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PubMed1996

Mathematical modeling of AVM physiology using compartmental network analysis: theoretical considerations and preliminary in vivo validation using a previously developed animal model.

The development of computer modeling technique of cerebral arteriovenous malformations using circuit network analysis, validated with a previously developed animal model is presented. Such a malformation and its vascular connections are rendered into a complex system of interconnecting tubes, which is then simulated by an analogous electrical circuit using commercially available computer software. This methodology was tested using a swine model, of which a detailed computer model was constructed from anatomic and angiographic measurements of the cranial vessels. Flow conditions, before and after creation of the in vivo model, were predicted from the computer model and compared with previously reported in vivo measurements. Detailed analysis of flow within the CAVM nidus was also performed. There was a good correlation between the computer and in vivo models regarding changes in flow and pressure drop across the rete. Flow mapping within the nidus showed localized directional flow that was determined by global inputs, consistent with functional compartmentalization. This method of computer modeling appears promising for studying clinically relevant aspects of cerebral arteriovenous malformation pathophysiology. To our knowledge it is the first computer model to demonstrate functional compartmentalization.

Animals↗
PubMed1999

Artificial neural network analysis (ANNA) of prostatic transrectal ultrasound.

BACKGROUND: Our purpose was to determine the diagnostic potential of a new, computerized method of interpreting transrectal ultrasound (TRUS) information by artificial neural network analysis (ANNA). This method was developed to resolve the current dilemma of visual differentiation between benign and malignant tissue on TRUS. To train and objectively evaluate ANNA, a new precise method of computerized virtual correlation of preoperative ultrasound findings and radical prostatectomy histopathology was devised. After training with this pathologically confirmed digitized TRUS information, ANNA was tested in a blinded study. METHODS: Following radical prostatectomy, 289 pathology whole-mount sections of 61 patients were correlated digitally with the corresponding TRUS slices. Specific selection of TRUS areas unequivocally identified on the correlated digitized pathohistology resulted in 553 pathology-confirmed representations (samples). Of these, 53 were used for training and 500 were subjected to blind analysis by ANNA. RESULTS: ANNA classified 378 (99%) of the 381 benign pathology-confirmed samples correctly as benign. The false-positive rate was 1% (n = 3). Of the 119 pathology-confirmed malignant samples, 94 (79%) were classified correctly; 25 (21%) were falsely classified as normal. Out of all 119 cancers, ANNA classified 60 (71%) of the hypoechoic cancers as malignant and 24 (29%) as benign. Surprisingly, 34 (97%) of the isoechoic cancers were correctly classified by ANNA, missing only one sample. CONCLUSIONS: The introduction of ANNA enhanced the accuracy of TRUS prostate cancer identification. Although not all malignant areas were detected, cancer was detected in each patient. The ability to detect isoechoic cancerous lesions appears to be the essential innovation over conventional TRUS interpretation.

Aged↗
PubMed2024

Comparison of the antibiotic resistance mechanisms in a gram-positive and a gram-negative bacterium by gene networks analysis.

Nowadays, the emergence of some microbial species resistant to antibiotics, both gram-positive and gram-negative bacteria, is due to changes in molecular activities, biological processes and their cellular structure in order to survive. The aim of the gene network analysis for the drug-resistant Enterococcus faecium as gram-positive and Salmonella Typhimurium as gram-negative bacteria was to gain insights into the important interactions between hub genes involved in key molecular pathways associated with cellular adaptations and the comparison of survival mechanisms of these two bacteria exposed to ciprofloxacin. To identify the gene clusters and hub genes, the gene networks in drug-resistant E. faecium and S. Typhimurium were analyzed using Cytoscape. Subsequently, the putative regulatory elements were found by examining the promoter regions of the hub genes and their gene ontology (GO) was determined. In addition, the interaction between milRNAs and up-regulated genes was predicted. RcsC and D920_01853 have been identified as the most important of the hub genes in S. Typhimurium and E. faecium, respectively. The enrichment analysis of hub genes revealed the importance of efflux pumps, and different enzymatic and binding activities in both bacteria. However, E. faecium specifically increases phospholipid biosynthesis and isopentenyl diphosphate biosynthesis, whereas S. Typhimurium focuses on phosphorelay signal transduction, transcriptional regulation, and protein autophosphorylation. The similarities in the GO findings of the promoters suggest common pathways for survival and basic physiological functions of both bacteria, including peptidoglycan production, glucose transport and cellular homeostasis. The genes with the most interactions with milRNAs include dpiB, rcsC and kdpD in S. Typhimurium and EFAU004_01228, EFAU004_02016 and EFAU004_00870 in E. faecium, respectively. The results showed that gram-positive and gram-negative bacteria have different mechanisms to survive under antibiotic stress. By deciphering their intricate adaptations, we can develop more effective therapeutic approaches and combat the challenges posed by multidrug-resistant bacteria.

Anti-Bacterial Agents↗
PubMed1985

A combined Bodian-Nissl stain for improved network analysis in neuronal cell culture.

Bodian and Nissl procedures were combined to stain dissociated mouse spinal cord cells cultured on coverslips. The Bodian technique stains fine neuronal processes in great detail as well as an intracellular fibrillar network concentrated around the nucleus and in proximal neurites. The Nissl stain clearly delimits neuronal cytoplasm in somata and in large dendrites. A combination of these techniques allows the simultaneous depiction of neuronal perikarya and all afferent and efferent processes. Costaining with little background staining by either procedure suggests high specificity for neurons. This procedure could be exploited for routine network analysis of cultured neurons.

Acetates↗
PubMed1995

Using social network analysis to study patterns of drug use among urban drug users at high risk for HIV/AIDS.

Few studies have examined the current social relationships of injecting drug users. This paper examines the structural and relationship characteristics of the social networks of injecting drug users, and the relation of social network characteristics to the HIV infection risk behavior of frequency of injecting heroin and cocaine. The study sample was comprised of 293 inner city injecting drug users in Baltimore, Maryland. Most participants (89%) reported at least one family member in their social network, and 44% listed their mother or step-mother in their network. Presence of family members in personal social networks was not related to patterns of drug use examined here; however, those who reported a partner in their personal social network injected significantly less often than those who did not report a partner. Network density and size of drug subnetworks were positively associated with frequency of drug injection. The results of this study suggest that social network analysis may be a useful tool for understanding the social context of HIV/AIDS risk behaviors.

Acquired Immunodeficiency Syndrome↗
PubMed2024

Decoding the genetic landscape of allergic rhinitis: a comprehensive network analysis revealing key genes and potential therapeutic targets.

BACKGROUND: Allergic Rhinitis (AR), an inflammatory affliction impacting the upper respiratory tract, has been registering a substantial surge in incidence across the globe. METHODS: We embarked on examination of differentially expressed genes (DEGs) and the Weighted Gene Co-Expression Network Analysis (WGCNA). With this armory of genes identified, we engaged the tools of Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). Our study continued with the establishment of a protein-protein interaction (PPI) network and the application of LASSO regression. Finally, we leveraged a docking model to elucidate potential drug-gene interactions involving these key genes. RESULTS: Through WGCNA and different express genes screening, PPI network was performed, identifying top 20 key genes, including CD44, CD69, CD274. LASSO regression identified three independent factors, STARD5, CST1, and CHAC1, that were significantly associated with AR. A predictive model was developed with an AUC value over 0.75. Also, 105 potential therapeutic agents were discovered, including Fluorouracil, Cyclophosphamide, Doxorubicin, and Hydrocortisone, offering promising therapeutic strategies for AR. CONCLUSION: By fuzing DEGs with key genes derived from WGCNA, this study has illuminated a comprehensive network of gene interactions involved in the pathogenesis of AR, paving the way for future biomarker and therapeutic target discovery in AR.

Humans↗
PubMed2001

Interorganizational relationships among HIV/AIDS service organizations in Baltimore: a network analysis.

A wide variety of organizations has become involved in providing medical and social services to people living with human immunodeficiency virus/acquired immunodeficiency syndrome (HIV/AIDS). Although there is much interest among policymakers, service providers, and clients in coordination among HIV/AIDS service organizations, few studies have used network analytic tools to examine existing systems of HIV-related care. In an effort to fill this gap, this study used network analysis methods to describe several aspects of the interorganizational relationships among 30 HIV/AIDS service agencies in Baltimore, Maryland. Client referrals to other organizations, client referrals from other organizations, exchange of information about shared clients, formal written linkage agreements for client referrals, and joint programs were each examined as a distinct type of network tie, with each the basis of a separate network among these 30 organizations. All of the networks except the one based on joint programs were relatively well connected, with most organizations either directly or indirectly linked. Most of the interorganizational collaboration occurred on a rather ad hoc basis for the purposes of meeting the more immediate needs presented by clients. Highly structured coordination involving substantial investment of resources and ongoing interagency activities appeared to be less common. The findings from this study also suggest that the providers in Baltimore tend to work directly with others as client needs arise rather than negotiating through "clearinghouse" types of organizations. Of the 30 HIV/AIDS service organizations, 5 were highly central in at least four of the five different types of networks. These five organizations--each having a critical role in the continuum of care--may be considered the most central core of the HIV/AIDS service delivery network in Baltimore. These organizations tend to be those that have been created specifically to provide HIV-related services or that specialize in HIV/AIDS care. This research can help policymakers understand how an HIV-related service delivery network may function and delineate key features of a network. In all communities, this type of assessment is critical to designing interventions to promote collaboration that are feasible within the context of existing interorganizational relationships. This type of data also has implications for informing activities to build the capacity of HIV/AIDS service organizations.

Acquired Immunodeficiency Syndrome↗
PubMed1997

"We decide, you carry it out": a social network analysis of multidisciplinary long-term care teams.

The purpose of this study was to describe the structure of multidisciplinary long-term care teams by identifying the pattern of relationships that develop amongst staff as they go about their work. Using a social network analysis approach, team members were classified as occupying the same structural position based on their patterns of relationships with other team members. The analysis was based on the results of a self-administered survey of 93 health care workers on three teams in the same multilevel geriatric care facility in Metropolitan Toronto. A common structure of the teams was identified consisting of two sub-teams: a multiprofessional sub-team and a nursing sub-team, each of which has a different structure indicating differential involvement in different types of teamwork. The multiprofessional sub-team has an "organic" structure and is mainly involved in teamwork that involves decision-making and problem-solving, whereas the nursing sub-team has a "mechanistic" structure and is mainly involved in task oriented work. The findings of this analysis indicate that while teamwork may be increasing the participation in decision-making by health professionals other than medicine, rather than flattening the hierarchical structure throughout the health care division of labour, its effects are limited to a group of higher status professionals. The clearly defined hierarchy remains for the lower status subdisciplines, and "I decide, you carry it out" has simply become "We decide, you carry it out".

Adult↗
PubMedDate not supplied

Markov Network Analysis: suggestions for innovations in covariance structure analysis.

Studies of aging offer special methodological challenges to the researcher in that he must often examine the change of multiple correlated variables over time. We present a set of procedures that are specifically designed to model change in such multivariate situations. These procedures, which we will call Markov Network Analysis, are directly applicable to modeling change from longitudinal or serial data. In such cases, the parameters of the model have dynamic interpretations, e.g., as coefficients in positive or negative feedback loops. In cross-sectional data, one cannot directly estimate the dynamic coefficients but the model does show how certain dynamic interpretations can be made. Statistically, maximum likelihood estimation procedures are developed and presented. In the development of the statistical model, it is shown how the bias of sequential hypothesis testing, a frequent occurrence in the estimation of complex covariance structure models, may be reduced.

Age Factors↗
PubMed1996

A biomathematical model of intracranial arteriovenous malformations based on electrical network analysis: theory and hemodynamics.

Hemodynamics play a significant role in the propensity of intracranial arteriovenous malformations (AVMs) to hemorrhage and in influencing both therapeutic strategies and their complications. AVM hemodynamics are difficult to quantitate, particularly within or in close proximity to the nidus. Biomathematical models represent a theoretical method of investigating AVM hemodynamics but currently provide limited information because of the simplicity of simulated anatomic and physiological characteristics in available models. Our purpose was to develop a new detailed biomathematical model in which the morphological, biophysical, and hemodynamic characteristics of an intracranial AVM are replicated more faithfully. The technique of electrical network analysis was used to construct the biomathematical AVM model to provide an accurate rendering of transnidal and intranidal hemodynamics. The model represented a complex, noncompartmentalized AVM with 4 arterial feeders (with simulated pial and transdural supply), 2 draining veins, and a nidus consisting of 28 interconnecting plexiform and fistulous components. Simulated vessel radii were defined as observed in human AVMs. Common values were assigned for normal systemic arterial pressure, arterial feeder pressures, draining vein pressures, and central venous pressure. Using an electrical analogy of Ohm's law, flow was determined based on Poiseuille's law given the aforementioned pressures and resistances of each nidus vessel. Circuit analysis of the AVM vasculature based on the conservation of flow and voltage revealed the flow rate through each vessel in the AVM network. Once the flow rate was established, the velocity, the intravascular pressure gradient, and the wall shear stress were determined. Total volumetric flow through the AVM was 814 ml/min. Hemodynamic analysis of the AVM showed increased flow rate, flow velocity, and wall shear stress through the fistulous component. The intranidal flow rate varied from 5.5 to 57.0 ml/min with and average of 31.3 ml/min for the plexiform vessels and from 595.1 to 640.1 ml/min with an average of 617.6 ml/min for the fistulous component. The blood flow velocity through the AVM nidus ranged from 11.7 to 121.1 cm/s with an average of 66.4 cm/s for the plexiform vessels and from 446.9 to 480 dyne/cm2 with an average of 463.5 dyne/cm2 for the fistulous component. The wall shear stress ranged in magnitude from 33.2 to 342.1 dyne/cm2 with an average of 187.7 dyne/cm2 for the plexiform vessels and from 315.9 to 339.7 cm/s with an average of 327.8 cm/s for the fistulous component. The described novel biomathematical model characterizes the transnidal and intranidal hemodynamics of an intracranial AVM more accurately than was possible previously. This model should serve as a useful research tool for further theoretical investigations of intracranial AVMs and their hemodynamic sequelae.

Biophysical Phenomena↗
PubMed1975

Network analysis of dendritic fields of pyramidal cells in neocortex and Purkinje cells in the cerebellum of the rat.

The connectivity within the dendritic array of Purkinje cells in the cerebellum and pyramidal cells of the neocortex of the rat, stained by the Golgi-Cox method, has been quantified by the method of network analysis. Connectivity was characterized either by applying the system of Strahler ordering, which assigns a relative order of magnitude to each branch of the arborescence or by the identification of unique topological branching patterns within the tree. The former method has been used to define the entire dendritic array of the Purkinje cell and the apical system of neocortical pyramids. It has been shown that the relation between the numbers of branches of successive Strahler order in Purkinje cells form an inverse geometric series in which the highest order is unity and the ratio between successive orders approximates to 3. On the other hand, the apical dendrites of neocortical pyramids exhibit two bifurcation ratios, i.e. a ratio of 3 between low orders and a ratio of 4 between higher orders. A computer simulation technique was used to generate networks of a size comparable with the Purkinje cell networks and grown according to two hypotheses namely, a 'terminal growth model' in which additional segments were added randomly to the terminal branches only and a 'segmental growth model' in which additional segments were added randomly to any branch within the array including terminal branches. Subsequent ordering of the simulated trees revealed that the relation between the numbers of successive orders for networks generated according to the 'segmental model' tended towards an inverse geometric series with a ratio of 4 and that generated according to the 'terminal model' tended towards a ratio of 3. This result showed that the dendritic tree of Purkinje cells grow in a manner indistinguishable from a system adding branches to random terminal segments and that neocortical apical dendrites add their collateral branches to random segments of the apical shaft but that the collateral branches themselves grow by random terminal branching. The possibility that such conclusions may be influenced by loss of branches incurred by either a failure of impregnation, by sectioning, or by environmental influences was investigated by means of a computer technique...

Animals↗
PubMed2026

Nested co-expression network analysis identifies compact gene clusters in a black box.

MOTIVATION: Digital analysis of biological systems requires methods capable of identifying both broad and nested gene modules reflecting complex biological processes. Existing transcriptomic methods often miss compact gene sets corresponding to subprocesses in specialized cell types, limiting insights into functional heterogeneity. RESULTS: We present Nested-WGCNA, a two-stage unsupervised network analysis algorithm designed to identify coarse-grained and fine-grained gene modules. Applied to bulk RNA-Seq data, Nested-WGCNA reveals stable modules reproducible across datasets. When validated against scRNA-Seq data, these modules correspond to both major and minor immune cell subtypes. Application to immunotherapy response datasets uncovers predictive and prognostic biomarkers, highlighting its utility in treatment stratification and biomarker discovery. AVAILABILITY: The NestedWGCNA source code and analysis pipeline are available on GitHub (https://github.com/ilyada/NestedWGCNA) and archived on Zenodo (https://doi.org/10.5281/zenodo.18959244).

Algorithms↗
PubMed1992

Network analysis of Korean health insurance policy-making process.

This study examines how the decision-making process evolved in Korea during the initial phases of introduction and implementation of National Health Insurance. This study analyses the official documents and interviews views made with government officials and related personnel. We used the method of network analysis and multidimensional scaling in order to demonstrate how the major participants in the decision-making process developed and changed under the contemporary political situations. In the pre-implementation stage around 1976, major concerns were concentrated around the issues of financial support for social insurance, the fee schedule and who ought to be covered first. The total number of participants of the health or health-related organization was 61, which included the President, the Minister of Health and Social Affairs, representatives of special interest groups, etc. In the actual implementation period of 1982, different issues were brought up by the major participants. The number of participants in this period declined to 44 with the deletion of 19 and with the addition of two newly formed health insurance organizations. By 1988, as the implementation reached its final decision period, disagreements were centered on progressive premium rating and the administration of National Health Insurance. The number of participants increased to 60 after the addition of 16 participants. The analysis of this paper may provide some insight for other countries which wish to establish National Health Insurance; as reference to the policy-making process, it may provide some suggestions for when to initiate and how to formulate National Health Insurance policies.

Humans↗
PubMed1993

Artificial neural network analysis of pyrolysis mass spectrometric data in the identification of Streptomyces strains.

Sixteen representatives of three morphologically distinct groups of streptomycetes were recovered from soil using selective isolation procedures. Duplicated batches of the test strains were examined by Curie-point pyrolysis mass spectrometry and the first data set used for conventional multivariate statistical analyses and as a training set for an artificial neural network. The second set of data was used for 'operational fingerprinting' and for testing the artificial neural network. All of the test strains were correctly identified using the artificial neural network whereas only fifteen of the sixteen strains were assigned to the correct group using the conventional operational fingerprinting procedure. Artificial neural network analysis of pyrolysis mass spectrometric data provides a rapid, cost-effective and reproducible way of identifying and typing large numbers of microorganisms.

Bacterial Typing Techniques↗
PubMed1991

Use of neural network analysis to classify electroencephalographic patterns against depth of midazolam sedation in intensive care unit patients.

The electroencephalographic (EEG) analog signal is complex and cannot easily be described by univariate variables. Clear visual changes in the EEG power spectrum can be present with little or no change in univariate variable values. A method that could produce a single value based on the total data available in the EEG power spectrum would be very useful in monitoring EEG changes. Neural network analysis is a technique that can take multiple inputs and produce a single output value using complicated processing patterns that require training to establish. We examined the usefulness of a series of neural network models to classify 63 EEG patterns against sedation level in 26 mechanically ventilated patients requiring midazolam for long-term sedation. During a stable period of sedation, a 4- to 60-minute period of EEG data was obtained concurrently with a sedation level from 1 (follows commands) to 7 (no or gag response to suctioning of the endotracheal tube). The EEG power spectrum was divided into equal frequency bands, and the log absolute powers in each of these bands were used as inputs for a series of neural network models. The output target was the sedation level associated with each set of EEG data. Networks were trained on a subset of EEG power/sedation score data pairs, and the ability to classify the remaining data pairs was tested. Using a t-test comparison with a random set of sedation levels, we found that trained neural network models classified EEG patterns against sedation level successfully (p less than 0.001).(ABSTRACT TRUNCATED AT 250 WORDS)

Adolescent↗
PubMed2026

Crosstalk mediators implicated in the Stevens-Johnson Syndrome through gene regulatory network analysis.

Stevens-Johnson syndrome (SJS) is a rare and severe mucocutaneous disorder often triggered by medications or infections. Our previous research identified that four key genes, Ikzf1, Ptger3, Mavs, and Tlr3 are involved in SJS susceptibility and the conjunctival epithelial innate immune response, demonstrating their role in regulating interferon-stimulated genes. However, the interplay among these regulatory factors remains unclear. This study aimed to elucidate the crosstalk mechanisms between the pathways regulated by these four genes in conjunctival epithelial cells. We constructed a comprehensive gene regulatory network using transcriptomic data from murine conjunctival epithelial cells under 16 distinct conditions, including polyI:C stimulation across wild-type, knockout, and transgenic backgrounds for the key genes. A targeted network analysis systematically identified numerous candidate genes mediating the crosstalk between the regulatory pathways initiated by Ikzf1, Ptger3, Mavs, and Tlr3. The identified candidates suggest the involvement of diverse signaling pathways previously unlinked to SJS pathology. Our findings suggest that the pathogenesis of SJS may arise not from the dysfunction of isolated genes but from the disruption of a balance maintained by intricate pathway crosstalk.

Animals↗
PubMed2026

Functional and Nutritional Potential of Chickpea Protein Hydrolysates: A Systematic Review and Plant-protein Network Analysis.

Chickpea is a protein-rich legume increasingly explored as a substrate for functional plant-based ingredients. Chickpea protein hydrolysates (CPHs) and chickpea-derived peptides (CPs), obtained through enzymatic hydrolysis or simulated gastrointestinal digestion, may provide technological and biological properties while supporting the valorization of chickpea fractions and by-products. This review integrates a network analysis of title-abstract terms from 5,728 unique Scopus and PubMed records on plant protein hydrolysates with a systematic review of 72 studies focused on CPH production, peptide characterization, bioactivity, and translational gaps. The evidence indicates that CPHs and CPs show promising antioxidant, antihypertensive, antidiabetic, anti-inflammatory, lipid-lowering, immunomodulatory, antimicrobial, and anticancer-related activities, mainly supported by biochemical assays, cell models, and animal studies. However, heterogeneous hydrolysis protocols, incomplete peptide characterization, inconsistent bioactivity methods, limited scale-up evidence, and the absence of human intervention trials restrict translation. Future studies should prioritize standardized protocols, mechanistic validation, bioavailability, sensory and regulatory assessment, food-matrix validation, and clinical trials.

Cicer↗
PubMed1995

Neural network analysis of quantitative histological factors to predict pathological stage in clinical stage I nonseminomatous testicular cancer.

A great deal of controversy exists in staging clinical stage I (CSI) nonseminomatous testicular germ cell tumors (NSGCT) because of the difficulty of distinguishing true stage I patients from those with occult retroperitoneal or distant metastases. The goal of this study was to quantitate primary tumor histologic factors and to apply these in a neural network computer analysis to determine if more accurate staging could be achieved. All available primary tumor histological slides from 93 CSI NSGCT patients were analyzed for vascular invasion (VI), lymphatic invasion (LI), tunical invasion (TI) and quantitative determination of percentage of the primary tumor composed of embryonal carcinoma (%EMB), yolk sac carcinoma (%YS), teratoma (%TER) and seminoma (%SEM). These patients had undergone retroperitoneal lymphadenectomy or follow-up such that final stage included 55 pathologic stage I and 38 stage II or higher lesions. Two investigators were provided identical datasets for neural network analysis; one experienced researcher used custom Kohonen and back propagation programs and one less experienced researcher used a commercially available program. For each experiment, a subset of data was used for training, and subsets were blindly used to test the accuracy of the networks. In the custom back propagation network, 86 of 93 patients were correctly staged for an overall accuracy of 92% (sensitivity 88%, specificity 96%). Using Neural Ware commercial software 74 of 93 (79.6%) were accurately staged when all 7 input variables were used; however, accuracy improved from 84.9 to 87.1% when 2, 4 and 5 of the variables were used. Quantitative histologic assessment of the primary tumor and neural network processing of data may provide clinically useful information in the CSI NSGCT population; however, the expertise of the network researcher appears to be important, and commercial software in general use may not be superior to standard regression analysis. Prospective testing of expert methodology should be instituted to confirm its utility.

Germinoma↗