Longitudinal study of Chinese patients with pernicious anaemia.

BACKGROUND: The survival of whites who have been treated for pernicious anaemia (PA) is unaffected, apart from incurring a greater risk of gastric cancers. The long term outcome of PA in Chinese is unknown. METHODS: A hospital based prospective longitudinal study of Chinese PA patients was conducted. Patients with known cancers were excluded. RESULTS: From 1994 to 2007, 199 intrinsic factor antibody (IFA) positive and 168 IFA negative patients were recruited. Both cohorts had similar baseline characteristics, except the IFA positive patients had more severe haematological findings and more thyrogastric immune features; also more IFA negative patients had type 2 diabetes mellitus and gastrointestinal (GI) disease or GI surgery. Both cohorts had a good haematological response but an unsatisfactory neurological response to treatment. Hypothyroidism developed in patients of both cohorts during follow-up. 24 IFA positive patients and 7 IFA negative patients developed cancers (p = 0.007) during follow-up. 20% of all cancers were gastric carcinoma. Mean survival of both cohorts was similar. Mean survival of IFA positive patients with and without cancers was 64 and 129 months, respectively (p<0.001), and that of IFA negative patients 36 and 126 months, respectively (p<0.001). Death rates were 31% in the IFA positive cohort and 21% in the IFA negative cohort (p = 0.028). Cancer related death rates of IFA positive and IFA negative cohorts were 37% and 14%, respectively (p = 0.014). CONCLUSION: The survival period of Chinese with PA who have received treatment is good, but there is an increased risk of gastric cancers. IFA positive patients have a higher risk of developing all types of cancers and cancer related deaths than IFA negative patients.

Automatic correction of artifact from single-trial event-related potentials by blind source separation using second order statistics only.

Event-related potentials (ERP) are in general masked by various kinds of artifacts. To attenuate the effects of artifacts, various schemes have been introduced, such as epoch rejection, electro-oculogram (EOG) regression and independent component analysis (ICA). However, none of the existing techniques can automatically remove various kinds of artifacts from a single ERP epoch. EOG regression cannot handle artifacts other than ocular ones. ICA incorporating higher order statistics (HOS) normally requires data with large number of time samples in order that the solution is robust. In this paper we blindly separate the multi-channel ERP into source components by estimating the correlation matrices of the data. Since only second order statistics (SOS) is involved, the process performs well at the single epoch level. Automatic artifact identification is performed in the source domain by introducing objective criteria for various artifacts. Criteria are based on time domain signal amplitude for blink and spurious peak artifact, scalp distribution of signal power for eye movement artifact and power distribution of frequency components for muscle artifact. The correction procedure can be completed by removing the identified artifactual sources from the raw multi-channel ERP.

Instantaneous frequency estimate of nonstationary phonocardiograph signals using hilbert spectrum.

A method for analyzing the nonlinear and non-stationary processes and investigating the instantaneous frequency of the practical medical signals is presented. The aim of this contribution is to explore the role that both empirical mode decomposition and Hilbert transform can be applied to play in phonocardiograph (PCG) signals. Hilbert transform is used to each intrinsic mode function to obtain the global time-frequency distribution of the underlying signal with a point of view of instantaneous frequency. Two kinds of clinical phonocardiograph signals with normal and abnormal cardiac functions were analyzed by using the proposed approach. The instantaneous frequency distributions of the PCG signals were also compared with the results by using the Morlet wavelet transform. Both simulation and experimental results were presented and discussed to demonstrate the power and effectiveness of the proposed approach.

An Improved Method for Discriminating ECG Signals using Typical Nonlinear Dynamic Parameters and Recurrence Quantification Analysis in Cardiac Disease Therapy.

The discrimination of ECG signals using nonlinear dynamic parameters is of crucial importance in the cardiac disease therapy and chaos control for arrhythmia defibrillation in the cardiac system. However, the discrimination results of previous studies using features such as maximal Lyapunov exponent (λmax) and correlation dimension (D2) alone are somewhat limited in recognition rate. In this paper, improved methods for computing λmaxand D2are purposed. Another parameter from recurrence quantification analysis is incorporated to the new multi-feature Bayesian classifier with λmaxand D2so as to improve the discrimination power. Experimental results have verified the prediction using Fisher discriminant that the maximal vertical line length (Vmax) from recurrence quantification analysis is the best to distinguish different ECG classes. Experimental results using the MIT-BIH Arrhythmia Database show improved and excellent overall accuracy (96.3%), average sensitivity (96.3%) and average specificity (98.15%) for discriminating sinus, premature ventricular contraction and ventricular flutter signals.

Free radical EPR spectroscopy analysis using blind source separation.

In this paper, we propose a novel approach for electron paramagnetic resonance (EPR) mixture spectra analysis based on blind source separation (BSS) technique. EPR spectrum of a free radical is often superimposed by overlapping spectra of other species. It is important and challenging to accurately identify and quantify the 'pure' spectra from such mixtures. In this study, an automated BSS method implementing independent component analysis is used to extract the components from mixed EPR spectra that contain overlapping components of different paramagnetic centers. To apply this method, there is no requirement to know the component spectra or the number of components in advance. The method is applied to analyze free radical EPR spectra which are collected from standard chemical system, cultured cell suspense, and ex vivo rat kidneys by spin trapping EPR technique. Results show that the BSS method proposed here is capable of identifying the component EPR spectra from mixtures with unknown compositions. The BSS technique can offer powerful aids in resolving spectral overlapping problems in general EPR spectroscopy analysis.

In vitro calibration and validation of a digital subtraction radiography system using scanned images.

OBJECTIVES: To calibrate and validate a digital subtraction radiography system using scanned images for quantification of alveolar bone changes by means of computer-assisted densitometric image analysis (CADIA) in vitro. MATERIALS AND METHODS: Noise levels were determined using 10 standardized periapical radiographs of the same lower molar region in a human dry skull. For validation of the system, radiographs were taken before and after bovine bone particles in measures with increments of 2 mg weighing from 2 to 20 mg were added into each socket of three dry skulls. Radiographs were developed and scanned into a computer with a flatbed scanner. After digitization, the images were subjected to alignment, normalization and subtraction. Appropriate regions of interest (ROIs) were selected and their CADIA values were calculated for the determination of noise levels, and correlations between the CADIA values and the actual bone mass were performed. RESULTS: When the threshold value was 7, the percentage of pixels deviating from the set threshold value was small (0-11.3%). There were statistically significant correlations between the actual bone mass and the CADIA value for anterior sockets (p<0.001, r2=0.89) and posterior sockets (p<0.001, r2=0.9). For pooled data of both anterior and posterior sockets, the correlation was also statistically significant (p<0.001, r2=0.88). CONCLUSIONS: A high and statistically significant correlation between the actual bone mass and CADIA value was obtained, which suggests that the system could be suitable for the detection of small alveolar bone changes.