Drug-related problems in a general paediatric ward of a tertiary care hospital in Malaysia

International Journal of Clinical Pharmacy - Background Drug-related problems are relatively common among hospitalised patients and may be detrimental to patients and even increase healthcare...     

Glycan-Based Near-infrared Fluorescent (NIRF) Imaging of Gastrointestinal Tumors: a Preclinical Proof-of-Concept In Vivo Study

Molecular Imaging and Biology - Aberrantly expressed glycans in cancer are of particular interest for tumor targeting. This proof-of-concept in vivo study aims to validate the use of aberrant Lewis...     

First molecular characterization of Giardia duodenalis from goats in Malaysia

In the present study, 310 faecal samples from goats from eight different farms in Malaysia were tested for the presence of Giardia using a PCR-coupled approach. The nested PCR for SSU amplified products of the expected size (～200 bp) from 21 of 310 (6.8%) samples. Sixteen of these 21 products could be sequenced successfully and represented six distinct sequence types. Phylogenetic analysis of the SSU sequence data using Bayesian Inference (BI) identified Giardia assemblages A, B and E. The identification of the ‘zoonotic’ assemblages A and B suggests that Giardia-infected goats represent a possible reservoir for human giardiasis in Malaysia.     

Computer-Based Identification of Type 2 Diabetic Subjects with and Without Neuropathy Using Dynamic Planter Pressure and Principal Component Analysis

Diabetes is a chronic disease that is characterized by an increased blood glucose level due to insulin resistance. Type 2 diabetes is common in middle aged and old people. In this work, we present a technique to analyze dynamic foot pressures images and classify them into normal, diabetes type 2 with and without neuropathy classes. Plantar pressure images were obtained using the F-Scan (Tekscan, USA) in-shoe measurement system. We used Principal Component Analysis (PCA) and extracted the eigenvalues from different regions of the foot image. The features extracted from region 1 of the foot pressure image, which were found to be clinically significant, were fed into the Fuzzy classifier (Sugeno model) for automatic classification. Our results show that the proposed method is able to identify the unknown class with an accuracy of 93.7%, sensitivity of 100%, and specificity of 83.3%. Moreover, in this work, we have proposed an integrated index using the eigenvalues to differentiate the normal subjects from diabetes with and without neuropathy subjects using just one number. This index will help the clinicians in easy and objective daily screening, and it can also be used as an adjunct tool to cross check their diagnosis.