Copro-molecular diagnosis of the Toxoplasmatinae subfamily in dog and cat populations in northern Iran.

OBJECTIVES: The oocysts of the Toxoplasmatinae subfamily (Neospora caninum, Hammondia hammondi and H. heydorni, and Besnoitia besnoiti) are morphologically similar to Toxoplasma gondii, and indistinguishable from each other. This study investigated the prevalence of the Toxoplasmatinae subfamily in dog and cat fecal samples using a nested polymerase chain reaction method. METHODS: Overall, 200 fecal samples from domestic dogs (n=120) and cats (n=80) were collected from 15 farms in northern Iran. The samples were homogenized in 2.5% potassium dichromate solution and subsequently concentrated with sucrose solution. DNA was extracted from samples using a genomic DNA kit. Specific primers and the 18S rDNA gene were used to screen and detect all Toxoplasmatinae oocysts. RESULTS: Overall, 2.5% (3 of 120) and 22.5% (18 of 80) of the fecal samples collected from dogs and cats were infected with Toxoplasmatinae. In dogs, 2 samples were positive for N. caninum and 1 sample was positive for T. gondii. In cats, all 18 positive samples belonged to T. gondii. No contamination with H. heydorni was observed in dog fecal samples or H. hammondi and B. besnoiti in cat fecal samples. A phylogenetic analysis revealed that the T. gondii (cat) and N. caninum (dog) found had similarities with parasites reported from other regions of the world. CONCLUSIONS: This is the first study to provide data on the epidemiology of Toxoplasmatinae oocysts in Iran. The findings suggest that public-health monitoring for the effective control of feces from cats and dogs and improved pet hygiene habits are needed.

A new prospect in magnetic nanoparticle-based cancer therapy: Taking credit from mathematical tissue-mimicking phantom brain models.

Distribution patterns/performance of magnetic nanoparticles (MNPs) was visualized by computer simulation and experimental validation on agarose gel tissue-mimicking phantom (AGTMP) models. The geometry of a complex three-dimensional mathematical phantom model of a cancer tumor was examined by tomography imaging. The capability of mathematical model to predict distribution patterns/performance in AGTMP model was captured. The temperature profile vs. hyperthermia duration was obtained by solving bio-heat equations for four different MNPs distribution patterns and correlated with cell death rate. The outcomes indicated that bio-heat model was able to predict temperature profile throughout the tissue model with a reasonable precision, to be applied for complex tissue geometries. The simulation results on the cancer tumor model shed light on the effectiveness of the studied parameters.