The Relationship Between Fibrinolytic Capacity and Metabolic Control in Insulin-Dependent Diabetes Mellitus.

The purpose of this study was to investigate the relationship between fibrinolytic capacity and metabolic control in insulin-dependent diabetes mellitus. Testing protocol was explained and informed consent was obtained from 26 insulin-dependent diabetics and 20 healthy controls who agreed to serve as subjects. Fibrinolytic capacity (FC) was measured as the change in euglobulin lysis time produced by 10 min of venous occlusion with a sphygmomanometer cuff inflated around the upper arm at a pressure midway between systolic and diastolic blood pressure. Metabolic control was evaluated by measurement of hemoglobin A1 (HbA1) using a minicolumn chromatographic technique. Fasting glucose (GLU), cholesterol (CHO), and triglyceride (TRI) were measured on a Coulter Chemistry Analyzer. Significant differences were found to exist between diabetics and controls, respectively, for FC (46.6 (+OR-) 19.3% vs. 64.2 (+OR-) 12.1%), HbA1 (10.3 (+OR-) 2.1% vs. 6.5 (+OR-) .9%), GLU (209 (+OR-) 102 mg% vs. 95 (+OR-) 7 mg%), and CHO (215 (+OR-) 56 mg% vs. 186 (+OR-) 25 mg%). No differences were found for TRI. Bivariate correlations were calculated between FC and HbA1 for diabetics only, for controls only, and for all subjects combined. Significant relationships were found for both the combined sample (r = -.33) and for controls (r = .54), but not for diabetics (r = -.08). Stepwise linear regression revealed that FC could best be explained in insulin-dependent diabetes by TRI (p = .03), and no significance could be attached to HbA1 (p = .46). It is concluded that the positive relationship noted between FC and HbA1 for normal subjects is spurious and that the reduced capacity for fibrinolytic activity in insulin-dependent diabetes is not directly related to metabolic control

Abstracts of Papers, 80th Annual Meeting of the Virginia Academy of Science, May 22-24, 2002, Hampton University, Hampton, Virginia

Abstracts of papers that were presented at the 80th annual meeting of the Virginia Academy of Science

Impacts of Diabetic Neuropathy on the Human Neuromuscular System

Diabetes mellitus (DM) imparts vascular and metabolic stressors that cause damage and dysfunction to the human nervous system. The disorder associated with this dysfunction is termed diabetic polyneuropathy (DPN). Although DPN has been associated with muscle weakness and atrophy, the extent of its impacts on the neuromuscular system is not well understood. The five studies presented in my thesis investigated how DPN affects the neuromuscular system in humans, from the motor neuron to skeletal muscle contractile properties, using a combination of electromyography (EMG), dynamometry and magnetic resonance imaging (MRI) techniques. The purpose of Studies 1 and 2 was to determine whether the neurogenic loss of motor units underlies the muscle weakness and atrophy associated with DPN, and to investigate how these changes may differ in an upper and lower limb muscle. I determined DPN patients feature reduced motor unit estimates (MUNEs) compared to controls, and progression of motor unit loss in DPN may follow a length-dependent pattern. The purpose of Study 3 was to assess the stability of neuromuscular transmission in patients with DPN compared with healthy controls, using a novel set of electrodiagnostic parameters obtained via quantitative EMG. I determined DPN patients have less stable neuromuscular transmission, and the feature intermittent conduction failure at a relatively low contraction intensity. The purpose of Study 4 was to investigate skeletal muscle contractile properties and morphology in DPN patients associated with the severity of muscle denervation. I determined DPN patients possess slowed muscle, with greater proportional amounts of non-contractile muscle tissue compared to controls. The purpose of Study 5 was to explore the fatigability of DPN patients during a sustained, maximal voluntary contraction (MVC). I determined DPN patients have less endurance than controls, and their increased fatigability may be associated with neuromuscular transmission failure. Overall these foundational explorations greatly expand our knowledge of how DPN can impact the neuromuscular system in humans. Furthermore, the studies contained within my thesis may help direct further useful studies and strategies to understand, and direct clinical support in those with DPN

Construction and validation of the APOCHIP, a spotted oligo-microarray for the study of beta-cell apoptosis

BACKGROUND: Type 1 diabetes mellitus (T1DM) is a autoimmune disease caused by a long-term negative balance between immune-mediated beta-cell damage and beta-cell repair/regeneration. Following immune-mediated damage the beta-cell fate depends on several genes up- or down-regulated in parallel and/or sequentially. Based on the information obtained by the analysis of several microarray experiments of beta-cells exposed to pro-apoptotic conditions (e.g. double stranded RNA (dsRNA) and cytokines), we have developed a spotted rat oligonucleotide microarray, the APOCHIP, containing 60-mer probes for 574 genes selected for the study of beta-cell apoptosis. RESULTS: The APOCHIP was validated by a combination of approaches. First we performed an internal validation of the spotted probes based on a weighted linear regression model using dilution series experiments. Second we profiled expression measurements in ten dissimilar rat RNA samples for 515 genes that were represented on both the spotted oligonucleotide collection and on the in situ-synthesized 25-mer arrays (Affymetrix GeneChips). Internal validation showed that most of the spotted probes displayed a pattern of reaction close to that predicted by the model. By using simple rules for comparison of data between platforms we found strong correlations (r(median)= 0.84) between relative gene expression measurements made with spotted probes and in situ-synthesized 25-mer probe sets. CONCLUSION: In conclusion our data suggest that there is a high reproducibility of the APOCHIP in terms of technical replication and that relative gene expression measurements obtained with the APOCHIP compare well to the Affymetrix GeneChip. The APOCHIP is available to the scientific community and is a useful tool to study the molecular mechanisms regulating beta-cell apoptosis

Improving intrusion detection systems using data mining techniques

Recent surveys and studies have shown that cyber-attacks have caused a lot of damage to organisations, governments, and individuals around the world. Although developments are constantly occurring in the computer security field, cyber-attacks still cause damage as they are developed and evolved by hackers. This research looked at some industrial challenges in the intrusion detection area. The research identified two main challenges; the first one is that signature-based intrusion detection systems such as SNORT lack the capability of detecting attacks with new signatures without human intervention. The other challenge is related to multi-stage attack detection, it has been found that signature-based is not efficient in this area. The novelty in this research is presented through developing methodologies tackling the mentioned challenges. The first challenge was handled by developing a multi-layer classification methodology. The first layer is based on decision tree, while the second layer is a hybrid module that uses two data mining techniques; neural network, and fuzzy logic. The second layer will try to detect new attacks in case the first one fails to detect. This system detects attacks with new signatures, and then updates the SNORT signature holder automatically, without any human intervention. The obtained results have shown that a high detection rate has been obtained with attacks having new signatures. However, it has been found that the false positive rate needs to be lowered. The second challenge was approached by evaluating IP information using fuzzy logic. This approach looks at the identity of participants in the traffic, rather than the sequence and contents of the traffic. The results have shown that this approach can help in predicting attacks at very early stages in some scenarios. However, it has been found that combining this approach with a different approach that looks at the sequence and contents of the traffic, such as event- correlation, will achieve a better performance than each approach individually

HLA class I supertype and supermotif definition by chemometric approaches.

Activation of cytotoxic T cells in human requires specific binding of antigenic peptides to human leukocyte antigen (HLA) molecules. HLA is the most polymorphic protein in the human body, currently 1814 different alleles collected in the HLA sequence database at the European Bioinformatics Institute. Most of the HLA molecules recognise different peptides. Also, some peptides can be recognised by several of HLA molecules. In the present project, all available class I HLA alleles are classified into supertypes. Super - binding motifs for peptides binding to some supertypes are defined where binding data are available. A variety of chemometric techniques are used in the project, including 2D and 3D QSAR techniques and different variable selection methods like SIMCA, GOLPE and genetic algorithm. Principal component analysis combined with molecular interaction fields calculation by the program GRID is used in the class I HLA classification. This thesis defines an HLA-A3 supermotif using two QSAR methods: the 3D-QSAR method CoMSIA, and a recently developed 2D-QSAR method, which is named the additive method. Four alleles with high phenotype frequency were included in the study: HLA-A*0301, HLA-A*1101, HLA-A*3101 and HLA- A*6801. An A*020T binding motif is also defined using amino acid descriptors and variable selection methods. Novel peptides have been designed according to the motifs and the binding affinity is tested experimentally. The results of the additive method are used in the online server, MHCPred, to predict binding affinity of unknown peptides. In HLA classification, the HLA-A, B and C molecules are classified into supertypes separately. A total of eight supertypes are observed for class I HLA, including A2, A3, A24, B7, B27, B44, CI and C4 supertype. Using the HLA classification, any newly discovered class I HLA molecule can be grouped into a supertype easily, thus simplifying the experimental function characterisation process

Big Data and Causality

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Causality analysis continues to remain one of the fundamental research questions and the ultimate objective for a tremendous amount of scientific studies. In line with the rapid progress of science and technology, the age of big data has significantly influenced the causality analysis on various disciplines especially for the last decade due to the fact that the complexity and difficulty on identifying causality among big data has dramatically increased. Data mining, the process of uncovering hidden information from big data is now an important tool for causality analysis, and has been extensively exploited by scholars around the world. The primary aim of this paper is to provide a concise review of the causality analysis in big data. To this end the paper reviews recent significant applications of data mining techniques in causality analysis covering a substantial quantity of research to date, presented in chronological order with an overview table of data mining applications in causality analysis domain as a reference directory

Multimodal optical measurement for study of lower limb tissue viability in patients with diabetes mellitus

According to the International Diabetes Federation, the challenge of early stage diagnosis and treatment effectiveness monitoring in diabetes is currently one of the highest priorities in modern healthcare. The potential of combined measurements of skin fluorescence and blood perfusion by the laser Doppler flowmetry method in diagnostics of low limb diabetes complications was evaluated. Using Monte Carlo probabilistic modeling, the diagnostic volume and depth of the diagnosis were evaluated. The experimental study involved 76 patients with type 2 diabetes mellitus. These patients were divided into two groups depending on the degree of complications. The control group consisted of 48 healthy volunteers. The local thermal stimulation was selected as a stimulus on the blood microcirculation system. The experimental studies have shown that diabetic patients have elevated values of normalized fluorescence amplitudes, as well as a lower perfusion response to local heating. In the group of people with diabetes with trophic ulcers, these parameters also significantly differ from the control and diabetes only groups. Thus, the intensity of skin fluorescence and level of tissue blood perfusion can act as markers for various degrees of complications from the beginning of diabetes to the formation of trophic ulcers