Networking Data and Intelligent Management in the Post-COVID19 Era: A Report on APNOMS 2021

This article presents a report on APNOMS 2021, which was held on September 8–10, 2021 in Tainan, Taiwan. The theme of APNOMS 2021 was "Networking Data and Intelligent Management in the Post-COVID19 Era.”. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

Theoretical Study on Raman Characteristic Peaks of Coronavirus Spike Protein Based on Deep Learning

COVID-19, which has lasted for a year, has caused great damage to the global economy. In order to control COVID-19 effectively, rapid detection of COVID-19 (SARS-CoV-2) is an urgent problem. Spike protein is the detection point of Raman spectroscopy to detect SARS-CoV-2. The construction of spike protein Raman characteristic peaks plays an important role in the rapid detection of SARS-CoV-2 using Raman technology. In this paper, we used Deep Neural Networks to construct the amide I and III characteristic peak model of spike proteins based on simplified exciton model, and combined with the experimental structures of seven coronaviruses (HCoV-229E, HCoV-HKUl, HCoV-NL63, HCoV-OC43, MERS-CoV, SARS-CoV, SARS-CoV-2) spike proteins, analyzed the differences of amide I and III characteristic peaks of seven coronaviruses. The results showed that seven coronaviruses could be divided into four groups according to the amide I and III characteristic peaks of spike proteins: SARS-CoV-2, SARS-CoV, MERS-CoV form a group;HCoV-HKUl, HCoV-NL63 form a group;HCoV-229E and HCoV-OC43 form a group independently. The frequency of amide I and III in the same group is relatively close,and it is difficult to distinguish spike proteins by the frequency of amide I and III ;the characteristic peaks of amide I and III in different groups are quite different, and spike proteins can be distinguished by Raman spectroscopy. The results provide a theoretical basis for the development of Raman spectroscopy for rapid detection of SARS-CoV-2. © 2022 Science Press. All rights reserved.

University Operations During a Pandemic: A Flexible Decision Analysis Toolkit

Modeling infection spread during pandemics is not new, with models using past data to tune simulation parameters for predictions. These help in understanding of the healthcare burden posed by a pandemic and responding accordingly. However, the problem of how college/university campuses should function during a pandemic is new for the following reasons: (i) social contact in colleges are structured and can be engineered for chosen objectives;(ii) the last pandemic to cause such societal disruption was more than 100 years ago, when higher education was not a critical part of society;(iii) not much was known about causes of pandemics, and hence effective ways of safe operations were not known;and (iv) today with distance learning, remote operation of an academic institution is possible. As one of the first to address this problem, our approach is unique in presenting a flexible simulation system, containing a suite of model libraries, one for each major component. The system integrates agent-based modeling and the stochastic network approach, and models the interactions among individual entities (e.g., students, instructors, classrooms, residences) in great detail. For each decision to be made, the system can be used to predict the impact of various choices, and thus enables the administrator to make informed decisions. Although current approaches are good for infection modeling, they lack accuracy in social contact modeling. Our agent-based modeling approach, combinedwith ideas from Network Science, presents a novel approach to contact modeling. A detailed case study of the University of Minnesota's Sunrise Plan is presented. For each decision made, its impact was assessed, and results were used to get a measure of confidence. We believe that this flexible tool can be a valuable asset for various kinds of organizations to assess their infection risks in pandemic-time operations, including middle and high schools, factories, warehouses, and small/medium-sized businesses.

DPP9: Comprehensive in silico analyses of LoF variants and associated gene signatures in HCC

Background: Dipeptidyl peptidase (DPP) 9, DPP8,DPP4 and fibroblast activation protein (FAP) are the four enzymatically active members of the S9b protease family. Associations of DPP9 or DPP8 with human liver cancer have not been examined. Genome-wide association studies have found that intronic single-nucleotide polymorphisms (SNPs) in DPP9 are associated with severeCOVID-19 and lung fibrosis. However, exonic SNPs in DPP9 and DPP9 loss of function (LoF) variants have not been explored. Methods: Large-scale human genetic databases including The Cancer Genome Atlas (TCGA) were interrogated. Results: We found that DPP8 and DPP9 are intolerant to LoF variants, which suggests that these enzymes, but not DPP4 and FAP, are crucial for life in humans. Uterine corpus endometrial carcinoma (UCEC) was the most commonly diagnosed cancer in patients with DPP9 LoF variants, and low DPP9 expression was associated with poor survival in UCEC. The two DPP9 intronic SNPs that have been associated with lung fibrosis and COVID-19 were not associated with liver fibrosis or cancer. All four enzymes were overexpressed in liver tumours. Increased DPP9 expression was associated with extreme obesity in HCC patients. There was no association between DPP9 expression intensity and HCC survival. However, high expression of all four DPP4- like genes was significantly associated with poor survival in HCC. Moreover, high expression of genes that positively correlated with overexpression of DPP4, DPP8 and DPP9 was associated with very poor survival in HCC. Enriched pathways analysis of these in-common correlated genes featured Toll-like receptor and SUMOylation pathways. Conclusion: This comprehensive data mining suggests that DPP9 is crucial for human survival and the DPP4 protease family is important in cancer pathogenesis.

Considerations for the Management of Oncology Patients During the COVID-19 Pandemic

Worldwide incidence and mortality due to the coronavirus disease 2019 (COVID-19) pandemic is greatest in the United States, with the initial epicenter in New York In Nassau County, New York, where we practice, our institution has had more than 2500 cases and has discharged from the hospital more than 1000 patients. As many academic and private institutions have swiftly shifted their clinical and research priorities to address the pandemic, data are emerging regarding both the impact of malignancy on COVID-19 outcomes as well as the challenges faced in assuring that cancer care remains unimpeded. Of concern, recent studies of cancer patients primarily in China and Italy have suggested that advanced malignancy is associated with increased susceptibility to severe COVID-19 infection. At present, more than 500 clinical trials are underway investigating the pathogenesis and treatment of COVID-19, including expanded use of oncology drugs, such as small molecular inhibitors of cytokine pathways. Here, we begin by reviewing the latest understanding of COVID-19 pathophysiology and then focus our attention on the impact of this virus on hematologic and oncologic practice. Finally, we highlight ongoing investigational treatment approaches that are so relevant to the care of oncology patients during this extraordinary pandemic.