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At least 1,531 records · Page 85Linked to original sources
PubMed1998

Determinants of rifabutin-associated uveitis in patients treated with rifabutin, clarithromycin, and ethambutol for Mycobacterium avium complex bacteremia: a multivariate analysis. Canadian HIV Trials Network Protocol 010 Study Group.

Uveitis occurred in a substantial proportion of AIDS patients receiving rifabutin, 600 mg daily, together with clarithromycin and ethambutol for treatment of Mycobacterium avium complex bacteremia. A case-control study was undertaken to examine potential risk factors for developing uveitis. Of eight parameters examined, only baseline body weight predicted the development of uveitis by both univariate and multivariate analyses (P = .001). The incidence of uveitis was 14% in patients weighing >65 kg, 45% in patients between 55 and 65 kg, and 64% in patients <55 kg. Concomitant therapy with fluconazole, a drug known to raise serum rifabutin concentrations, was not associated with an increased incidence of uveitis. The risk of uveitis was markedly reduced when rifabutin was given at 300 mg daily in combination with clarithromycin and ethambutol.

AIDS-Related Opportunistic Infections↗
PubMed2005

Complex networks and simple models in biology.

The analysis of molecular networks, such as transcriptional, metabolic and protein interaction networks, has progressed substantially because of the power of models from statistical physics. Increasingly, the data are becoming so detailed--though not always complete or correct--that the simple models are reaching the limits of their usefulness. Here, we will discuss how network information can be described and to some extent quantified. In particular statistics offers a range of tools, such as model selection, which have not yet been widely applied in the analysis of biological networks. We will also outline a number of present challenges posed by biological network data in systems biology, and the extent to which these can be addressed by new developments in statistics, physics and applied mathematics.

Animals↗
PubMedDate not supplied

Bioprocess fault detection by nonlinear multivariate analysis: application of an artificial autoassociative neural network and wavelet filter bank.

A nonlinear multivariate analysis, artificial autoassociative neural network (AANN), was applied to bioprocess fault detection. In an optimal production process of a recombinant yeast with a temperature controllable expression system, faults in test cases with faulty temperature sensors and plasmid instability of recombinant cells could be detected by the AANN. Since the raw data of measured variables included high-frequency noise, a wavelet filter bank (WFB) was applied to noise elimination before training of the AANN. The filtering performance of the WFB was compared with those of some classical first-order digital filters. The filtered signals at several resolution scales by the WFB were employed as the training data of the AANN. The computing time and summation of square of errors in training were compared, and the appropriate degree of the noise filtering and the density of the training data of the AANN were discussed. The performance of the feature capturing by the AANN was compared with that by a linear multivariate analysis, principal component analysis. A J index defined in this paper, using inputs and outputs of the AANN, was used for fault detection successfully. The output of the first unit of the trained AANN functioned effectively for the discrimination of the data in the abnormal cases from the data in the normal cases.

Algorithms↗
PubMed2004

The JAK-STAT signaling network in the human B-cell: an extreme signaling pathway analysis.

Large-scale models of signaling networks are beginning to be reconstructed and corresponding analysis frameworks are being developed. Herein, a reconstruction of the JAK-STAT signaling system in the human B-cell is described and a scalable framework for its network analysis is presented. This approach is called extreme signaling pathway analysis and involves the description of network properties with systemically independent basis vectors called extreme pathways. From the extreme signaling pathways, emergent systems properties of the JAK-STAT signaling network have been characterized, including 1), a mathematical definition of network crosstalk; 2), an analysis of redundancy in signaling inputs and outputs; 3), a study of reaction participation in the network; and 4), a delineation of 85 correlated reaction sets, or systemic signaling modules. This study is the first such analysis of an actual biological signaling system. Extreme signaling pathway analysis is a topologically based approach and assumes a balanced use of the signaling network. As large-scale reconstructions of signaling networks emerge, such scalable analyses will lead to a description of the fundamental systems properties of signal transduction networks.

B-Lymphocytes↗
PubMed1994

Data qualification: logic analysis applied toward neural network training.

For neural networks to develop good internal representations for pattern mapping, noise in the training set data must be controlled. Because of the many difficulties associated with manually validating training data, we have focused on using decision table techniques as a practical, domain-independent means of optimizing training set formulation. Decision tables provide a variety of mechanisms whereby training set data can be processed to remove ambiguity, contradictions, and other noise. In addition to serving as data filters, decision tables can be used in the evaluation of neural network training.

Algorithms↗
PubMed1994

Mixed polymer networks in the direct analysis of pharmaceuticals in urine by capillary electrophoresis.

Two-component polymer mixtures of polyethylene oxide-polydextran have been investigated as unique separation media for capillary electrophoresis. The effects of concentration of the individual polymers and their mixtures on the electroosmotic velocity and electrophoretic mobility of small pharmaceutical compounds were investigated. The molecular masses of polymers, buffer concentrations and percentages of organic solvents and cyclodextrins were varied to explore their effects on the separation process. The plate height against field strength curves were also generated for a better understanding of the kinetic processes involved. The two-component polymer mixtures were found as stable and selective media for the analysis of an anti-ulcer drug famotidine directly in untreated urine.

Buffers↗
PubMed2005

A novel dengue fever (DF) and dengue haemorrhagic fever (DHF) analysis using artificial neural network (ANN).

Dengue fever (DF) is an acute febrile viral disease frequently presented with headache, bone or joint and muscular pains, and rash. A significant percentage of DF patients develop a more severe form of disease, known as dengue haemorrhagic fever (DHF). DHF is the complication of DF. The main pathophysiology of DHF is the development of plasma leakage from the capillary, resulting in haemoconcentration, ascites, and pleural effusion that may lead to shock following defervescence of fever. Therefore, accurate prediction of the day of defervescence of fever is critical for clinician to decide on patient management strategy. To date, no known literature describes of any attempt to predict the day of defervescence of fever in DF patients. This paper describes a non-invasive prediction system for predicting the day of defervescence of fever in dengue patients using artificial neural network. The developed system bases its prediction solely on the clinical symptoms and signs and uses the multilayer feed-forward neural networks (MFNN). The results show that the proposed system is able to predict the day of defervescence in dengue patients with 90% prediction accuracy.

Dengue↗
PubMed2006

CoryneRegNet 3.0--an interactive systems biology platform for the analysis of gene regulatory networks in corynebacteria and Escherichia coli.

CoryneRegNet is an ontology-based data warehouse for the reconstruction and visualization of transcriptional regulatory interactions in prokaryotes. To extend the biological content of CoryneRegNet, we added comprehensive data on transcriptional regulations in the model organism Escherichia coli K-12, originally deposited in the international reference database RegulonDB. The enhanced web interface of CoryneRegNet offers several types of search options. The results of a search are displayed in a table-based style and include a visualization of the genetic organization of the respective gene region. Information on DNA binding sites of transcriptional regulators is depicted by sequence logos. The results can also be displayed by several layouters implemented in the graphical user interface GraphVis, allowing, for instance, the visualization of genome-wide network reconstructions and the homology-based inter-species comparison of reconstructed gene regulatory networks. In an application example, we compare the composition of the gene regulatory networks involved in the SOS response of E. coli and Corynebacterium glutamicum. CoryneRegNet is available at the following URL: http://www.cebitec.uni-bielefeld.de/groups/gi/software/coryneregnet/.

Corynebacterium glutamicum↗
PubMed2006

Hierarchical microimaging for multiscale analysis of large vascular networks.

There is a wide range of diseases and normal physiological processes that are associated with alterations of the vascular system in organs. Ex vivo imaging of large vascular networks became feasible with recent developments in microcomputed tomography (microCT). Current methods permit to visualize only limited numbers of physically excised regions of interests (ROIs) from larger samples. We developed a method based on modified vascular corrosion casting (VCC), scanning electron microscopy (SEM), and desktop and synchrotron radiation microCT (SRmicroCT) technologies to image vasculature at increasing levels of resolution, also referred to as hierarchical imaging. This novel approach allows nondestructive 3D visualization and quantification of large microvascular networks, while retaining a precise anatomical context for ROIs scanned at very high resolution. Scans of entire mouse brain VCCs were performed at 16-microm resolution with a desktop microCT system. Custom-made navigation software with a ROI selection tool enabled the identification of anatomical brain structures and precise placement of multiple ROIs. These were then scanned at 1.4-microm voxel size using SRmicroCT and a local tomography setup. A framework was developed for fast sample positioning, precise selection of ROIs, and sequential high-throughput scanning of a large numbers of brain VCCs. Despite the use of local tomography, exceptional image quality was achieved with SRmicroCT. This method enables qualitative and quantitative assessment of vasculature at unprecedented resolution and volume with relatively high throughput, opening new possibilities to study vessel architecture and vascular alterations in models of disease.

Animals↗
PubMed2025

Comparative analysis of convolutional neural network models for the histopathological differentiation of acinic cell carcinoma and secretory carcinoma.

OBJECTIVE: Although artificial intelligence tools show promise for enhancing the diagnosis of head and neck lesions, few studies have tested these resources for the microscopic diagnosis of salivary gland tumors. Specifically, the microscopic differentiation between acinic cell carcinoma and secretory carcinoma has never been addressed in this context. Therefore, this exploratory study aimed to comparatively evaluate the feasibility of applying convolutional neural networks for the microscopic differentiation between acinic cell carcinomas and secretory carcinomas. METHODS: A cross-sectional study using whole-slide images from 46 patients with acinic cell carcinoma (n = 26) or secretory carcinoma (n = 20) was conducted. Eight CNNs (ResNet-50, InceptionV3, VGG16, Xception, MobileNet, DenseNet121, EfficientNetB0, and EfficientNetV2B0) were trained and evaluated for accuracy, sensitivity, specificity, F1-score, and AUC. Performance was measured in training, validation, and test subsets. Accuracy and loss curves were also presented. RESULTS: InceptionV3 demonstrated the best overall performance, with the lowest loss (1.39), highest accuracy (0.81), sensitivity (0.90), and F1-score (0.81). VGG16 achieved the highest AUC (0.86) and precision (0.77). DenseNet121 showed the lowest performance in terms of accuracy (0.65) and F1-Score (0.52), but the highest specificity (0.85). CONCLUSION: This proof-of-concept study suggests that convolutional neural networks may be feasible tools to support the microscopic differentiation between acinic cell carcinoma and secretory carcinoma. The performance of these models critically depends on the size of the dataset and the quality of annotations. The findings should be interpreted cautiously given the limited dataset and potential sources of bias. Further validation with larger, multicenter datasets is needed before any clinical application can be considered.

Humans↗
PubMed2005

Alteration of the perceived path of a non-pursued target during smooth pursuit: analysis by a neural network model.

During pursuit of a circularly moving target, the perceived movement of a second circularly moving target is altered. The perceived movement of the non-pursued target is different from both its real movement path and its retinal path. In the present paper this phenomenon is studied using a physiologically based neural network model. Simulation results were compared to psychophysical findings in human subjects. Model simulations enabled us to suggest an explanation for this phenomenon in terms of underlying physiological mechanisms and to estimate the contribution of the efferent eye-movement signal to the perceptual process.

Adult↗
PubMed1998

MHC haplotype analysis by artificial neural networks.

Conventional matching is based on numbers of alleles shared between donor and recipient. This approach, however, ignores the degree of relationship between alleles and haplotypes, and therefore the actual degree of difference. To address this problem, we have compared family members using a block matching technique which reflects differences in genomic sequences. All parents and siblings had been genotyped using conventional MHC typing so that haplotypes could be assigned and relatives could be classified as sharing 0, 1 or 2 haplotypes. We trained an Artificial Neural Network (ANN) with subjects from 6 families (85 comparisons) to distinguish between relatives. Using the outputs of the ANN, we developed a score, the Histocompatibility Index (HI), as a measure of the degree of difference. Subjects from a further 3 families (106 profile comparisons) were tested. The HI score for each comparison was plotted. We show that the HI score is trimodal allowing the definition of three populations corresponding to approximately 0, 1 or 2 haplotype sharing. The means and standard deviations of the three populations were found. As expected, comparisons between family members sharing 2 haplotypes resulted in high HI scores with one exception. More interestingly, this approach distinguishes between the 1 and 0 haplotype groups, with some informative exceptions. This distinction was considered too difficult to attempt visually. The approach provides promise in the quantification of degrees of histocompatibility.

Electrophoresis, Agar Gel↗
PubMedDate not supplied

Quantification and description of fracture network by MRI image analysis.

The contribution of fractures to total porosity and their geometrical descriptions have been studied by Image Analysis applied to 1H Magnetic Resonance Imaging (MRI). Reservoirs of different lithology were acquired with MSME 2D quantitative and 3D sequences. An image analysis procedure, developed ad hoc, was then applied to these acquisitions and the petrophysical parameters computed. These parameters range from fracture porosity to fracture density.

Magnetic Resonance Imaging↗