Seroprevalence of Toxoplasmosis among Hemato-oncology patients in Kelantan.

Toxoplasmosis is a zoonotic disease caused by Toxoplasma gondii that is prevalent in humans and animals. This study was aimed to determine the seroprevalence of T. gondii infection among hemato-oncology patients and its association with sociodemographic and behavioural characteristics. This cross-sectional study was conducted at the Hospital Universiti Sains Malaysia (USM) involving 56 blood samples from hemato-oncology patients. Anti-T. gondii IgG and IgM antibodies and IgG avidity were determined using enzyme-linked immunosorbent assays (ELISA). The association of T. gondii exposure, sociodemographic, and behavioural characteristics were assessed by a questionnaire and face-to-face interviews. Twenty-eight (50%) patients were seropositive for T. gondii antibodies, where 27 (48.21%) patients were IgG+/IgM- and one patient (1.79%) was IgG+/IgM+ with high avidity index, indicating infection of more than 20 weeks. A univariate analysis showed that age, gender, ethnicity, marital status, educational level, employment status, stem cell transplant, blood transfusion, close contact with cats, water supply, and consumption of undercooked meat were not significantly associated with Toxoplasma seropositivity (p < 0.05). Our study has demonstrated, for the first time, the serological evidence of T. gondii exposure among hemato-oncology patients in Hospital USM. Our findings indicated that latent toxoplasmosis was relatively prevalence among our patients. Therefore, serological screening tests should be considered for immunocompromised patients as well as the implementation of health education programmes to encourage a healthy lifestyle and the consumption of healthy food among them.

A novel segmentation approach for noisy medical images using intuitionistic fuzzy divergence with neighbourhood-based membership function.

Medical image segmentation demands higher segmentation accuracy especially when the images are affected by noise. This paper proposes a novel technique to segment medical images efficiently using an intuitionistic fuzzy divergence-based thresholding. A neighbourhood-based membership function is defined here. The intuitionistic fuzzy divergence-based image thresholding technique using the neighbourhood-based membership functions yield lesser degradation of segmentation performance in noisy environment. Its ability in handling noisy images has been validated. The algorithm is independent of any parameter selection. Moreover, it provides robustness to both additive and multiplicative noise. The proposed scheme has been applied on three types of medical image datasets in order to establish its novelty and generality. The performance of the proposed algorithm has been compared with other standard algorithms viz. Otsu's method, fuzzy C-means clustering, and fuzzy divergence-based thresholding with respect to (1) noise-free images and (2) ground truth images labelled by experts/clinicians. Experiments show that the proposed methodology is effective, more accurate and efficient for segmenting noisy images.

Diffusion MR imaging for cerebral abscess.

We report a case of cerebral abscess, which ruptured into the lateral ventricle. The radiological features on conventional MRI, appearance on both diffusion weighted images (DWI) and apparent diffusion coefficient (ADC) maps are described.