ABSTRACT
Migration plays a crucial role in epidemic spreading, and its dynamic can be studied by metapopulation model. Instead of the uniform mixing hypothesis, we adopt networked metapopulation to build the model of the epidemic spreading and the individuals' migration. In these populations, individuals are connected by contact network and populations are coupled by individuals migration. With the network mean-field and the gravity law of migration, we establish the N-seat intertwined SIR model and obtain its basic reproduction number â 0 . Meanwhile, we devise a non-markov Node-Search algorithm for model statistical simulations. Through the static network migration ansatz and â 0 formula, we discover that migration will not directly increase the epidemic replication capacity. But when â 0 > 1 , the migration will make the susceptive population evolve from metastable state (disease-free equilibrium) to stable state (endemic equilibrium), and then increase the influence area of epidemic. Re-evoluting the epidemic outbreak in Wuhan, top 94 cities empirical data validate the above mechanism. In addition, we estimate that the positive anti-epidemic measures taken by the Chinese government may have reduced 4 million cases at least during the first wave of COVID-19, which means those measures, such as the epidemiological investigation, nucleic acid detection in medium-high risk areas and isolation of confirmed cases, also play a significant role in preventing epidemic spreading after travel restriction between cities.
ABSTRACT
BACKGROUND: Traditional Chinese medicine (TCM) is regarded as a large database containing hundreds to thousands of chemical constituents that can be further developed as clinical drugs, such as artemisinin in Artemisia annua. However, effectively exploring novel candidates is still a challenge faced by researchers. PURPOSE: In this work, an integrated strategy combining chemical profiling, molecular networking, chemical isolation, and activity evaluation (CMCA strategy) was proposed and applied to systematically characterize and screen novel candidates, and Forsythiae fructus (FF) was used as an example. STUDY DESIGN: It contained four parts. First, the chemical compounds in FF were detected by ultra-high-performance liquid chromatography-mass spectrometry (UPLC/Q-TOF MS) with data-dependent acquisition, and further, the targeted compounds were screened out based on an in-house database. In the meantime, the representative MS/MS fragmentation behaviors of different chemical structure types were summarized. Second, homologous constituents were grouped and organized based on feature-guided molecular networking, and the nontargeted components with homologous mass fragmentation behaviors were characterized. Third, the novel compounds were isolated and unambiguously identified by nuclear magnetic resonance (NMR). Finally, the anti-angiotensin-converting enzyme 2 (ACE2) activities of isolated chemical constituents were further evaluated by in vitro experiments. RESULTS: A total of 278 compounds were profiled in FF, including 151 targeted compounds and 127 nontargeted compounds. Among them, 16 were unambitiously identified by comparison with reference standards. Moreover, 25 were classified into potential novel compounds. Two novel compounds were unambiguously identified by using conventional chromatographic methods, and they were named phillyrigeninside D (peak 254) and forsythenside O (peak 155). Furthermore, the ACE2 activity of the compounds in FF was evaluated by modern pharmacological methods, and among them, suspensaside A was confirmed to present obvious anti-ACE2 activity. CONCLUSION: Our work provides meaningful information for revealing potential FF candidates for the treatment of COVID-19, along with new insight for exploring novel candidates from complex systems.