Wildlife shares grazing areas with herders in the eastern Qinghai-Tibet Plateau, and humans can be infected by zoonotic nematodes through direct contact with animals or contaminated water. In this study, fecal samples (n = 296) from wild carnivores were collected to explore the infection rate and molecular genetic characteristics of nematodes by stratified random sampling in the survey areas. Host species and the nematodes they carried were then identified using 16S rRNA and 18S rRNA gene sequencing, respectively. Statistical analysis, neutrality tests, genetic diversity analysis and Bayesian inferred trees were performed to complete the study. In total, 10 species of nematodes were detected in 240 feces from six species of carnivores identified (including dominant Vulpes ferrilata and Vulpes vulpes), namely Uncinaria stenocephala, Toxascaris sp., Crenosoma vulpis, Parapharyngodon bainae, Oesophagostomum muntiacum, Aspiculuris tetraptera, Mastophorus muris, Nematodirus spathiger, Muellerius capillaris, and Molineus patens. Among these nematodes, U. stenocephala (35.83%, 86/240) and Toxascaris sp. (14.58%, 35/240) were detected at higher rates than the other nematodes (χ2 = 516.909, p < 0.05). Of 17 and 18 haplotypes were found based on the ITS1 gene for U. stenocephala and nad1 gene for Toxascaris sp., respectively. For the first time, using molecular methods, we report the infection of V. ferrilata by U. stenocephala, a potential zoonotic parasite, and suggest Toxascaris sp. may be a newly discovered nematode that lives within the fox intestine.
Background Cryptosporidiosis is a zoonotic intestinal infectious disease caused by Cryptosporidium spp., and its transmission is highly influenced by climate factors. In the present study, the potential spatial distribution of Cryptosporidium in China was predicted based on ecological niche models for cryptosporidiosis epidemic risk warning and prevention and control. Methods The applicability of existing Cryptosporidium presence points in ENM analysis was investigated based on data from monitoring sites in 2011–2019. Cryptosporidium occurrence data for China and neighboring countries were extracted and used to construct the ENMs, namely Maxent, Bioclim, Domain, and Garp. Models were evaluated based on Receiver Operating Characteristic curve, Kappa, and True Skill Statistic coefficients. The best model was constructed using Cryptosporidium data and climate variables during 1986‒2010, and used to analyze the effects of climate factors on Cryptosporidium distribution. The climate variables for the period 2011‒2100 were projected to the simulation results to predict the ecological adaptability and potential distribution of Cryptosporidium in future in China. Results The Maxent model (AUC = 0.95, maximum Kappa = 0.91, maximum TSS = 1.00) fit better than the other three models and was thus considered the best ENM for predicting Cryptosporidium habitat suitability. The major suitable habitats for human-derived Cryptosporidium in China were located in some high-population density areas, especially in the middle and lower reaches of the Yangtze River, the lower reaches of the Yellow River, and the Huai and the Pearl River Basins (cloglog value of habitat suitability > 0.9). Under future climate change, non-suitable habitats for Cryptosporidium will shrink, while highly suitable habitats will expand significantly (χ2 = 76.641, P < 0.01; χ2 = 86.836, P < 0.01), and the main changes will likely be concentrated in the northeastern, southwestern, and northwestern regions. Conclusions The Maxent model is applicable in prediction of Cryptosporidium habitat suitability and can achieve excellent simulation results. These results suggest a current high risk of transmission and significant pressure for cryptosporidiosis prevention and control in China. Against a future climate change background, Cryptosporidium may gain more suitable habitats within China. Constructing a national surveillance network could facilitate further elucidation of the epidemiological trends and transmission patterns of cryptosporidiosis, and mitigate the associated epidemic and outbreak risks. Graphical Abstract
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