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Results for “network analysis”

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At least 523 records · Page 29Linked to original sources
PubMed2002

Combinatorial complexity of pathway analysis in metabolic networks.

Elementary flux mode analysis is a promising approach for a pathway-oriented perspective of metabolic networks. However, in larger networks it is hampered by the combinatorial explosion of possible routes. In this work we give some estimations on the combinatorial complexity including theoretical upper bounds for the number of elementary flux modes in a network of a given size. In a case study, we computed the elementary modes in the central metabolism of Escherichia coli while utilizing four different substrates. Interestingly, although the number of modes occurring in this complex network can exceed half a million, it is still far below the upper bound. Hence, to a certain extent, pathway analysis of central catabolism is feasible to assess network properties such as flexibility and functionality.

Computer Simulation↗
PubMed2025

Stage-dependent proteomic alterations in aqueous humor of diabetic retinopathy patients based on data-independent acquisition and parallel reaction monitoring.

BACKGROUND: Diabetic retinopathy (DR), a microvascular complication of diabetes mellitus (DM), represents the predominant cause of preventable vision loss in working-age populations globally. While the pathophysiological mechanisms underlying DR progression remain incompletely understood, our study employs comprehensive proteomic profiling of aqueous humor (AH) to identify stage-specific biomarkers and therapeutic targets in type 2 diabetes mellitus (T2DM) patients across DR progression. METHODS: Utilizing data-independent acquisition (DIA) mass spectrometry, we quantified AH proteomes in a discovery cohort comprising 24 subjects: 18 T2DM patients stratified by DR severity [6 non-DR, 6 non-proliferative DR (NPDR), 6 proliferative DR (PDR)] and 6 cataract controls without diabetes (non-DM). Validation cohort analysis (including 10 AH samples in each group) was performed using parallel reaction monitoring (PRM) strategy for verification of target proteins. Comprehensive bioinformatics analyses included gene set enrichment analysis (GSEA), weighted gene co-expression network analysis (WGCNA), Kyoto encyclopedia of genes and genomes (KEGG) enrichment analysis, protein-protein interaction (PPI) network construction, receiver operating characteristic (ROC) curve analysis, and ConnectivityMap (Cmap)-based drug prediction. RESULTS: Proteomic profiling identified 739 quantifiable AH proteins (62% extracellular) with clear separation among the four clinical stages in the discovery cohort. GSEA uncovered altered expression of proteins mainly related to complement and coagulation cascades, folate metabolism, and the selenium micronutrient network in patients with DR. WGCNA-derived protein modules yielded 83 PRM-validated targets, including 5 hub proteins differentiating NPDR from non-DR and 33 hub proteins showed significant upregulation in PDR versus NPDR comparison. Clinical correlation analysis identified F2, FGG, FGB, RBP4, AMBP, VTN, C8A, CPB2, and C2 associated with clinical traits. C6, FAM3C, SPP1, and JCHAIN levels were altered post-anti-VEGF treatment. Pharmacological prediction identified potential therapeutic compounds, including perindopril, triciribine, and XAV-939 for NPDR, and topiramate, triciribine, and vecuronium for PDR. CONCLUSION: This study established a comprehensive AH proteomic signature of DR progression, offering insights into the pathogenesis of DR and highlighting potential biomarkers and novel therapeutic targets.

Humans↗
PubMed2005

Geometric diffusions for the analysis of data from sensor networks.

Harmonic analysis on manifolds and graphs has recently led to mathematical developments in the field of data analysis. The resulting new tools can be used to compress and analyze large and complex data sets, such as those derived from sensor networks or neuronal activity datasets, obtained in the laboratory or through computer modeling. The nature of the algorithms (based on diffusion maps and connectivity strengths on graphs) possesses a certain analogy with neural information processing, and has the potential to provide inspiration for modeling and understanding biological organization in perception and memory formation.

Algorithms↗
PubMed1986

The uses of spatial analysis in medical geography: a review.

This paper is a review of how geographers and others have used spatial analysis to study disease and health care delivery patterns. Point, line, area and surface patterns, as well as map comparisons and relative spaces are discussed. Problems encountered in applying spatial analytic techniques in medical geography are pointed out. The paper is intended to stimulate discussion about where medical geography can and should go in this area of study. Point pattern techniques include standard distance, standard deviational ellipses, gradient analysis and space and space-time clustering. Line methods include random walks, vectors and graph theory or network analysis. Under areas, location quotients, standardized mortality ratios, Poisson probabilities, space and space-time clustering, autocorrelation measures and hierarchical clustering are discussed. Surface techniques mentioned are isolines and trend surfaces. For map comparisons, Lorenz curves, coefficients of areal correspondence and correlation coefficients have been used. Case-control matching, acquaintance networks, multidimensional scaling and cluster analysis are examples of methods that are based on relative or non-metric spaces. The review gives rise to the discussion of several general points: problems encountered in spatial analysis, theory building and verification, the appropriate role of technique and computer use. Some suggestions are made for further use of spatial analytic techniques in medical geography: Monte Carlo simulation of point patterns, network analysis to study referral systems and health care for pastoralists, geographic information systems to assess environmental risk, difference mapping for disease and risk factor map comparisons and multidimensional scaling to measure social distance.

Cross-Sectional Studies↗
PubMed2006

The effect of oxygen on biochemical networks and the evolution of complex life.

The evolution of oxygenic photosynthesis and ensuing oxygenation of Earth's atmosphere represent a major transition in the history of life. Although many organisms retreated to anoxic environments, others evolved to use oxygen as a high-potential redox couple while concomitantly mitigating its toxicity. To understand the changes in biochemistry and enzymology that accompanied adaptation to O2, we integrated network analysis with information on enzyme evolution to infer how oxygen availability changed the architecture of metabolic networks. Our analysis revealed the existence of four discrete groups of networks of increasing complexity, with transitions between groups being contingent on the presence of key metabolites, including molecular oxygen, which was required for transition into the largest networks.

Adaptation, Physiological↗
PubMed2003

Use of mammography screening among older Samoan women in Los Angeles county: a diffusion network approach.

Minority migrant populations, such as older Samoan women, are likely to underuse preventive health services, including mammography screening. The purpose of this paper is to explore how informal (lay peers from churches) and formal (health care providers) health communication networks influence mammography screening use among older Samoan women. To do so, we apply diffusion of innovation theory and network analysis to understand how interpersonal networks may affect mammography use in this urban-dwelling, migrant population. The data come from a survey of 260 Samoan women, aged 50 years or older, who attended 39 randomly sampled Samoan churches in Los Angeles County (USA) between 1996 and 1997. Retrospective data, based over a 20-year period from this sample's year of first use of mammography screening, suggest that interpersonal networks may have accounted for the dramatic increase in the rate of adoption within the past 5 years of the survey. Using this information, we categorized women into mutually exclusive stages of mammography use and regressed these stages of mammography use on formal (had a provider referral) and informal (level of connectedness with peers in churches) health communication networks. The results indicated that being well-connected within women's informal, church-based health communication networks increased the likelihood of being in the decision (planned to have) and implementation and confirmation (had a recent mammogram) stages, but having a provider referral for a mammogram (formal networks) only increased the likelihood of being in the latter stages compared to women in the knowledge and persuasion stages. Formal and informal health communication networks influence recent use of mammography screening, but informal networks, in and of themselves, are also influential on future intention to use mammography screening.

Aged↗
PubMed2026

Proteomic profiling identifies systemic drivers of blood-brain barrier injury in sickle cell disease.

Sickle cell disease (SCD) causes brain injury and cognitive disability. Systemic inflammation and endothelial injury are central to SCD pathophysiology, yet the relationship between systemic drivers of blood-brain barrier (BBB) disruption and brain injury remains understudied. This cross-sectional study assessed whole-brain and regional BBB permeability (Ktrans) using dynamic contrast-enhanced magnetic resonance imaging for 37 adults with SCD in steady state and 37 adults without SCD. Cerebral oxygen extraction fraction (OEF) and white matter mean diffusivity (MD) measured tissue hypoxia and microstructural injury, respectively. The SCD cohort showed elevated Ktrans compared with controls (3.6 &#xd7; 10-4&#xb7;min-1 vs 2.58 &#xd7; 10-4&#xb7;min-1; 95% confidence interval [CI] median difference, 0.36 &#xd7; 10-4&#xb7;min-1 to 1.30 &#xd7; 10-4&#xb7;min-1; P< .001), indicating BBB disruption. In SCD, white matter Ktrans was associated with MD (&#x3b2;, 6.25 [95% CI, 1.72-10.77]; P = .008), independent of OEF (&#x3b2;, 0.22 [95% CI, 0.09-0.35]), and silent cerebral infarcts (&#x3b2;, 0.01 [95% CI, 0.00-0.02]). The interaction (P = .037) between Ktrans and OEF on MD suggested a combined, deleterious effect of BBB disruption and hypoxia on microstructural injury. High-throughput plasma proteomics followed by differential expression analysis, and weighted gene correlation network analysis in a subset of 61 participants revealed that 79 proteins associated with BBB permeability belonged to iron homeostasis, response to hypoxia, immune dysregulation, extracellular matrix degradation, lipoprotein homeostasis, and arginine-proline metabolism pathways. All pathways were independently associated with microstructural injury. BBB permeability was a mediator of brain injury for all pathways except extracellular matrix degradation. Targeting specific systemic pathways to protect the BBB may represent a therapeutic approach to preserve brain health in SCD.

Humans↗