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At least 1,243 records · Page 69Linked to original sources
PubMed2001

Risk factors for renal allograft survival from pediatric cadaver donors: an analysis of united network for organ sharing data.

BACKGROUND: The shortage of cadaveric donors for kidney transplantation has prompted many centers to use cadaver kidneys from pediatric donors. Use of kidneys from pediatric donors has been shown to have a lower graft survival. METHODS: Recipients receiving cadaver kidneys from pediatric and adult donors between 1988 and 1995 were analyzed. The data were obtained from United Network of Organ Sharing database. The actuarial kidney transplant graft survival was estimated by the Kaplan-Meier method. A logistic regression analysis was used to identify various risk factors for 1-year graft failure. Odds ratios (OR) were estimated for various risk factors. RESULTS: Kidney transplant survival rates for donor age <18 years (n=12,838) at 1, 2, 3, 4, and 5 years were 81.5%, 76.3%, 71.3%, 66.4%, and 61.7%, respectively. The corresponding results for adult donors from age 18 to 50 years (n=35, 442) were 83.5%, 78.4%, 73.1%, 67.9%, and 62.4%, respectively, Log-rank test P<0.01. Pediatric donors were further divided into three groups according to donor age: group I (0-5 years), group II (6-11 years), and group III (12-17 years). The actuarial survival rates for 1, 3, and 5 years for group I (n=2198) were 73.6%, 63.3%, and 55.6%, respectively. The corresponding values for group II (n=2873) were 78.0%, 67.5%, and 57.8% and for group III (n=7767) were 85%, 75.0%, and 64.8%, respectively, P<0.01. Although the recipients of group I had lower graft survival, en bloc grafts (n=751) had much better 1-, 3-, and 5-year graft survival rates (76.3%, 67.7%, and 60.7%, respectively) compared with single grafts (n=1447; 72.2%, 61.1%, and 53.2%, P=0.02) from donors 0 to 5 years. Graft thrombosis as a cause of graft failure was seen in 10% of group I compared with 6% in group II and 5% in group III. In group I, lower OR were seen when an en bloc transplant was performed (0.688, P<0.01) and when donor body weight was>15 kg (0.547, P<0.01). However, OR were elevated in recipients of previous transplants (1.556, P<0.01), with prolonged cold ischemic time (1.097, P=0.03), for black recipients (1.288, P=0.03), and for recipients with body mass index> or =25 (1.286, P=0.02). Progressive increase in the donor age was associated with lower OR in group II (0.894, P<0.01). CONCLUSIONS: (1) Overall, poorer graft survival was seen in pediatric donor transplants, (2) transplant kidney survival with en bloc kidneys was better than a single kidney from donors 0-5 years, (3) progressive increase in donor age was associated with improved graft survival when the donors were 6-11 years, whereas progressive increase in donor weight was associated with improved graft survival when the donors were 0-5 years.

Adolescent↗
PubMed2006

Robustness analysis of biochemical network models.

Biological systems that have been experimentally verified to be robust to significant changes in their environments require mathematical models that are themselves robust. In this context, a necessary condition for model robustness is that the model dynamics should not be sensitive to small variations in the model's parameters. Robustness analysis problems of this type have been extensively studied in the field of robust control theory and have been found to be very difficult to solve in general. The authors describe how some tools from robust control theory and nonlinear optimisation can be used to analyse the robustness of a recently proposed model of the molecular network underlying adenosine 3',5'-cyclic monophosphate (cAMP) oscillations observed in fields of chemotactic Dictyostelium cells. The network model, which consists of a system of seven coupled nonlinear differential equations, accurately reproduces the spontaneous oscillations in cAMP observed during the early development of D. discoideum. The analysis by the authors reveals, however, that very small variations in the model parameters can effectively destroy the required oscillatory dynamics. A biological interpretation of the analysis results is that correct functioning of a particular positive feedback loop in the proposed model is crucial to maintaining the required oscillatory dynamics.

Animals↗
PubMed2001

Comparison of the prediction of extremely low birth weight neonatal mortality by regression analysis and by neural networks.

AIMS: To compare the prediction of mortality in individual extremely low birth weight (ELBW) neonates by regression analysis and by artificial neural networks. STUDY DESIGN: A database of 23 variables on 810 ELBW neonates admitted to a tertiary care center was divided into training, validation, and test sets. Logistic regression and neural network models were developed on the training set, validated, and outcome (mortality) predicted on the test set. Stepwise regression identified significant variables in the full set. Regression models and neural networks were then tested using data sets with only the identified significant variables, and then with variables excluded one at a time. RESULTS: The area under the curve (AUC) of receiver operating characteristic (ROC) curves for neural networks and regression was similar (AUC 0.87+/-0.03; p=0.31). Birthweight or gestational age and the 5-min Apgar score contributed most to AUC. CONCLUSIONS: Both neural networks and regression analysis predicted mortality with reasonable accuracy. For both models, analyzing selected variables was superior to full data set analysis. We speculate neural networks may not be superior to regression when no clear non-linear relationships exist.

Area Under Curve↗
PubMed2004

Flexible multi-scale fitting of atomic structures into low-resolution electron density maps with elastic network normal mode analysis.

A novel method is presented for the quantitative flexible docking of high-resolution structure into low-resolution maps of macromolecular complexes from electron microscopy. This method uses a linear combination of low-frequency normal modes from elastic network description of the molecular framework in an iterative manner to deform the structure optimally to conform to the low-resolution electron density map. The methodology utilizes gradient following techniques in collective normal modes to locally optimize the overall correlation coefficient between computed and measured electron density. To evaluate the performance of our approach, several proteins, which undergo large conformational changes, have been studied. We demonstrate that refinement based on normal mode analysis provides an accurate and fast alternative for the flexible fitting of high-resolution structure into a low-resolution density map. Additionally, we show that lower resolution (multi-scale) structural models can be used for the normal mode searching in lieu of fully atomic models with little loss of overall accuracy.

Algorithms↗
PubMed2004

Analysis of gene network regulating yeast multidrug resistance by artificial activation of transcription factors: involvement of Pdr3 in salt tolerance.

We established a strategy to constitutively activate Zn(2)Cys(6)-type protein by fusing its DNA-binding domain with the VP16 trans-activation domain. To explore gene network regulating yeast multidrug resistance, the strategy was applied to Pdr1, Pdr3 and Yrr1, known to regulate multidrug resistance, as well as three uncharacterized Yrr1-related transcription factors. DNA microarray analysis revealed that all of the six mutants induce typical drug transporter genes including SNQ2 and YOR1, suggesting redundancy in regulation. On the other hand, each displays a unique spectrum of targets, which is coincident with the phylogenetic tree of the transcription factors and presumably reflects their functional specification. Indeed, careful analysis of target genes specific to each transcription factor led us to reveal an unexpected role for Pdr3 in salt tolerance. The strategy would thus contribute not only to identify target genes but to reveal redundancy and specificity in complex gene regulatory networks.

ATP-Binding Cassette Transporters↗
PubMed2005

Calibration of a Hall effect displacement measurement system for complex motion analysis using a neural network.

Biomechanics studies often require the analysis of position and orientation. Although a variety of transducer and camera systems can be utilized, a common inexpensive alternative is the Hall effect sensor. Hall effect sensors have been used extensively for one-dimensional position analysis but their non-linear behavior and cross-talk effects make them difficult to calibrate for effective and accurate two- and three-dimensional position and orientation analysis. The aim of this study was to develop and calibrate a displacement measurement system for a hydraulic-actuation joystick used for repetitive motion analysis of heavy equipment operators. The system utilizes an array of four Hall effect sensors that are all active during any joystick movement. This built-in redundancy allows the calibration to utilize fully connected feed forward neural networks in conjunction with a Microscribe 3D digitizer. A fully connected feed forward neural network with one hidden layer containing five neurons was developed. Results indicate that the ability of the neural network to accurately predict the x, y and z coordinates of the joystick handle was good with r(2) values of 0.98 and higher. The calibration technique was found to be equally as accurate when used on data collected 5 days after the initial calibration, indicating the system is robust and stable enough to not require calibration every time the joystick is used. This calibration system allowed an infinite number of joystick orientations and positions to be found within the range of joystick motion.

Algorithms↗
PubMed1998

From specific gene regulation to genomic networks: a global analysis of transcriptional regulation in Escherichia coli.

Because a large number of molecular mechanisms involved in gene regulation have been described during the last decades, it is now becoming possible to address questions about the global structure of gene regulatory networks, at least in the case of some of the best-characterized organisms. This paper presents a global characterization of the transcriptional regulation in Escherichia coli on the basis of the current data. The connectivity of the corresponding network was evaluated by analyzing the distribution of the number of genes regulated by a given regulatory protein, and the distribution of the number of regulatory genes regulating a given regulated gene. The mean connectivity found (between 2 and 3) shows a rather loosely interconnected structure. Special emphasis is given to circular sequences of interactions ("circuits") because of their critical dynamical properties. Only one-element circuits were found, in which negative autoregulation is the dominant architecture. These global properties are discussed in light of several pertinent theoretical approaches, as well as in terms of physiological and evolutionary considerations.

Bacterial Proteins↗
PubMed2005

Ab initio prediction of thermodynamically feasible reaction directions from biochemical network stoichiometry.

Analysis of the stoichiometric structure of metabolic networks provides insights into the relationships between structure, function, and regulation of metabolic systems. Based on knowledge of only reaction stoichiometry, certain aspects of network functionality and robustness can be predicted. Current theories focus on breaking a metabolic network down into non-decomposable pathways able to operate in steady state. The physics underlying these theories is based on mass balance and the laws of thermodynamics. However, due to the inherent nonlinearity of the thermodynamic constraints on metabolic fluxes, computational analysis of large-scale biochemical systems can be expensive. In this study, it is shown how the feasible reaction directions may be determined by either computing the allowable ranges under the mass-balance and thermodynamic constraints or by analyzing the stoichiometric structure of the network. The computed reaction directions translate into a set of linear constraints necessary for thermodynamic feasibility. This set of necessary linear constraints is shown to be sufficient to guarantee feasibility in certain cases, thus translating the nonlinear thermodynamic constraints to linear. We show that for a reaction network of 44 internal reactions representing energy metabolism, the computed linear inequality constraints represent necessary and sufficient conditions for thermodynamic feasibility.

Cell Physiological Phenomena↗
PubMed1994

Neural network for automatic analysis of motility data.

Continuous recording of intraluminal pressures for extended periods of time is currently regarded as a valuable method for detection of esophageal motor abnormalities. A subsequent automatic analysis of the resulting motility data relies on strict mathematical criteria for recognition of pressure events. Due to great variation in events, this method often fails to detect biologically relevant pressure variations. We have tried to develop a new concept for recognition of pressure events based on a neural network. Pressures were recorded for over 23 hours in 29 normal volunteers by means of a portable data recording system. A number of pressure events and non-events were selected from 9 recordings and used for training the network. The performance of the trained network was then verified on recordings from the remaining 20 volunteers. The accuracy and sensitivity of the two systems were comparable. However, the neural network recognized pressure peaks clearly generated by muscular activity that had escaped detection by the conventional program. In conclusion, we believe that neurocomputing has potential advantages for automatic analysis of gastrointestinal motility data.

Adult↗
PubMed2025

Exploring biosynthetic potential of the endophytic Penicillium turbatum BLH34 using whole-genome sequence analysis and molecular networking.

An in-depth genomic and metabolomic investigation was conducted on the endophytic fungus Penicillium turbatum BLH34, isolated from Macleaya cordata. Hybrid sequencing (Illumina-Nanopore) generated a high-quality 27.9&#x2009;Mb genome (GC 48.6%) encoding 9798 proteins, with functional annotation linking 5350 genes to the NCBI non-redundant database and 3404 to KEGG pathways. AntiSMASH analysis uncovered 35 biosynthetic gene clusters (BGCs), 23 of which lacked homology to known pathways, highlighting BLH34's potential for novel metabolite discovery. Molecular networking (GNPS) and LC-MS/MS identified 19 specialised metabolites, including antimicrobial polyketides. Bioassays demonstrated potent inhibition against Staphylococcus aureus (36&#x2009;mm), Bacillus subtilis (28&#x2009;mm) and Escherichia coli (24&#x2009;mm), underscoring its pharmaceutical relevance.

Penicillium↗
PubMed2006

Dynamic modelling and analysis of biochemical networks: mechanism-based models and model-based experiments.

Systems biology applies quantitative, mechanistic modelling to study genetic networks, signal transduction pathways and metabolic networks. Mathematical models of biochemical networks can look very different. An important reason is that the purpose and application of a model are essential for the selection of the best mathematical framework. Fundamental aspects of selecting an appropriate modelling framework and a strategy for model building are discussed. Concepts and methods from system and control theory provide a sound basis for the further development of improved and dedicated computational tools for systems biology. Identification of the network components and rate constants that are most critical to the output behaviour of the system is one of the major problems raised in systems biology. Current approaches and methods of parameter sensitivity analysis and parameter estimation are reviewed. It is shown how these methods can be applied in the design of model-based experiments which iteratively yield models that are decreasingly wrong and increasingly gain predictive power.

Algorithms↗
PubMed2001

Spectrum-analysis and neural networks for imaging to detect and treat prostate cancer.

Conventional B-mode ultrasound currently is the standard means of imaging the prostate for guiding prostate biopsies and planning brachytherapy to treat prostate cancer. Yet B-mode images do not adequately display cancerous lesions of the prostate. Ultrasonic tissue-type imaging based on spectrum analysis of radiofrequency (rf) echo signals has shown promise for overcoming the limitations of B-mode imaging for visualizing prostate tumors. This method of tissue-type imaging utilizes nonlinear classifiers, such as neural networks, to classify tissue based on values of spectral parameter and clinical variables. Two- and three-dimensional images based on these methods demonstrate potential for guiding prostate biopsies and targeting radiotherapy of prostate cancer. Two-dimensional images are being generated in real time in ultrasound scanners used for real-time biopsy guidance and have been incorporated into commercial dosimetry software used for brachytherapy planning. Three-dimensional renderings show promise for depicting locations and volumes of cancer foci for disease evaluation to assist staging and treatment planning, and potentially for registration or fusion with CT images for targeting external-beam radiotherapy.

Biopsy↗
PubMed2005

Networks in phylogenetic analysis: new tools for population biology.

Phylogenetic analysis has changed greatly in the past decade, including the more widespread appreciation of the idea that evolutionary histories are not always tree-like, and may, thus, be best represented as reticulated networks rather than as strictly dichotomous trees. Reconstructing such histories in the absence of a bifurcating speciation process is even more difficult than the usual procedure, and a range of alternative strategies have been developed. There seem to be two basic uses for a network model of evolution: the display of real but unobservable evolutionary events (i.e. a hypothesis of the true phylogenetic history), and the display of character conflict within the data itself (i.e. a summary of the data). These two general approaches are briefly reviewed here, and the strengths and weaknesses of the different implementations are compared and contrasted. Each network methodology seems to have limitations in terms of how it responds to increasing complexity (e.g. conflict) in the data, and therefore each is likely to be more appropriate for one of the two uses than for the other. Several examples using parasitological data sets illustrate the uses of networks within the context of population biology.

Algorithms↗
PubMed2025

Flux-sum coupling analysis of metabolic network models.

Metabolites acting as substrates and regulators of all biochemical reactions play an important role in maintaining the functionality of cellular metabolism. Despite advances in the constraint-based framework for genome-scale metabolic modeling, we lack reliable proxies for metabolite concentrations that can be efficiently determined and that allow us to investigate the relationship between metabolite concentrations in specific metabolic states in the absence of measurements. Here, we introduce a constraint-based approach, the flux-sum coupling analysis (FSCA), which facilitates the study of the interdependencies between metabolite concentrations by determining coupling relationships based on the flux-sum of metabolites. Application of FSCA on metabolic models of Escherichia coli, Saccharomyces cerevisiae, and Arabidopsis thaliana showed that the three coupling relationships are present in all models and pinpointed similarities in coupled metabolite pairs. Using the available concentration measurements of E. coli metabolites, we demonstrated that the coupling relationships identified by FSCA can capture the qualitative associations between metabolite concentrations and that flux-sum is a reliable proxy for metabolite concentration. Therefore, FSCA provides a novel tool for exploring and understanding the intricate interdependencies between the metabolite concentrations, advancing the understanding of metabolic regulation, and improving flux-centered systems biology approaches.

Escherichia coli↗
PubMed2003

Topological analysis of metabolic networks based on Petri net theory.

Petri net concepts provide additional tools for the modelling of metabolic networks. Here, the similarities between the counterparts in traditional biochemical modelling and Petri net theory are discussed. For example the stoichiometry matrix of a metabolic network corresponds to the incidence matrix of the Petri net. The flux modes and conservation relations have the T-invariants, respectively, P-invariants as counterparts. We reveal the biological meaning of some notions specific to the Petri net framework (traps, siphons, deadlocks, liveness). We focus on the topological analysis rather than on the analysis of the dynamic behaviour. The treatment of external metabolites is discussed. Some simple theoretical examples are presented for illustration. Also the Petri nets corresponding to some biochemical networks are built to support our results. For example, the role of triose phosphate isomerase (TPI) in Trypanosoma brucei metabolism is evaluated by detecting siphons and traps. All Petri net properties treated in this contribution are exemplified on a system extracted from nucleotide metabolism.

Algorithms↗