Comparison of the clinical process and outcomes in patients with fever who visited the emergency department before and after coronavirus infection 2019 outbreak (preprint)

Rationale, aims and objectives: The coronavirus infection 2019 (COVID-19) pandemic has affected the emergency department (ED) management. Its viral transmission necessitates the use of isolation rooms and personal protective equipment for treating suspected patients, such as those with fever. This delays the time until the first encounter with the patients, thereby increasing the length of stay (LOS) in the ED. We aimed to compare delays in the ED LOS and clinical processes between the COVID-19 period and pre-COVID-19 period. Moreover, we intended to evaluate if the aforementioned delay affected patient outcomes. Methods: We conducted a single-center, retrospective study in Korea. Patients with fever were compared between the “COVID-19 period” from March 2020 to August 2020 and the “pre-COVID-19 period” from March 2019 to September 2019. We compared the overall ED LOS and individual time variable, including initial diagnostic tests (laboratory tests, radiography), specific diagnostic test (computed tomography), and treatment processes (antibiotics). A logistic regression analysis was conducted to identify the association between hospital admission and patient data. Results: We enrolled 931 and 749 patients during pre- and COVID-19 periods, respectively. Patients with fever remained in the ED for a longer duration during the COVID-19 period (pre-COVID-19:207.7±102.7 min vs. during COVID-19: 223.5±119.4 min, p=0.004). The total time for performing laboratory tests and radiography displayed significant differences between the two periods, particularly from the time of patient arrival in the ED to the time of issuing the order. The time until antibiotic administration was delayed in the COVID-19 period (pre-COVID-19:195.8±103.3 min vs. during COVID-19: 216.9±108.4 min, p=0.003). The logistic regression analysis for hospital admission identified ED LOS as an independent factor in both periods. Conclusion: The delay until encountering patients with fever resulted in longer ED LOS during the COVID-19 period; however, it possibly did not increase the hospital admission rates.

Epidemiological characteristics of infected patients aged 60 years and older during the outbreak of SARS-CoV-2 Omicron variant in Sanya, Hainan in 2022

Objective: To analyze the epidemic characteristics of coronavirus disease 2019 (COVID-19) cases aged 60 years or older during the outbreak epidemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant in Sanya City, Hainan Province, August-September 2022, and to provide scientific and effective basis for formulating strategies for the prevention and treatment of COVID-19 in the elderly in the future. Methods: The data of 1 785 COVID-19 cases aged 60 years reported from August 1 to September 8, 2022 in Sanya City, and 537 local COVID-19 inpatients aged 60 years treated in Sanya Central Hospital were collected and analyzed using WPS Office 2018 software for data cleaning, database assistance, and drawing, and SPSS 22.0 software was used to statistically describe and analyze the counting data. Results: A total of 1 785 local COVID-19 cases aged 60 years were reported in China's Disease Prevention and Control Information System, including 1 509 confirmed cases (84.54%) and 276 asymptomatic cases (15.46%). Of the confirmed cases, 1 448 were mild cases (95.96%), 40 were common cases (2.65%), 10 were severe cases (0.66%), and 11 were critical cases (0.73%), and no deaths were reported. The ratio of male to female was 0.88:1. The youngest was 60 years old, the oldest age was 107 years old, with a median age of 67 years old. Most of them (1 559 cases, 87.34%) were non-social employed personnel. Among them, 952 (53.33%) were from Tianya District and 733 (41.06%) from Jiyang District. Among the 537 COVID-19 elderly infected patients hospitalized in Sanya Central Hospital, infected patients with underlying diseases accounted for 287 (53.45%) cases of hypertension, diabetes and other types of basic diseases (chronic kidney disease, hyperlipidemia, chronic gastritis, gastric ulcer, etc.), and there were serious infections. Conclusions: Most of the elderly infected with Omicron variants were mild cases with few symptoms, but the elderly infected with Omicron variants were difficult to recover or even worsen the condition due to old age and underlying diseases. Therefore, by analyzing the various aspects of elderly infected cases, scientific and effective basis can be provided to prevent and control the epidemic situation in the future, and reduce the occurrence of severe, critical and death cases of COVID-19 in the elderly.

OView-AI Supporter for Classifying Pneumonia, Pneumothorax, Tuberculosis, Lung Cancer Chest X-ray Images Using Multi-Stage Superpixels Classification.

The deep learning approach has recently attracted much attention for its outstanding performance to assist in clinical diagnostic tasks, notably in computer-aided solutions. Computer-aided solutions are being developed using chest radiography to identify lung diseases. A chest X-ray image is one of the most often utilized diagnostic imaging modalities in computer-aided solutions since it produces non-invasive standard-of-care data. However, the accurate identification of a specific illness in chest X-ray images still poses a challenge due to their high inter-class similarities and low intra-class variant abnormalities, especially given the complex nature of radiographs and the complex anatomy of the chest. In this paper, we proposed a deep-learning-based solution to classify four lung diseases (pneumonia, pneumothorax, tuberculosis, and lung cancer) and healthy lungs using chest X-ray images. In order to achieve a high performance, the EfficientNet B7 model with the pre-trained weights of ImageNet trained by Noisy Student was used as a backbone model, followed by our proposed fine-tuned layers and hyperparameters. Our study achieved an average test accuracy of 97.42%, sensitivity of 95.93%, and specificity of 99.05%. Additionally, our findings were utilized as diagnostic supporting software in OView-AI system (computer-aided application). We conducted 910 clinical trials and achieved an AUC confidence interval (95% CI) of the diagnostic results in the OView-AI system of 97.01%, sensitivity of 95.68%, and specificity of 99.34%.

Does digital transformation of enterprises help reduce the cost of equity capital.

Digital economy is regarded as the main economic form following agricultural economy and industrial economy. And the digital transformation has given enterprises new development momentum. Can it reduce the equity capital cost? This paper uses text analysis obtained by crawling the annual reports from 2010 to 2021 and investigates the impact of digital transformation on the corporate equity capital cost. The results show that: 1) Digital transformation will reduce the equity capital cost; 2) The digital transformation has a heterogeneous impact on the equity capital cost of enterprises with different scales, natures and levels of leverage, which is more significant for large-scale enterprises, state-owned enterprises and highly leveraged enterprises; 3) Digital transformation mainly affects the equity capital cost by improving enterprise value, rather than by increasing analysts' attention and influencing the level of corporate risk bearing.

Fit-failure rate associated with simulated reuse and extended use of N95 respirators assessed by a quantitative fit test.

OBJECTIVE: We quantitatively assessed the fit failure rate of N95 respirators according to the number of donning/doffing and hours worn. DESIGN: Experimental study. SETTING: A tertiary-care referral center in South Korea. PARTICIPANTS: In total, 10 infection control practitioners participated in the fit test. METHODS: The first experiment comprised 4 consecutive 1-hour donnings and fit tests between each donning. The second experiment comprised 2 consecutive 3-hour donnings and fit tests between each donning. The final experiment comprised fit tests after an 1-hour donning or a 2-hour donning. RESULTS: For 1-hour donnings, 60%, 70%, and 90% of the participants had fit failures after 2, 3, and 4 consecutive donnings, respectively. For 3-hour donnings, 50% had fit failure after the first donning and 70% had failures after 2 consecutive donnings. All participants passed the fit test after refitting whenever fit failure occurred. The final experiment showed that 50% had fit failure after a single use of 1 hour, and 30% had fit failure after a single use of 2 hours. CONCLUSIONS: High fit-failure rates were recorded after repeated donning and extended use of N95 respirators. Caution is needed for reuse (≥1 time) and extended use (≥1 hour) of N95 respirators in high-risk settings such as those involving aerosol-generating procedures. Although adequate refitting may recover the fit factor, the use of clean gloves and strict hand hygiene afterward should be ensured when touching the outer surfaces of N95 respirators for refitting.

Sustainability and the Australian international higher education industry: towards a multidimensional model

Purpose>This paper aims to explore how the Australian Government represented issues of sustainability in Australian international higher education (IHE) policies and how it framed efforts to foster enhanced strategies for sustainable development of IHE. This research calls for a change from one-dimensional economic sustainability to a more multi-dimensional conception of sustainability, including emphasizing the role of the political sphere in issues of sustainability.Design/methodology/approach>This paper uses discourse analysis of policy documents, specifically Bacchi’s (2009) “what is the problem represented to be” approach, to explore the problematization behind selected government policies related to IHE in Australia.Findings>This research identified existing challenges and factors that have affected the sustainability of Australian IHE and examined how the Australian Government constructed this issue. In light of this approach, a theoretical model is proposed from internal resource analysis and external industry and foreign market structure analysis to help foster more sustainable development of IHE.Research limitations/implications>This study is based on policy document analysis. Consequently, future empirical research is needed to examine the impact of these policies and further substantiate the findings of this study.Practical implications>This paper proposes a theoretical model for strategy making that helps gain and maintain sustainable competitive advantage in IHE from a more integrated perspective;such an approach enables more systemic thinking on strategy proposals and offers a reference for future practice. This research will contribute to policy design for the sustainability of the Australian IHE industry and promote change from a one-dimensional economic sustainability to a more multi-dimensional sustainability approach, thereby offering a point of reference for other countries that face similar issues.Social implications>This study points out the need to broaden the business focus, expand the value created from shareholder value to the common good and change “inside-out” economic perspectives to “outside-in” integrated perspectives for business, including the IHE industry.Originality/value>The sustainability of IHE has become an important concern in Australian policies but is an area for further inquiry in academic discussion and research. By closely examining government policies, particularly from a discursive approach (after Bacchi), this paper makes a contribution to policy design for the sustainability of the Australian IHE industry, helping to promote a more multi-dimensional approach to sustainability.

A Case Series of Olfactory Dysfunction in Imported COVID-19 Patients: A 12-Month Follow-Up Study.

Objective: The scientific community knows little about the long-term influence of coronavirus disease 2019 (COVID-19) on olfactory dysfunction (OD). With the COVID-19 pandemic ongoing worldwide, the risk of imported cases remains high. In China, it is necessary to understand OD in imported cases. Methods: A prospective follow-up design was adopted. A total of 11 self-reported patients with COVID-19 and OD from Xi'an No. 8 Hospital were followed between August 19, 2021, and December 12, 2021. Demographics, clinical characteristics, laboratory and radiological findings, and treatment outcomes were analyzed at admission. We surveyed the patients via telephone for recurrence and sequelae at the 1-, 6-, and 12-month follow-up. Results: Eleven patients with OD were enrolled; of these, 54.5% (6/11) had hyposmia and 45.5% (5/11) had anosmia. 63.6% (7/11) reported OD before or on the day of admission as their initial symptom; of these, 42.9% (3/7) described OD as the only symptom. All patients in the study received combined treatment with traditional Chinese medicine and Western medicine, and 72.7% (8/11) had partially or fully recovered at discharge. In terms of OD recovery at the 12-month follow-up, 45.5% (5/11) reported at least one sequela, 81.8% (9/11) had recovered completely, 18.2% (2/11) had recovered partially, and there were no recurrent cases. Conclusions: Our data revealed that OD frequently presented as the initial or even the only symptom among imported cases. Most OD improvements occurred in the first 2 weeks after onset, and patients with COVID-19 and OD had favorable treatment outcomes during long-term follow-up. A better understanding of the pathogenesis and appropriate treatment of OD is needed to guide clinicians in the care of these patients.

Deep Learning in Multi-Class Lung Diseases' Classification on Chest X-ray Images.

Chest X-ray radiographic (CXR) imagery enables earlier and easier lung disease diagnosis. Therefore, in this paper, we propose a deep learning method using a transfer learning technique to classify lung diseases on CXR images to improve the efficiency and accuracy of computer-aided diagnostic systems' (CADs') diagnostic performance. Our proposed method is a one-step, end-to-end learning, which means that raw CXR images are directly inputted into a deep learning model (EfficientNet v2-M) to extract their meaningful features in identifying disease categories. We experimented using our proposed method on three classes of normal, pneumonia, and pneumothorax of the U.S. National Institutes of Health (NIH) data set, and achieved validation performances of loss = 0.6933, accuracy = 82.15%, sensitivity = 81.40%, and specificity = 91.65%. We also experimented on the Cheonan Soonchunhyang University Hospital (SCH) data set on four classes of normal, pneumonia, pneumothorax, and tuberculosis, and achieved validation performances of loss = 0.7658, accuracy = 82.20%, sensitivity = 81.40%, and specificity = 94.48%; testing accuracy of normal, pneumonia, pneumothorax, and tuberculosis classes was 63.60%, 82.30%, 82.80%, and 89.90%, respectively.

Rapid Degradation of SARS-CoV-2 Spike S Protein by A Specific Serine Protease.

The S protein of SARS-CoV-2 is a crucial structural and functional component for virus entry. Due to the constant mutation of the virus, there are very limited ways to prevent and control COVID-19. This experiment used a macroscopic SDS-PAGE method and proved that the S protein of wild-type SARS-CoV-2 virus, especially the S1 subunit, is very sensitive to alkaline serine protease with acidic pI (ASPNJ), NJ represents Neanthes japonica (Izuka) from which ASP is purified). ASPNJ cleaves proteins when the carbonyl group of the peptide bond is contributed by arginine or lysine. ASPNJ can degrade the S protein very quickly and effectively in vitro with relative selectivity. It can be inferred that the S, S1 and RBD of SARS-CoV-2 variants can also be easily degraded by ASPNJ. This rapid and strong degradation of the S protein by ASPNJ may become a potential new treatment strategy.

Multi-Class Classification of Lung Diseases Using CNN Models

In this study, we propose a multi-class classification method by learning lung disease images with Convolutional Neural Network (CNN). As the image data for learning, the U.S. National Institutes of Health (NIH) dataset divided into Normal, Pneumonia, and Pneumothorax and the Cheonan Soonchunhyang University Hospital dataset including Tuberculosis were used. To improve performance, preprocessing was performed with Center Crop while maintaining the aspect ratio of 1:1. As a Noisy Student of EfficientNet B7, fine-tuning learning was performed using the weights learned from ImageNet, and the features of each layer were maximally utilized using the Multi GAP structure. As a result of the experiment, Benchmarks measured with the NIH dataset showed the highest performance among the tested models with an accuracy of 85.32%, and the four-class predictions measured with data from Soonchunhyang University Hospital in Cheonan had an average accuracy of 96.1%, an average sensitivity of 92.2%, an average specificity of 97.4%, and an average inference time of 0.2 s.

Impact of COVID-19 on Acute Stroke Presentation in a Designated COVID-19 Hospital.

Objectives: Thousands of designated COVID-19 hospitals have been set up in China to fight the ongoing COVID-19 pandemic. Anecdotal reports indicate a falling rate of acute stroke diagnoses in these hospitals during the COVID-19 period. We conducted an exploratory single-center analysis to estimate the change in acute stroke presentation at the designated COVID-19 hospitals. Methods: This retrospective observational study included all patients admitted to Yongchuan Hospital Affiliated to Chongqing Medical University with acute stroke between January 24 and March 10, 2020. Patient demographics, characteristics of the stroke, treatment details, and clinical outcomes were compared with those of patients admitted in the corresponding period in the year before (2019, "the pre-COVID-19 period"). Subgroup analysis was performed in the ischemic and hemorrhagic stroke groups. Results: A total of 110 patients presented with acute stroke symptoms during the COVID-19 pandemic, compared with 173 patients in the pre-COVID-19 period. A higher proportion of stroke patients presented to the hospital via emergency medical services during the pandemic (48.2 vs. 31.8%, p = 0.006). There was a lower proportion of ischemic stroke patients (50.9 vs. 65.3%, p = 0.016) than in the preceding year. There were significantly fewer patients with 90-day modified Rankin Scale score ≥3 in the COVID-19 period compared with the pre-COVID-19 period (17.3 vs. 30.6%, p = 0.012). Among patients with ischemic stroke, the mean time from patient arrival to vessel puncture for emergency endovascular therapy in the COVID-19 period was shorter than that in the pre-COVID-19 period (109.18 ± 71.39 vs. 270.50 ± 161.51 min, p = 0.002). Among patients with hemorrhagic stroke, the rate of emergency surgical operation in the COVID-19 period was higher than that in the pre-COVID-19 period (48.1 vs. 30.0%, p = 0.047). The mean time from patient arrival to emergency surgical operation (15.31 ± 22.89 vs. 51.72 ± 40.47 min, p = 0.002) was shorter in the COVID-19 period than in the pre-COVID-19 period. Conclusions: Although fewer acute stroke patients sought medical care in this designated COVID-19 hospital during the COVID-19 pandemic, this type of hospital was more efficient for timely treatment of acute stroke. Recognizing how acute strokes presented in designated COVID-19 hospitals will contribute to appropriate adjustments in strategy for dealing with acute stroke during COVID-19 and future pandemics.

Global view on virus infection in non-human primates and implications for public health and wildlife conservation.

Viruses can be transmitted from animals to humans (and vice versa) and across animal species. As such, host-virus interactions and transmission have attracted considerable attention. Non-human primates (NHPs), our closest evolutionary relatives, are susceptible to human viruses and certain pathogens are known to circulate between humans and NHPs. Here, we generated global statistics on VI-NHPs based on a literature search and public data mining. In total, 140 NHP species from 12 families are reported to be infected by 186 DNA and RNA virus species, 68.8% of which are also found in humans, indicating high potential for crossing species boundaries. The top 10 NHP species with high centrality in the NHP-virus network include two great apes (Pan troglodytes, Pongo pygmaeus) and eight Old World monkeys (Macaca mulatta, M. fascicularis, M. leonina, Papio cynocephalus, Cercopithecus ascanius, C. erythrotis, Chlorocebus aethiops, and Allochrocebus lhoesti). Given the wide distribution of Old World monkeys and their frequent contact with humans, there is a high risk of virus circulation between humans and such species. Thus, we suggest recurring epidemiological surveillance of NHPs, specifically Old World monkeys that are in frequent contact with humans, and other effective measures to prevent potential circulation and transmission of viruses. Avoidance of false positives and sampling bias should also be a focus in future work.

Nosocomial Outbreak of COVID-19 in a Hematologic Ward.

BACKGROUND: Coronavirus disease 2019 (COVID-19) outbreaks occur in hospitals in many parts of the world. In hospital settings, the possibility of airborne transmission needs to be investigated thoroughly. MATERIALS AND METHODS: There was a nosocomial outbreak of COVID-19 in a hematologic ward in a tertiary hospital, Seoul, Korea. We found 11 patients and guardians with COVID-19 through vigorous contact tracing and closed-circuit television monitoring. We found one patient who probably had acquired COVID-19 through airborne-transmission. We performed airflow investigation with simulation software, whole-genome sequencing of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). RESULTS: Of the nine individuals with COVID-19 who had been in the hematologic ward, six stayed in one multi-patient room (Room 36), and other three stayed in different rooms (Room 1, 34, 35). Guardian in room 35 was close contact to cases in room 36, and patient in room 34 used the shared bathroom for teeth brushing 40 minutes after index used. Airflow simulation revealed that air was spread from the bathroom to the adjacent room 1 while patient in room 1 did not used the shared bathroom. Airflow was associated with poor ventilation in shared bathroom due to dysfunctioning air-exhaust, grill on the door of shared bathroom and the unintended negative pressure of adjacent room. CONCLUSION: Transmission of SARS-CoV-2 in the hematologic ward occurred rapidly in the multi-patient room and shared bathroom settings. In addition, there was a case of possible airborne transmission due to unexpected airflow.

Standardization of ELISA protocols for serosurveys of the SARS-CoV-2 pandemic using clinical and at-home blood sampling.

The extent of SARS-CoV-2 infection throughout the United States population is currently unknown. High quality serology is key to avoiding medically costly diagnostic errors, as well as to assuring properly informed public health decisions. Here, we present an optimized ELISA-based serology protocol, from antigen production to data analyses, that helps define thresholds for IgG and IgM seropositivity with high specificities. Validation of this protocol is performed using traditionally collected serum as well as dried blood on mail-in blood sampling kits. Archival (pre-2019) samples are used as negative controls, and convalescent, PCR-diagnosed COVID-19 patient samples serve as positive controls. Using this protocol, minimal cross-reactivity is observed for the spike proteins of MERS, SARS1, OC43 and HKU1 viruses, and no cross reactivity is observed with anti-influenza A H1N1 HAI. Our protocol may thus help provide standardized, population-based data on the extent of SARS-CoV-2 seropositivity, immunity and infection.

The Attitude towards Preventive Measures and Knowledge of COVID-19 Inpatients with Severe Mental Illness in Economically Underdeveloped Areas of China.

Patients with severe mental illnesses (SMI) were at high risk of infection during Coronavirus Diseases 2019 (COVID-19) pandemic. This study examined hospitalized SMI patients' attitude and knowledge towards the COVID-19 infection. A cross-sectional survey was conducted in five psychiatric hospitals located in Gansu province, the most economically underdeveloped area in China. Patients' attitude towards preventive measures and knowledge of COVID-19 were measured by a self-report questionnaire. A total of 925 hospitalized patients with SMI were recruited. Of them, 84.8% (95%CI: 82.4%-87.1%) had positive attitudes towards preventive measures of the COVID-19 outbreak. Being married (OR: 1.55, 95%CI: 1.05-2.30) and a higher educational level (OR: 1.63, 95%CI: 1.12-2.38) were independently associated with positive attitudes towards COVID-19 preventive measures, whereas higher educational level was associated with better knowledge of the COVID-19 outbreak (ß: 0.231, P < 0.001). Patients mainly received COVID-19 relevant knowledge from public media (58.9%), followed by their clinicians (33.2%). Most hospitalized SMI patients in economically underdeveloped areas in China showed positive attitudes towards COVID-19 preventive measures. However, public health education on COVID-19 relevant knowledge by mental health professionals was inadequate to reduce the risk of transmission and infection.

Optimizing high-yield production of SARS-CoV-2 soluble spike trimers for serology assays.

The SARS-CoV-2 spike trimer is the primary antigen for several serology assays critical to determining the extent of SARS-CoV-2 exposure in the population. Until stable cell lines are developed to increase the titer of this secreted protein in mammalian cell culture, the low yield of spike protein produced from transient transfection of HEK293 cells will be a limiting factor for these assays. To improve the yield of spike protein and support the high demand for antigens in serology assays, we investigated several recombinant protein expression variables by altering the incubation temperature, harvest time, chromatography strategy, and final protein manipulation. Through this investigation, we developed a simplified and robust purification strategy that consistently yields 5 mg of protein per liter of expression culture for two commonly used forms of the SARS-CoV-2 spike protein. We show that these proteins form well-behaved stable trimers and are consistently functional in serology assays across multiple protein production lots.