ABSTRACT
BACKGROUND: A One Health approach has been increasingly mainstreamed by the international community, as it provides for holistic thinking in recognizing the close links and inter-dependence of the health of humans, animals and the environment. However, the dearth of real-world evidence has hampered application of a One Health approach in shaping policies and practice. This study proposes the development of a potential evaluation tool for One Health performance, in order to contribute to the scientific measurement of One Health approach and the identification of gaps where One Health capacity building is most urgently needed. METHODS: We describe five steps towards a global One Health index (GOHI), including (i) framework formulation; (ii) indicator selection; (iii) database building; (iv) weight determination; and (v) GOHI scores calculation. A cell-like framework for GOHI is proposed, which comprises an external drivers index (EDI), an intrinsic drivers index (IDI) and a core drivers index (CDI). We construct the indicator scheme for GOHI based on this framework after multiple rounds of panel discussions with our expert advisory committee. A fuzzy analytical hierarchy process is adopted to determine the weights for each of the indicators. RESULTS: The weighted indicator scheme of GOHI comprises three first-level indicators, 13 second-level indicators, and 57 third-level indicators. According to the pilot analysis based on the data from more than 200 countries/territories the GOHI scores overall are far from ideal (the highest score of 65.0 out of a maximum score of 100), and we found considerable variations among different countries/territories (31.8-65.0). The results from the pilot analysis are consistent with the results from a literature review, which suggests that a GOHI as a potential tool for the assessment of One Health performance might be feasible. CONCLUSIONS: GOHI-subject to rigorous validation-would represent the world's first evaluation tool that constructs the conceptual framework from a holistic perspective of One Health. Future application of GOHI might promote a common understanding of a strong One Health approach and provide reference for promoting effective measures to strengthen One Health capacity building. With further adaptations under various scenarios, GOHI, along with its technical protocols and databases, will be updated regularly to address current technical limitations, and capture new knowledge.
Subject(s)
One Health , Forecasting , Global HealthABSTRACT
BACKGROUND: COVID-19 pandemic continues, clarifying signatures in clinical characters and antibody responses between severe and non-severe COVID-19 cases would benefit the prognosis and treatment. METHODS: In this study, 119 serum samples from 37 severe or non-severe COVID-19 patients from the First People's Hospital of Yueyang were collected between January 25 and February 18 2020. The clinical features, antibody responses targeting SARS-CoV-2 spike protein (S) and its different domains, SARS-CoV-2-specific Ig isotypes, IgG subclasses, ACE2 competitive antibodies, binding titers with FcγIIa and FcγIIb receptors, and 14 cytokines were comprehensively investigated. The differences between severe and non-severe groups were analyzed using Mann-Whitney U test or Fisher's exact test. RESULTS: Severe group including 9 patients represented lower lymphocyte count, higher neutrophil count, higher level of LDH, total bile acid (TBA) (P < 1 × 10-4), r-glutaminase (P = 0.011), adenosine deaminase (P < 1 × 10-4), procalcitonin (P = 0.004), C-reactive protein (P < 1 × 10-4) and D-dimer (P = 0.049) compared to non-severe group (28 patients). Significantly, higher-level Igs targeting S, different S domains (RBD, RBM, NTD, and CTD), FcγRIIa and FcγRIIb binding capability were observed in a severe group than that of a non-severe group, of which IgG1 and IgG3 were the main IgG subclasses. RBD-IgG were strongly correlated with S-IgG both in severe and non-severe group. Additionally, CTD-IgG was strongly correlated with S-IgG in a non-severe group. Positive RBD-ACE2 binding inhibition was strongly associated with high titers of antibody (S-IgG1, S-IgG3, NTD-IgG, RBD-IgA, NTD-IgA, and CTD-IgA) especially RBD-IgG and CTD-IgG in the severe group, while in the non-severe group, S-IgG3, RBD-IgG, NTD-IgG, and NTD-IgM were correlated with ACE2 blocking rate. S-IgG1, NTD-IgM and S-IgM were negatively associated with illness day in a severe group, while S-IgG3, RBD-IgA, CTD-IgA in the severe group (r = 0.363, P = 0.011) and S-IgG1, NTD-IgA, CTD-IgA in the non-severe group were positively associated with illness day. Moreover, GRO-α, IL-6, IL-8, IP-10, MCP-1, MCP-3, MIG, and BAFF were also significantly elevated in the severe group. CONCLUSION: Antibody detection provides important clinical information in the COVID-19 process. The different signatures in Ig isotypes, IgG subclasses, antibody specificity between the COVID-19 severe and non-severe group will contribute to future therapeutic and preventive measures development.