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At least 73 records · Page 4Linked to original sources
PubMed2026

Examining early-phase symptom trajectories in interpersonal psychotherapy versus antidepressant medication for adults with depression: A dynamic time warp network analysis.

BACKGROUND: Depression is characterized by substantial symptom heterogeneity, which is often concealed when examining total severity scores. Analyzing symptom-level change can improve our understanding of treatment effects and recovery processes. This study, therefore, examined dynamic symptom networks during early-phase interpersonal psychotherapy (IPT) and selective serotonin reuptake inhibitor (SSRI) antidepressant treatment, assessing patterns of symptom change across as well as differences between treatments. METHODS: Using weekly item-level Hamilton Depression Rating Scale (HAM-D) data from a randomized clinical trial comparing IPT and SSRIs for adults with depression, this preregistered study examined symptom trajectories in the first six weeks of treatment with Dynamic Time Warping (DTW). RESULTS: Depressive symptom trajectories and DTW-based symptom networks were largely similar for IPT and SSRI. In both conditions, changes in somatic symptoms of anxiety and middle insomnia tended to precede improvements in depressed mood. CONCLUSIONS: Early symptom change may occur outside the core affective domain, underscoring the importance of monitoring symptoms broadly. Symptom-level patterns may reflect patients' stage of recovery and provide clinically relevant information beyond total severity scores. The absence of differences in improvement patterns between IPT and SSRI suggest few indications for treatment selection based on baseline symptom profiles. Future research should replicate and extend these findings to subsequent treatment phases using more frequent assessments and a broader range of interventions.

Humans↗
PubMed1996

Introduction of a new laboratory test: an econometric approach with the use of neural network analysis.

We designed a simulation model for the assessment of the financial risks involved when a new diagnostic test is introduced in the laboratory. The model is based on a neural network consisting of ten neurons and assumes that input entities can have assigned appropriate uncertainty. Simulations are done on a 1-day interval basis. Risk analysis completes the model and the financial effects are evaluated for a selected time period. The basic output of the simulation consists of total expenses and income during the simulation time, net present value of the project at the end of simulation, total number of control samples during simulation, total number of patients evaluated and total number of used kits.

Chemistry, Clinical↗
PubMed1988

Network analysis of an arterial tree.

The arterial tree of a Sprague-Dawley rat was casted and carefully mapped with the aim of comparing its network characteristics with those suggested by the classical model of an arterial tree. It is shown that if the tree is to be measured accurately, the concept of 'whole vessels' on which the classical model is based must be abandoned since such vessels do not actually exist in the network, nor can they be accurately defined. The concept of 'vessel segments' is proposed instead and its use is demonstrated. A total of 1313 vessel segments in the arterial tree of the rat are mapped and divided into well defined 'levels'. The length and diameter of each segment are measured and the distribution and averages of these at different levels are presented as indicators of the branching characteristics of the tree.

Animals↗
PubMedDate not supplied

Artificial neural network analysis of 1H nuclear magnetic resonance spectroscopic data from human plasma.

Nuclear magnetic resonance (NMR) spectroscopy is finding increasing use in studies of plasma and lipoproteins in health and disease, including cancer. Analysis of the NMR data is not straightforward due to complex systems and also partly unknown underlying biochemistry. Here we demonstrate how artificial neural networks can be utilised in biomedical NMR. Their quantification power is illustrated by establishing lipoprotein lipid quantification directly from plasma 1H NMR data. The biochemical rationale for this example is elucidated on the basis of the relative weights of the spectral inputs in a trained network. A novel application of a Kohonen-type network architecture to classify plasma 1H NMR spectra is also presented.

Humans↗
PubMed1993

Network analysis of functional auditory pathways mapped with fluorodeoxyglucose: associative effects of a tone conditioned as a Pavlovian excitor or inhibitor.

The purpose of this study was to examine how opposite learned associative properties of the same auditory stimulus are represented by the pattern of network interactions between auditory system structures. [14C(U)]2-fluoro-2-deoxyglucose (FDG) autoradiography was used to compare mean auditory system activity and interregional correlations resulting from the presentation of a tone trained as either a Pavlovian conditioned excitor or inhibitor. Rats were trained with reinforced trials of the conditioned excitor (A+) intermixed with non-reinforced trials of a tone-light compound (AX-). For the Conditioned Excitor group, the tone was the excitor (A+), while for the Conditioned Inhibitor group the tone was the inhibitor (X-). After conditioning, both groups were injected with FDG and presented with the same tone. Structural equation models, constructed from the anatomical connections between auditory regions and their interregional correlations in FDG uptake, were used to calculate path coefficients representing the network interactions. The opposite associative significance of the tone was reflected as functional changes in the interactions between parallel auditory pathways. Direct covariance effects through lemniscal pathways from the ventral cochlear nucleus were similar in absolute magnitude but differed in sign between the Excitor and Inhibitor network models. Extra-auditory influences on the dorsal cochlear nucleus were greater for the tone-inhibitor, reflecting possible interactions of this nucleus with extra-auditory regions. The different associative effects of the tone suggest that central auditory pathways can code not only the physical qualities, but also the associative significance of auditory stimuli. These findings demonstrate that neural network interactions differentiate the associative effects of tones in the brain. It is proposed that associative learning is a distributed property of neural networks and that such a property can be understood by considering the interactions between component parts of the network.

Acoustic Stimulation↗
PubMed2026

Integrative Network Analysis of Bioactive Compounds from Punica granatum L. Peel: Multi-Target Mechanisms in Wound Healing.

BACKGROUND: Wound-healing agents often have limited efficacy and require prolonged recovery times, prompting growing interest in developing herbal-based formulations. Among these, Punica granatum L. has attracted considerable attention because of its high polyphenolic content. Despite its widespread use, the precise pharmacological targets underlying its wound-healing effects remain poorly understood and require systematic investigation. OBJECTIVES: This study aimed to elucidate the underlying pharmacological mechanisms of the topical wound-healing properties of P. granatum L. using a network pharmacology approach. METHODS: Bioactive compounds of P. granatum L. and their potential target genes were identified using the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), Similarity Ensemble Approach (SEA), and SwissTargetPrediction databases. Wound healing-related genes were retrieved from the GeneCards database. Genes intersecting P. granatum L. targets and wound healing-associated genes were subjected to functional enrichment analyses, including protein-protein interaction (PPI), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. The PPI network was further analyzed using Cytoscape, and the phytoconstituent-target interaction network was visualized using Gephi. These findings were validated using molecular docking. RESULTS: A total of 40 intersecting genes were identified as potential P. granatum L. targets involved in wound healing. Among these, EGFR, PTPN11, HRAS, IGF1R, and ESR1 were identified as key hub genes. Functional enrichment analysis indicated that the most significantly enriched signaling pathways included the MAPK, PI3K-Akt, EGFR tyrosine kinase inhibitor resistance, focal adhesion, and FoxO signaling pathways. Molecular docking analysis confirmed favorable binding of quercetin and ellagic acid to the hub targets EGFR, IGF1R, and ESR1. CONCLUSIONS: These findings elucidate the pharmacological pathways underlying P. granatum-mediated wound healing and suggest that P. granatum L. acts as a multi-target modulator in the wound-healing process.

Focal Adhesion↗
PubMed2025

DigiNet: Optimizing personalized care for patients with stage IV non-small cell lung cancer (NSCLC) through a digitally connected provider network-analysis plan of a prospective multicenter cohort trial.

PURPOSE: The German sector-based healthcare system poses a major challenge to continuous patient monitoring and long-term follow-up, both essential for generating high-quality, longitudinal real-world data. The national Network for Genomic Medicine (nNGM) bridges the inpatient and outpatient care sectors to provide comprehensive molecular diagnostics and personalized treatment for non-small cell lung cancer (NSCLC) patients in Germany. Building on the established nNGM infrastructure, the DigiNet study aims to evaluate the impact of digitally integrated, personalized care on overall survival (OS) and the optimization of treatment pathways, compared to routine care. METHODS: DigiNet is a prospective, controlled, non-randomized multicenter cohort study including patients with stage IV NSCLC in two study regions (East and West) in Germany. The results of molecular diagnostics and clinical information, along with the entire treatment data are documented in a shared database. A board of lung cancer specialists monitors critical events. Patients digitally complete quality of life questionnaires, with results visualized for physicians. To assess the impact of this personalized digital care, a population-based control group will be identified by matching cohorts within the involved cancer registries. The primary endpoint is OS, and secondary endpoints comprise time on first-line treatment and hospitalization rates. Furthermore, a health economic and business economic evaluation will be conducted. Qualitative interviews with patients and physicians will be performed to assess barriers and facilitating factors for implementing the DigiNet intervention. ETHICS: The study protocol was reviewed and approved by the Ethics Committee of the University Hospital of Cologne (21-1521). TRIAL REGISTRATION: NCT05818449, registered retrospectively on December 12, 2022.

Humans↗
PubMed1994

Neural network analysis of flow cytometric data for 40 marine phytoplankton species.

Flow cytometry data (time of flight, horizontal and vertical forward light scatter, 90 degrees light scatter, and "red" and "orange" integral fluorescence) were collected for laboratory cultures of 40 species of marine phytoplankton, from the following taxonomic classes, the Dinophyceae, Bacillariophyceae, Prymnesiophyceae, Cryptophyceae, and other flagellates. Single-hidden-layer "back-propagation" neural networks were trained to discriminate between species by recognising patterns in their flow cytometric signatures, and network performance was assessed using an independent test data set. Two approaches were adopted employing: (1) a hierarchy of small networks, the first identifying to which major taxonomic group a cell belonged, and then a network for that taxonomic group identified to species, and (2) a single large network. Discriminating some of the major taxonomic groups was successful but others less so. With networks for specific groups, cryptophyte species were all identified reliably (probability of correct classification always being > 0.75); in the other groups half of the species were identified reliably. With the large network, dinoflagellates, cryptomonads, and flagellates were identified almost as well as by networks specific for these groups. The application of neural computing techniques to identification of such a large number of species represents a significant advance from earlier studies, although further development is required.

Flow Cytometry↗
PubMed1996

Positron emission tomography of 5-HT reuptake sites in the human brain with C-11 McN5652 extraction of characteristic images by artificial neural network analysis.

The goal of this work was to identify the distribution of serotonin transporters in the human brain with [11C](+)McN5652/PET. Four healthy volunteers were studied. To determine non-specific binding, a PET study was also performed with the inactive enantiomer [11C](-)McN5652 as well as with [11C](+)McN5652 after pretreatment with fluoxetine. For pattern extraction the PET data sets were analyzed by a back-propagation neural network. Two pharmacokinetic patterns and two characteristic images were separated; one representing specific binding, the other representing non-specific binding. The specific binding image showed characteristic distribution of serotonin transporters with [11C](+)McN5652. The pattern images demonstrated an improvement in image quality compared to the original PET images (reduced variance, higher region-to-cerebellum ratio, good correlation with known density of serotonin transporters). The non-specific binding images extracted from [11C](-)McN5652/PET were similar to those of [11C](-)McN5652 and [11C](-)McN5652 with fluoxetine. Thus, PET studies obtained with [11C](+)McN5652 largely represent the regional distribution of the serotonin transporters and the inactive enantiomer [11C](-)McN5652 shows the distribution of its nonspecific binding.

Adult↗
PubMed1995

Network analysis of brain cognitive function using metabolic and blood flow data.

Functional neuroimaging has become a powerful tool for investigating the neurobiological foundations of cognition. An overview is presented of the two major strategies by which such data are currently analyzed. One strategy compares the pattern of activity between two (or more) tasks, looking for those brain areas that show significant changes. The second investigates the functional relationships between regional activities in an attempt to determine the systems-level neural networks mediating the tasks. Object and spatial visual processing tasks are used to illustrate each of these strategies.

Animals↗
PubMed1995

Evaluation of muscle diseases using artificial neural network analysis of 31P MR spectroscopy data.

Dermatomyositis is an autoimmune disease characterized by an erythematous rash and severe muscle weakness. 31P Magnetic resonance spectroscopy (MRS) provides quantitative data for longitudinal monitoring of disease status and responses to immunosuppressive therapy. A disease variant, amyopathic dermatomyositis, presents with a typical rash but no clinical muscle weakness. However, metabolic abnormalities in the oxidative capacity of muscles of amyopathic patients during exercise were detected with 31P MRS. Because MRS provided the best quantitative data for evaluating dermatomyositis, the 31P metabolic parameters derived from the MR spectra were further processed using an artificial neural network (XERION). The neural network analyses provided additional clinical information from the weighted correlations of multiple 31P parameters, namely, inorganic phosphate, phosphocreatine, ATP, phosphodiesters, and selected ratios. This investigation analyzes the relative importance of the various metabolic parameters for accurate patient characterization and provides insights into the pathogenesis of the disease.

Adenosine Triphosphate↗
PubMed1984

Glomerular hemodynamics and vascular structure in uremia: a network analysis of glomerular path lengths and maximal blood transit times computed for a microvascular model reconstructed from subserial ultrathin sections.

The dimensions of individual capillary segments were determined from a glomerular model constructed on the basis of electron micrographs of subserial ultrathin sections of kidney tissue from a rat made chronically uremic by subtotal nephrectomy. Hemodynamic calculations used computer programs for node pressure analysis and for the determination of path lengths and transit times from afferent to efferent arteriole. The afferent arteriole divided into five primary capillary segments. Three of these carried 84% of the flow through 632 paths, which accounted for 54% of the capillary endothelial area. Their mean path length was 520.97 micron, mean of segment numbers per path 17.66, and mean transit time 0.50 sec. The remaining two capillary segments carried 16% of the flow and led to paths accounting for the remaining 46% of the capillary area. The number of paths from the afferent arteriole through these two segments was 21,244. Mean path length from the initial node through these two segments was 852.18 micron, mean of segment numbers per path 29.62, and mean transit time 20.76 sec. The most striking difference between the two sets of paths was in transit time. This asymmetry would tend to reduce the filtration surface and the Kf (hydraulic conductance x filtering surface area) by causing early filtration pressure equilibrium in much of the capillary network, and suggests an intrinsic glomerular aspect of chronic renal insufficiency.

Animals↗
PubMed1995

Cartilage matrix protein forms a type II collagen-independent filamentous network: analysis in primary cell cultures with a retrovirus expression system.

Cartilage matrix protein (CMP) is expressed specifically in mature cartilage and consists of two von Willebrand factor A domains (CMP-A1 and CMP-A2) that are separated by an epidermal growth factor-like domain, and a coiled-coil tail domain at the carboxyl terminal end. We have shown previously that CMP interacts with type II collagen-containing fibrils in cartilage. In this study, we describe a type II collagen-independent CMP filament and we analyze the structural requirement for the formation of this type of filament. Recombinant wild-type CMP and two mutant forms were expressed in chick primary cell cultures using a retrovirus expression system. In chondrocytes, the wild-type virally encoded CMP is able to form disulfide bonded trimers and to assemble into filaments. Filaments also form with CMP whose Cys455 and Cys457 in the tail domain were mutagenized to prevent interchain disulfide bond formation. Therefore, intermolecular disulfide bonds are not necessary for the assembly of CMP into filaments. Both the wild-type and the double cysteine mutant also form filaments in fibroblasts, indicating that chondrocyte-specific factors are not required for filament formation. A truncated form of CMP that consists only of the CMP-A2 domain and the tail domain can form trimers but fails to form filaments, indicating that the deleted CMP-A1 domain and/or the epidermal growth factor domain are necessary for filament assembly but not for trimer formation. Furthermore, the expression of the virally encoded truncated CMP in chondrocyte culture disrupts endogenous CMP filament formation. Together these data suggest a role for CMP in cartilage matrix assembly by forming filamentous networks that require participation and coordination of individual domains of CMP.

Amino Acid Sequence↗
PubMed2026

Enzyme-Metabolite Network Analysis of Endometrial Cancer-Derived Extracellular Vesicles Through Integrated Proteomics and Metabolomics.

Endometrial cancer (EC) is the most common gynecological malignancy in high-income countries. Extracellular vesicles (EVs) are key mediators of intercellular communication and metabolic reprogramming, but their molecular cargo in EC remains poorly characterized. EVs were isolated from four EC cell lines representing Type I and Type II subtypes (AN3CA, ISHIKAWA, HEC1A, and KLE). Untargeted metabolomics was performed by HILIC-LC-MS/MS, proteomics by data-independent acquisition (DIA) mass spectrometry, and multi-omics integration using MetaboAnalyst and OmicsNet. Metabolomic profiling identified 1463 annotated features and revealed significant differences among EC cell lines (PERMANOVA, p = 0.002). Twenty-eight differentially abundant metabolites, including lactic acid, succinic acid, and uric acid, were identified. Proteomic analysis quantified 8513 proteins with subtype-specific expression patterns. Integrated analysis revealed seven significantly enriched pathways, including glycolysis/gluconeogenesis, central carbon metabolism in cancer, and the pentose phosphate pathway. Increased LDHA abundance in metastatic AN3CA-derived EVs was confirmed by Western blot (p = 0.047). EC-derived EVs display subtype- and metastatic-status-specific metabolo-proteomic signatures, with glycolysis, TCA cycle remodeling, and central carbon metabolism as convergent pathway signatures of molecular reprogramming. These findings establish a multi-omics framework for characterizing EV cargo in EC and identify candidate enzyme-metabolite nodes for future biomarker validation in patient-derived specimens.

Female↗
PubMed1995

Pharmacologic modification of tumor blood flow and interstitial fluid pressure in a human tumor xenograft: network analysis and mechanistic interpretation.

Various vasoactive agents have been used to modify tumor blood flow with the ultimate goal of improving cancer detection and treatment, with widely disparate results. Furthermore, the lack of mechanistic interpretations has hindered understanding of how these agents affect the different physiological parameters involved in perfusion. Thus, there is a need to develop a unified framework for understanding the interrelated physiological effects of pharmacological and physical agents. The goals of this study were (1) to develop a mathematical model which helps determine the location and magnitude of changes in the vascular resistance of tumor and normal tissues and (2) to test the model with our experimental studies and by comparison with results from the literature. The systemic and interstitial pressures and relative tumor blood flow were measured before and after administration of angiotensin II, epinephrine, norepinephrine, nitroglycerin, and hydralazine in SCID mice bearing LS174T human colon adenocarcinoma xenografts. A mathematical model was developed in analogy to electrical circuits which examined the pressure, flow, and resistance relationships for arterial and venous segments of the vasculature of a tumor and surrounding normal tissue. Vasoconstrictor-induced increases in the mean arterial blood pressure led to increases in tumor blood flow and interstitial pressure with the magnitude of change dependent on the agent (percentage change in blood flow: angiotensin > epinephrine > norepinephrine). The vasodilating agents induced decreases in tumor blood flow in parallel to the induced decreases in the systemic pressure, but only the long-acting arterial vasodilator hydralazine was capable of effecting a decrease in tumor interstitial pressure. The model was also found to be consistent with other data available in the literature on norepinephrine, pentoxifylline, nicotinamide, and hemodilution, and was useful in providing input as to the location and degree of the physiological effects of these agents. The results of the data and model show that the steal phenomenon is the dominant mechanism for redistribution of host blood flow to the tumor. However, some degree of arterial control was found to be present in the tumors. Moreover, the parallel increases in tumor interstitial pressure and blood flow contradict any hypothesis suggesting that elevated interstitial fluid pressure precipitates chronic vascular collapse, thus decreasing blood flow.

Angiotensin II↗
PubMed1985

Modeling the electrical behavior of anatomically complex neurons using a network analysis program: passive membrane.

We describe the application of a popular and widely available electrical circuit simulation program called SPICE to modeling the electrical behavior of neurons with passive membrane properties and arbitrarily complex dendritic trees. Transient responses may be calculated at any location in the cell model following current, voltage or conductance perturbations at any point. A numbering method is described for binary trees which is helpful in transforming complex dendritic structures into a coded list of short cylindrical dendritic segments suitable for input to SPICE. Individual segments are modeled as isopotential compartments comprised of a parallel resistor and capacitor, representing the transmembrane impedance, in series with one or two core resistors. Synaptic current is modeled by a current source controlled by the local membrane potential and an "alpha-shaped" voltage, thus simulating a conductance change in series with a driving potential. Extensively branched test cell circuits were constructed which satisfied the equivalent cylinder constraints (Rall 1959). These model neurons were perturbed by independent current sources and by synaptic currents. Responses calculated by SPICE are compared with analytical results. With appropriately chosen model parameters, extremely accurate transient calculations may be obtained. Details of the SPICE circuit elements are presented, along with illustrative examples sufficient to allow implementation of passive nerve cell models on a number of common computers. Methods for modeling excitable membrane are presented in the companion paper (Bunow et al. 1985).

Animals↗