Detection of possible variant forms of hemoglobin in HbA1c measurements by chromatography liquid ion exchange high resolution (HPLC)

Background: The glycosylated hemoglobin (HbA1c) is used to help monitor the degree of a diabetic’s hyperglycemia. Security and accuracy of the methods used in its detection are affected by variants forms of Hb or elevations in levels of Fetal Hb (HbF). These interference are the result of a change in the haemoglobin total net charge of the variant due of a substitution of one amino acid in the remaining amino terminal of the beta chain. International Standardization for HbA1c values (NGSP) not include interference assessment as part of the certification program. Therefore, the effect of each variant or the lifting of the HbF on HbA1c result should be examined in each sample depending on the detected variant and the method used for the detection of the same. The objectives were: to describe the possible variants of Hb and their interference in HbA1c measurement by our method, after the implementation of a computer program for their detection. To identify some variants detected by chromatography liquid ion exchange high resolution (HPLC) with DNA molecular sequencing.

Assessment of Severe Apnoea through Voice Analysis, Automatic Speech, and Speaker Recognition Techniques

This study is part of an ongoing collaborative effort between the medical and the signal processing communities to promote research on applying standard Automatic Speech Recognition (ASR) techniques for the automatic diagnosis of patients with severe obstructive sleep apnoea (OSA). Early detection of severe apnoea cases is important so that patients can receive early treatment. Effective ASR-based detection could dramatically cut medical testing time. Working with a carefully designed speech database of healthy and apnoea subjects, we describe an acoustic search for distinctive apnoea voice characteristics. We also study abnormal nasalization in OSA patients by modelling vowels in nasal and nonnasal phonetic contexts using Gaussian Mixture Model (GMM) pattern recognition on speech spectra. Finally, we present experimental findings regarding the discriminative power of GMMs applied to severe apnoea detection. We have achieved an 81% correct classification rate, which is very promising and underpins the interest in this line of inquiry.