Mapping the cellular biogeography of human bone marrow niches using single-cell transcriptomics and proteomic imaging.

Non-hematopoietic cells are essential contributors to hematopoiesis. However, heterogeneity and spatial organization of these cells in human bone marrow remain largely uncharacterized. We used single-cell RNA sequencing (scRNA-seq) to profile 29,325 non-hematopoietic cells and discovered nine transcriptionally distinct subtypes. We simultaneously profiled 53,417 hematopoietic cells and predicted their interactions with non-hematopoietic subsets. We employed co-detection by indexing (CODEX) to spatially profile over 1.2 million cells. We integrated scRNA-seq and CODEX data to link predicted cellular signaling with spatial proximity. Our analysis revealed a hyperoxygenated arterio-endosteal neighborhood for early myelopoiesis, and an adipocytic localization for early hematopoietic stem and progenitor cells (HSPCs). We used our CODEX atlas to annotate new images and uncovered mesenchymal stromal cell (MSC) expansion and spatial neighborhoods co-enriched for leukemic blasts and MSCs in acute myeloid leukemia (AML) patient samples. This spatially resolved, multiomic atlas of human bone marrow provides a reference for investigation of cellular interactions that drive hematopoiesis.

Promoter-Enhancer Communication Occurs Primarily within Insulated Neighborhoods.

Metazoan chromosomes are sequentially partitioned into topologically associating domains (TADs) and then into smaller sub-domains. One class of sub-domains, insulated neighborhoods, are proposed to spatially sequester and insulate the enclosed genes through self-association and chromatin looping. However, it has not been determined functionally whether promoter-enhancer interactions and gene regulation are broadly restricted to within these loops. Here, we employed published datasets from murine embryonic stem cells (mESCs) to identify insulated neighborhoods that confine promoter-enhancer interactions and demarcate gene regulatory regions. To directly address the functionality of these regions, we depleted estrogen-related receptor ß (Esrrb), which binds the Mediator co-activator complex, to impair enhancers of genes within 222 insulated neighborhoods without causing mESC differentiation. Esrrb depletion reduces Mediator binding, promoter-enhancer looping, and expression of both nascent RNA and mRNA within the insulated neighborhoods without significantly affecting the flanking genes. Our data indicate that insulated neighborhoods represent functional regulons in mammalian genomes.

Making Connections: Neurodevelopmental Changes in Brain Connectivity After Adverse Experiences in Early Adolescence.

Adverse childhood experiences have been linked to detrimental mental health outcomes in adulthood. This study investigates a potential neurodevelopmental pathway between adversity and mental health outcomes: brain connectivity. We used data from the prospective, longitudinal Adolescent Brain Cognitive Development (ABCD) study (N ≍ 12.000, participants aged 9-13 years, male and female) and assessed structural brain connectivity using fractional anisotropy (FA) of white matter tracts. The adverse experiences modeled included family conflict and traumatic experiences. K-means clustering and latent basis growth models were used to determine subgroups based on total levels and trajectories of brain connectivity. Multinomial regression was used to determine associations between cluster membership and adverse experiences. The results showed that higher family conflict was associated with higher FA levels across brain tracts (e.g., t (3) = -3.81, ß = -0.09, p bonf = 0.003) and within the corpus callosum (CC), fornix, and anterior thalamic radiations (ATR). A decreasing FA trajectory across two brain imaging timepoints was linked to lower socioeconomic status and neighborhood safety. Socioeconomic status was related to FA across brain tracts (e.g., t (3) = 3.44, ß = 0.10, p bonf = 0.01), the CC and the ATR. Neighborhood safety was associated with FA in the Fornix and ATR (e.g., t (1) = 3.48, ß = 0.09, p bonf = 0.01). There is a complex and multifaceted relationship between adverse experiences and brain development, where adverse experiences during early adolescence are related to brain connectivity. These findings underscore the importance of studying adverse experiences beyond early childhood to understand lifespan developmental outcomes.

Predicting lncRNA-disease associations based on combining selective similarity matrix fusion and bidirectional linear neighborhood label propagation.

Recent studies have revealed that long noncoding RNAs (lncRNAs) are closely linked to several human diseases, providing new opportunities for their use in detection and therapy. Many graph propagation and similarity fusion approaches can be used for predicting potential lncRNA-disease associations. However, existing similarity fusion approaches suffer from noise and self-similarity loss in the fusion process. To address these problems, a new prediction approach, termed SSMF-BLNP, based on organically combining selective similarity matrix fusion (SSMF) and bidirectional linear neighborhood label propagation (BLNP), is proposed in this paper to predict lncRNA-disease associations. In SSMF, self-similarity networks of lncRNAs and diseases are obtained by selective preprocessing and nonlinear iterative fusion. The fusion process assigns weights to each initial similarity network and introduces a unit matrix that can reduce noise and compensate for the loss of self-similarity. In BLNP, the initial lncRNA-disease associations are employed in both lncRNA and disease directions as label information for linear neighborhood label propagation. The propagation was then performed on the self-similarity network obtained from SSMF to derive the scoring matrix for predicting the relationships between lncRNAs and diseases. Experimental results showed that SSMF-BLNP performed better than seven other state of-the-art approaches. Furthermore, a case study demonstrated up to 100% and 80% accuracy in 10 lncRNAs associated with hepatocellular carcinoma and 10 lncRNAs associated with renal cell carcinoma, respectively. The source code and datasets used in this paper are available at: https://github.com/RuiBingo/SSMF-BLNP.

Joint deep autoencoder and subgraph augmentation for inferring microbial responses to drugs.

Exploring microbial stress responses to drugs is crucial for the advancement of new therapeutic methods. While current artificial intelligence methodologies have expedited our understanding of potential microbial responses to drugs, the models are constrained by the imprecise representation of microbes and drugs. To this end, we combine deep autoencoder and subgraph augmentation technology for the first time to propose a model called JDASA-MRD, which can identify the potential indistinguishable responses of microbes to drugs. In the JDASA-MRD model, we begin by feeding the established similarity matrices of microbe and drug into the deep autoencoder, enabling to extract robust initial features of both microbes and drugs. Subsequently, we employ the MinHash and HyperLogLog algorithms to account intersections and cardinality data between microbe and drug subgraphs, thus deeply extracting the multi-hop neighborhood information of nodes. Finally, by integrating the initial node features with subgraph topological information, we leverage graph neural network technology to predict the microbes' responses to drugs, offering a more effective solution to the 'over-smoothing' challenge. Comparative analyses on multiple public datasets confirm that the JDASA-MRD model's performance surpasses that of current state-of-the-art models. This research aims to offer a more profound insight into the adaptability of microbes to drugs and to furnish pivotal guidance for drug treatment strategies. Our data and code are publicly available at: https://github.com/ZZCrazy00/JDASA-MRD.

Multiplex imaging reveals spatially resolved DNA-damage response neighborhoods in TP53-mutated myelodysplastic neoplasms.

While increased DNA damage is a well-described feature of myelodysplastic syndrome (MDS) and acute myeloid leukemia (AML), it is unclear whether all lineages and all regions of the marrow are homogeneously affected. In this study, we performed immunohistochemistry on formalin-fixed, paraffin-embedded whole-section bone marrow biopsies using a well-established antibody to detect pH2A.X (phosphorylated histone variant H2A.X) that recognizes DNA double-strand breaks. Focusing on TP53-mutated and complex karyotype MDS/AML, we find a greater pH2A.X+ DNA damage burden compared to TP53 wild-type neoplastic cases and non-neoplastic controls. To understand how double-strand breaks vary between lineages and spatially in TP53-mutated specimens, we applied a low-multiplex immunofluorescence staining and spatial analysis protocol to visualize pH2A.X+ cells with p53 protein staining and lineage markers. pH2A.X marked predominantly mid- to late-stage erythroids, whereas early erythroids and CD34+ blasts were relatively spared. In a prototypical example, these pH2A.X+ erythroids were organized locally as distinct colonies, and each colony displayed pH2A.X+ puncta at a synchronous level. This highly coordinated immunophenotypic expression was also seen for p53 protein staining and among presumed early myeloid colonies. Neighborhood clustering analysis showed distinct marrow regions differentially enriched in pH2A.X+/p53+ erythroid or myeloid colonies, indicating spatial heterogeneity of DNA-damage response and p53 protein expression. The lineage and architectural context within which DNA damage phenotype and oncogenic protein are expressed is relevant to current therapeutic developments that leverage macrophage phagocytosis to remove leukemic cells in part due to irreparable DNA damage. © 2024 The Pathological Society of Great Britain and Ireland.

Environmental inequality in the neighborhood networks of urban mobility in US cities.

Research has made clear that neighborhoods impact the health and well-being of their residents. A related strand of research shows that neighborhood disadvantage is geographically clustered. Because the neighborhoods of low-income and minority populations tend to be more disadvantaged, neighborhood conditions help explain racial and socioeconomic inequalities. These strands of research restrict processes of neighborhood influence to operate only within and between geographically contiguous neighbors. However, we are underestimating the role of neighborhood conditions in explaining inequality if disadvantage extends beyond the residential and extralocal environments into a network of neighborhoods spanning the urban landscape based on where residents move within a city. I use anonymized mobile phone data to measure exposure to air pollution among residents of poor and minority neighborhoods in 88 of the most populous US cities. I find that residents from minority and poor neighborhoods travel to neighborhoods that have greater air pollution levels than the neighborhoods that residents from White and nonpoor neighborhoods visit. Hispanic neighborhoods exhibit the greatest overall pollution burden, Black/White and Asian/White disparities are greatest at the network than residential scale, and the socioeconomic advantage of lower risk exposure is highest for residents from White neighborhoods. These inequalities are notable given recent declines in segregation and air pollution levels in American cities.

A comprehensive analysis integrating phenotypic assessment uncovering thornless cultivar lineages in Aralia elata.

Aralia elata is an Araliaceae woody plant species found in Northeastern Asia. To understand how genetic pools are distributed for A.elata clones, we were to analyze the population structure of A.elata cultivars and identify how these are correlated with thorn-related phenotype which determines the utility of A.elata. We found that the de novo assembled genome of 'Yeongchun' shared major genomic compartments with the public A.elata genome assembled from the wild-type from China. To identify the population structure of the 32 Korean and Japanese cultivars, we identified 44 SSR markers and revealed three main sub-clusters using ΔK analysis with one isolated cultivar. Machine-learning based clustering with thorn-related phenotype correlated moderately with population structure based on SSR analysis suggested multi-layered genetic regulation of thorn-related phenotypes. Thus, we revealed genetic lineage of A.elata and uncovered isolated cultivar which can provide new genetic material for further breeding.

Neighborhood-level variability in asthma-related emergency department visits in Central Texas.

BACKGROUND: The extent to which incidence rates of asthma-related emergency department (ED) visits vary from neighborhood to neighborhood and predictors of neighborhood-level asthma ED visit burden are not well understood. OBJECTIVE: We aimed to describe the census tract-level spatial distribution of asthma-related ED visits in Central Texas and identify neighborhood-level characteristics that explain variability in neighborhood-level asthma ED visit rates. METHODS: Conditional autoregressive models were used to examine the spatial distribution of asthma-related ED visit incidence rates across census tracts in Travis County, Texas, and assess the contribution of census tract characteristics to their distribution. RESULTS: There were distinct patterns in ED visit incidence rates at the census tract scale. These patterns were largely unexplained by socioeconomic or selected built environment neighborhood characteristics. However, racial and ethnic composition explained 33% of the variability of ED visit incidence rates across census tracts. The census tract predictors of ED visit incidence rates differed by racial and ethnic group. CONCLUSIONS: Variability in asthma ED visit incidence rates are apparent at smaller spatial scales. Most of the variability in census tract-level asthma ED visit rates in Central Texas is not explained by racial and ethnic composition or other neighborhood characteristics.

Exploring gene-patient association to identify personalized cancer driver genes by linear neighborhood propagation.

BACKGROUND: Driver genes play a vital role in the development of cancer. Identifying driver genes is critical for diagnosing and understanding cancer. However, challenges remain in identifying personalized driver genes due to tumor heterogeneity of cancer. Although many computational methods have been developed to solve this problem, few efforts have been undertaken to explore gene-patient associations to identify personalized driver genes. RESULTS: Here we propose a method called LPDriver to identify personalized cancer driver genes by employing linear neighborhood propagation model on individual genetic data. LPDriver builds personalized gene network based on the genetic data of individual patients, extracts the gene-patient associations from the bipartite graph of the personalized gene network and utilizes a linear neighborhood propagation model to mine gene-patient associations to detect personalized driver genes. The experimental results demonstrate that as compared to the existing methods, our method shows competitive performance and can predict cancer driver genes in a more accurate way. Furthermore, these results also show that besides revealing novel driver genes that have been reported to be related with cancer, LPDriver is also able to identify personalized cancer driver genes for individual patients by their network characteristics even if the mutation data of genes are hidden. CONCLUSIONS: LPDriver can provide an effective approach to predict personalized cancer driver genes, which could promote the diagnosis and treatment of cancer. The source code and data are freely available at https://github.com/hyr0771/LPDriver .

Mediating pathways between neighborhood disadvantage and cardiovascular risk: Quasi-experimental evidence from a Danish refugee dispersal policy.

Research has documented that neighborhood disadvantage is associated with increased cardiovascular disease risk, but it is unclear which mechanistic pathways mediate this association across the life course. Leveraging a natural experiment in which refugees to Denmark were quasi-randomly assigned to neighborhoods across the country during 1986-1998 and using 30 years of follow-up data from population and health registers, we assessed whether and how individual-level poverty, unstable employment, and poor mental health mediate the relation between neighborhood disadvantage and the risk of hypertension, hyperlipidemia, and type 2 diabetes among Danish refugees (N= 40,811). Linear probability models using the discrete time-survival framework showed that neighborhood disadvantage was associated with increased risk of hypertension (0.05 percentage points [pp] per year [95%CI -0.00, 0.10]); hyperlipidemia (0.03 pp per year [95%CI -0.01, 0.07]), and diabetes (0.01 pp per year (95%CI -0.02, 0.03)). The Baron-Kenny product-of-coefficients method for counterfactual mediation analysis indicated that cumulative income mediated 6%-28% of the disadvantage effect on these outcomes. We find limited evidence of mediation by unstable employment and poor mental health. This study informs our theoretical understanding of the pathways linking neighborhood disadvantage with cardiovascular disease risk and identifies income security as a promising point of intervention in future research.

Neighborhood eviction trajectories and odds of moderate and serious psychological distress during pregnancy among African American women.

African American mothers are unjustly burdened by both residential evictions and psychological distress. We quantified associations between trajectories of neighborhood evictions over time and the odds of moderate and serious psychological distress (MPD and SPD, respectively) during pregnancy among African American women. We linked publicly available data on neighborhood eviction filing and judgment rates to preconception and during-pregnancy addresses from the Life-course Influences on Fetal Environments (LIFE) Study (2009-2011; n = 808). Multinomial logistic regression-estimated odds of MPD and SPD during pregnancy that were associated with eviction filing and judgment rate trajectories incorporating preconception and during-pregnancy addresses (each categorized as low, medium, or high, with two 9-category trajectory measures). Psychological distress was measured with the Kessler Psychological Distress Scale (K6) (K6 scores 5-12 = MPD, and K6 scores ≥13 = SPD). MPD was reported in 60% of the sample and SPD in 8%. In adjusted models, higher neighborhood eviction filing and judgment rates, as compared with low/low rates, during the preconception and pregnancy periods were associated with 2- to 4-fold higher odds of both MPD and SPD during pregnancy among African American women. In future studies, researchers should identify mechanisms of these findings to inform timely community-based interventions and effective policy solutions to ensure the basic human right to housing for all. This article is part of a Special Collection on Mental Health.

HERVK-mediated regulation of neighboring genes: implications for breast cancer prognosis.

Human endogenous retroviruses (HERVs) are the remnants of ancient retroviral infections integrated into the human genome. Although most HERVs are silenced or rendered inactive by various regulatory mechanisms, they retain the potential to influence the nearby genes. We analyzed the regulatory map of 91 HERV-Ks on neighboring genes in human breast cancer and investigated the impact of HERV-Ks on the tumor microenvironment (TME) and prognosis of breast cancer. Nine RNA-seq datasets were obtained from GEO and NCBI SRA. Differentially expressed genes and HERV-Ks were analyzed using DESeq2. Validation of high-risk prognostic candidate genes using TCGA data. These included Overall survival (multivariate Cox regression model), immune infiltration analysis (TIMER), tumor mutation burden (maftools), and drug sensitivity analysis (GSCA). A total of 88 candidate genes related to breast cancer prognosis were screened, of which CD48, SLAMF7, SLAMF1, IGLL1, IGHA1, and LRRC8A were key genes. Functionally, these six key genes were significantly enriched in some immune function-related pathways, which may be associated with poor prognosis for breast cancer (p = 0.00016), and the expression levels of these genes were significantly correlated with the sensitivity of breast cancer treatment-related drugs. Mechanistically, they may influence breast cancer development by modulating the infiltration of various immune cells into the TME. We further experimentally validated these genes to confirm the results obtained from bioinformatics analysis. This study represents the first report on the regulatory potential of HERV-K in the neighboring breast cancer genome. We identified three key HERV-Ks and five neighboring genes that hold promise as novel targets for future interventions and treatments for breast cancer.

Where you live matters: Area deprivation predicts poor survival and liver transplant waitlisting.

Social determinants of health (SDOH) are important predictors of poor clinical outcomes in chronic diseases, but their associations among the general cirrhosis population and liver transplantation (LT) are limited. We conducted a retrospective, multiinstitutional analysis of adult (≥18-years-old) patients with cirrhosis in metropolitan Chicago to determine the associations of poor neighborhood-level SDOH on decompensation complications, mortality, and LT waitlisting. Area deprivation index and covariates extracted from the American Census Survey were aspects of SDOH that were investigated. Among 15 101 patients with cirrhosis, the mean age was 57.2 years; 6414 (42.5%) were women, 6589 (43.6%) were non-Hispanic White, 3652 (24.2%) were non-Hispanic Black, and 2662 (17.6%) were Hispanic. Each quintile increase in area deprivation was associated with poor outcomes in decompensation (sHR [subdistribution hazard ratio] 1.07; 95% CI 1.05-1.10; P < .001), waitlisting (sHR 0.72; 95% CI 0.67-0.76; P < .001), and all-cause mortality (sHR 1.09; 95% CI 1.06-1.12; P < .001). Domains of SDOH associated with a lower likelihood of waitlisting and survival included low income, low education, poor household conditions, and social support (P < .001). Overall, patients with cirrhosis residing in poor neighborhood-level SDOH had higher decompensation, and mortality, and were less likely to be waitlisted for LT. Further exploration of structural barriers toward LT or optimizing health outcomes is warranted.

Neutrophils seeking new neighbors: radiotherapy affects the cellular framework and the spatial organization in a murine breast cancer model.

Neutrophils are known to contribute in many aspects of tumor progression and metastasis. The presence of neutrophils or neutrophil-derived mediators in the tumor microenvironment has been associated with poor prognosis in several types of solid tumors. However, the effects of classical cancer treatments such as radiation therapy on neutrophils are poorly understood. Furthermore, the cellular composition and distribution of immune cells in the tumor is of increasing interest in cancer research and new imaging technologies allow to perform more complex spatial analyses within tumor tissues. Therefore, we aim to offer novel insight into intra-tumoral formation of cellular neighborhoods and communities in murine breast cancer. To address this question, we performed image mass cytometry on tumors of the TS/A breast cancer tumor model, performed spatial neighborhood analyses of the tumor microenvironment and quantified neutrophil-extracellular trap degradation products in serum of the mice. We show that irradiation with 2 × 8 Gy significantly alters the cellular composition and spatial organization in the tumor, especially regarding neutrophils and other cells of the myeloid lineage. Locally applied radiotherapy further affects neutrophils in a systemic manner by decreasing the serum neutrophil extracellular trap concentrations which correlates positively with survival. In addition, the intercellular cohesion is maintained due to radiotherapy as shown by E-Cadherin expression. Radiotherapy, therefore, might affect the epithelial-mesenchymal plasticity in tumors and thus prevent metastasis. Our findings underscore the growing importance of the spatial organization of the tumor microenvironment, particularly with respect to radiotherapy, and provide insight into potential mechanisms by which radiotherapy affects epithelial-mesenchymal plasticity and tumor metastasis.

Associations of neighborhood sociodemographic environment with mortality and circulating metabolites among low-income black and white adults living in the southeastern United States.

BACKGROUND: Residing in a disadvantaged neighborhood has been linked to increased mortality. However, the impact of residential segregation and social vulnerability on cause-specific mortality is understudied. Additionally, the circulating metabolic correlates of neighborhood sociodemographic environment remain unexplored. Therefore, we examined multiple neighborhood sociodemographic metrics, i.e., neighborhood deprivation index (NDI), residential segregation index (RSI), and social vulnerability index (SVI), with all-cause and cardiovascular disease (CVD) and cancer-specific mortality and circulating metabolites in the Southern Community Cohort Study (SCCS). METHODS: The SCCS is a prospective cohort of primarily low-income adults aged 40-79, enrolled from the southeastern United States during 2002-2009. This analysis included self-reported Black/African American or non-Hispanic White participants and excluded those who died or were lost to follow-up ≤ 1 year. Untargeted metabolite profiling was performed using baseline plasma samples in a subset of SCCS participants. RESULTS: Among 79,631 participants, 23,356 deaths (7214 from CVD and 5394 from cancer) were documented over a median 15-year follow-up. Higher NDI, RSI, and SVI were associated with increased all-cause, CVD, and cancer mortality, independent of standard clinical and sociodemographic risk factors and consistent between racial groups (standardized HRs among all participants were 1.07 to 1.20 in age/sex/race-adjusted model and 1.04 to 1.08 after comprehensive adjustment; all P < 0.05/3 except for cancer mortality after comprehensive adjustment). The standard risk factors explained < 40% of the variations in NDI/RSI/SVI and mediated < 70% of their associations with mortality. Among 1110 circulating metabolites measured in 1688 participants, 134 and 27 metabolites were associated with NDI and RSI (all FDR < 0.05) and mediated 61.7% and 21.2% of the NDI/RSI-mortality association, respectively. Adding those metabolites to standard risk factors increased the mediation proportion from 38.4 to 87.9% and 25.8 to 42.6% for the NDI/RSI-mortality association, respectively. CONCLUSIONS: Among low-income Black/African American adults and non-Hispanic White adults living in the southeastern United States, a disadvantaged neighborhood sociodemographic environment was associated with increased all-cause and CVD and cancer-specific mortality beyond standard risk factors. Circulating metabolites may unveil biological pathways underlying the health effect of neighborhood sociodemographic environment. More public health efforts should be devoted to reducing neighborhood environment-related health disparities, especially for low-income individuals.

A weighted bilinear neural collaborative filtering approach for drug repositioning.

Drug repositioning is an efficient and promising strategy for traditional drug discovery and development. Many research efforts are focused on utilizing deep-learning approaches based on a heterogeneous network for modeling complex drug-disease associations. Similar to traditional latent factor models, which directly factorize drug-disease associations, they assume the neighbors are independent of each other in the network and thus tend to be ineffective to capture localized information. In this study, we propose a novel neighborhood and neighborhood interaction-based neural collaborative filtering approach (called DRWBNCF) to infer novel potential drugs for diseases. Specifically, we first construct three networks, including the known drug-disease association network, the drug-drug similarity and disease-disease similarity networks (using the nearest neighbors). To take the advantage of localized information in the three networks, we then design an integration component by proposing a new weighted bilinear graph convolution operation to integrate the information of the known drug-disease association, the drug's and disease's neighborhood and neighborhood interactions into a unified representation. Lastly, we introduce a prediction component, which utilizes the multi-layer perceptron optimized by the α-balanced focal loss function and graph regularization to model the complex drug-disease associations. Benchmarking comparisons on three datasets verified the effectiveness of DRWBNCF for drug repositioning. Importantly, the unknown drug-disease associations predicted by DRWBNCF were validated against clinical trials and three authoritative databases and we listed several new DRWBNCF-predicted potential drugs for breast cancer (e.g. valrubicin and teniposide) and small cell lung cancer (e.g. valrubicin and cytarabine).

Predicting miRNA-disease associations via learning multimodal networks and fusing mixed neighborhood information.

MOTIVATION: In recent years, a large number of biological experiments have strongly shown that miRNAs play an important role in understanding disease pathogenesis. The discovery of miRNA-disease associations is beneficial for disease diagnosis and treatment. Since inferring these associations through biological experiments is time-consuming and expensive, researchers have sought to identify the associations utilizing computational approaches. Graph Convolutional Networks (GCNs), which exhibit excellent performance in link prediction problems, have been successfully used in miRNA-disease association prediction. However, GCNs only consider 1st-order neighborhood information at one layer but fail to capture information from high-order neighbors to learn miRNA and disease representations through information propagation. Therefore, how to aggregate information from high-order neighborhood effectively in an explicit way is still challenging. RESULTS: To address such a challenge, we propose a novel method called mixed neighborhood information for miRNA-disease association (MINIMDA), which could fuse mixed high-order neighborhood information of miRNAs and diseases in multimodal networks. First, MINIMDA constructs the integrated miRNA similarity network and integrated disease similarity network respectively with their multisource information. Then, the embedding representations of miRNAs and diseases are obtained by fusing mixed high-order neighborhood information from multimodal network which are the integrated miRNA similarity network, integrated disease similarity network and the miRNA-disease association networks. Finally, we concentrate the multimodal embedding representations of miRNAs and diseases and feed them into the multilayer perceptron (MLP) to predict their underlying associations. Extensive experimental results show that MINIMDA is superior to other state-of-the-art methods overall. Moreover, the outstanding performance on case studies for esophageal cancer, colon tumor and lung cancer further demonstrates the effectiveness of MINIMDA. AVAILABILITY AND IMPLEMENTATION: https://github.com/chengxu123/MINIMDA and http://120.79.173.96/.

MNMDCDA: prediction of circRNA-disease associations by learning mixed neighborhood information from multiple distances.

Emerging evidence suggests that circular RNA (circRNA) is an important regulator of a variety of pathological processes and serves as a promising biomarker for many complex human diseases. Nevertheless, there are relatively few known circRNA-disease associations, and uncovering new circRNA-disease associations by wet-lab methods is time consuming and costly. Considering the limitations of existing computational methods, we propose a novel approach named MNMDCDA, which combines high-order graph convolutional networks (high-order GCNs) and deep neural networks to infer associations between circRNAs and diseases. Firstly, we computed different biological attribute information of circRNA and disease separately and used them to construct multiple multi-source similarity networks. Then, we used the high-order GCN algorithm to learn feature embedding representations with high-order mixed neighborhood information of circRNA and disease from the constructed multi-source similarity networks, respectively. Finally, the deep neural network classifier was implemented to predict associations of circRNAs with diseases. The MNMDCDA model obtained AUC scores of 95.16%, 94.53%, 89.80% and 91.83% on four benchmark datasets, i.e., CircR2Disease, CircAtlas v2.0, Circ2Disease and CircRNADisease, respectively, using the 5-fold cross-validation approach. Furthermore, 25 of the top 30 circRNA-disease pairs with the best scores of MNMDCDA in the case study were validated by recent literature. Numerous experimental results indicate that MNMDCDA can be used as an effective computational tool to predict circRNA-disease associations and can provide the most promising candidates for biological experiments.

The Impact of Social Deprivation on Anterior Urethral Stricture Recurrence After Urethroplasty - A Trauma and Urologic Reconstructive Network of Surgeons (TURNS) Analysis.

INTRODUCTION AND OBJECTIVES: Several factors influence recurrence after urethral stricture repair. The impact of socioeconomic factors on stricture recurrence after urethroplasty is poorly understood. This study aims to assess the impact that social deprivation, an area-level measure of disadvantage, has on urethral stricture recurrence after urethroplasty. METHODS: We performed a retrospective review of patients undergoing urethral reconstruction by surgeons participating in a collaborative research group. Home zip code was used to calculate Social Deprivation Indices (SDI; 0-100), which quantifies the level of disadvantage across several sociodemographic domains collected in the American Community Survey. Patients without zip code data were excluded from the analysis. The Cox Proportional Hazards model was used to study the association between SDI and the hazard of functional recurrence, adjusting for stricture characteristics as well as age and body mass index. RESULTS: Median age was 46.0 years with a median follow up of 367 days for the 1452 men included in the study. Patients in the fourth SDI quartile (worst social deprivation) were more likely to be active smokers with traumatic and infectious strictures compared to the first SDI quartile. Patients in the fourth SDI quartile had 1.64 times the unadjusted hazard of functional stricture recurrence vs patients in the first SDI quartile (95% CI 1.04-2.59). Compared to anastomotic ± excision, substitution only repair had 1.90 times the unadjusted hazard of recurrence. The adjusted hazard of recurrence was 1.08 per 10-point increase in SDI (95% CI 1.01-1.15, P = .027). CONCLUSIONS: Patient social deprivation identifies those at higher risk for functional recurrence after anterior urethral stricture repair, offering an opportunity for preoperative counseling and postoperative surveillance. Addressing these social determinants of health can potentially improve outcomes in reconstructive surgery.