensin 1-7. Genetic ablation or pharmacologic inhibition of ACE2 or Mas receptor augments, whereas ACE2 activation or angiotensin 1-7 treatment attenuates, progression of experimental AAAs. The potential influences of SARS-CoV-2 on AAA pathogenesis include augmented ACE-angiotensin II-AT1 receptor activity resulting from decreased reciprocal ACE2-angiotensin 1-7-Mas activation; increased production of proaneurysmal mediators stimulated by viral spike proteins in ACE2-negative myeloid cells or by ACE2-expressing vascular structural cells; augmented local or systemic cross-talk between viral targeted nonvascular, nonleukocytic ACE2-expressing cells via ligand recognition of their cognate leukocyte receptors; and hypoxemia and increased systemic inflammatory tone experienced during severe COVID-19 illness. CONCLUSIONS: COVID-19 may theoretically influence AAA disease through multiple SARS-CoV-2-induced mechanisms. Further investigation and clinical follow-up will be necessary to determine whether and to what extent the COVID-19 pandemic will influence the prevalence, progression, and lethality of AAA disease in the coming decade.

Association Between Clinical Characteristics and Short-Term Outcomes in Adult Male COVID-19 Patients With Mild Clinical Symptoms: A Single-Center Observational Study.

Majority of patients with 2019 novel coronavirus infection (COVID-19) exhibit mild symptoms. Identification of COVID-19 patients with mild symptoms who might develop into severe or critical illness is essential to save lives. We conducted an observational study in a dedicated make-shift hospital for adult male COVID-19 patients with mild symptoms between February and March 2020. Baseline characteristics, medical history, and clinical presentation were recorded. Laboratory tests and chest computed tomography were performed. Patients were observed until they were either transferred to a hospital for advanced care owing to disease exacerbation or were discharged after improvement. Patients were grouped based on their chest imaging findings or short-term outcomes. A total of 125 COVID-19 patients with mild symptoms were enrolled. Of these, 7 patients were transferred for advanced care while 118 patients were discharged after improvement and showed no disease recurrence during an additional 28-day follow-up period. Eighty-five patients (68.0%) had abnormal chest imaging findings. Patients with abnormal chest imaging findings were more likely to have disease deterioration and require advanced care as compared to those with normal chest imaging findings. Patients with deteriorated outcomes were more likely to have low peripheral blood oxygen saturation and moderately-elevated body temperature. There were no significant differences between patients with deteriorated or improved outcomes with respect to age, comorbidities, or other clinical symptoms (including nasal congestion, sore throat, cough, hemoptysis, sputum production, shortness of breath, fatigue, headache, nausea or vomiting, diarrhea). Abnormal chest imaging findings, low peripheral blood oxygen saturation, and elevated temperature were associated with disease deterioration in adult male COVID-19 patients with mild clinical symptoms. Clinical Trial Registration: https://register.clinicaltrials.gov/prs/app/action/SelectProtocol?sid=S0009RA3&selectaction=Edit&uid=U0003F4L&ts=2&cx=-ajpsbw, identifier NCT04346602.

Darwin's Neural Network: AI-based Strategies for Rapid and Scalable Cell and Coronavirus Screening.

Recent advances in the interdisciplinary scientific field of machine perception, computer vision, and biomedical engineering underpin a collection of machine learning algorithms with a remarkable ability to decipher the contents of microscope and nanoscope images. Machine learning algorithms are transforming the interpretation and analysis of microscope and nanoscope imaging data through use in conjunction with biological imaging modalities. These advances are enabling researchers to carry out real-time experiments that were previously thought to be computationally impossible. Here we adapt the theory of survival of the fittest in the field of computer vision and machine perception to introduce a new framework of multi-class instance segmentation deep learning, Darwin's Neural Network (DNN), to carry out morphometric analysis and classification of COVID19 and MERS-CoV collected in vivo and of multiple mammalian cell types in vitro.