Study on the Impact of Supply Chain Dynamic Capabilities on Long-Term Performance of Enterprises

The risk of frequent disasters is becoming a huge challenge for enterprises and their supply chains. In particular, sudden global public health events have brought a great test to the supply chain. How to make sustainable planning and preparedness and smoothly carry out supply chain operations and obtain sustainable firm performance in the complex market environment requires urgent attention from industries and academia. The different effects of supply chain operational capability and dynamic capability on the long-term performance and short-term performance of enterprises are still unclear;therefore, a model was established to discuss this. Based on the theory of dynamic capability, a relational model between supply chain dynamic capability, supply chain operational capability, and firm performance was constructed, a hypothesis testing method and Amos software were used to verify the set model, and the mechanisms of supply chain dynamic capability and supply chain operational capability on firm performance were discussed. The empirical results show that supply chain operational capability has a mediating effect on supply chain dynamic capability and firm performance, and supply chain dynamic capability has a moderating impact on supply chain operational capability and firm performance. The supply chain and its enterprises should cultivate and continuously improve the supply chain dynamic capability as soon as possible, so that in the face of emergencies, the supply chain operation capability can be reasonably configured to avoid damage, improve firm performance, and gain competitive advantages.

Meta-analysis of the prevalence of anxiety and depression among frontline healthcare workers during the COVID-19 pandemic

Objective To systematically review the prevalence of anxiety and depression among frontline healthcare workers during the coronavirus disease 2019 (COVID-19) pandemic. Methods Computers were used to search CNKI, VIP, WanFang Data, PubMed, and other Chinese and English databases. The search period was limited to December 2019 to April 2022. Cross-sectional studies collected data on the prevalence of anxiety and depression among frontline healthcare workers since the onset of COVID-19. The STATA 15.1 software was used for the meta-analysis of the included literature. Results A total of 30 studies were included, with a sample size of 18,382 people. The meta-analysis results showed that during the COVID-19 pandemic, the total prevalence of anxiety among frontline healthcare workers was 43.00%, with a 95% confidence interval (CI) of 0.36–0.50, and the total prevalence of depression was 45.00%, with a 95% CI of 0.37–0.52. The results of the subgroup analysis showed that prevalence of anxiety and depression in women, married individuals, those with children, and nurses was relatively high. Frontline healthcare workers with a bachelor's degree or lower had a higher prevalence of anxiety. The prevalence of depression was higher among frontline healthcare workers with intermediate or higher professional titles. Conclusion During the COVID-19 pandemic, the prevalence of anxiety and depression among frontline healthcare workers was high. In the context of public health emergencies, the mental health status of frontline healthcare workers should be given full attention, screening should be actively carried out, and targeted measures should be taken to reduce the risk of COVID-19 infection among frontline healthcare workers. Systematic review registration http://www.crd.york.ac.uk/PROSPERO/, identifier: CRD42022344706.

Families coping with the COVID-19 pandemic: Risk perceptions associated with preventive intention across three generations of Chinese families

While generational differences in coping with the threat of the global COVID-19 crisis were widely discussed in Western societies, a more careful look from the family level is needed in collectivistic societies like China. This study conducted an online survey among three generations of Chinese families between late January and late March in 2020. The study examined 1380 individuals (college students [G1]: N = 762, M-age = 20.47 + 2.45, 78.1% female;parents [G2]: N = 386, M-age = 47.64 + 4.08, 51.3% female;grandparents [G3]: N = 232, M-age = 73.50 + 8.57, 54.3% female) and their cognitions, affect, and preventive intentions toward COVID-19. The investigation ultimately yielded 226 pairs of family data. The results showed generational differences in the above variables. Perceived severity showed a significant total effect on preventive intention for all three generations, and perceived societal risk showed a significant (total) effect on preventive intention only for G3. Perceived severity was linked to preventive intentions through negative affect for those with lower self-efficacy in G1 and G2. Perceived societal risk was also linked to preventive intention through negative affect for those with low self-efficacy for G2. Moreover, cluster analyses identified three types of families with different epidemic coping patterns: stand-by families (48.23%), precautious families (35.40%), and insensitive families (16.37%). This research provides theoretical and practical implications for understanding the disparities in epidemic prevention between different generations and families. Findings show insights for improving the government's communication strategies.

[Critical coronavirus disease 2019 complicated with heparin resistance in 2 patients].

OBJECTIVE: To explore the diagnosis process and treatment experience of severe coronavirus disease 2019 (COVID-19) patients with heparin resistance (HR). METHODS: The medical team of the First People's Hospital of Lianyungang admitted 2 severe COVID-19 patients with HR in intensive care unit (ICU) during their support to the designated hospital for the treatment of COVID-19 patients in Lianyungang City in November 2021. The clinical features, laboratory examinations, imaging features, treatment and prognosis of the two patients were analyzed. RESULTS: Both severe COVID-19 patients received mechanical ventilation, 1 patient was treated with extracorporeal membrane oxygenation (ECMO) support. Both patients were complicated with lower extremity deep venous thrombosis and HR phenomenon under routine dose anticoagulant therapy. The maximum daily dose of unfractionated heparin exceeded 35 000 U (up to 43 200 U), the 2 patients failed to meet the standard of anticoagulation treatment, and the course of disease was prolonged. After that, argatroban was given 0.4 µg×kg-1×min-1 combined with anticoagulant therapy, the activated partial thromboplastin time (APTT) of patients undergoing ECMO could be maintained at 55-60 seconds and the activated coagulation time (ACT) of them could be maintained at 180-200 seconds. After ECMO support or later sequential mechanical ventilation, both patients recovered and were discharged, and deep venous thrombosis was also effectively controlled. CONCLUSIONS: HR phenomenon often occurs during the treatment of severe COVID-19 patients, the anticoagulation regimen should be adjusted in time, and the anticoagulation effect combined with argatroban is clear.

Survivors of COVID-19 exhibit altered amplitudes of low frequency fluctuation in the brain: a resting-state functional magnetic resonance imaging study at 1-year follow-up.

Although some short-term follow-up studies have found that individuals recovering from coronavirus disease 2019 (COVID-19) exhibit anxiety, depression, and altered brain microstructure, their long-term physical problems, neuropsychiatric sequelae, and changes in brain function remain unknown. This observational cohort study collected 1-year follow-up data from 22 patients who had been hospitalized with COVID-19 (8 males and 11 females, aged 54.2 ± 8.7 years). Fatigue and myalgia were persistent symptoms at the 1-year follow-up. The resting state functional magnetic resonance imaging revealed that compared with 29 healthy controls (7 males and 18 females, aged 50.5 ± 11.6 years), COVID-19 survivors had greatly increased amplitude of low-frequency fluctuation (ALFF) values in the left precentral gyrus, middle frontal gyrus, inferior frontal gyrus of operculum, inferior frontal gyrus of triangle, insula, hippocampus, parahippocampal gyrus, fusiform gyrus, postcentral gyrus, inferior parietal angular gyrus, supramarginal gyrus, angular gyrus, thalamus, middle temporal gyrus, inferior temporal gyrus, caudate, and putamen. ALFF values in the left caudate of the COVID-19 survivors were positively correlated with their Athens Insomnia Scale scores, and those in the left precentral gyrus were positively correlated with neutrophil count during hospitalization. The long-term follow-up results suggest that the ALFF in brain regions related to mood and sleep regulation were altered in COVID-19 survivors. This can help us understand the neurobiological mechanisms of COVID-19-related neuropsychiatric sequelae. This study was approved by the Ethics Committee of the Second Xiangya Hospital of Central South University (approval No. 2020S004) on March 19, 2020.

A Deep Adversarial Model for Segmentation Assisted COVID-19 Diagnosis Using CT Images (preprint)

The outbreak of coronavirus disease 2019(COVID-19) is spreading rapidly around the world, resulting in a global pandemic. Imaging techniques such as computed tomography (CT) play an essential role in the diagnosis and treatment of the disease since lung infection or pneumonia is a common complication. However, training a deep network to learn how to diagnose COVID-19 rapidly and accurately in CT images and segment the infected regions like a radiologist is challenging. Since the infectious area are difficult to distinguish, and manually annotation the segmentation results is time-consuming. To tackle these problems, we propose an efficient method based on a deep adversarial network to segment the infection regions automatically. Then the predicted segment results can assist the diagnosis network in identifying the COVID-19 samples from the CT images. On the other hand, a radiologist-like segmentation network provides detailed information of the infectious regions by separating areas of ground-glass, consolidation, and pleural effusion, respectively. Our method can accurately predict the COVID-19 infectious probability and provides lesion regions in CT images with limited training data. Additionally, we have established a public dataset for multitask learning. Extensive experiments on diagnoses and segmentation show superior performance over state-of-the-art methods.

Depression and anxiety and assciated factors among college students returned to campus during the outbreak of COVID-19

Objective: The purpose of this study was to investigate the state of depression and anxiety and associated factors of back-to-school college students during the outbreak of COVID-19, so as to provide theoretical basis for emotional counseling and psychological crisis intervention after long-term school closure due to epidemic outbreak.

Analysis of mental health and influencing factors of college students in the online learning period during the outbreak of COVID-19

Objective: To investigate the mental health and influencing factors of college students during online learning under the coronavirus disease 2019 (COVID-19) epidemic, and to provide a scientific basis for mental health education.

Sleep quality and influencing factors of college students returning to school during COVID-19 epidemic

Objective: To investigate the sleep quality and influencing factors of the first batch of college students returning to school during COVID-19 epidemic, so as to provide scientific basis for taking corresponding measures.

Outcomes of COVID-19 in Patients With Cancer: Report From the National COVID Cohort Collaborative (N3C).

PURPOSE: Variation in risk of adverse clinical outcomes in patients with cancer and COVID-19 has been reported from relatively small cohorts. The NCATS' National COVID Cohort Collaborative (N3C) is a centralized data resource representing the largest multicenter cohort of COVID-19 cases and controls nationwide. We aimed to construct and characterize the cancer cohort within N3C and identify risk factors for all-cause mortality from COVID-19. METHODS: We used 4,382,085 patients from 50 US medical centers to construct a cohort of patients with cancer. We restricted analyses to adults ≥ 18 years old with a COVID-19-positive or COVID-19-negative diagnosis between January 1, 2020, and March 25, 2021. We followed N3C selection of an index encounter per patient for analyses. All analyses were performed in the N3C Data Enclave Palantir platform. RESULTS: A total of 398,579 adult patients with cancer were identified from the N3C cohort; 63,413 (15.9%) were COVID-19-positive. Most common represented cancers were skin (13.8%), breast (13.7%), prostate (10.6%), hematologic (10.5%), and GI cancers (10%). COVID-19 positivity was significantly associated with increased risk of all-cause mortality (hazard ratio, 1.20; 95% CI, 1.15 to 1.24). Among COVID-19-positive patients, age ≥ 65 years, male gender, Southern or Western US residence, an adjusted Charlson Comorbidity Index score ≥ 4, hematologic malignancy, multitumor sites, and recent cytotoxic therapy were associated with increased risk of all-cause mortality. Patients who received recent immunotherapies or targeted therapies did not have higher risk of overall mortality. CONCLUSION: Using N3C, we assembled the largest nationally representative cohort of patients with cancer and COVID-19 to date. We identified demographic and clinical factors associated with increased all-cause mortality in patients with cancer. Full characterization of the cohort will provide further insights into the effects of COVID-19 on cancer outcomes and the ability to continue specific cancer treatments.

A Continuously Benchmarked and Crowdsourced Challenge for Rapid Development and Evaluation of Models to Predict COVID-19 Diagnosis and Hospitalization.

Importance: Machine learning could be used to predict the likelihood of diagnosis and severity of illness. Lack of COVID-19 patient data has hindered the data science community in developing models to aid in the response to the pandemic. Objectives: To describe the rapid development and evaluation of clinical algorithms to predict COVID-19 diagnosis and hospitalization using patient data by citizen scientists, provide an unbiased assessment of model performance, and benchmark model performance on subgroups. Design, Setting, and Participants: This diagnostic and prognostic study operated a continuous, crowdsourced challenge using a model-to-data approach to securely enable the use of regularly updated COVID-19 patient data from the University of Washington by participants from May 6 to December 23, 2020. A postchallenge analysis was conducted from December 24, 2020, to April 7, 2021, to assess the generalizability of models on the cumulative data set as well as subgroups stratified by age, sex, race, and time of COVID-19 test. By December 23, 2020, this challenge engaged 482 participants from 90 teams and 7 countries. Main Outcomes and Measures: Machine learning algorithms used patient data and output a score that represented the probability of patients receiving a positive COVID-19 test result or being hospitalized within 21 days after receiving a positive COVID-19 test result. Algorithms were evaluated using area under the receiver operating characteristic curve (AUROC) and area under the precision recall curve (AUPRC) scores. Ensemble models aggregating models from the top challenge teams were developed and evaluated. Results: In the analysis using the cumulative data set, the best performance for COVID-19 diagnosis prediction was an AUROC of 0.776 (95% CI, 0.775-0.777) and an AUPRC of 0.297, and for hospitalization prediction, an AUROC of 0.796 (95% CI, 0.794-0.798) and an AUPRC of 0.188. Analysis on top models submitting to the challenge showed consistently better model performance on the female group than the male group. Among all age groups, the best performance was obtained for the 25- to 49-year age group, and the worst performance was obtained for the group aged 17 years or younger. Conclusions and Relevance: In this diagnostic and prognostic study, models submitted by citizen scientists achieved high performance for the prediction of COVID-19 testing and hospitalization outcomes. Evaluation of challenge models on demographic subgroups and prospective data revealed performance discrepancies, providing insights into the potential bias and limitations in the models.

A highly sensitive stretchable strain sensor based on multi-functionalized fabric for respiration monitoring and identification

Wearable strain sensors have generated considerable recent research interest due to their huge potential in the real-time detection of human body deformation. State-of-the-art strain sensors are normally fabricated through conductive networks with a single sensing element, which always faces the challenge of either limited stretchability or inferior quality in sensitivity. In this work, we report a highly sensitive strain sensor based on a multi-functionalized fabric through carbonization and polymer-assisted copper deposition. The sensor shows high sensitivity (Gauge factor∼3557.6 in the strain range from 0 to 48%), and outstanding stretchability up to the strain of 300%, which is capable of detecting different types of deformation of the human body. By integrating the high-performance sensor with a deep learning network, we demonstrate a high accuracy of respiration monitoring and emergency alarm system, showing the enormous application potential of the sensor in personal and public healthcare.

A Panel Regression Analysis for the COVID-19 Epidemic in the United States: From the Quarantine Policy and Environmental Perspective (preprint)

Background. The COVID-19 epidemic has had an extreme impact on society. This study aimed to discuss this epidemic in the U.S. and explore the association between COVID-19 daily incidence rate and influencing factors including people’s implementation of states’ quarantine policy and environmental factors including temperature, humidity and so on.Methods. Data of 50 states in U.S. were used as the research subjects. A panel data model was established based on the daily incidence rate and influencing factors from 15 March to 30 September, 2020. The period was analyzed both unsegmented and segmented. The k-means clustering method was used to cluster the states, and panel linear regression method was used for correlation analysis.Results. The characteristics of the daily incidence rate and factors of the three categories were different after clustering. The daily residents at home, proportion of travel people, humidity and incidence rate were negatively correlated, while the daily temperature and incidence rate were positively correlated after unsegmented multivariate analysis. While after segmented analysis, the air pressure and the temperature showed a trend that was negatively correlated with the daily incidence rate respectively in the first and the fifth segment, other indicators showed the analogous results. At the same time, this study also completed the regression analysis after classification of the three groups. Compared with results without classification, there was a decrease of the number of significant independent variables.Conclusions. The spread of COVID-19 in 50 states in U.S. was related to quarantine measures, temperature and humidity. The progress of the epidemic would be relatively slow if people chose to stay at home. Besides, the increase in temperature (＜84.2℉) could be conducive to the spread of the epidemic, while the increase in relative humidity (40~70%) might inhibit the spread of the virus to a certain degree.

HIV self-testing partially filled the HIV testing gap among men who have sex with men in China during the COVID-19 pandemic: results from an online survey.

INTRODUCTION: HIV self-testing (HIVST) is a useful strategy to promote HIV testing among key populations. This study aimed to understand HIV testing behaviours among men who have sex with men (MSM) and specifically how HIVST was used during the coronavirus disease 2019 (COVID-19) measures in China when access to facility-based testing was limited. METHODS: An online cross-sectional study was conducted to recruit men who have sex with men (MSM) in China from May to June of 2020, a period when COVID-19 measures were easing. Data on socio-demographic characteristics, sexual behaviours and HIV testing in the three months before and during COVID-19 measures (23 January 2020) were collected. Chi-square test and logistic regression were used for analyses. RESULTS: Overall, 685 MSM were recruited from 135 cities in 30 provinces of China, whose mean age was 28.8 (SD: 6.9) years old. The majority of participants self-identified as gay (81.9%) and had disclosed their sexual orientation (66.7%). In the last three months, 69.6% ever had sex with men, nearly half of whom had multiple sexual partners (47.2%). Although the overall HIV testing rates before and during COVID-19 measures were comparable, more MSM self-tested for HIV during COVID-19 measures (52.1%) compared to before COVID-19 measures (41.6%, p = 0.038). Fewer MSM used facility-based HIV testing during COVID-19 measures (42.9%) compared to before COVID-19 measures (54.1%, p = 0.038). Among 138 facility-based testers before COVID-19 measures, 59.4% stopped facility-based testing during COVID-19 measures. Among 136 self-testers during COVID-19 measures, 58.1% had no HIV self-testing before COVID-19 measures. Multivariable logistic regression showed that having sex with other men in the last three months (adjusted odds ratio, aOR = 2.04, 95% CI: 1.38 to 3.03), self-identifying as gay (aOR = 2.03, 95% CI: 1.31 to 3.13), ever disclosing their sexual orientation (aOR = 1.72, 95% CI: 1.19 to 2.50) and tested for HIV in three months before COVID-19 measures (aOR = 4.74, 95% CI: 3.35 to 6.70) were associated with HIV testing during COVID-19 measures. CONCLUSIONS: Facility-based HIV testing decreased and HIVST increased among MSM during COVID-19 measures in China. MSM successfully accessed HIVST as substitute for facility-based testing, with no overall decrease in HIV testing rates.

Epidemiological characteristics of clustering outbreaks of 51 cases of COVID-19 in

Objective: To analyze the occurrence characteristics and epidemic regularity of a new epidemic situation of COVID-19, and we provide scientific basis for formulating strategies and measures for epidemic prevention and control.

A novel biparatopic hybrid antibody-ACE2 fusion that blocks SARS-CoV-2 infection: implications for therapy.

In the absence of a proven effective vaccine preventing infection by SARS-CoV-2, or a proven drug to treat COVID-19, the positive results of passive immune therapy using convalescent serum provide a strong lead. We have developed a new class of tetravalent, biparatopic therapy, 89C8-ACE2. It combines the specificity of a monoclonal antibody (89C8) that recognizes the relatively conserved N-terminal domain of the viral Spike (S) glycoprotein, and the ectodomain of ACE2, which binds to the receptor-binding domain of S. This molecule shows exceptional performance in vitro, inhibiting the interaction of recombinant S1 to ACE2 and transduction of ACE2-overexpressing cells by S-pseudotyped lentivirus with IC50s substantially below 100 pM, and with potency approximately 100-fold greater than ACE2-Fc itself. Moreover, 89C8-ACE2 was able to neutralize authentic viral infection in a standard 96-h co-incubation assay at low nanomolar concentrations, making this class of molecule a promising lead for therapeutic applications.