ABSTRACT
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.
ABSTRACT
Background: With the worldwide spread of COVID-19, people’s health and social order have been exposed to enormous risks. After encountering patients who test positive again after discharge, our study analyzed the pathogenesis to further assess the risk and possibility of virus reactivation. Methods: : A separate microarray was acquired from the Integrated Gene Expression System (GEO), and its samples were divided into two groups: a “convalescent-RTP” group consisting of recovery and “retesting-positive” (RTP) patients (group CR) and a “health-RTP” group consisting of healthy control and RTP patients (group HR). The enrichment analysis was performed with R software, obtaining the gene ontology (GO) and Kyoto pluripotent stem cells (KEGG) of the genes and genomes. Subsequently, the protein–protein interaction (PPI) networks of each group were established and the hub genes were discovered using the cytoHubba plug-in. Results: : In this study, 20 differentially expressed genes were identified, and 6622 genes were identified in the group CR, consisting of 5003 up-regulated and 1619 down-regulated genes. Meanwhile, 7335 genes were screened in the group HR, including 4323 up-regulated and 3012 down-regulated ones. The GO and KEGG analysis of the two groups revealed significant enrichment of these differentially expressed genes in pathways associated with immune response and apoptosis. In the PPI network constructed, 10 hub genes in group CR were identified, including TP53BP1, SNRPD1, SNRPD2, SF3B1, SNRNP200, MRPS16, MRPS9, CALM1, PPP2R1A, YWHAZ. Similarly, TP53BP1, RPS15, EFTUD2, MRPL16, MRPL17, MRPS14, RPL35A, MRPL32, MRPS6, POLR2G were selected as hub genes. Conclusions: : Using the messenger ribonucleic acid (mRNA) expression data from GSE166253, we explore the pathogenesis of retesting positive in COVID-19 from the immune mechanism and molecular level. We found TP53BP1, SNRPD1 and SNRPD2 as hub genes in RTP patients. Hence, their regulatory pathway is vital to the management and prognostic prediction of RTP patients, rendering the further study of these hub genes necessary.