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At least 469 records · Page 26Linked to original sources
PubMed2000

The metabolic topography of essential blepharospasm: a focal dystonia with general implications.

OBJECTIVE: To determine the metabolic topography of essential blepharospasm (EB). BACKGROUND: EB is a cranial dystonia of unknown etiology and anatomic localization. The authors have used 18F-fluorodeoxyglucose (FDG) and PET with network analysis to identify distinctive patterns of regional metabolic abnormality associated with idiopathic torsion dystonia (ITD), as well as sleep induction during PET imaging to suppress involuntary movements, thereby reducing this potential confound in the analysis. METHODS: Six patients with EB and six normal volunteers were scanned with FDG-PET. Scans were performed twice: once in wakefulness and once following sleep induction. The authors used statistical parametric mapping to compare glucose metabolism between patients with EB and control subjects in each condition. They also quantified the expression of the previously identified ITD-related metabolic networks in each subject in both conditions. RESULTS: With active involuntary movements during wakefulness, the EB group exhibited hypermetabolism of the cerebellum and pons. With movement suppression during sleep, the EB group exhibited superior-medial frontal hypometabolism in a region associated with cortical control of eyelid movement. Network analysis demonstrated a specific metabolic covariance pattern associated with ITD was also expressed in the patients with EB in both the sleep and wake conditions. CONCLUSION: These findings suggest that the clinical manifestations of EB are associated with abnormal metabolic activity in the pons and cerebellum, whereas the functional substrate of the disorder may be associated with abnormalities in cortical eyelid control regions. Furthermore, ITD-related networks are expressed in patients with EB, suggesting a functional commonality between both forms of primary dystonia.

Adult↗
PubMed2007

Qualitative network models and genome-wide expression data define carbon/nitrogen-responsive molecular machines in Arabidopsis.

BACKGROUND: Carbon (C) and nitrogen (N) metabolites can regulate gene expression in Arabidopsis thaliana. Here, we use multi-network analysis of microarray data to identify molecular networks regulated by C and N in the Arabidopsis root system. RESULTS: We used the Arabidopsis whole genome Affymetrix gene chip to explore global gene expression responses in plants exposed transiently to a matrix of C and N treatments. We used ANOVA analysis to define quantitative models of regulation for all detected genes. Our results suggest that about half of the Arabidopsis transcriptome is regulated by C, N or CN interactions. We found ample evidence for interactions between C and N that include genes involved in metabolic pathways, protein degradation and auxin signaling. To provide a global, yet detailed, view of how the cell molecular network is adjusted in response to the CN treatments, we constructed a qualitative multi-network model of the Arabidopsis metabolic and regulatory molecular network, including 6,176 genes, 1,459 metabolites and 230,900 interactions among them. We integrated the quantitative models of CN gene regulation with the wiring diagram in the multi-network, and identified specific interacting genes in biological modules that respond to C, N or CN treatments. CONCLUSION: Our results indicate that CN regulation occurs at multiple levels, including potential post-transcriptional control by microRNAs. The network analysis of our systematic dataset of CN treatments indicates that CN sensing is a mechanism that coordinates the global and coordinated regulation of specific sets of molecular machines in the plant cell.

Arabidopsis↗
PubMed2002

Internet infrastructures and health care systems: a qualitative comparative analysis on networks and markets in the British National Health Service and Kaiser Permanente.

BACKGROUND: The Internet and emergent telecommunications infrastructures are transforming the future of health care management. The costs of health care delivery systems, products, and services continue to rise everywhere, but performance of health care delivery is associated with institutional and ideological considerations as well as availability of financial and technological resources. OBJECTIVE: to identify the effects of ideological differences on health care market infrastructures including the Internet and telecommunications technologies by a comparative case analysis of two large health care organizations: the British National Health Service and the California-based Kaiser Permanente health maintenance organization. METHODS: A qualitative comparative analysis focusing on the British National Health Service and the Kaiser Permanente health maintenance organization to show how system infrastructures vary according to market dynamics dominated by health care institutions ("push") or by consumer demand ("pull"). System control mechanisms may be technologically embedded, institutional, or behavioral. RESULTS: The analysis suggests that telecommunications technologies and the Internet may contribute significantly to health care system performance in a context of ideological diversity. CONCLUSIONS: The study offers evidence to validate alternative models of health care governance: the national constitution model, and the enterprise business contract model. This evidence also suggests important questions for health care policy makers as well as researchers in telecommunications, organizational theory, and health care management.

Computer Communication Networks↗
PubMed2006

Computer-aided diagnosis of emphysema in COPD patients: neural-network-based analysis of lung shape in digital chest radiographs.

Several abnormalities of the shape of lung fields (depression and flattening of the diaphragmatic contours, increased retrosternal space) are indicative of emphysema and can be accurately imaged by digital chest radiography. In this work, we aimed at developing computational descriptors of the shape of the lung silhouette able to capture the alterations associated with emphysema. We analyzed two-sided digital chest radiographs from a sample of 160 patients with chronic obstructive pulmonary disease (COPD), 60 of which were affected by emphysema, and from 160 subjects with normal lung function. Two different description schemes were considered: a first one based on lung-silhouette curvature features, and a second one based on a minimal-polyline approximation of the lung shape. Both descriptors were employed to recognize alterations of the lung shape using classifiers based on multilayer neural networks of the feed-forward type. Results indicate that pulmonary emphysema can be reliably diagnosed or excluded by using digital chest radiographs and a proper computational aid. Two-sided chest radiographs provide more accurate discrimination than single-view analysis. The minimal-polyline approximation provided significantly better results than those obtained from curvature-based features. Emphysema was detected, in the entire dataset, with an accuracy of about 90% (sensitivity 88%, specificity 90%) by using the minimal-polyline approximation.

Humans↗
PubMed1997

Adaptive classification of two-dimensional gel electrophoretic spot patterns by neural networks and cluster analysis.

The interpretation of two-dimensional gel electrophoresis spot profiles can be facilitated by statistical and machine learning programs. Two different approaches to classification of spot profiles - cluster analysis and neural networks - are discussed. Neural networks for two different model patterns were designed and an algorithm for training of the net for the classification was developed. It was shown that the performance of neural networks is higher compared to cluster and principal component analysis. The possibility of combining both approaches into one process can increase reliability and speed of classification. Artificially created training sets with added random noise can be used for network training. The analysis was applied on the Streptomyces coelicolor developmental two-dimensional (2-D) gel database.

Cluster Analysis↗
PubMed2026

Blood and gut virome remodeling in gastric cancer: Anellovirus expansion and novel virus discovery.

Gastric cancer (GC) is a prevalent malignancy worldwide, yet effective early diagnostic tools remain lacking, and the role of the virome, a key component of the tumor microenvironment, in GC progression is largely unknown. This study aimed to characterize the virome landscapes in peripheral blood and feces of GC patients versus healthy controls, and to identify viral signatures associated with GC onset and metastasis. We performed viral metagenomic sequencing on pooled libraries from 100 GC patients (45 non-metastatic, 55 metastatic) and 50 healthy controls, followed by taxonomic annotation, diversity assessment, LEfSe differential abundance testing, and co-occurrence network analysis. In blood, the GC virome shifted from a bacteriophage-dominated profile in controls to one overwhelmingly dominated by Anelloviridae (> 80%), with significantly decreased alpha diversity. In contrast, the gut virome of GC patients showed increased alpha diversity and coexistence of diverse bacteriophages. LEfSe identified betatorquevirus in blood as a key discriminatory taxon for GC. Network analysis revealed negative correlations between Anelloviridae and multiple bacteriophage families, suggesting niche competition. We also discovered 67 provisional novel anellovirus species and one novel gemykibivirus in GC patient blood. Collectively, our findings indicate that GC is associated with compartment-specific virome remodeling in blood and gut, and that expansion of blood anelloviruses holds promise as a non-invasive biomarker. This study provides a foundational resource for understanding the virome's role in GC.

Humans↗
PubMed1977

The heart of what's the matter. The semantics of illness in Iran.

Our understanding of the psychosocial and cultural dimensions of disease and illness is limited not merely by a lack of empirical knowledge but also by an inadequate medical semantics. The empiricist theories of medical language commonly employed both by comparative ethnosemantic studies and by medical theory are unable to account for the integration of illness and the language of high medical traditions into distinctive social and symbolic contexts. A semantic network analysis conceives the meaning of illness categories to be constituted not primarily as an ostensive relationship between signs and natural disease entities but as a 'syndrome' of symbols and experiences which typically 'run together' for the members of a society. Such analysis dirests our attention to the patterns of associations which provide meaning to elements of a medical lexicon and to the constitution of that meaning through the use of medical discourse to articulate distinctive configurations of social stress and to negotiate relief for the sufferer. This paper provides a critical discussion of medical semantics and develops a semantic network analysis of 'heart distress', a folk illness in Iran.

Adolescent↗
PubMed1997

Pretreatment prediction of the chemotherapeutic response of human glioma cell cultures using nuclear magnetic resonance spectroscopy and artificial neural networks.

Both tumor metabolism and its response to cytotoxic drugs are intrinsic properties of tumor cells. It is therefore likely that there is a relationship between the two properties, however subtle and complex, wherein the metabolic characteristics of tumor cells can reflect the inherent response (resistance or sensitivity) of these cells to cytotoxic drugs. We used artificial neural network analysis to show that it is possible to distinguish, prior to treatment, between drug-resistant and drug-sensitive human glioma cell cultures from their metabolic profiles, as given by high-resolution proton nuclear magnetic resonance spectra of the cell extracts, and to predict their cellular response to the chemotherapeutic drug 1-(2-chloroethyl)-3-cyclohexyl-1-nitrosourea in vitro. The results suggest that neural network analysis of tumor nuclear magnetic resonance spectra has potential as a prognostic tool for determining treatment of gliomas, ultimately noninvasively, and may be used to provide information about the metabolic pathways involved in drug response that may be helpful in developing novel treatments for these tumors.

Antineoplastic Agents, Alkylating↗
PubMed1994

The contribution of influence and selection to adolescent peer group homogeneity: the case of adolescent cigarette smoking.

Understanding the homogeneity of peer groups requires identification of peer groups and consideration of influence and selection processes. Few studies have identified adolescent peer groups, however, or examined how they become homogeneous. This study used social network analysis to identify peer groups (cliques), clique liaisons, and isolates among adolescents in 5 schools at 2 data collection rounds (N = 926). Cigarette smoking was the behavior of interest. Influence and selection contributed about equally to peer group smoking homogeneity. Most smokers were not peer group members, however, and selection provided more of an explanation than influence for why isolates smoke. The results suggest the importance of using social network analysis in studies of peer group influence and selection.

Adolescent↗
PubMed2006

Individuals with diabetes mellitus with and without depressive symptoms: could social network explain the comorbidity?

OBJECTIVES: The main objective of the present study was to obtain an approximate prevalence of depressive symptoms in a community sample of persons with diabetes mellitus and to discover whether social networks could explain comorbidity of diabetes mellitus and depressive symptoms. METHOD: Subjects were persons with diabetes mellitus, members of the Diabetes Association of Ljubljana (N = 396, average age: 62.9 +/- 13.4, average duration of diabetes: 17.2 +/- 10.6). Firstly, subjects were screened with CESD (Centre for Epidemiological Studies Depression Scale) and demographic data were also gathered. Secondly, two samples (individuals with and without depressive symptoms) were compared on the basis of social network analysis. RESULTS: The prevalence of depressive symptoms was higher among persons with diabetes in comparison with general population. Individuals with depressive symptoms compared with individuals without depressive symptoms were less dissatisfied with diabetes (p = 0.011), and had better informational (p = 0.039) and instrumental support (p = 0.046), relations between them and support givers were closer (p = 0.050), more important and less conflicting (p = 0.042). Compared groups did not differ in quantitative characteristics of social networks (eg. size of the network or the network gender structure). CONCLUSIONS: The community sample results in Slovenia support the already reported association between diabetes mellitus and depression. Furthermore, social network analysis offered some potentially relevant explanation for comorbidity of diabetes mellitus and depressive symptoms.

Adult↗
PubMed2000

Adult-size kidneys without acute tubular necrosis provide exceedingly superior long-term graft outcomes for infants and small children: a single center and UNOS analysis. United Network for Organ Sharing.

BACKGROUND: Infants with end-stage renal disease are at highest risk for early graft loss and mortality of any subgroup undergoing renal transplantation. This study evaluates the influence of donor tissue mass and acute tubular necrosis (ATN) on graft survival and incidence of acute rejection episodes in infant and small child recipients of living donor (LD) and cadaver (CAD) adult-size kidneys (ASKs), pediatric CAD kidneys and combined kidney-liver transplants. Methods. Kidney transplants in infants and small children at a single center and those reported to the UNOS Scientific Renal Transplant Registry were analyzed. At Stanford, multi-variate analysis was conducted on 45 consecutive renal allograft recipients weighing < or = 15 kg, mean weight 11.2 +/- 2.6 kg. The UNOS Registry results in age groups 0-2.5 (n=548) and 2.5-5 years (n=743) were compared with age groups 6-12, 13-18, and the lowest risk adult group of 19-45 years. STANFORD RESULTS. Graft survival was 97.8 +/- 0.0 at 2 years and 84.6 +/- 0.1% at 8 years. The incidence of biopsy proven rejection was 8.8% in the first 3 months and 15.5% over the 8-year follow-up. None of the pediatric CAD kidneys had ATN. Rejection episodes were restricted to the pediatric CAD kidneys alone (3/3), with no kidney rejections in the combined pediatric CAD kidney-liver transplants (0/6; P=0.003). Four ASK transplants had ATN (1 postoperative and 3 late), and all predisposed to subsequent acute rejection episodes (4/4), whereas there were no rejection episodes in ASK transplants without ATN (0/32; P<0.001). At 3 years posttransplantation, mean serum creatinines were worse in ASKs with ATN (1.5 vs. 0.9 mg/dL; P<0.001) and in all grafts with rejection episodes (1.2 vs. 0.9 mg/dL; P<0.05). UNOS RESULTS: Among the 5 age groups studied, significantly better (P<0.001) long-term graft survival rates were observed in allograft recipients in the 2 youngest age groups with ASKs without ATN: 82 +/- 3% and 81 +/- 3% for LD and 70 +/- 7% and 78 +/- 4% for CAD recipients in the 0-2.5 and 2.5- to 5-year age groups, respectively, at 6 years after transplantation. Moreover, the projected graft half-lives after the 1st year in the LD groups without ATN were at least equivalent to those of HLA-identical sibling recipients ages 19-45 years: 26.3 +/- 5 and 29.3 +/- 6 years for the 0- to 2.5- and 2.5- to 5-year age groups, respectively, and 23.3 +/- 1 years for HLA-identical transplants. The graft half-lives for CAD recipients without ATN ages 0-2.5 and 2.5-5 yearswere equivalent or better than those for LD transplants without ATN in recipients aged 19-45 years: 15.4+/- 7 and 23.7 +/- 8 years versus 15.0 +/- 0.3 years. Mean serum creatinines were superior in the 2 younger recipient age groups compared with older age groups. CONCLUSIONS: Increased donor tissue mass of the ASK or kidney-liver transplants, in the absence of ATN, seems to confer a protective effect to infant and small child recipients of these allografts. This is manifested by a prolonged rejection-free state in the single center experience and enhanced graft survival and function in the UNOS analysis, comparable to HLA identical sibling transplants for LD infant and small child recipients and to LD adult results for CAD infant and small child recipients. To optimize this protective effect by whatever mechanism, absolute avoidance of ATN is essential in infant recipients of ASK or combined kidney-liver transplants.

Adolescent↗
PubMed1997

Artificial neural networks for molecular sequence analysis.

Artificial neural networks provide a unique computing architecture whose potential has attracted interest from researchers across different disciplines. As a technique for computational analysis, neural network technology is very well suited for the analysis of molecular sequence data. It has been applied successfully to a variety of problems, ranging from gene identification, to protein structure prediction and sequence classification. This article provides an overview of major neural network paradigms, discusses design issues, and reviews current applications in DNA/RNA and protein sequence analysis.

Algorithms↗
PubMed2001

Functional brain networks in Parkinson's disease.

With the advent of new methods of network analysis, we have utilized metabolic data acquired through positron emission tomography (PET) to identify disease-related patterns of functional pathology in the movement disorders. In Parkinson's disease (PD), we have used [(18)F]-fluorodeoxyglucose (FDG)/PET to identify a disease-related regional metabolic covariance pattern characterized by lentiform and thalamic hypermetabolism associated with regional metabolic decrements in the lateral premotor cortex, the supplementary motor area, the dorsolateral prefrontal cortex, and the parieto-occipital association regions. The expression of this network is modulated in a predictable fashion by levodopa therapy and by stereotaxic interventions for PD.We have extended this network analytical approach from studies of glucose metabolism in the resting state to dynamic studies of brain activation during motor performance. These PET studies utilized [(15)O]-water (H(2) (15)O) to measure cerebral blood flow activation responses during the execution of simple and complex motor tasks. In addition to the modulation of abnormal resting metabolic networks, effective PD therapy can enhance brain activation responses during motor execution, with specific regional associations with improvements in timing and spatial accuracy.This approach is also useful in identifying specific brain networks mediating the learning of sequential information. We have found that the normal relationship between brain networks and learning performance are altered in the earliest stages of PD with a functional shift from striatal to cortical processing. Brain activation PET studies during therapeutic interventions for PD demonstrate how normal brain-behavior relationships can be restored with successful therapy. Thus, functional brain imaging with network analysis can provide insights into the mechanistic basis of basal ganglia disorders and their treatment.

Antiparkinson Agents↗
PubMed2001

Metabolic network abnormalities in early Huntington's disease: an [(18)F]FDG PET study.

UNLABELLED: The identification of discrete patterns of altered functional brain circuitry in preclinical Huntington's disease (HD) gene carriers is important to understanding the pathophysiology of this disorder and could be useful as a biologic disease marker. The purpose of this study was to use PET imaging of regional cerebral glucose metabolism to identify abnormal networks of brain regions that are specifically related to the preclinical phase of HD. METHODS: Eighteen presymptomatic HD gene carriers, 13 early-stage HD patients, and 8 age-matched gene-negative relatives were scanned using PET with [(18)F]FDG to quantify regional glucose utilization. A network modeling strategy was applied to the FDG PET data to identify disease-related regional metabolic covariance patterns in the preclinical HD cohort. The outcome measures were the region weights defining the metabolic topography of the HD gene carriers and the subject scores quantifying the expression of the pattern in individual subjects. RESULTS: Network analysis of the presymptomatic carriers and the gene-negative control subjects revealed a significant metabolic covariance pattern characterized by caudate and putamenal hypometabolism but also included mediotemporal metabolic reductions as well as relative metabolic increases in the occipital cortex. Subject scores for this pattern were abnormally elevated in the preclinical group compared with those of the control group (P < 0.005) and in the early symptomatic group compared with those of the presymptomatic group (P < 0.005). CONCLUSION: These findings show that FDG PET with network analysis can be used to identify specific patterns of abnormal brain function in preclinical HD. The presence of discrete patterns of metabolic abnormality in preclinical HD carriers may provide a useful means of quantifying the rate of disease progression during the earliest phases of this illness.

Adult↗
PubMed2005

The impact of prehospital endotracheal intubation on outcome in moderate to severe traumatic brain injury.

BACKGROUND: Although early intubation to prevent the mortality that accompanies hypoxia is considered the standard of care for severe traumatic brain injury (TBI), the efficacy of this approach remains unproven. METHODS: Patients with moderate to severe TBI (Head/Neck Abbreviated Injury Scale [AIS] score 3+) were identified from our county trauma registry. Logistic regression was used to explore the impact of prehospital intubation on outcome, controlling for age, gender, mechanism, Glasgow Coma Scale score, Head/Neck AIS score, Injury Severity Score, and hypotension. Neural network analysis was performed to identify patients predicted to benefit from prehospital intubation. RESULTS: A total of 13,625 patients from five trauma centers were included; overall mortality was 22.9%, and 19.3% underwent prehospital intubation. Logistic regression revealed an increase in mortality with prehospital intubation (odds ratio, 0.36; 95% confidence interval, 0.32-0.42; p < 0.001). This was true for all patients, for those with severe TBI (Head/Neck AIS score 4+ and/or Glasgow Coma Scale score of 3-8), and with exclusion of patients transported by aeromedical crews. Patients intubated in the field versus the emergency department had worse outcomes. Neural network analysis identified a subgroup of patients with more significant injuries as potentially benefiting from prehospital intubation. CONCLUSION: Prehospital intubation is associated with a decrease in survival among patients with moderate-to-severe TBI. More critically injured patients may benefit from prehospital intubation but may be difficult to identify prospectively.

Abbreviated Injury Scale↗
PubMed1995

Computer-assisted decision support systems for patient management in an intensive care unit.

The application of the intelligent monitoring techniques of case-based reasoning and neural network analysis to physician decision making concerning patient care in an Intensive Car Unit (ICU) is described. Case-based reasoning offers a model for quickly matching--using a predetermined hierarchical structure--a single patient's parameters (text or numeric) to similar parameters contained in a clinical database. The output produces a group of patients which may be set to match exactly on certain characteristics and may also be set to match "as closely as possible" on a gradient of patient properties. Clinicians may thus use the system to find the group of the closest matching cases to their current patient. Aspects of the ICU history of the selected group may then be displayed graphically (e.g., mortality, length of stay, hours of ventilation, procedures utilized, and complications encountered). Neural network analysis is a pattern recognition technique which uses a training set of patient data (text or numeric) to seek mathematical relationships between various subsets of patient parameters. The discovered relationships from the training set are then applied to estimate the outcomes (e.g., mortality, length of stay, hours of ventilation) of new patients. The effects of these intelligent monitoring techniques are scheduled to be tested in a field trial held in a regional referral center ICU.

Algorithms↗
PubMed2006

Investigating innovation in complex health care supply networks: an initial conceptual framework.

This paper explores the constraints and enablers of the process of innovation within the context of UK health care supply networks. Building on a comprehensive literature review of established and recent innovation and supply network research, the paper presents three levels of supply network analysis: sector level supply networks, focal organization supply networks and dyadic supply relationships. The paper reports on the first round of fieldwork conducted with 12 different health care organizations. The three different levels are applied during analysis and the findings are considered in terms of the key themes that have emerged and the practical and theoretical challenges that they represent.

Diffusion of Innovation↗
PubMed1999

[Analysis of mass optimization of manned spacecraft ECLSS thermo-hydraulic network].

OBJECTIVE: To reduce the weight of manned spacecraft Environment Control and Life Support System (ECLSS). METHOD: Based on network analysis theory, the flow and thermo-hydraulic network composed of gas and liquid loops in manned spacecraft ECLSS was explored to reduce the weight of ECLSS. The physical models and mathematical models of flow, heat transfer and weight calculation in the network were established. The thermodynamic parameters and weight of the network were calculated on the bases of energy balance, heat transfer relation and the component weight relation. And influencing factors on the system weight were discussed. RESULT: (1) There is an optimal pipe diameter in the system and the diameter is influenced by flow rate to a large extent; (2) The weight can be reduced by raising inlet temperature properly; (3) The best heat exchange layout makes the weight lightest. CONCLUSION: The obtained results are of importance for reducing launching weight of manned spacecraft.

Air Conditioning↗