Plantar fascia ultrasound images characterization and classification using support vector machine

The examination of plantar fascia (PF) ultrasound (US) images is subjective and based on the visual perceptions and manual biometric measurements carried out by medical experts. US images feature extraction, characterization and classification have been widely introduced for improving the accuracy of medical assessment, reducing its subjective nature and the time required by medical experts for PF pathology diagnosis. In this paper, we develop an automated supervised classification approach using the Support Vector Machine (Linear and Kernel) to distinguishes between symptomatic and asymptomatic PF cases. Such an approach will facilitate the characterization and the classification of the PF area for the identification of patients with inferior heel pain at risk of plantar fasciitis. Six feature sets were extracted from the segmented PF region. Additionally, features normalization, features ranking and selection analysis using an unsupervised infinity selection method were introduced for the characterization and the classification of symptomatic and asymptomatic PF subjects. The performance of the classifiers was assessed using confusion matrix attributes and some derived performance measures including recall, specificity, balanced accuracy, precision, F-score and Matthew’s correlation coefficient. Using the best selected features sets, Linear SVM and Kernel SVM achieved an F-Score of 97.06 and 98.05 respectively

Acquisition of naturally occurring antibody responses to recombinant protein domains of Plasmodium falciparum erythrocyte membrane protein 1

Background: Antibodies targeting variant antigens expressed on the surface of Plasmodium falciparum infected erythrocytes have been associated with protection from clinical malaria. The precise target for these antibodies is unknown. The best characterized and most likely target is the erythrocyte surface-expressed variant protein family Plasmodium falciparum erythrocyte membrane protein 1 (PfEMP1). Methods: Using recombinant proteins corresponding to five domains of the expressed A4 var gene, A4 PfEMP1, the naturally occurring antibody response was assessed, by ELISA, to each domain in serum samples obtained from individuals resident in two communities of differing malaria transmission intensity on the Kenyan coast. Using flow cytometry, the correlation in individual responses to each domain with responses to intact A4-infected erythrocytes expressing A4 PfEMP1 on their surface as well as responses to two alternative parasite clones and one clinical isolate was assessed. Results: Marked variability in the prevalence of responses between each domain and between each transmission area was observed, as wasa strong correlation between age and reactivity with some but not all domains. Individual responses to each domain varied strikingly, with some individuals showing reactivity to all domains and others with no reactivity to any, this was apparent at all age groups. Evidence for possible cross-reactivity in responses to the domain DBL4γ was found. Conclusion: Individuals acquire antibodies to surface expressed domains of a highly variant protein. The finding of potential cross-reactivity in responses to one of these domains is an important initial finding in the consideration of potential vaccine targets

Techniques of EMG signal analysis: detection, processing, classification and applications

Electromyography (EMG) signals can be used for clinical/biomedical applications, Evolvable Hardware Chip (EHW) development, and modern human computer interaction. EMG signals acquired from muscles require advanced methods for detection, decomposition, processing, and classification. The purpose of this paper is to illustrate the various methodologies and algorithms for EMG signal analysis to provide efficient and effective ways of understanding the signal and its nature. We further point up some of the hardware implementations using EMG focusing on applications related to prosthetic hand control, grasp recognition, and human computer interaction. A comparison study is also given to show performance of various EMG signal analysis methods. This paper provides researchers a good understanding of EMG signal and its analysis procedures. This knowledge will help them develop more powerful, flexible, and efficient applications

Effect of var gene disruption on switching in Plasmodium falciparum.

The molecular mechanisms underpinning switching of variant antigens on the surface of Plasmodium falciparum-infected erythrocytes are poorly understood. We tested the hypothesis that insertional disruption of the A4var gene, one of two var genes located within the subtelomeric region of one end of chromosome 13, would result in a preferential switch in transcription to the adjacent R29var gene upon rosette selection. In this way, we aimed to mimic the preferential transcription of R29var in rosetting R29 parasites, a parasite line in which the A4var gene is deleted through a chromosome end truncation. Initial analysis of the knock-out parasite lines shows that the insertional disruption of the A4var gene prevents A4 PfEMP1 expression, but that switching transcription to other var gene variants is unaffected. Furthermore, analysis of var transcription in the knock-out parasite line during rosette selection shows that, rather than facilitating a switch to R29var gene transcription, this event was suppressed in the transfectants. These data, and the implications for epigenetic transcriptional control of var genes, are discussed

Pattern recognition for bivariate process mean shifts using feature-based artificial neural network

In multivariate quality control, the artificial neural networks (ANN)-based pattern recognition schemes generally performed better for monitoring bivariate process mean shifts and provided more efficient information for diagnosing the source variable(s) compared to the traditional multivariate statistical process control charting. However, these schemes revealed disadvantages in term of reference bivariate patterns in identifying the joint effect and excess false alarms in identifying stable process condition. In this study, feature-based ANN scheme was investigated for recognizing bivariate correlated patterns. Feature-based input representation was utilized into an ANN training and testing towards strengthening discrimination capability between bivariate normal and bivariate mean shift patterns. Besides indicating an effective diagnosis capability in dealing with low correlation bivariate patterns, the proposed scheme promotes a smaller network size and better monitoring capability as compared to the raw data-based ANN scheme

Smoking increases failure rate of operation for established non-union of the scaphoid bone

The aim of the study was to investigate the effect of smoking on the operative treatment of established non-union of the carpal scaphoid. Case notes and radiographs of patients undergoing bone grafting and screw fixation of scaphoid non-unions were reviewed. There were 34 patients that had undergone 37 operations for established non-union of the carpal scaphoid bone. There were two female patients, and the average age was 26.8 years (range 13.4 years to 52.9 years). The median delay to operation was 11.9 months. The overall success rate of the operation (internal fixation and autologous bone grafting) was 59.5% (22/37), but there was a significant association between non-union and smoking (P=0.02 for Fisher’s exact test). In non-smokers (n=17) the success rate was 82.4%, but this dropped to 40.0% among smokers (P<0.01). We concluded that smoking was significantly associated with failure of operative treatment of established non-union of the scaphoid bone