Application of probabilistic neural network in the clinical diagnosis of cancers based on clinical chemistry data

As a recently developed and powerful classification tool, probabilistic neural network was used to distinguish cancer patients from healthy persons according to the levels of nucleosides in human urine. Two datasets (containing 32 and 50 patterns, respectively) were investigated and the total consistency rate obtained was 100% for dataset 1 and 94% for dataset 2. To evaluate the performance of probabilistic neural network, linear discriminant analysis and learning vector quantization network, were also applied to the classification problem. The results showed that the predictive ability of the probabilistic neural network is stronger than the others in this study. Moreover, the recognition rate for dataset 2 can achieve to 100% if combining, these three methods together, which indicated the promising potential of clinical diagnosis by combining different methods. (C) 2002 Elsevier Science B.V. All rights reserved.

DNA diagnosis by capillary electrophoresis and microfabricated electrophoretic devices

DNA diagnosis is experiencing an impressive progression towards the development of novel technology to identity various clinically relevant categories of genetic changes and to meet the exponential growth of genomics. The introduction of capillary electrophoresis has dramatically accelerated the completion of the first draft of the human DNA sequence in the Human Genome Project, and thus, has become the method of choice for analysis of various genetic variants. The recent development of microfabricated electrophoretic devices has led to the possibility of integrating multiple sample handling with the actual measurement steps required for automation of molecular diagnostics. This review highlights the most recent progress in capillary electrophoresis and electrophoretic microdevices for DNA-based diagnostics, including the important areas of genotyping for point mutation, single nucleotide polymorphisms, short tandem repeats and organism identification. The application of these techniques for infectious and genetic disease diagnosis, as well as forensic identification purpose, are covered. The promising development and the challenges for techinical problems are also discussed.

Artificial neural network classification based on high-performance liquid chromatography of urinary and serum nucleosides for the clinical diagnosis of cancer

Nucleosides in human urine and serum have frequently been studied as a possible biomedical marker for cancer, acquired immune deficiency syndrome (AIDS) and the whole-body turnover of RNAs. Fifteen normal and modified nucleosides were determined in 69 urine and 42 serum samples using high-performance liquid chromatography (HPLC). Artificial neural networks have been used as a powerful pattern recognition tool to distinguish cancer patients from healthy persons. The recognition rate for the training set reached 100%. In the validating set, 95.8 and 92.9% of people were correctly classified into cancer patients and healthy persons when urine and serum were used as the sample for measuring the nucleosides. The results show that the artificial neural network technique is better than principal component analysis for the classification of healthy persons and cancer patients based on nucleoside data. (C) 2002 Elsevier Science B.V. All rights reserved.

Targeted RNA interference of cyclin A(2) mediated by functionalized single-walled carbon nanotubes induces proliferation arrest and apoptosis in chronic myelogenous leukemia K562 cells

Cyclin A(2) plays critical role in DNA replication, transcription, and cell cycle regulation. Its overexpression has been detected and related to many types of cancers including leukemia, suggesting that suppression of cyclin A(2) would be an attractive strategy to prevent tumor progression. Herein, we apply functionalized single wall carbon nanotubes (f-SWNTs) to carry small interfering RNA (siRNA) into K562 cells and determine whether inhibition of cyclin A(2) would be a potential therapeutic target for chronic myelogenous leukemia.

Artificial neural network applied to diagnosis of lung cancer

Artificial neural network(ANN) approach was applied to classification of normal persons and lung cancer patients based on the metal content of hair and serum samples obtained by inductively coupled plasma atomic emission spectrometry (ICP-AES) for the two groups. This method was verified with independent prediction samples and can be used as an aiding means of the diagnosis of lung cancer. The case of predictive classification with one element missing in the prediction samples was studied in details, The significance of elements in hair and serum samples for classification prediction was also investigated.

Hypothesis Formation and Evaluation in Medical Diagnosis

This thesis describes some aspects of a computer system for doing medical diagnosis in the specialized field of kidney disease. Because such a system faces the spectre of combinatorial explosion, this discussion concentrates on heuristics which control the number of concurrent hypotheses and efficient "compiled" representations of medical knowledge. In particular, the differential diagnosis of hematuria (blood in the urine) is discussed in detail. A protocol of a simulated doctor/patient interaction is presented and analyzed to determine the crucial structures and processes involved in the diagnosis procedure. The data structure proposed for representing medical information revolves around elementary hypotheses which are activated when certain disposing of findings, activating hypotheses, evaluating hypotheses locally and combining hypotheses globally is examined for its heuristic implications. The thesis attempts to fit the problem of medical diagnosis into the framework of other Artifcial Intelligence problems and paradigms and in particular explores the notions of pure search vs. heuristic methods, linearity and interaction, local vs. global knowledge and the structure of hypotheses within the world of kidney disease.

Simultaneous and ultrarapid determination of reactive oxygen species and reduced glutathione in apoptotic leukemia cells by microchip electrophoresis

A microchip electrophoresis method coupled with laser-induced fluorescence (LIF) detection was established for simultaneous determination of two kinds of intracellular signaling molecules (reactive oxygen species, ROS, and reduced glutathione, GSH) related to apoptosis and oxidative stress. As the probe dihydrorhodamine-123 (DHR123) can be converted intracellularly by ROS to the fluorescent rhodamine-123 (Rh123), and the probe naphthalene-2,3-dicarboxaldehyde (NDA) can react quickly with GSH to produce a fluorescent adduct, rapid determination of Rh-123 and GSH was achieved on a glass microchip within 27 s using a 20 mm borate buffer (pH 9.2). The established method was tested to measure the intracellular ROS and GSH levels in acute promyelocytic leukemia (APL)-derived NB4 cells. An elevation of intracellular ROS and depletion of GSH were observed in apoptotic N134 cells induced by arsenic trioxide (AS(2)O(3)) at low concentration (1-2 mu m). Buthionine sulfoximine (BSO), in combination with AS(2)O(3) enhanced the decrease of reduced GSH to a great extent. The combined treatment of AS(2)O(3) and hydrogen peroxide (H2O2) led to an inverse relationship between the concentrations of ROS and GSH obtained, showing the proposed method can readily evaluate the generation of ROS, which occurs simultaneously with the consumption of the inherent antioxidant.

Automatic diagnosis of task faults in flexible manufacturing systems

Hardy, N. W., Barnes, D. P., Lee, L. H. (1989). Automatic diagnosis of task faults in flexible manufacturing systems. Robotica, 7 (1):25-35

Taking advantage of an old concept, "illegitimate transcription", for a proposed novel method of genetic diagnosis of McArdle disease

McArdle disease is a metabolic disorder caused by pathogenic mutations in the PYGM gene. Timely diagnosis can sometimes be difficult with direct genomic analysis, which requires additional studies of cDNA from muscle transcripts. Although the "nonsense-mediated mRNA decay" (NMD) eliminates tissue-specific aberrant transcripts, there is some residual transcription of tissue-specific genes in virtually all cells, such as peripheral blood mononuclear cells (PBMCs).We studied a subset of the main types of PYGM mutations (deletions, missense, nonsense, silent, or splicing mutations) in cDNA from easily accessible cells (PBMCs) in 12 McArdle patients.Analysis of cDNA from PBMCs allowed detection of all mutations. Importantly, the effects of mutations with unknown pathogenicity (silent and splicing mutations) were characterized in PBMCs. Because the NMD mechanism does not seem to operate in nonspecific cells, PBMCs were more suitable than muscle biopsies for detecting the pathogenicity of some PYGM mutations, notably the silent mutation c.645G>A (p.K215=), whose effect in the splicing of intron 6 was unnoticed in previous muscle transcriptomic studies.We propose considering the use of PBMCs for detecting mutations that are thought to cause McArdle disease, particularly for studying their actual pathogenicity.

Manifestações orais em crianças em tratamento oncológico

Projeto de Pós-Graduação/Dissertação apresentado à Universidade Fernando Pessoa como parte dos requisitos para obtenção do grau de Mestre em Medicina Dentária