An index for multidimensional assessment of swine health

Building composite indices in the age of big data - Application to honey bee exposure to infectious and parasitic agents

Pollinator insects play a crucial role in maintaining biodiversity and agricultural production worldwide. Yet they are subject to various infectious and parasitic agents (IPAs). To better assess their exposure to IPAs, discriminative and quantitative molecular methods have been developed. These tools produce large datasets that need to be summarised so as to be interpreted. In this paper, we described the calculation of three types of composite indices (numerical, ordinal, nominal) to characterize the honey bee exposure to IPAs in 128 European sites. Our summarizing methods are based on component-based factorial analyses. The indices summarised the dataset of eight IPAs quantified at two sampling times, into synthetic values providing different yet complementary information. Because our dataset included two sampling times, we used Multiple Factor Analysis (MFA) to synthetize the information. More precisely, the numerical and ordinal indices were generated from the first component of MFA, whereas the nominal index used the first main components of MFA combined with a clustering analysis (Hierarchical Clustering on components). The numerical index was easy to calculate and to be used in further statistical analyses. However, it contained only about 20% of the original information. Containing the same amount of original information, the ordinal index was much easier to interpret. These two indices summarised information in a unidimensional manner. Instead, the nominal index summarised information in a multidimensional manner, which retained much more information (94%). In the practical example, the three indices showed an antagonistic relationship between N. ceranae and DWV-B. These indices represented a toolbox where scientists could pick one composite index according to the aim pursued. Indices could be used in further statistical analyses but could also be used by policy makers and public instances to characterize a given sanitary situation at a site level for instance.

Gender gap reduction and the one health benefits

Several factors including gender, age groups, cultures and social conditions may affect significantly the risk of diseases and their clinical evolution. Unfortunately, little research has been carried out on these aspects and, consequently few guidelines or interventions have been implemented. In particular, gender is considered a main determinant of inequalities in living conditions, access to health services and, thus, in health protection. Focusing on the gender gap, we propose an ecological approach to find relationships between quantitative indicators of the gender gap dimension, the environmental performance index and the life expectancy at birth as summary of human health index in 155 countries. We speculated on the consequences of wider gender gaps to the population and environmental health. We further explore these relationships considering gender gap and environmental aspect subindexes, to identify determinants that should be addressed to maximize the One Health effect. We found that the gender gap in educational attainment followed by the political empowerment were strongly correlated with life expectancy, environmental health, and ecosystem vitality. Addressing gender issues, particularly the education attainment and political empowerment, can provide positive impact beyond the social dimension and the population health, and gender should be component of the One Health approach. We recommend gender targeted interventions that integrate these aspects into One Health national policies.

Development of a multiplex qRT-PCR assay for detection of African swine fever virus, classical swine fever virus and porcine reproductive and respiratory syndrome virus

BACKGROUND

African swine fever virus (ASFV), classical swine fever virus (CSFV), and porcine reproductive and respiratory syndrome virus (PRRSV) are still prevalent in many regions of China. Co-infections make it difficult to distinguish their clinical symptoms and pathological changes. Therefore, a rapid and specific method is needed for the differential detection of these pathogens.

OBJECTIVES

The aim of this study was to develop a multiplex real-time quantitative reverse transcription polymerase chain reaction (multiplex qRT-PCR) for the simultaneous differential detection of ASFV, CSFV, and PRRSV.

METHODS

Three pairs of primers and TaqMan probes targeting the ASFV p72 gene, CSFV 5' untranslated region, and PRRSV ORF7 gene were designed. After optimizing the reaction conditions, including the annealing temperature, primer concentration, and probe concentration, multiplex qRT-PCR for simultaneous and differential detection of ASFV, CSFV, and PRRSV was developed. Subsequently, 1,143 clinical samples were detected to verify the practicality of the assay.

RESULTS

The multiplex qRT-PCR assay could specifically and simultaneously detect the ASFV, CSFV, and PRRSV with a detection limit of 1.78 × 10⁰ copies for the ASFV, CSFV, and PRRSV, but could not amplify the other major porcine viruses, such as pseudorabies virus, porcine circovirus type 1 (PCV1), PCV2, PCV3, foot-and-mouth disease virus, porcine parvovirus, atypical porcine pestivirus, and Senecavirus A. The assay had good repeatability with coefficients of variation of intra- and inter-assay of less than 1.2%. Finally, the assay was used to detect 1,143 clinical samples to evaluate its practicality in the field. The positive rates of ASFV, CSFV, and PRRSV were 25.63%, 9.36%, and 17.50%, respectively. The co-infection rates of ASFV+CSFV, ASFV+PRRSV, CSFV+PRRSV, and ASFV+CSFV+PRRSV were 2.45%, 2.36%, 1.57%, and 0.17%, respectively.

CONCLUSIONS

The multiplex qRT-PCR developed in this study could provide a rapid, sensitive, specific diagnostic tool for the simultaneous and differential detection of ASFV, CSFV, and PRRSV.