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PubMed2003

The Database of Quantitative Cellular Signaling: management and analysis of chemical kinetic models of signaling networks.

MOTIVATION: Analysis of cellular signaling interactions is expected to pose an enormous informatics challenge, perhaps even larger than analyzing the genome. The complex networks arising from signaling processes are traditionally represented as block diagrams. A key step in the evolution toward a more quantitative understanding of signaling is to explicitly specify the kinetics of all chemical reaction steps in a pathway. Technical advances in proteomics and high-throughput protein interaction assays promise a flood of such quantitative data. While annotations, molecular information and pathway connectivity have been compiled in several databases, and there are several proposals for general cell model description languages, there is currently little experience with databases of chemical kinetics and reaction level models of signaling networks. RESULTS: The Database of Quantitative Cellular Signaling is a repository of models of signaling pathways. It is intended both to serve the growing field of chemical-reaction level simulation of signaling networks, and to anticipate issues in large-scale data management for signaling chemistry. AVAILABILITY: The Database of Quantitative Cellular Signaling is available at http://doqcs.ncbs.res.in. Links to the signaling model simulator, GENESIS/Kinetikit are at http://www.ncbs.res.in/~bhalla/kkit/index.html and are also provided from within the database. The database source code is available under the GNU Public License.

Abstracting and Indexing↗

PubMed1978

The effect of refeeding after neonatal starvation on Purkinje cell dendritic growth in the rat.

Male rats, undernourished from birth until 30 days by restricting access to the lactating dam, were given ad libitum food supplies unitl 80 days. Body weight, whole brain weight and cerebellar remained significantly lower in these animals than in normally fed controls. Significant deficits in the area of the molecular and granular layers persisted at 80 days, although there was some recovery during the refeeding period. At the same time granule and Purkinje cell density declined, suggesting that the areal recovery was due to the expansion of the interneuronal matrix. Granule cell numbers remained unchanged between 30 and 80 days. Network analysis of Golgi-Cox preparations indicated a 28% decrease in overall size of the Purkinje cell dendritic networks, due primarily to a deficit in segment frequency which remained unchanged throughout the rehabilitation period. Segment length did, however, show some recovery; distal segments from 80-day experimental networks were significantly longer than those of 30-day undernourished animals. The results of the topological analysis suggested that dendritic remodelling had taken place during the rehabilitation period. The failure to observe a complete recovery in the size and morphology of the dendritic network may be explained in terms of recent suggestions that dendritic development is influenced both by the metabolism of the neurons themselves, and by the number and density of adjacent afferent axons. It is suggested that refeeding from 30 days leads to the recovery of Purkinje cell metabolism, but is unable to restore the parallel fiber deficit.

Animals↗

PubMed2026

Metagenomic insights into microbial drivers of organic micropollutant removal in wastewater-impacted riverbank filtration.

Organic micropollutants (OMPs) in wastewater treatment plant (WWTP) effluent pose persistent risks to aquatic ecosystems and drinking water sources. Riverbank filtration (RBF) is a nature-based treatment process, yet the compartment-specific roles of riverbed sediment and downstream soil in OMP attenuation remain poorly resolved under wastewater-impacted conditions. Here, we combined targeted chemical analysis, OMP property compilation, shotgun metagenomics, EnviPath-based biotransformation annotation, and exploratory network analysis to investigate OMP attenuation in a laboratory-scale RBF system treating real WWTP effluent for 10 months. Nineteen OMPs were monitored along a sequential sediment-soil filtration pathway. Sediment preferentially attenuated hydrophilic or charged compounds, including lidocaine, amantadine, and sotalol, whereas soil contributed more strongly to the attenuation of naproxen, atenolol, and losartan. Metagenomic profiling revealed distinct microbial communities and functional gene repertoires between sediment and soil after long-term operation. Sediment harbored higher relative abundances of genes associated with oxidative xenobiotic transformation, including cytochrome P450-related enzymes, demethylases, dehydrogenases, oxidases, and aromatic compound degradation pathways. An exploratory Spearman network further identified associations among microbial genera, EnviPath-annotated candidate biotransformation genes, and OMP removal rates, including 17 KO-OMP links supported by both correlation and pathway annotation. These findings indicate that sediment and soil develop complementary microbial functional potentials that may support compound-specific OMP attenuation. This study provides a mechanistic basis for optimizing sediment-soil configurations in wastewater-impacted RBF systems and for improving nature-based barriers against diverse OMP mixtures.

Wastewater↗

PubMed1999

The determination of three subcutaneous adipose tissue compartments in non-insulin-dependent diabetes mellitus women with artificial neural networks and factor analysis.

The optical device LIPOMETER allows for non-invasive, quick, precise and safe determination of subcutaneous fat distribution, so-called subcutaneous adipose tissue topography (SAT-Top). In this paper, we show how the high-dimensional SAT-Top information of women with type-2 diabetes mellitus (non-insulin-dependent diabetes mellitus (NIDDM)) and a healthy control group can be analysed and represented in low-dimensional plots by applying factor analysis and special artificial neural networks. Three top-down sorted subcutaneous adipose tissue compartments are determined (upper trunk, lower trunk, legs). NIDDM women provide significantly higher upper trunk obesity and significantly lower leg obesity ('apple' type), as compared with their healthy control group. Further, we show that the results of the applied networks are very similar to the results of factor analysis.

Adipose Tissue↗

PubMed1998

Organizational characteristics associated with agency position in community care networks.

This study examines how organizational characteristics affect agency participation and centrality in community service networks. We find that the network structure of agency relations varies for administrative and client-related activities among the 69 agencies studied, which include all but the most isolated agencies serving people with physical disabilities in a single community. In identifying structurally equivalent groups using network analysis, we find that all types of agencies except HMOs are found throughout community service networks. Analyses show that among the five types of relations, minimal intergroup activity occurs within funding and planning networks and that organizational size and ownership are the best organizational predictors of network location and centrality. Non-profits are the most central for planning and client referrals, and large agencies are the most central for funding. We explore the implications of these findings, particularly for sustaining cooperation within the service networks and for the role of non-profits and medical providers in the community.

Community Networks↗

PubMed2026

Integrated Genome Mining and Bioactivity-Guided Isolation of Antimicrobial Peptides from Bacillus amyloliquefaciens BS4.

Bacterial resistance remains a critical global health challenge, driving the continuous search for novel antimicrobial agents. Bacillus amyloliquefaciens is a recognized repository of bioactive metabolites; however, its full biosynthetic potential requires integrated genomic and experimental validation. This study characterized the antimicrobial profile of B. amyloliquefaciens BS4 through a hybrid pipeline. Genome sequencing and de novo assembly revealed a 3.9 Mb chromosome with a G + C content of 46.14%. Functional annotation identified 3,887 coding sequences, including pathways for siderophore biosynthesis and a complete bacilysin biosynthetic cluster. BGC analysis using antiSMASH v7.1.0 and BAGEL4 identified 18 biosynthetic gene clusters, while similarity network analysis via BiG-SCAPE highlighted unique singleton BGCs, indicating untapped biosynthetic diversity. Although in silico screening via Macrel predicted two putative cationic antimicrobial peptides (AMPs), bioactivity-guided purification utilizing sequential RP-HPLC, and de novo sequencing revealed a distinct set of four active peptides. Notably, three of these sequences were identified as fragments derived from the BclA exosporium protein family, highlighting the structural proteome as a non-canonical source of antimicrobials. The purified fractions exhibited activity against M. luteus and E. coli, while displaying no significant hemolytic activity or cytotoxicity, even above the MIC values. Molecular docking further supported the interaction of these candidates with bacterial targets. Overall, this hybrid strategy effectively uncovers the antimicrobial complexity of BS4, revealing 'cryptic' peptide candidates with therapeutic potential.

Bacillus amyloliquefaciens BS4↗

PubMed2000

Neural network modeling of risk assessment in child protective services.

The advantages of using neural network methodology for the modeling of complex social science data are demonstrated, and neural network analysis is applied to Washington State Child Protective Services risk assessment data. Neural network modeling of the association between social worker overall assessment of risk and the 37 separate risk factors from the State of Washington Risk Assessment Matrix is shown to provide case classification results superior to linear or logistic multiple regression. The improvement in case prediction and classification accuracy is attributed to the superiority of neural networks for modeling nonlinear relationships between interacting variables; in this respect the mathematical framework of neural networks is a better approximation to the actual process of human decision making than linear, main effects regression. The implications of this modeling advantage for evaluating social science data within the framework of ecological theories are discussed.

Child↗

PubMed2000

A miniature integrated device for automated multistep genetic assays.

A highly integrated monolithic device was developed that automatically carries out a complex series of molecular processes on multiple samples. The device is capable of extracting and concentrating nucleic acids from milliliter aqueous samples and performing microliter chemical amplification, serial enzymatic reactions, metering, mixing and nucleic acid hybridization. The device, which is smaller than a credit card, can manipulate over 10 reagents in more than 60 sequential operations and was tested for the detection of mutations in a 1.6 kb region of the HIV genome from serum samples containing as few as 500 copies of the RNA. The elements in this device are readily linked into complex, flexible and highly parallel analysis networks for high throughput sample preparation or, conversely, for low cost portable DNA analysis instruments in point-of-care medical diagnostics, environmental testing and defensive biological agent detection.

DNA↗

PubMed2025

Radiogenomic MRI biomarkers for noninvasive prediction of GPC3 expression and tumor microenvironment in hepatocellular carcinoma.

BACKGROUND: Glypican-3 (GPC3) is frequently overexpressed in hepatocellular carcinoma (HCC) and plays a key role in immune and metabolic remodeling of the tumor microenvironment. Reliable noninvasive biomarkers for predicting GPC3 status could improve patient stratification and support precision immunotherapy. METHODS: This multicenter retrospective study included 274 patients with pathologically confirmed hepatocellular carcinoma from three institutions, 34 external cases with MRI from The Cancer Imaging Archive, and 363 transcriptomic profiles from The Cancer Genome Atlas. Contrast-enhanced T1-weighted imaging and diffusion-weighted imaging were analyzed. Tumor and peritumoral regions were segmented manually and radiomic features extracted using PyRadiomics. Feature selection was performed with correlation filtering and least absolute shrinkage and selection operator regression. Machine learning classifiers including logistic regression, random forest, support vector machine, k-nearest neighbor, and decision tree were trained with 10-fold cross-validation and tested on independent external cohorts. A radiomics score was calculated for each patient. Radiogenomic analysis correlated radiomics scores with transcriptomic data using weighted gene co-expression network analysis. Hub genes and enriched pathways were identified, and immune infiltration and predicted immunotherapy response were assessed using computational methods. RESULTS: The random forest model using contrast-enhanced T1-weighted imaging achieved an area under the curve of 0.966 in training and 0.935 in internal validation. The integrated contrast-enhanced T1-weighted imaging plus diffusion-weighted imaging model reached an internal validation area under the curve of 0.979. In external testing, the best performance was obtained with a support vector machine model (area under the curve 0.756). Radiomics scores were significantly correlated with GPC3 expression (R = 0.78, p < 0.05). Transcriptomic analysis identified a 10-gene signature enriched in hypoxia and lipid metabolism pathways that stratified patients into prognostic subgroups (concordance index 0.720, hazard ratio 4.07, p < 0.0001). High-risk patients had greater immune infiltration and a lower predicted immune evasion score, suggesting a potential benefit from immunotherapy. CONCLUSIONS: MRI-based radiomics models can noninvasively predict GPC3 expression in hepatocellular carcinoma. Radiomics scores reflect underlying hypoxia and lipid metabolism pathways and stratify patients by prognosis and predicted immunotherapy response. These findings support radiogenomics as a translational approach to imaging-guided precision treatment in hepatocellular carcinoma.

Humans↗

PubMed2026

Bioinformatics and Quantitative Real-Time Polymerase Chain Reaction Analysis of SUCNR1 and GPR37L1 in Schizophrenia.

Schizophrenia is a severe, complex, and multifactorial mental disorder involving numerous genetic susceptibility elements, leading to substantial disability, morbidity, and mortality. Despite significant progress in understanding its pathophysiology and etiology, specific diagnostic biomarkers for schizophrenia remain elusive. This study aimed to identify candidate molecular markers associated with schizophrenia. An integrated bioinformatics analysis was performed on the public microarray dataset GSE54913. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses revealed that the most significantly enriched GO terms were related to channel activity, including passive transmembrane transporter activity, ion channel activity, gated channel activity, and substrate-specific channel activity. The top five enriched KEGG pathways were insulin secretion, cAMP signaling pathway, nucleotide excision repair, TNF signaling pathway, and glutathione metabolism. Validation was conducted using quantitative real-time polymerase chain reaction (qRT-PCR) on an independent sample set from Wuhan Rongjun Youfu Hospital. The qRT-PCR results were largely consistent with the microarray analysis (Pearson r = 0.89, 95% CI: 0.66-0.97). Protein-protein interaction (PPI) network analysis identified two hub genes, SUCNR1 and GPR37L1, which were significantly associated with the GO term 'ion channel activity' and enriched in the KEGG pathway 'insulin secretion'. Furthermore, SUCNR1 expression showed a negative correlation with verbal memory scores (r = -0.54, P = 0.015), whereas GPR37L1 expression showed a positive correlation (r = 0.59, P = 0.0034). These findings suggest that altered SUCNR1 and GPR37L1 expression may be associated with schizophrenia and may represent candidate molecular markers for further investigation.

Humans↗

PubMed2005

Are scale-free networks robust to measurement errors?

BACKGROUND: Many complex random networks have been found to be scale-free. Existing literature on scale-free networks has rarely considered potential false positive and false negative links in the observed networks, especially in biological networks inferred from high-throughput experiments. Therefore, it is important to study the impact of these measurement errors on the topology of the observed networks. RESULTS: This article addresses the impact of erroneous links on network topological inference and explores possible error mechanisms for scale-free networks with an emphasis on Saccharomyces cerevisiae protein interaction networks. We study this issue by both theoretical derivations and simulations. We show that the ignorance of erroneous links in network analysis may lead to biased estimates of the scale parameter and recommend robust estimators in such scenarios. Possible error mechanisms of yeast protein interaction networks are explored by comparisons between real data and simulated data. CONCLUSION: Our studies show that, in the presence of erroneous links, the connectivity distribution of scale-free networks is still scale-free for the middle range connectivities, but can be greatly distorted for low and high connecitivities. It is more appropriate to use robust estimators such as the least trimmed mean squares estimator to estimate the scale parameter gamma under such circumstances. Moreover, we show by simulation studies that the scale-free property is robust to some error mechanisms but untenable to others. The simulation results also suggest that different error mechanisms may be operating in the yeast protein interaction networks produced from different data sources. In the MIPS gold standard protein interaction data, there appears to be a high rate of false negative links, and the false negative and false positive rates are more or less constant across proteins with different connectivities. However, the error mechanism of yeast two-hybrid data may be very different, where the overall false negative rate is low and the false negative rates tend to be higher for links involving proteins with more interacting partners.

Algorithms↗

PubMedDate not supplied

MatrixExplorer: a dual-representation system to explore social networks.

MatrixExplorer is a network visualization system that uses two representations: node-link diagrams and matrices. Its design comes from a list of requirements formalized after several interviews and a participatory design session conducted with social science researchers. Although matrices are commonly used in social networks analysis, very few systems support the matrix-based representations to visualize and analyze networks. MatrixExplorer provides several novel features to support the exploration of social networks with a matrix-based representation, in addition to the standard interactive filtering and clustering functions. It provides tools to reorder (layout) matrices, to annotate and compare findings across different layouts and find consensus among several clusterings. MatrixExplorer also supports Node-link diagram views which are familiar to most users and remain a convenient way to publish or communicate exploration results. Matrix and node-link representations are kept synchronized at all stages of the exploration process.

Algorithms↗

PubMed2026

Identification and evaluation of glutamine-related gene characteristics based on multi-omics to predict the prognosis of patients with colorectal cancer.

BACKGROUND: Colorectal cancer (CRC), a prevalent malignancy of the gastrointestinal tract, ranks among the leading causes of cancer-related morbidity and mortality. Its clinical course is marked by high fatality and poor prognosis. Elucidating the mechanisms underlying CRC initiation and recurrence is therefore critical for identifying novel therapeutic targets. METHODS: This study incorporated two datasets, TCGA-CRC and GSE17537. A total of 84 glutamine metabolism-related genes (GMRGs) were identified, and differential expression analysis was conducted using the TCGA-CRC dataset. Weighted Gene Co-expression Network Analysis (WGCNA) was applied to determine gene modules most strongly associated with GMRG scores. Single-cell RNA sequencing (scRNA-seq) was utilized to characterize key cellular clusters and to identify differentially expressed genes (DEGs) between high and low glutamine metabolism (GM) groups. Overlapping GMRGs were visualized using the ggVennDiagram package in R. A CRC risk prediction model was developed through Cox proportional hazards and LASSO regression analyses, with performance evaluated by ROC curves. Cell type enrichment across 64 immune and stromal populations was assessed via xCell, and intergroup differences were tested using the Wilcoxon rank-sum test. TIDE scores were used to estimate immunotherapy responsiveness, while oncoPredict facilitated drug sensitivity profiling. PCOLCE2 expression in CRC was validated by RT-qPCR and Western blotting. Its functional role was examined through CCK-8 assays, invasion and migration tests, flow cytometry, and glutamate quantification. RESULTS: ScRNA-seq analysis identified two key cell populations and 437 DEGs associated with GM status. WGCNA pinpointed the MEgreen module as most significantly correlated with GMRG scores, encompassing 1075 genes. Integration of DEGs, module genes, and GM-related DEGs yielded 60 candidate genes for downstream analysis. A GMRG-based prognostic model comprising six genes (SRPX, CXCL1, GPX3, PCOLCE2, CLU, SEMA3E) demonstrated strong predictive performance. Prognostic gene expression correlated with immune and stromal infiltration patterns, as indicated by Spearman correlation analysis. The high-risk group exhibited diminished predicted response to immunotherapy (TIDE scores). Drug sensitivity analysis identified four compounds—Dasatinib-51, WH-4-023-56, TWS-119-366, and LDN-193189-478—with elevated efficacy in high-risk CRC cases. PCOLCE2 expression was significantly reduced in CRC tissues. Functional assays revealed that PCOLCE2 knockdown did not substantially affect cell proliferation but significantly impaired invasion and migration in CRC cells, increased apoptosis, and suppressed both glutamine uptake and glutamate production—highlighting its oncogenic role. CONCLUSION: Six GMRGs—SRPX, CXCL1, GPX3, PCOLCE2, CLU, and SEMA3E—were identified as key components of a robust prognostic model for CRC. These findings offer valuable insights into CRC pathogenesis and potential therapeutic strategies. Notably, this study provides the first evidence implicating PCOLCE2 as a tumor-promoting factor in CRC.

Glutamine↗

PubMed1995

Automated grading of astrocytomas based on histomorphometric analysis of Ki-67 and Feulgen stained paraffin sections. Classification results of neuronal networks and discriminant analysis.

In stereotactically obtained astrocytoma biopsies, four morphometric nuclear parameters were determined with the use of an image analysis system. A special Ki-67 (MIB1)/Feulgen stain made it possible to quantify the essential characteristics of gliomas of the astrocytoma/glioblastoma group: growth pattern, cellularity, proliferation tendency and nucleus pleomorphism. A grading scale based on a cluster analysis resembling the WHO-scheme, which is suitable for automated astrocytoma grading, was developed. Large back propagation neural networks were used and their results compared with those of a classical multivariate discriminant classification analysis. It is possible to show that the neural network technology is superior to the statistical approach for automated astrocytoma grading. Based on the results of our study we believe neural network technology to be useful for tumour grading problems. The presented approach can be generalized for the automated grading of other tumour entities.

Astrocytoma↗

PubMed2006

Connectivity matrix method for analyses of biological networks and its application to atom-level analysis of a model network of carbohydrate metabolism.

An approach for analysis of biological networks is proposed. In this approach, named the connectivity matrix (CM) method, all the connectivities of interest are expressed in a matrix. Then, a variety of analyses are performed on GNU Octave or Matlab. Each node in the network is expressed as a row vector or numeral that carries information defining or characterising the node itself. Information about connectivity itself is also expressed as a row vector or numeral. Thus, connection of node n1 to node n2 through edge e is expressed as [n1, n2, e], a row vector formed by the combination of three row vectors or numerals, where n1, n2 and e indicate two different nodes and one connectivity, respectively. All the connectivities in any given network are expressed as a matrix, CM, each row of which corresponds to one connectivity. Using this CM method, intermetabolite atom-level connectivity is investigated in a model metabolic network composed of the reactions for glycolysis, oxidative decarboxylation of pyruvate, citric acid cycle, pentose phosphate pathway and gluconeogenesis.

Adaptation, Physiological↗

PubMed2026

New insights into diagnostic values and mechanisms of ferroptosis associated with immune infiltration in diabetic kidney disease.

The pathogenesis of diabetic kidney disease (DKD) is complex and closely related to ferroptosis and immune dysregulation, but the relevance is unclear. The present study investigates the potential mechanisms of ferroptosis-related genes (FRGs) in DKD and their relationship with the immune-inflammatory response. It searches for new diagnostic biomarkers to help diagnose and treat DKD. Four Gene Expression Omnibus (GEO) datasets, GSE30528, GSE30529 and GSE30122 as the test set, and GSE96804 for validation, were analyzed. FRGs were obtained from GeneCards, and 47 ferroptosis-related differentially expressed genes (FRDEGs) were identified by intersecting with DKD-related differentially expressed genes. Functional enrichment analyses, including Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, Gene Set Enrichment Analysis and Gene Set Variation Analysis, revealed that these FRDEGs are primarily associated with ferroptosis, hypoxia response and immune inflammation. Subsequently, the weighted gene co-expression network analysis (WGCNA) was employed to expand the ferroptosis-related gene network, and intersection of the 47 FRDEGs with key WGCNA module genes yielded 10 key genes. Based on the 10 key genes, the least absolute shrinkage and selection operator and support vector machine algorithms identified three hub genes [chemokine ligand 5 (CCL5), forkhead box C1 (FOXC1) and lactotransferrin (LTF)] for DKD diagnosis. Receiver operating characteristic curves confirmed their diagnostic value, with FOXC1 and LTF validated in the independent dataset. Immune infiltration analysis via CIBERSORT revealed eight immune cell types with significantly different infiltration levels between the DKD and control group in the integrated GEO datasets. Notably, both LTF and CCL5 showed a significant positive correlation with gamma delta T cells (γδT). Quantitative PCR results confirmed differential expression of the three hub genes in the DKD group, with elevated expression observed in DKD mice following intervention with rosiglitazone and hyperoside.

bioinformatics analysis↗

PubMed2026

Genome-Wide Characterization of β-Glucosidase (TaBGLU) Genes in Bread Wheat and Their Expression Under Drought, Cold, and Combined Stress.

Glycoside hydrolase 1 (GH1) β-glucosidases were known to activate hormone conjugates and defense metabolites, yet their genomic organization and stress-response dynamics in wheat remained incompletely defined. We therefore performed an integrated characterization of TaBGLUs spanning phylogeny, gene structure and conserved motifs, subcellular localization, promoter cis-elements, Gene Ontology enrichment, protein-protein interaction networks, and targeted expression profiling. Wheat TaBGLUs partitioned into well-supported clades that shared canonical GH1 catalytic residues and a largely conserved motif scaffold. Subcellular localization predictions indicated predominant nuclear and chloroplast targeting, with a smaller cohort directed to secretory or endomembrane compartments. Promoters were enriched for light-responsive, hormone-related (ABA, JA/SA, auxin, GA) and stress-associated (MYB/WRKY, heat, low temperature) cis-elements, and functional annotations were consistent with roles in carbohydrate and cell-wall metabolism, hormone homeostasis, and defense. Network analysis revealed a densely connected TaBGLU submodule embedded within broader carbohydrate and defense interaction networks, suggesting coordinated or cooperative functions. Expression profiling under cold, drought, and combined drought and cold demonstrated broad stress inducibility, with early activation detected by 6 h, cold-responsive maxima typically at 12 h, drought-responsive peaks predominating at 24 h, and combined stress eliciting both earlier and more sustained expression maxima between 12-24 h. Representative strongly responsive genes included TaBGLU20, TaBGLU44, TaBGLU6, and TaBGLU23, which showed pronounced late induction under combined stress, TaBGLU30, which exhibited an earlier combined-stress peak, and TaBGLU12, which displayed a marked late drought-specific response. Taken together, this integrated genomic, regulatory, and expression atlas refined the wheat BGLU repertoire relative to previous gene model inventories, highlighted candidate TaBGLUs with central network positions and strong stress inducibility, and provided concrete entry points for functional validation and breeding for improved stress resilience.

Triticum↗

PubMed1995

HIV transmission in sexual networks: an empirical analysis.

Risk behaviour and egocentric sexual network data collected from a large random sample of young gay men in San Francisco were analysed to assess the importance of sexual mixing (i.e. sexual networks) in the acquisition of HIV. These data were collected in 1993, during wave one of a longitudinal cohort study of HIV transmission in gay men; the seroprevalence level in the sample was 18%. We identify recent sexual mixing patterns and we demonstrate that seropositives and seronegatives have very different age-stratified sexual mixing patterns. We show that sexual mixing can explain the current seroprevalence patterns in the young gay community; seroprevalence levels in risk groups reflect the degree of sexual mixing with the older (and more heavily infected) age group. Our results suggest that seropositives became infected with HIV not simply owing to an increased rate of acquisition of sex partners, but also as a result of their sexual mixing pattern. We develop and apply a simple methodology that uses the sexual network data in combination with risk behaviour data to estimate the future number of seroconverters. Our methodology is validated by testing our predictions against the observed seroconversion data collected during wave two of the cohort study in 1994. Our analyses empirically demonstrate (for the first time) the significance of sexual mixing as a risk factor for HIV transmission.

Adolescent↗