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PubMed2026

Integrated Network Pharmacology and Molecular Docking Analysis of Sishen Decoction Identifies Potential Targets and Pathways in Gout.

Gout is a disease characterized by hyperuricemia and the deposition of urate crystals in joints and soft tissues, leading to recurrent acute arthritis. Its increasing prevalence imposes substantial clinical and socioeconomic burdens. Sishen Decoction (SSD) has been used in the treatment of gout, but its potential molecular mechanisms remain unclear. This study applied an integrated network pharmacology and molecular docking approach to identify potential targets and signaling pathways associated with SSD in gout. Active compounds and corresponding targets of SSD were retrieved from the Traditional Chinese Medicine Systems Pharmacology Database (TCMSP), while gout-related targets were collected from the GeneCards and Online Mendelian Inheritance in Man (OMIM) databases. Overlapping targets were identified and used to construct a drug-component-target-disease network. A protein-protein interaction (PPI) network was established using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed, followed by molecular docking using the docking server analysis module. A total of 37 bioactive compounds were associated with 116 overlapping gout-related targets. The top hub targets included TP53, IL6, IL1B, TNF, AKT1, EGFR, CASP3, JUN, BCL2, and MMP9. GO analysis suggested that these targets are involved in gene expression regulation and signal transduction. KEGG enrichment analysis indicated significant associations with the mitogen-activated protein kinase (MAPK), phosphoinositide 3-kinase/protein kinase B (PI3K-Akt), interleukin-17 (IL-17), and tumor necrosis factor (TNF) signaling pathways. Molecular docking predicted favorable interactions between key compounds and hub targets, with all binding energies of ≤-5 kcal/mol. These computational findings provide potential mechanistic hypotheses for the action of SSD in gout and may support future experimental validation.

Molecular Docking Simulation↗
PubMed2005

YANA - a software tool for analyzing flux modes, gene-expression and enzyme activities.

BACKGROUND: A number of algorithms for steady state analysis of metabolic networks have been developed over the years. Of these, Elementary Mode Analysis (EMA) has proven especially useful. Despite its low user-friendliness, METATOOL as a reliable high-performance implementation of the algorithm has been the instrument of choice up to now. As reported here, the analysis of metabolic networks has been improved by an editor and analyzer of metabolic flux modes. Analysis routines for expression levels and the most central, well connected metabolites and their metabolic connections are of particular interest. RESULTS: YANA features a platform-independent, dedicated toolbox for metabolic networks with a graphical user interface to calculate (integrating METATOOL), edit (including support for the SBML format), visualize, centralize, and compare elementary flux modes. Further, YANA calculates expected flux distributions for a given Elementary Mode (EM) activity pattern and vice versa. Moreover, a dissection algorithm, a centralization algorithm, and an average diameter routine can be used to simplify and analyze complex networks. Proteomics or gene expression data give a rough indication of some individual enzyme activities, whereas the complete flux distribution in the network is often not known. As such data are noisy, YANA features a fast evolutionary algorithm (EA) for the prediction of EM activities with minimum error, including alerts for inconsistent experimental data. We offer the possibility to include further known constraints (e.g. growth constraints) in the EA calculation process. The redox metabolism around glutathione reductase serves as an illustration example. All software and documentation are available for download at http://yana.bioapps.biozentrum.uni-wuerzburg.de. CONCLUSION: A graphical toolbox and an editor for METATOOL as well as a series of additional routines for metabolic network analyses constitute a new user-friendly software for such efforts.

Algorithms↗
PubMed2001

Feature extraction and classification of breast cancer on dynamic magnetic resonance imaging using artificial neural network.

A neural network system was designed to extract and analyze the quantitative data from time-intensity profile. These data was used to predict the outcome of biopsy in a group of patients with histopathologically proved breast lesions. The performance of an artificial neural network (ANN) was compared with radiologists using a database with 120 patients' records each of which consisted of 14 quantitative parameters mostly derived directly from time-intensity profile. The network was trained and tested using the jackknife method and its performance was then compared with that of the radiologists in terms of sensitivity, specificity and accuracy using receiver operating characteristic curve (ROC) analysis. The network was able to classify correctly 107 of 120 original cases and yielded a better diagnostic accuracy (89%), compared with that of the radiologist (79%) by performing a constructive association between extracted quantitative data and corresponding pathological results (r=0.72, P<0.001).

Adolescent↗
PubMed2007

Neural network analyses of infrared spectra for classifying cell wall architectures.

About 10% of plant genomes are devoted to cell wall biogenesis. Our goal is to establish methodologies that identify and classify cell wall phenotypes of mutants on a genome-wide scale. Toward this goal, we have used a model system, the elongating maize (Zea mays) coleoptile system, in which cell wall changes are well characterized, to develop a paradigm for classification of a comprehensive range of cell wall architectures altered during development, by environmental perturbation, or by mutation. Dynamic changes in cell walls of etiolated maize coleoptiles, sampled at one-half-d intervals of growth, were analyzed by chemical and enzymatic assays and Fourier transform infrared spectroscopy. The primary walls of grasses are composed of cellulose microfibrils, glucuronoarabinoxylans, and mixed-linkage (1 --> 3),(1 --> 4)-beta-D-glucans, together with smaller amounts of glucomannans, xyloglucans, pectins, and a network of polyphenolic substances. During coleoptile development, changes in cell wall composition included a transient appearance of the (1 --> 3),(1 --> 4)-beta-D-glucans, a gradual loss of arabinose from glucuronoarabinoxylans, and an increase in the relative proportion of cellulose. Infrared spectra reflected these dynamic changes in composition. Although infrared spectra of walls from embryonic, elongating, and senescent coleoptiles were broadly discriminated from each other by exploratory principal components analysis, neural network algorithms (both genetic and Kohonen) could correctly classify infrared spectra from cell walls harvested from individuals differing at one-half-d interval of growth. We tested the predictive capabilities of the model with a maize inbred line, Wisconsin 22, and found it to be accurate in classifying cell walls representing developmental stage. The ability of artificial neural networks to classify infrared spectra from cell walls provides a means to identify many possible classes of cell wall phenotypes. This classification can be broadened to phenotypes resulting from mutations in genes encoding proteins for which a function is yet to be described.

Algorithms↗
PubMed2005

Fractal and Fourier analysis of the hepatic sinusoidal network in normal and cirrhotic rat liver.

The organization of the hepatic microvascular network has been widely studied in recent years, especially with regard to cirrhosis. This research has enabled us to recognize the distinctive vascular patterns in the cirrhotic liver, compared with the normal liver, which may explain the cause of liver dysfunction and failure. The aim of this study was to compare normal and cirrhotic rat livers by means of a quantitative mathematical approach based on fractal and Fourier analyses performed on photomicrographs and therefore on discriminant analysis. Vascular corrosion casts of livers belonging to the following three experimental groups were studied by scanning electron microscopy: normal rats, CCl(4)-induced cirrhotic rats and cirrhotic rats after ligation of the bile duct. Photomicrographs were taken at a standard magnification; these images were used for the mathematical analysis. Our experimental design found that use of these different analyses reaches an efficiency of over 94%. Our analyses demonstrated a higher complexity of the normal hepatic sinusoidal network in comparison with the cirrhotic network. In particular, the morphological changes were more marked in the animals with bile duct-ligation cirrhosis compared with animals with CCl(4)-induced cirrhosis. The present findings based on fractal and Fourier analysis could increase our understanding of the pathophysiological alterations of the liver, and may have a diagnostic value in future clinical research.

Algorithms↗
PubMed2005

Predictive neural networks for gene expression data analysis.

Gene expression data generated by DNA microarray experiments have provided a vast resource for medical diagnosis and disease understanding. Most prior work in analyzing gene expression data, however, focuses on predictive performance but not so much on deriving human understandable knowledge. This paper presents a systematic approach for learning and extracting rule-based knowledge from gene expression data. A class of predictive self-organizing networks known as Adaptive Resonance Associative Map (ARAM) is used for modelling gene expression data, whose learned knowledge can be transformed into a set of symbolic IF-THEN rules for interpretation. For dimensionality reduction, we illustrate how the system can work with a variety of feature selection methods. Benchmark experiments conducted on two gene expression data sets from acute leukemia and colon tumor patients show that the proposed system consistently produces predictive performance comparable, if not superior, to all previously published results. More importantly, very simple rules can be discovered that have extremely high diagnostic power. The proposed methodology, consisting of dimensionality reduction, predictive modelling, and rule extraction, provides a promising approach to gene expression analysis and disease understanding.

Animals↗
PubMed1995

Temporal coding in the guinea-pig auditory cortex as revealed by optical imaging and its pattern-time-series analysis.

The neural network structure of a guinea-pig's primary auditory cortex is estimated by applying pattern-time-series analysis to the auditory evoked responses. Spatiotemporal patterns in click-evoked responses, observed by optical recording with voltage-sensitive dye, are analyzed by time series analysis using a multivariable autoregressive (MAR) model. Oscillatory neural activities with a distribution of about 10 approximately 40 Hz in the click-induced evoked responses are found in the cortical response field. The cortical regions where the distributed neural oscillations are generated are identified by pattern-time-series analysis. In addition, two types of cortico-cortical connections, unilateral and bilateral connections between the cortical points, are speculated to be the causes of oscillatory neural activity transfer. It can be said that the so-called synchronized neural oscillation, in the sense of coherency or correlation between the two evoked responses at the oscillatory frequency, does not necessarily represent real cortico-cortical neural connections at the evoked response points.

Animals↗
PubMed2006

Analysis of co-aggregation of cancer based on registry data.

OBJECTIVE: An exploratory analysis of co-aggregation of cancers using registry-based data. METHODS: We utilized sibships from over 18,000 families who had been recruited to the NCI-sponsored multi-institutional Cancer Genetics Network. The analysis assesses co-aggregation at the individual and family level and adjusts for ascertainment. RESULTS: We found statistically significant familial co-aggregation of lung cancer with pancreatic (adjusted p < 0.001), prostate (adjusted p < 0.003), and colorectal cancers (adjusted p = 0.004). In addition, we found significant familial co-aggregation of pancreatic and colorectal cancers (adjusted p = 0.018), and co-aggregation of hematopoietic and (non-ovarian) gynecologic cancers (adjusted p = 0.01). CONCLUSION: This analysis identified familial aggregation of cancers for which a genetic component has yet to be established.

Female↗
PubMed1991

Application of neural computing in pharmaceutical product development.

Neural computing technology is capable of solving problems involving complex pattern recognition. This technology is applied here to pharmaceutical product development. The most commonly used computational algorithm, the delta back-propagation network, was utilized to recognize the complex relationship between the formulation variables and the in vitro drug release parameters for a hydrophilic matrix capsule system. This new computational technique was also compared with the response surface methodology (RSM). Artificial neural network (ANN) analysis was able to predict the response values for a series of validation experiments more precisely than RSM. ANN may offer an alternative to RSM because it allows for the development of a system that can incorporate literature and experimental data to solve common problems in the pharmaceutical industry.

Algorithms↗
PubMed2004

Melanoma diagnosis by Raman spectroscopy and neural networks: structure alterations in proteins and lipids in intact cancer tissue.

Melanoma is the most aggressive skin cancer. The specificity and sensitivity of clinical diagnosis varies from around 40% to 80%. Here, we investigated whether the chemical changes in the melanoma tissue detected by Raman spectroscopy and neural networks can be used for diagnostic purposes. Near-infrared Fourier transform Raman spectra were obtained from samples of melanoma (n=22) and other skin tumors that can be clinically confused with melanoma: pigmented nevi (n=41), basal cell carcinoma (n=48), seborrheic keratoses (n=23), and normal skin (n=89). A sensitivity analysis of spectral frequencies used by a neural network was performed to determine the importance of the individual components in the Raman spectra. Visual inspection of the Raman spectra suggested that melanoma could be differentiated from pigmented nevi, basal cell carcinoma, seborrheic keratoses, and normal skin due to the decrease in the intensity of the amide I protein band around 1660 cm-1. Moreover, melanoma and basal cell carcinoma showed an increase in the intensity of the lipid-specific band peaks around 1310 cm-1 and 1330 cm-1, respectively. Band alterations used in the visual inspection were also independently identified by a neural network for melanoma diagnosis. The sensitivity and specificity for diagnosis of melanoma achieved by neural network analysis of Raman spectra were 85% and 99%, respectively. We propose that neural network analysis of near-infrared Fourier transform Raman spectra could provide a novel method for rapid, automated skin cancer diagnosis on unstained skin samples.

Diagnosis, Differential↗
PubMedDate not supplied

Electronic prescribing and HIPAA privacy regulation.

Electronic prescribing offers the prospect of safer medication management, but fulfillment of that promise depends on ready access to personal health information from many sources, thus raising new concerns about information privacy and security. Federal privacy regulations under the Health Insurance Portability and Accountability Act of 1996 (HIPAA) limit the sharing of health information by providers, and particularly may discourage information sharing over distributed computer networks. This analysis finds that although HIPAA has only a limited effect on current e-prescribing practices, future electronic prescribing systems will likely fall short of their potential benefits, absent policy refinements designed to encourage clinically appropriate, networked sharing of patient health information.

Clinical Pharmacy Information Systems↗
PubMed1991

[New method for the analysis of two-way network crosses].

Two-way network crosses are often related to indigenous breeds which have two notable characteristics: 1. individuals within-breed are different, 2. the differences between-breeds are different too. Because of this, regular Least-square analysis method can not be used in this type of test. This paper proposed a new method, which was based on the new model and the best liner unbiased estimation theory. An example was given to show the usage.

Crosses, Genetic↗
PubMed2001

[Identification of epileptogenic networks from modeling and nonlinear analysis of SEEG signals].

This work is focused on the study of the organization of the epileptogenic zone (E.Z.) in humans based on the analysis of stereo-electroencephalographic (SEEG) signals with signal processing methods, and more especially those dedicated to the estimation of signal interdependencies. In order to evaluate quantities provided by these methods and in order to relate them to the notion of functional coupling between cerebral structures, we developed a neurophysiologically relevant model able to generate EEG signals from organized networks of neural populations. We showed that the model can produce realistic multichannel epileptiform signals (when compared to real SEEG signals) under certain conditions (excitation/inhibition ratio within populations, uni/bi-directional coupling between populations). In this paper, the model framework is used to evaluate the performances of nonlinear regression analysis as a method to characterize couplings between cerebral structures from the SEEG signals they produce. Two quantities, a nonlinear correlation coefficient and a direction index, respectively related to coupling parameters in the model (degree/direction) are presented. These two quantities are measured on real SEEG signals recorded in patients suffering from temporal lobe epilepsy and candidate to surgical treatment. Results show that the characterization of functional couplings leads to the identification of networks referred to as 'epileptogenic networks', which might be responsible for the triggering of seizures. These results also corroborate our previous results on the classification of temporal lobe epilepsies, showing that there exist recurrent seizure patterns that can be classified on the basis of interactions between medial and lateral neocortical structures.

Algorithms↗
PubMed2004

Stability analysis of asynchronous states in neuronal networks with conductance-based inhibition.

Oscillations in networks of inhibitory interneurons have been reported at various sites of the brain and are thought to play a fundamental role in neuronal processing. This Letter provides a self-contained analytical framework that allows numerically efficient calculations of the population activity of a network of conductance-based integrate-and-fire neurons that are coupled through inhibitory synapses. Based on a normalization equation this Letter introduces a novel stability criterion for a network state of asynchronous activity and discusses its perturbations. The analysis shows that, although often neglected, the reversal potential of synaptic inhibition has a strong influence on the stability as well as the frequency of network oscillations.

Membrane Potentials↗
PubMed2006

Effect of ionic solutes on the hydrogen bond network dynamics of water: power spectral analysis of aqueous NaCl solutions.

To understand the modifications of the hydrogen bond network of water by ionic solutes, power spectra as well as static distributions of the potential energies of tagged solvent molecules and solute ions have been computed from molecular dynamics simulations of aqueous NaCl solutions. The key power spectral features of interest are the presence of high-frequency peaks due to localized vibrational modes, the existence of a multiple time scale or 1/falpha frequency regime characteristic of networked liquids, and the frequency of crossover from 1/falpha type behavior to white noise. Hydrophilic solutes, such as the sodium cation and the chloride anion, are shown to mirror the multiple time scale behavior of the hydrogen bond network fluctuations, unlike hydrophobic solutes which display essentially white noise spectra. While the power spectra associated with tagged H2O molecules are not very sensitive to concentration in the intermediate frequency 1/falpha regime, the crossover to white noise is shifted to lower frequencies on going from pure solvent to aqueous alkali halide solutions. This suggests that new and relatively slow time scales enter the picture, possibly associated with processes such as migration of water molecules from the hydration shell to the bulk or conversion of contact ion pairs into solvent-separated ion pairs which translate into variations in equilibrium transport properties of salt solutions with concentration. For anions, cations, and solvent molecules, the trends in the alpha exponents of the multiple time scale region and the self-diffusivities are found to be strongly correlated.

Journal Article↗
PubMedDate not supplied

[Calculation of parameters of neuronal network model on the basis of analysis of power spectra of rat brain biopotentials].

On the basis of neuronal network a linearized model of power spectrum of the network stable activity was calculated with the approximation of external input by "white" noise. The power spectrum function obtained was used for approximation of the power spectra of the rats motor cortex potentials by the least squares method. As a result the network parameters modelling excitatory and inhibitory cellular and synaptic mechanisms were calculated for two rat strains differing in seizure readiness. As a result of calculations genetically predisposed to seizures KM rats were assumed to differ from unpredisposed to seizures Wistar rats in the increase of efficacy of neuronal interactions (excitatory and inhibitory) as a consequence of the enhanced neuronal "reactivity".

Animals↗
PubMed2024

Exclusive enteral nutrition initiates individual protective microbiome changes to induce remission in pediatric Crohn's disease.

Exclusive enteral nutrition (EEN) is a first-line therapy for pediatric Crohn's disease (CD), but protective mechanisms remain unknown. We established a prospective pediatric cohort to characterize the function of fecal microbiota and metabolite changes of treatment-naive CD patients in response to EEN (German Clinical Trials DRKS00013306). Integrated multi-omics analysis identified network clusters from individually variable microbiome profiles, with Lachnospiraceae and medium-chain fatty acids as protective features. Bioorthogonal non-canonical amino acid tagging selectively identified bacterial species in response to medium-chain fatty acids. Metagenomic analysis identified high strain-level dynamics in response to EEN. Functional changes in diet-exposed fecal microbiota were further validated using gut chemostat cultures and microbiota transfer into germ-free Il10-deficient mice. Dietary model conditions induced individual patient-specific strain signatures to prevent or cause inflammatory bowel disease (IBD)-like inflammation in gnotobiotic mice. Hence, we provide evidence that EEN therapy operates through explicit functional changes of temporally and individually variable microbiome profiles.

Crohn Disease↗
PubMed2006

Spatio-temporal modeling and analysis of fMRI data using NARX neural network.

This paper presents spatio-temporal modeling and analysis methods to fMRI data. Based on the nonlinear autoregressive with exogenous inputs (NARX) model realized by the Bayesian radial basis function (RBF) neural networks, two methods (NARX-1 and NARX-2) are proposed to capture the unknown complex dynamics of the brain activities. Simulation results on both synthetic and real fMRI data clearly show that the proposed schemes outperform the conventional t-test method in detecting the activated regions of the brain.

Bayes Theorem↗