Sarcocystis sp. are coccidian protozoans belonging to the Apicomplexa phylum. As other members of this phylum, they are obligate intracellular parasites having a complex cellular machinery for the invasion of host cells. Sarcocystis spp. display dixenous life cycles, involving a predator and a prey as definitive and intermediate hosts, respectively. Specifically, these parasites develop sarcocysts in the tissues of their intermediate hosts, ranging in size from microscopic to visible to the naked eye, depending on the species. When definitive hosts consume sarcocysts, infective forms are produced in the digestive system and discharged into the environment via feces. Consumption of oocyst-contaminated water and pasture by the intermediate host completes the parasitic cycle. More than 200 Sarcocystis spp. have been described infecting wildlife, domestic animals, and humans, some of which are of economic or public health importance. Interestingly, Old World camelids (dromedary, domestic Bactrian camel, and wild Bactrian camel) and New World or South American camelids (llama, alpaca, guanaco, and vicuña) can each be infected by two different Sarcocystis species: Old World camelids by S. cameli (producing micro- and macroscopic cysts) and S. ippeni (microscopic cysts); and South American camelids by S. aucheniae (macroscopic cysts) and S. masoni (microscopic cysts). Large numbers of Old and New World camelids are bred for meat production, but the finding of macroscopic sarcocysts in carcasses significantly hampers meat commercialization. This review tries to compile the information that is currently accessible regarding the biology, epidemiology, phylogeny, and diagnosis of Sarcocystis spp. infecting Old and New World camelids. In addition, knowledge gaps will be identified to encourage research that will lead to the control of these parasites.

Enterobacteriaceae can contaminate meat during various stages of processing, including slaughter, evisceration, and butchering, causing foodborne illnesses. The purpose of the study was to investigate the prevalence of Enterobacteriaceae associated with carcasses obtained from slaughterhouses and raw cut meat collected from butcher shops. A total of 120 samples of camels and cattle were identified using biochemistry and PCR testing. Total viable count (TVC) was range from 4.91 to 5.37 Log10 CFU/g in slaughterhouses and butcher shops. E. coli was predominating with 84 (70%) among all samples, where the camels had the highest with 100% and sheep the lowest with 30%. Salmonella spp. was confirmed in 40% of camel samples, 47.5% of cattle samples, and 32.5% in sheep samples. Twenty-five Enterobacteriaceae genera were confirmed using PCR. Where sheep’s samples had the highest occurrence of Enterobacteriaceae with 15 different genera followed by camels and cattle samples with 14 different genera. The prevalence of Enterobacteriaceae among camel, cattle, and sheep carcasses raises significant concerns regarding food safety. Adherence to good hygiene practices throughout animal slaughtering is crucial to minimize the risk of infection and transmission and ensure food safety.

Ticks and tick-borne pathogens (TBPs) are major constraints to camel health and production, yet epidemiological data on their diversity and impact on dromedary camels are limited. We sur-veyed the diversity of ticks and TBPs associated with camels and co-grazing sheep at 12 sites in Marsabit County, northern Kenya. We screened blood and ticks (858 pools) collected from 296 camels and 77 sheep for bacterial and protozoan TBPs by high-resolution melting analysis and sequencing of PCR products. Hyalomma (75.7%), Amblyomma (17.6%) and Rhipicephalus (6.7%) spp. ticks were morphologically identified and confirmed by molecular analyses. We detected TBP DNA in 80.1% of blood samples from 296 healthy camels. “Candidatus Anaplasma camelii”, “Candidatus Ehrlichia regneryi” and Coxiella burnetii were detected in both camels and associ-ated ticks, and Ehrlichia chaffeensis, Rickettsia africae, Rickettsia aeschlimannii and Coxiella endo-symbionts were detected in camel ticks. We also detected Ehrlichia ruminantium, responsible for heartwater disease in ruminants, in Amblyomma ticks infesting camels and sheep and in sheep blood, indicating its endemicity in Marsabit. Our findings also suggest that camels and/or the ticks infesting them are reservoirs of zoonotic Q fever (C. burnetii), ehrlichiosis (E. chaffeensis), and rickettsiosis (R. africae), which pose a public health threat to pastoralist communities.

The finding of important explanatory variables for the location parameter and the scale parameter of the generalized extreme value (GEV) distribution, when the latter is used for the modelling of annual streamflow maxima, is known to have reduced the uncertainties in inferences, as estimated through regional flood frequency analysis frameworks. However, important explanatory variables have not been found for the GEV shape parameter, despite its critical significance, which stems from the fact that it determines the behaviour of the upper tail of the distribution. Here we examine the nature of the shape parameter by revealing its relationships with basin attributes. We use a dataset that comprises information about daily streamflow and forcing, climatic indices, topographic, land cover, soil and geological characteristics of 591 basins with minimal human influence in the contiguous United States. We propose a framework that uses random forests and linear models to find (a) important predictor variables of the shape parameter and (b) an interpretable model with high predictive performance. The process of study comprises of assessing the predictive performance of the models, selecting a parsimonious predicting model and interpreting the results in an ad-hoc manner. The findings suggest that the shape parameter mostly depends on climatic indices, while the selected prediction model results in more than 20% higher accuracy in terms of RMSE compared to a naïve approach. The implications are important, since incorporating the regression model into regional flood frequency analysis frameworks can considerably reduce the predictive uncertainties.