Construction of a quality evaluation system for emergency pre-examination and triage under the normalization of COVID-19 epidemic based on the Servqual model

Objective: To build a quality evaluation system for emergency pre-test and triage under a normal COVID-19 epidemic, based on the Servqual model, and to improve the effect of emergency epidemic prevention and control and the quality of emergency pre-testing and triage under the normalization of COVID-19 epidemics. Methods: Based on the Servqual model, a quality evaluation system of emergency pre-examination and triage under the normalization of COVID-19 epidemic was developed. Two rounds of expert consultation were conducted by the Delphi method, involving 15 experts. Results: The recovery rates for the two rounds of expert consultation were 100%, the expert authority coefficient was 0.954, and the Kendall coordination coefficients were 0.273 and 0.182 . The coefficients of variation of the three-level indicators of the two rounds of expert consultation were 0.003-0.275 and 0.052-0.125, respectively. An evaluation system including 5 first-level indicators, 14 second-level indicators and 42 third-level indicators of 5 dimensions (feasibility, reliability, responsiveness, security, empathy) of the Servqual model was established. Conclusion: Based on the Servqual model, a quality evaluation system for emergency pre-examination and triage under the normalization of the COVID-19 epidemic situation was completed. It reflects the medical services, triage and epidemic prevention and control required to provide the requisite quality of emergency pre-examination and triage under the normalization of the COVID-19 epidemic situation. It provides a reference for the quality supervision of emergency pre-examination and triage under the normalization of the COVID-19 epidemic situation.

Effects of Physical Activity and Circadian Rhythm Differences on the Mental Health of College Students in Schools Closed by COVID-19.

Purpose: Since the prolonged sequestration management that was implemented in order to achieve lower infection and mortality rates, there has been a surge in depression worldwide. The correlation between the physical activity level and the detection rate of a depressed mood in college students should be of wide concern. A large number of studies have focused on the association between physical activity levels and a negative mood, but circadian rhythm differences seem to be strongly associated with both physical activity levels and mental illness. Therefore, this paper will examine the correlation between physical activity levels, circadian rhythm differences, and mental health levels in college students. METHODS: Data were collected through a web-based cross-sectional survey. In June and December 2022, questionnaires were administered to college students from three universities in Anhui, China. In addition to socio-demographic information, measures included the International Physical Activity Questionnaire-Short Form (IPAQ-SF), Morning and Evening Questionnaire-5 Items (MEQ-5), and Symptom Check List90 (SCL-90) scales. Correlation analysis was used to understand the relationship between physical activity and circadian rhythm differences in the three aspects of college student's mental health. RESULTS: The analysis of the data led to the conclusion that 28.4% of the 1241 college students in this survey had psychological disorders. The physical activity level of male students was higher than that of female students, but the risk of having depressive tendencies was higher in female students than in male students. There was a significant negative correlation between the physical activity level and scl-90 scores (p < 0.01), which indicates that higher physical activity levels are associated with higher mental health. Circadian rhythm differences and scl-90 scores were significantly positively correlated among college students (p < 0.01), and night-type people had a higher risk of mental illness than intermediate-type and early-morning-type people. CONCLUSIONS: During the period of closed administration due to COVID-19, school college students experienced large and high levels of negative emotional phenomena due to reduced physical activity and public health emergencies. This study showed significant correlations between both physical activity levels and circadian rhythmicity differences and the degree of mental health of college students.

Amplification-free CRISPR/Cas detection technology: challenges, strategies, and perspectives.

Rapid and accurate molecular diagnosis is a prerequisite for precision medicine, food safety, and environmental monitoring. The clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated (Cas)-based detection, as a cutting-edged technique, has become an immensely effective tool for molecular diagnosis because of its outstanding advantages including attomolar level sensitivity, sequence-targeted single-base specificity, and rapid turnover time. However, the CRISPR/Cas-based detection methods typically require a pre-amplification step to elevate the concentration of the analyte, which may produce non-specific amplicons, prolong the detection time, and raise the risk of carryover contamination. Hence, various strategies for target amplification-free CRISPR/Cas-based detection have been developed, aiming to minimize the sensitivity loss due to lack of pre-amplification, enable detection for non-nucleic acid targets, and facilitate integration in portable devices. In this review, the current status and challenges of target amplification-free CRISPR/Cas-based detection are first summarized, followed by highlighting the four main strategies to promote the performance of target amplification-free CRISPR/Cas-based technology. Furthermore, we discuss future perspectives that will contribute to developing more efficient amplification-free CRISPR/Cas detection systems.

Multi-Criteria Decision Analysis In Healthcare: Scientometric And Bibliometric Analysis

IntroductionMulti-criteria decision analysis (MCDA) is a useful tool in complex decision-making situations and has been used in medical fields to evaluate treatment options and drug selection. We aimed to provide valuable insights on the use of MCDA in health care through examining the research focus of existing studies, major fields, major applications, most productive authors and countries, and most common journals in the domain using a scientometric and bibliometric analysis.MethodsPublications related to MCDA in health care were identified by searching the Web of Science Core Collection on 14 July 2021. Three bibliometric software programs (VOSviewer, Bibliometrix, and CiteSpace) were used to conduct the analysis.ResultsA total of 410 publications were identified from 196 academic journals (average yearly growth rate of 32% from 1999 to 2021), with 23,637 co-cited references by 871 institutions from 70 countries or regions. The USA was the most productive country (n=80), while the Universiti Pendidikan Sultan Idris (n=16), Université de Montréal (n= 13), and Syreon Research Institute (n=12) were the most productive institutions. The biggest nodes in every cluster of author networks were Aos Alaa Zaidan, Mireille Goetghebeur, and Zoltan Kalo. The top journals in terms of number of articles (n=17) and citations (n=1,673) were Value in Health and the Journal of Medical Systems, respectively. The research hotspots mainly included the analytic hierarchy process (AHP), decision-making, health technology assessment, and healthcare waste management. In the recent literature there was more emphasis on coronavirus disease 2019 (COVID-19) and fuzzy Technique for Order Preference by Similarities to Ideal Solution (TOPSIS). Big data, telemedicine, TOPSIS, and the fuzzy AHP, which are well-developed and important themes, may be the trends in future research.ConclusionsThis study provides a holistic picture of the MCDA-related literature published in health care. MCDA has a broad application in different topic areas and would be helpful for practitioners, researchers, and decision makers working in health care when faced with complex decisions. It can be argued that the door is still open for improving the role of MCDA in health care, both in its technologies and its application.

Towards a global One Health index: a potential assessment tool for One Health performance.

BACKGROUND: A One Health approach has been increasingly mainstreamed by the international community, as it provides for holistic thinking in recognizing the close links and inter-dependence of the health of humans, animals and the environment. However, the dearth of real-world evidence has hampered application of a One Health approach in shaping policies and practice. This study proposes the development of a potential evaluation tool for One Health performance, in order to contribute to the scientific measurement of One Health approach and the identification of gaps where One Health capacity building is most urgently needed. METHODS: We describe five steps towards a global One Health index (GOHI), including (i) framework formulation; (ii) indicator selection; (iii) database building; (iv) weight determination; and (v) GOHI scores calculation. A cell-like framework for GOHI is proposed, which comprises an external drivers index (EDI), an intrinsic drivers index (IDI) and a core drivers index (CDI). We construct the indicator scheme for GOHI based on this framework after multiple rounds of panel discussions with our expert advisory committee. A fuzzy analytical hierarchy process is adopted to determine the weights for each of the indicators. RESULTS: The weighted indicator scheme of GOHI comprises three first-level indicators, 13 second-level indicators, and 57 third-level indicators. According to the pilot analysis based on the data from more than 200 countries/territories the GOHI scores overall are far from ideal (the highest score of 65.0 out of a maximum score of 100), and we found considerable variations among different countries/territories (31.8-65.0). The results from the pilot analysis are consistent with the results from a literature review, which suggests that a GOHI as a potential tool for the assessment of One Health performance might be feasible. CONCLUSIONS: GOHI-subject to rigorous validation-would represent the world's first evaluation tool that constructs the conceptual framework from a holistic perspective of One Health. Future application of GOHI might promote a common understanding of a strong One Health approach and provide reference for promoting effective measures to strengthen One Health capacity building. With further adaptations under various scenarios, GOHI, along with its technical protocols and databases, will be updated regularly to address current technical limitations, and capture new knowledge.

Clustered Regularly Interspaced short palindromic repeats-Based Microfluidic System in Infectious Diseases Diagnosis: Current Status, Challenges, and Perspectives.

Mitigating the spread of global infectious diseases requires rapid and accurate diagnostic tools. Conventional diagnostic techniques for infectious diseases typically require sophisticated equipment and are time consuming. Emerging clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated proteins (Cas) detection systems have shown remarkable potential as next-generation diagnostic tools to achieve rapid, sensitive, specific, and field-deployable diagnoses of infectious diseases, based on state-of-the-art microfluidic platforms. Therefore, a review of recent advances in CRISPR-based microfluidic systems for infectious diseases diagnosis is urgently required. This review highlights the mechanisms of CRISPR/Cas biosensing and cutting-edge microfluidic devices including paper, digital, and integrated wearable platforms. Strategies to simplify sample pretreatment, improve diagnostic performance, and achieve integrated detection are discussed. Current challenges and future perspectives contributing to the development of more effective CRISPR-based microfluidic diagnostic systems are also proposed.

Emotional discourse analysis of COVID-19 patients and their mental health: A text mining study.

COVID-19 has caused negative emotional responses in patients, with significant mental health consequences for the infected population. The need for an in-depth analysis of the emotional state of COVID-19 patients is imperative. This study employed semi-structured interviews and the text mining method to investigate features in lived experience narratives of COVID-19 patients and healthy controls with respect to five basic emotions. The aim was to identify differences in emotional status between the two matched groups of participants. The results indicate generally higher complexity and more expressive emotional language in healthy controls than in COVID-19 patients. Specifically, narratives of fear, happiness, and sadness by COVID-19 patients were significantly shorter as compared to healthy controls. Regarding lexical features, COVID-19 patients used more emotional words, in particular words of fear, disgust, and happiness, as opposed to those used by healthy controls. Emotional disorder symptoms of COVID-19 patients at the lexical level tended to focus on the emotions of fear and disgust. They narrated more in relation to self or family while healthy controls mainly talked about others. Our automatic emotional discourse analysis potentially distinguishes clinical status of COVID-19 patients versus healthy controls, and can thus be used to predict mental health disorder symptoms in COVID-19 patients.

Knowledge Mapping of Multicriteria Decision Analysis in Healthcare: A Bibliometric Analysis.

Objective: Multicriteria decision analysis (MCDA) is a useful tool in complex decision-making situations, and has been used in medical fields to evaluate treatment options and drug selection. This study aims to provide valuable insights into MCDA in healthcare through examining the research focus of existing studies, major fields, major applications, most productive authors and countries, and most common journals in the domain. Methods: A bibliometric analysis was conducted on the publication related to MCDA in healthcare from the Web of Science Core Collection (WoSCC) database on 14 July 2021. Three bibliometric software (VOSviewer, R-bibliometrix, and CiteSpace) were used to conduct the analysis including years, countries, institutes, authors, journals, co-citation references, and keywords. Results: A total of 410 publications were identified with an average yearly growth rate of 32% (1999-2021), from 196 academic journals with 23,637 co-citation references by 871 institutions from 70 countries/regions. The United States was the most productive country (n = 80). Universiti Pendidikan Sultan Idris (n = 16), Université de Montréal (n = 13), and Syreon Research Institute (n = 12) were the top productive institutions. A A Zaidan, Mireille Goetghebeur and Zoltan Kalo were the biggest nodes in every cluster of authors' networks. The top journals in terms of the number of articles (n = 17) and citations (n = 1,673) were Value in Health and Journal of Medical Systems, respectively. The extant literature has focused on four aspects, including the analytic hierarchy process (AHP), decision-making, health technology assessment, and healthcare waste management. COVID-19 and fuzzy TOPSIS received careful attention from MCDA applications recently. MCDA in big data, telemedicine, TOPSIS, and fuzzy AHP is well-developed and an important theme, which may be the trend in future research. Conclusion: This study uncovers a holistic picture of the performance of MCDA-related literature published in healthcare. MCDA has a broad application on different topics and would be helpful for practitioners, researchers, and decision-makers working in healthcare to advance the wheel of medical complex decision-making. It can be argued that the door is still open for improving the role of MCDA in healthcare, whether in its methodology (e.g., fuzzy TOPSIS) or application (e.g., telemedicine).

Development of a Genetically Engineered Bivalent Vaccine against Porcine Epidemic Diarrhea Virus and Porcine Rotavirus.

Porcine epidemic diarrhea virus (PEDV) is an enteric coronavirus that causes acute diarrhea, vomiting, dehydration, and a high mortality rate in neonatal piglets. In recent years, PEDV has been associated with co-infections with other swine enteric viruses, including porcine rotavirus (PoRV), resulting in increased mortality among newborn piglets. In this paper, we developed a bivalent vaccine against PEDV and PoRV by constructing a recombinant PEDV encoding PoRV VP7 (rPEDV-PoRV-VP7). The recombinant virus was constructed by replacing the entire open reading frame 3 (ORF3) in the genome of an attenuated PEDV strain YN150 with the PoRV VP7 gene using reverse genetic systems. Similar plaque morphology and replication kinetics were observed in Vero cells with the recombinant PEDV compared to the wild-type PEDV. It is noteworthy that the VP7 protein could be expressed stably in rPEDV-PoRV-VP7-infected cells. To evaluate the immunogenicity and safety of rPEDV-PoRV-VP7, 10-day-old piglets were vaccinated with the recombinant virus. After inoculation, no piglet displayed clinical symptoms such as vomiting, diarrhea, or anorexia. The PoRV VP7- and PEDV spike-specific IgG in serum and IgA in saliva were detected in piglets after rPEDV-PoRV-VP7 vaccination. Moreover, both PoRV and PEDV neutralizing antibodies were produced simultaneously in the inoculated piglets. Collectively, we engineered a recombinant PEDV expressing PoRV VP7 that could be used as an effective bivalent vaccine against PEDV and PoRV.

Emotional Experiences of COVID-19 Patients in China: A Qualitative Study.

This study explored the emotional experiences of COVID-19 patients in China. Thirty-four patients diagnosed with COVID-19 participated in semi-structured telephone interviews. We used qualitative methods to investigate the distribution patterns and characteristics of patients' emotional experiences. The results indicated that emotional experiences showed different characteristics at different stages during isolation and treatment. COVID-19 patients' emotional discourse encompassed eight main themes, namely, feelings of shock at the diagnosis, yearning for future life, attachment to one's family, depression during the treatment, self-restriction due to probable contagiousness, powerlessness about the disease, open-mindedness about death, and faith in the joint efforts to fight COVID-19. These themes related to experiences concerning infection, isolation, outlook on life and death, stigma, and macro-identity. The findings suggest that the unexpected experience of COVID-19 infection exacerbated patients' negative emotions. COVID-19 patients' emotional stress stemmed from isolated environments, physiological effects of the disease, panic about the unknown, and realistic economic pressure. The government, medical staff, family members of patients, and the media should therefore work together to ensure proper emotional care for COVID-19 patients.

Feasibility Studies of Nebulized SARS-CoV-2 Neutralizing Antibody in Mice and Cynomolgus Monkeys.

PURPOSE: Neutralizing antibodies, administrated through intravenous infusion, have shown to be highly efficacious in treating mild and moderate COVID-19 caused by SARS-CoV-2 infection in the lung. However, antibodies do not transport across the plasma-lung barrier efficiently, and up to 100 mg/kg dose was used in human causing significant supply and cost burdens. This study was to explore the feasibility of nebulized antibodies inhalation delivery as an alternative route. METHODS: HB27, a potent RBD-specific humanized monoclonal antibody (Zhu et al. in National Sci Rev. 8:nwaa297, 2020), showed excellent protection against SARS-CoV-2 in animal model and good safety profile in clinical studies. The pharmacokinetics and preliminary safety of HB27 administrated through the respiratory tract were studied in mice and cynomolgus monkeys here. RESULTS: At a single 5 mg/kg dose, the peak HB27 concentration in mice pulmonary epithelial lining fluid (ELF) reached 857.8 µg/mL, 670-fold higher than the PRNT90 value of 1.28 µg/mL, and maintained above PRNT90 over 240 h. In contrast, when administrated by intravenous injection at a 5 mg/kg dose, the antibody concentrations in mice ELF were below PRNT90 value throughout, and were about 50-fold lower than that in the serum. In cynomolgus monkeys administrated with a single dose through inhalation, the antibody concentration in ELF remained high within 3 days. No drug-related safety concerns were observed in the studies. CONCLUSIONS: The study demonstrated that nebulized neutralizing antibody delivery though inhalation could be a more efficient and efficacious alternative approach for treating COVID-19 and other respiratory infectious diseases, and warrants further evaluation in clinical studies.

Understanding systemic risk induced by climate change

The systemic risk induced by climate change represents one of the most prominent threats facing humanity and has attracted increasing attention since the outbreak of the COVID-19 pandemic at the end of 2019. The existing literature highlights the importance of systemic risk induced by climate change, but there are still deficiencies in understanding its dynamics and assessing the risk. Aiming to bridge this gap, this study develops a theoretical framework and employs two cases to illustrate the concept, origin, occurrence, propagation, evolution, and assessment framework of systemic risk induced by climate change. The key findings include: 1) systemic risk induced by climate change derives from the rapid growth of greenhouse gas emissions, increasingly complex connections among different socioeconomic systems, and continuous changes in exposure and vulnerability;2) systemic risk induced by climate change is a holistic risk generated by the interconnection, interaction, and dynamic evolution of different types of single risks, and its fundamental, defining feature is cascading effects. The extent of risk propagation and its duration depend on the characteristics of the various discrete risks that are connected to make up the systemic risk;3) impact domains, severity of impact, and probability of occurrences are three core indicators in systemic risk assessment, and the impact domains should include the economy, society, homeland security, human health, and living conditions. We propose to deepen systemic risk research from three aspects: to develop theories to understand the mechanism of systemic risk;to conduct empirical research to assess future risks;and to develop countermeasures to mitigate the risk.

Abrupt emissions reductions during COVID-19 contributed to record summer rainfall in China.

Record rainfall and severe flooding struck eastern China in the summer of 2020. The extreme summer rainfall occurred during the COVID-19 pandemic, which started in China in early 2020 and spread rapidly across the globe. By disrupting human activities, substantial reductions in anthropogenic emissions of greenhouse gases and aerosols might have affected regional precipitation in many ways. Here, we investigate such connections and show that the abrupt emissions reductions during the pandemic strengthened the summer atmospheric convection over eastern China, resulting in a positive sea level pressure anomaly over northwestern Pacific Ocean. The latter enhanced moisture convergence to eastern China and further intensified rainfall in that region. Modeling experiments show that the reduction in aerosols had a stronger impact on precipitation than the decrease of greenhouse gases did. We conclude that through abrupt emissions reductions, the COVID-19 pandemic contributed importantly to the 2020 extreme summer rainfall in eastern China.

Fast climate responses to aerosol emission reductions during the COVID-19 pandemic. (Special Section: The COVID-19 pandemic: linking health, society, and environment.)

The reduced human activities and associated decreases in aerosol emissions during the COVID-19 pandemic are expected to affect climate. Assuming emission changes during lockdown, back-to-work and post-lockdown stages of COVID-19, climate model simulations show a surface warming over continental regions of the Northern Hemisphere. In January-March, there was an anomalous warming of 0.05-0.15 K in eastern China, and the surface temperature increase was 0.04-0.07 K in Europe, eastern United States, and South Asia in March-May. The longer the emission reductions undergo, the warmer the climate would become. The emission reductions explain the observed temperature increases of 10-40% over eastern China relative to 2019. A southward shift of the ITCZ is also seen in the simulations. This study provides an insight into the impact of COVID-19 pandemic on global and regional climate and implications for immediate actions to mitigate fast global warming.

CT radiomics facilitates more accurate diagnosis of COVID-19 pneumonia: compared with CO-RADS.

BACKGROUND: Limited data was available for rapid and accurate detection of COVID-19 using CT-based machine learning model. This study aimed to investigate the value of chest CT radiomics for diagnosing COVID-19 pneumonia compared with clinical model and COVID-19 reporting and data system (CO-RADS), and develop an open-source diagnostic tool with the constructed radiomics model. METHODS: This study enrolled 115 laboratory-confirmed COVID-19 and 435 non-COVID-19 pneumonia patients (training dataset, n = 379; validation dataset, n = 131; testing dataset, n = 40). Key radiomics features extracted from chest CT images were selected to build a radiomics signature using least absolute shrinkage and selection operator (LASSO) regression. Clinical and clinico-radiomics combined models were constructed. The combined model was further validated in the viral pneumonia cohort, and compared with performance of two radiologists using CO-RADS. The diagnostic performance was assessed by receiver operating characteristics curve (ROC) analysis, calibration curve, and decision curve analysis (DCA). RESULTS: Eight radiomics features and 5 clinical variables were selected to construct the combined radiomics model, which outperformed the clinical model in diagnosing COVID-19 pneumonia with an area under the ROC (AUC) of 0.98 and good calibration in the validation cohort. The combined model also performed better in distinguishing COVID-19 from other viral pneumonia with an AUC of 0.93 compared with 0.75 (P = 0.03) for clinical model, and 0.69 (P = 0.008) or 0.82 (P = 0.15) for two trained radiologists using CO-RADS. The sensitivity and specificity of the combined model can be achieved to 0.85 and 0.90. The DCA confirmed the clinical utility of the combined model. An easy-to-use open-source diagnostic tool was developed using the combined model. CONCLUSIONS: The combined radiomics model outperformed clinical model and CO-RADS for diagnosing COVID-19 pneumonia, which can facilitate more rapid and accurate detection.

Genomic, epigenomic, and immune subtype analysis of CTSL/B and SARS-CoV-2 receptor ACE2 in pan-cancer.

SARS-coronavirus 2 (SARS-CoV-2) has been spreading widely and posing an international challenge for both healthcare and society. At present, cancer has been identified as an individual risk factor for COVID-19. Angiotensin converting enzyme 2 (ACE2) and Cathepsin L/Cathepsin B (CTSL/B), which act as the receptor and entry-associated proteases of SARS-CoV-2 respectively, are pivotal for SARS-CoV-2 infection. To investigate the possible SARS-CoV-2 infection risk of pan-cancer, we analyzed the genetic alterations, RNA expression, DNA methylation, and the association with immune subtypes of ACE2 and CTSL/B with the prognosis in pan-cancer. Results showed the upregulation of CTSL/B and ACE2 in Pancreatic adenocarcinoma (PAAD) and Stomach adenocarcinoma (STAD) and demonstrated a positive correlation between copy number alteration (CNA) and gene expression for CTSB in PAAD and STAD. Hypomethylation and a negative correlation of gene expression and methylation for CTSB were detected in PAAD. In addition, ACE2 and CTSL/B are overexpressed in the IFN-gamma immune subtype of ovarian serous Cystadenocarcinoma (OV), Cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), and Bladder urothelial carcinoma (BLCA). Our study presents a bioinformatics assessment for the potential risk of SARS-CoV-2 infection in pan-cancer.

Analysis of early characteristics of patients with novel coronavirus infection in Kunming City, Yunnan Province, China

To analyze the epidemiological characteristics and early clinical characteristics of patients with new coronavirus (2019-nCoV) infection in Kunming, and to provide evidence for clinical diagnosis and treatment. The epidemiological history, clinical symptoms, and laboratory test data of 41 of 2019-nCoV confirmed patients admitted to the isolation ward of The third People's Hospital of Kunming from January 23 to February 10, 2020 were retrospectively analyzed. The vast majority of 41 2019-nCoV confirmed patients were imported cases, with incubation periods ranging from 2 to 20 days;severe and critically ill patients were Severe and critically ill patients were older, had higher BMI, similar clinical symptoms, and lymphocytes than patients with mild and general type Lower counts, earlier changes in blood gas, higher erythrocyte sedimentation, lower CA and FE, and more significant decreases in CD4 and CD8 counts. The changes of lymphocyte count, blood gas, ESR, CA and FE and T lymphocyte subsets will appear earlier in the week before the disease is aggravated, which has a certain early warning effect on the severity of the disease.