Bayesian mixed model inference for genetic association under related samples with brain network phenotype.

Genetic association studies for brain connectivity phenotypes have gained prominence due to advances in noninvasive imaging techniques and quantitative genetics. Brain connectivity traits, characterized by network configurations and unique biological structures, present distinct challenges compared to other quantitative phenotypes. Furthermore, the presence of sample relatedness in the most imaging genetics studies limits the feasibility of adopting existing network-response modeling. In this article, we fill this gap by proposing a Bayesian network-response mixed-effect model that considers a network-variate phenotype and incorporates population structures including pedigrees and unknown sample relatedness. To accommodate the inherent topological architecture associated with the genetic contributions to the phenotype, we model the effect components via a set of effect network configurations and impose an inter-network sparsity and intra-network shrinkage to dissect the phenotypic network configurations affected by the risk genetic variant. A Markov chain Monte Carlo (MCMC) algorithm is further developed to facilitate uncertainty quantification. We evaluate the performance of our model through extensive simulations. By further applying the method to study, the genetic bases for brain structural connectivity using data from the Human Connectome Project with excessive family structures, we obtain plausible and interpretable results. Beyond brain connectivity genetic studies, our proposed model also provides a general linear mixed-effect regression framework for network-variate outcomes.

Rhizobacterial Bacillus enrichment in soil enhances smoke tree resistance to Verticillium wilt.

Verticillium wilt, caused by the soilborne fungus Verticillium dahliae, poses a serious threat to the health of more than 200 plant species worldwide. Although plant rhizosphere-associated microbiota can influence plant resistance to V. dahliae, empirical evidence underlying Verticillium wilt resistance of perennial trees is scarce. In this study, we systemically investigated the effect of the soil microbiota on the resistance of smoke trees (Cotinus coggygria) to Verticillium wilt using field, greenhouse and laboratory experiments. Comparative analysis of the soil microbiota in the two stands of smoke trees suggested that Bacillus represented the most abundant and key microbial genus related to potential disease suppression. Smoke tree seedlings were inoculated with isolated Bacillus strains, which exhibited disease suppressiveness and plant growth-promoting properties. Furthermore, repletion of Bacillus agents to disease conducive soil significantly resulted in reduced incidence of smoke tree wilt and increased resistance of the soil microbiota to V. dahliae. Finally, we explored a more effective combination of Bacillus agents with the fungicide propiconazole to combat Verticillium wilt. The results establish a foundation for the development of an effective control for this disease. Overall, this work provides a direct link between Bacillus enrichment and disease resistance of smoke trees, facilitating the development of green control strategies and measurements of soil-borne diseases.

Comparison of clinical outcomes of supercapsular percutaneously-assisted approach total hip arthroplasty versus conventional posterior approach for total hip arthroplasty in adults: a systematic review and meta-analysis.

OBJECTIVE: This meta-analysis was aimed to compare the postoperative clinical outcomes between the supercapsular percutaneously assisted total hip (SuperPATH, SP) and conventional posterior/posterolateral approach (PA) for total hip arthroplasty in patients who have failed conservative treatment for hip-related disorders. METHODS: PRISMAP guidelines were followed in this systematic review. CNKI, Wanfang, PubMed, Embase, Cochrane, Web of Science databases and the reference list grey literature were searched for studies according to the search strategy. Endnote (version 20) was used to screen the searched studies according to the inclusion and exclusion criterias and extract the data from the eligible studied. RR and 95% CI were used for dichotomous variables and MD and 95% CI were used for continuous variables. All analyses and heterogeneity of outcomes were analysed by Review Manage (version 5.4). Publication bias of included studies was analysed by Stata (version 16.0). RESULTS: Thirty-six randomized control studies were included. Compared to PA group, SP group had a shorter incision length, less intraoperative blood loss, a shorter length of hospital stay and do activities earlier. Hip function (HHS) was significantly improved within three months postoperatively. Pain of hip (VAS) was significantly reduced within one month postoperatively. The state of daily living (BI) was significantly improved within three months. Patients' overall health status (SF-36) improved significantly postoperatively. There was no difference in postoperative complications between the two approaches. PA had a shorter operative time and a higher accuracy of prosthesis placement. CONCLUSION: The advantages of SuperPATH include accelerated functional recovery and less trauma associated with surgery. However, it required a longer operative time and implantation of the prosthesis was less accurate than that of PA.

Bayesian sparse heritability analysis with high-dimensional neuroimaging phenotypes.

Heritability analysis plays a central role in quantitative genetics to describe genetic contribution to human complex traits and prioritize downstream analyses under large-scale phenotypes. Existing works largely focus on modeling single phenotype and currently available multivariate phenotypic methods often suffer from scaling and interpretation. In this article, motivated by understanding how genetic underpinning impacts human brain variation, we develop an integrative Bayesian heritability analysis to jointly estimate heritabilities for high-dimensional neuroimaging traits. To induce sparsity and incorporate brain anatomical configuration, we impose hierarchical selection among both regional and local measurements based on brain structural network and voxel dependence. We also use a nonparametric Dirichlet process mixture model to realize grouping among single nucleotide polymorphism-associated phenotypic variations, providing biological plausibility. Through extensive simulations, we show the proposed method outperforms existing ones in heritability estimation and heritable traits selection under various scenarios. We finally apply the method to two large-scale imaging genetics datasets: the Alzheimer's Disease Neuroimaging Initiative and United Kingdom Biobank and show biologically meaningful results.

Bayesian interaction selection model for multimodal neuroimaging data analysis.

Multimodality or multiconstruct data arise increasingly in functional neuroimaging studies to characterize brain activity under different cognitive states. Relying on those high-resolution imaging collections, it is of great interest to identify predictive imaging markers and intermodality interactions with respect to behavior outcomes. Currently, most of the existing variable selection models do not consider predictive effects from interactions, and the desired higher-order terms can only be included in the predictive mechanism following a two-step procedure, suffering from potential misspecification. In this paper, we propose a unified Bayesian prior model to simultaneously identify main effect features and intermodality interactions within the same inference platform in the presence of high-dimensional data. To accommodate the brain topological information and correlation between modalities, our prior is designed by compiling the intermediate selection status of sequential partitions in light of the data structure and brain anatomical architecture, so that we can improve posterior inference and enhance biological plausibility. Through extensive simulations, we show the superiority of our approach in main and interaction effects selection, and prediction under multimodality data. Applying the method to the Adolescent Brain Cognitive Development (ABCD) study, we characterize the brain functional underpinnings with respect to general cognitive ability under different memory load conditions.

Impact of genetic polymorphisms on the risk of epilepsy amongst patients with acute brain injury: A systematic review.

BACKGROUND AND PURPOSE: The genetics of late seizure or epilepsy secondary to traumatic brain injury (TBI) or stroke are poorly understood. We undertook a systematic review to test the association of single-nucleotide polymorphisms (SNPs) with the risk of post-traumatic epilepsy (PTE) and post-stroke epilepsy (PSE). METHODS: We followed methods from our prespecified protocol on PROSPERO to identify indexed articles for this systematic review. We collated the association statistics from the included articles to assess the association of SNPs with the risk of epilepsy amongst TBI or stroke patients. We assessed study quality using the Q-Genie tool. We report odds ratios (OR) and hazard ratios with 95% confidence intervals (CIs). RESULTS: The literature search yielded 420 articles. We included 16 studies in our systematic review, of which seven were of poor quality. We examined published data on 127 SNPs from 32 genes identified in PTE and PSE patients. Eleven SNPs were associated with a significantly increased risk of PTE. Three SNPs, TRMP6 rs2274924, ALDH2 rs671, and CD40 -1C/T, were significantly associated with an increased risk of PSE, while two, AT1R rs12721273 and rs55707609, were significantly associated with reduced risk. The meta-analysis for the association of the APOE ɛ4 with PTE was nonsignificant (OR 1.8, CI 0.6-5.6). CONCLUSIONS: The current evidence on the association of genetic polymorphisms in epilepsy secondary to TBI or stroke is of low quality and lacks validation. A collaborative effort to pool genetic data linked to epileptogenesis in stroke and TBI patients is warranted.

Unhealthy lifestyles and clusters status among 3637 adolescents aged 11-23 years: a school-based cross-sectional study in China.

BACKGROUND: Unhealthy lifestyles are risk factors for non-communicable diseases (NCDs) and tend to be clustered, with a trajectory that extends from adolescence to adulthood. This study investigated the association of diets, tobacco, alcohol, physical activity (PA), screen time (ST) and sleep duration (SD) in a total of six lifestyles, separately and as cumulative lifestyle scores, with sociodemographic characteristics among school-aged adolescents in the Chinese city of Zhengzhou. METHODS: In the aggregate, 3,637 adolescents aged 11-23 years were included in the study. The questionnaire collected data on socio-demographic characteristics and lifestyles. Healthy and unhealthy lifestyles were identified and scored, depending on the individual score (0 and 1 for healthy and unhealthy lifestyles respectively), with a total score between 0 and 6. Based on the sum of the dichotomous scores, the number of unhealthy lifestyles was calculated and divided into three clusters (0-1, 2-3, 4-6). Chi-square test was used to analyze the group difference of lifestyles and demographic characteristics, and multivariate logistic regression was used to explore the associations between demographic characteristics and the clustering status of unhealthy lifestyles. RESULTS: Among all participants, the prevalence of unhealthy lifestyles was: 86.4% for diet, 14.5% for alcohol, 6.0% for tobacco, 72.2% for PA, 42.3% for ST and 63.9% for SD. Students who were in university, female, lived in country (OR = 1.725, 95% CI: 1.241-2.398), had low number of close friends (1-2: OR = 2.110, 95% CI: 1.428-3.117; 3-5: OR = 1.601, 95% CI: 1.168-2.195), and had moderate family income (OR = 1.771, 95% CI: 1.208-2.596) were more likely to develop unhealthy lifestyles. In total, unhealthy lifestyles remain highly prevalent among Chinese adolescents. CONCLUSION: In the future, the establishment of an effective public health policy may improve the lifestyle profile of adolescents. Based on the lifestyle characteristics of different populations reported in our findings, lifestyle optimization can be more efficiently integrated into the daily lives of adolescents. Moreover, it is essential to conduct well-designed prospective studies on adolescents.

Pursuing sources of heterogeneity in modeling clustered population.

Researchers often have to deal with heterogeneous population with mixed regression relationships, increasingly so in the era of data explosion. In such problems, when there are many candidate predictors, it is not only of interest to identify the predictors that are associated with the outcome, but also to distinguish the true sources of heterogeneity, that is, to identify the predictors that have different effects among the clusters and thus are the true contributors to the formation of the clusters. We clarify the concepts of the source of heterogeneity that account for potential scale differences of the clusters and propose a regularized finite mixture effects regression to achieve heterogeneity pursuit and feature selection simultaneously. We develop an efficient algorithm and show that our approach can achieve both estimation and selection consistency. Simulation studies further demonstrate the effectiveness of our method under various practical scenarios. Three applications are presented, namely, an imaging genetics study for linking genetic factors and brain neuroimaging traits in Alzheimer's disease, a public health study for exploring the association between suicide risk among adolescents and their school district characteristics, and a sport analytics study for understanding how the salary levels of baseball players are associated with their performance and contractual status.

Using machine learning to predict heavy drinking during outpatient alcohol treatment.

BACKGROUND: Accurate clinical prediction supports the effective treatment of alcohol use disorder (AUD) and other psychiatric disorders. Traditional statistical techniques have identified patient characteristics associated with treatment outcomes. However, less work has focused on systematically leveraging these associations to create optimal predictive models. The current study demonstrates how machine learning can be used to predict clinical outcomes in people completing outpatient AUD treatment. METHOD: We used data from the COMBINE multisite clinical trial (n = 1383) to develop and test predictive models. We identified three priority prediction targets, including (1) heavy drinking during the first month of treatment, (2) heavy drinking during the last month of treatment, and (3) heavy drinking between weekly/bi-weekly sessions. Models were generated using the random forest algorithm. We used "leave sites out" partitioning to externally validate the models in trial sites that were not included in the model training. Stratified model development was used to test for sex differences in the relative importance of predictive features. RESULTS: Models predicting heavy alcohol use during the first and last months of treatment showed internal cross-validation area under the curve (AUC) scores ranging from 0.67 to 0.74. AUC was comparable in the external validation using data from held-out sites (AUC range = 0.69 to 0.72). The model predicting between-session heavy drinking showed strong classification accuracy in internal cross-validation (AUC = 0.89) and external test samples (AUC range = 0.80 to 0.87). Stratified analyses showed substantial sex differences in optimal feature sets. CONCLUSION: Machine learning techniques can predict alcohol treatment outcomes using routinely collected clinical data. This technique has the potential to greatly improve clinical prediction accuracy without requiring expensive or invasive assessment methods. More research is needed to understand how best to deploy these models.

Bayesian network mediation analysis with application to the brain functional connectome.

The brain functional connectome, the collection of interconnected neural circuits along functional networks, facilitates a cutting-edge understanding of brain functioning, and has a potential to play a mediating role within the effect pathway between an exposure and an outcome. While existing mediation analytic approaches are capable of providing insight into complex processes, they mainly focus on a univariate mediator or mediator vector, without considering network-variate mediators. To fill the methodological gap and accomplish this exciting and urgent application, in the article, we propose an integrative mediation analysis under a Bayesian paradigm with networks entailing the mediation effect. To parameterize the network measurements, we introduce individually specified stochastic block models with unknown block allocation, and naturally bridge effect elements through the latent network mediators induced by the connectivity weights across network modules. To enable the identification of truly active mediating components, we simultaneously impose a feature selection across network mediators. We show the superiority of our model in estimating different effect components and selecting active mediating network structures. As a practical illustration of this approach's application to network neuroscience, we characterize the relationship between a therapeutic intervention and opioid abstinence as mediated by brain functional sub-networks.

Grading meningiomas utilizing multiparametric MRI with inclusion of susceptibility weighted imaging and quantitative susceptibility mapping.

BACKGROUND AND PURPOSE: The ability to predict high-grade meningioma preoperatively is important for clinical surgical planning. The purpose of this study is to evaluate the performance of comprehensive multiparametric MRI, including susceptibility weighted imaging (SWI) and quantitative susceptibility mapping (QSM) in predicting high-grade meningioma both qualitatively and quantitatively. METHODS: Ninety-two low-grade and 37 higher grade meningiomas in 129 patients were included in this study. Morphological characteristics, quantitative histogram analysis of QSM and ADC images, and tumor size were evaluated to predict high-grade meningioma using univariate and multivariate analyses. Receiver operating characteristic (ROC) analyses were performed on the morphological characteristics. Associations between Ki-67 proliferative index (PI) and quantitative parameters were calculated using Pearson correlation analyses. RESULTS: For predicting high-grade meningiomas, the best predictive model in multivariate logistic regression analyses included calcification (ß=0.874, P=0.110), peritumoral edema (ß=0.554, P=0.042), tumor border (ß=0.862, P=0.024), tumor location (ß=0.545, P=0.039) for morphological characteristics, and tumor size (ß=4×10-5, P=0.004), QSM kurtosis (ß=-5×10-3, P=0.058), QSM entropy (ß=-0.067, P=0.054), maximum ADC (ß=-1.6×10-3, P=0.003), ADC kurtosis (ß=-0.013, P=0.014) for quantitative characteristics. ROC analyses on morphological characteristics resulted in an area under the curve (AUC) of 0.71 (0.61-0.81) for a combination of them. There were significant correlations between Ki-67 PI and mean ADC (r=-0.277, P=0.031), 25th percentile of ADC (r=-0.275, P=0.032), and 50th percentile of ADC (r=-0.268, P=0.037). CONCLUSIONS: Although SWI and QSM did not improve differentiation between low and high-grade meningiomas, combining morphological characteristics and quantitative metrics can help predict high-grade meningioma.

A Systematic Review of 639 Patients with Biopsy-confirmed Nephrogenic Systemic Fibrosis.

Background Although nephrogenic systemic fibrosis (NSF) affects the use of gadolinium-based contrast agents (GBCAs) in MRI, there continues to be limited knowledge because of the small number of patients with NSF. Purpose To perform a systematic review of NSF. Materials and Methods PubMed database was searched by using the term "Nephrogenic systemic fibrosis" from January 2000 to February 2019. Articles reporting details on individual patients with NSF diagnosis on the basis of both clinical presentations and biopsy confirmation were included. Data were pooled and authors were contacted for clarifications. Rates of NSF were compared through 2008 versus after 2008 and for group I versus group II GBCAs, assuming equal market share. Results Included were 639 patients from 173 articles. Data regarding sex were found for 295 men and 254 women. Age at NSF symptom onset was reported for 177 patients (mean, 49 years ± 16 [standard deviation]; age range, 6-87 years). There were 529 patients with documented exposure to GBCAs including gadodiamide (n = 307), gadopentetate dimeglumine (n = 49), gadoversetamide (n = 6), gadobutrol (n = 1), gadobenate dimeglumine (n = 1), multiple (n = 41), and unknown (n = 120). Among patients with previous exposure, only seven patients were administered GBCA after 2008, yielding a lower rate of NSF after 2008 (P < .001). There were motion limitations in 70.8% (296 of 418) of patients, indicating a more serious debilitation. Associated factors reported for NSF included exposure to GBCA group I (P < .001), dialysis, proinflammatory conditions, hyperphosphatemia, ß-blockers, and epoetin. For 341 patients with follow-up, 12 patients were cured and 72 patients partially improved including one during pregnancy. Among those 84 patients reported as cured or improved, in 34 patients cure or improvement occurred after renal function restoration. Four deaths were attributed to NSF. Conclusion Although 639 patients with biopsy-confirmed nephrogenic systemic fibrosis were reported, only seven were after gadolinium-based contrast agent exposure after 2008, indicating that regulatory actions and practice changes have been effective preventive measures. Improvement and sometimes cure with renal function restoration are now possible. © RSNA, 2019 See also the editorial by Davenport in this issue.

Relationship of Seminal Megavesicles, Prostate Median Cysts, and Genotype in Autosomal Dominant Polycystic Kidney Disease.

BACKGROUND: Autosomal dominant polycystic kidney disease (ADPKD) can involve prostate and seminal vesicles but the potential interrelationship of these findings and associations with PKD gene mutation locus and type is unknown. PURPOSE: To determine the interrelationship of seminal megavesicles (seminal vesicles with lumen diameter > 10mm) and prostatic cysts in ADPKD and to determine whether there are associations with PKD gene mutations. STUDY TYPE: Retrospective, case control. POPULATION: Male ADPKD subjects (n = 92) with mutations in PKD1 (n = 71; 77%) or PKD2 (n = 21; 23%), and age/gender-matched controls without ADPKD (n = 92). FIELD STRENGTH/SEQUENCE: 1.5T, axial/coronal T2 -weighted MR images. ASSESSMENT: Reviewers blinded to genotype independently measured seminal vesicle lumen diameter and prevalence of cysts in prostate, kidney, and liver. STATISTICAL TESTS: Nonparametric tests for group comparisons and univariate and multivariable logistic regression analyses to identify associations of megavesicles and prostate median cysts with mutations and renal/hepatic cyst burden. RESULTS: Seminal megavesicles were found in 23 of 92 ADPKD (25%) subjects with PKD1 (22/71, 31%) or PKD2 (n = 1/21, 5%) mutations, but in only two control subjects (P < 0.0001). Prostate median cysts were found in 17/92 (18%) ADPKD subjects, compared with only 6/92 (7%) controls (P = 0.01), and were correlated with seminal vesicle diameters (ρ = 0.24, P = 0.02). Nonmedian prostate cyst prevalence was identical between ADPKD and controls (7/92, 8%). After adjusting for age, estimated glomerular filtration rate, and height-adjusted total kidney volume, ADPKD subjects with megavesicles were 10 times more likely to have a PKD1 than a PKD2 mutation. Among PKD1 subjects, seminal megavesicles occurred more frequently with nontruncating mutations with less severe kidney involvement. DATA CONCLUSION: ADPKD is associated with prostate median cysts near ejaculatory ducts. These cysts correlate with seminal megavesicles (dilated to >10 mm) which predict a 10-fold greater likelihood of PKD1 vs. PKD2 mutation. Cysts elsewhere in the prostate are not related to ADPKD. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:894-903.

Immediate Allergic Reactions to Gadolinium-based Contrast Agents: A Systematic Review and Meta-Analysis.

Purpose To perform a systematic review and meta-analysis to determine if there are differences in rates of immediate allergic events between classes of gadolinium-based contrast agents (GBCAs). Materials and Methods PubMed and Google Scholar databases were searched for studies in which rates of immediate adverse events to GBCAs were reported. The American College of Radiology classification system was used to characterize allergic-like events as mild, moderate, or severe, and the total number of administrations of each GBCA was recorded. Where necessary, authors of studies were contacted to clarify data and eliminate physiologic reactions. Relative risks of GBCA types were estimated by using the Mantel-Haenszel type method. Results Nine studies in which immediate reactions to GBCA were recorded from a total of 716 978 administrations of GBCA met the criteria for inclusion and exclusion. The overall rate of patients who had immediate allergic-like reactions was 9.2 per 10 000 administrations and the overall rate of severe immediate allergic-like reactions was 0.52 per 10 000 administrations.. The nonionic linear chelate gadodiamide had the lowest rate of reactions, at 1.5 (95% confidence interval [CI]: 0.74, 2.4) per 10 000 administrations, which was significantly less than that of linear ionic GBCAs at 8.3 (95% CI: 7.5, 9.2) per 10 000 administrations (relative risk, 0.19 [95% CI: 0.099, 0.36]; P < .00001) and less than that for nonionic macrocyclic GBCAs at 16 (95% CI: 14, 19) per 10 000 administrations (relative risk, 0.12 [95% CI: 0.05, 0.31]; P < .001). GBCAs known to be associated with protein binding had a higher rate of reactions, at 17 (95% CI: 15, 20) per 10 000 administrations compared with the same chelate classification without protein binding, at 5.2 (95% CI: 4.5, 6.0) per 10 000 administrations (relative risk, 3.1 [95% CI: 2.4, 3.8]; P < .0001). Conclusion These data show the lowest rate of immediate allergic adverse events with use of the nonionic linear GBCA gadodiamide in comparison with those of ionic linear or nonionic macrocyclic GBCAs. A higher rate of immediate allergic adverse events was associated with ionicity, protein binding, and macrocyclic structure. © RSNA, 2017 An earlier incorrect version of this article appeared online. This article was corrected on August 31, 2017.

Dentate Nucleus Signal Intensity Decrease on T1-weighted MR Images after Switching from Gadopentetate Dimeglumine to Gadobutrol.

Purpose To determine if the increased dentate nucleus signal intensity following six or more doses of a linear gadolinium-based contrast agent (GBCA) (gadopentetate dimeglumine) changes at follow-up examinations performed with a macrocyclic GBCA (gadobutrol). Materials and Methods This retrospective study included 13 patients with increased dentate nucleus signal intensity following at least six (range, 6-18) gadopentetate dimeglumine administrations who then underwent at least 12 months of follow-up imaging with multiple (range, 3-29) gadobutrol-enhanced magnetic resonance (MR) examinations. Dentate nucleus-to-pons and dentate nucleus-to-cerebellar peduncle signal intensity ratios were measured by two radiologists blinded to all patient information, and changes were analyzed by using the paired t test and linear regression. Results The mean dentate nucleus-to-pons and dentate nucleus-to-cerebellar peduncle signal intensity ratios increased after gadopentetate dimeglumine administration, from 0.98 ± 0.03 to 1.10 ± 0.03 (P < .0001) and from 0.98 ± 0.030 to 1.09 ± 0.02 (P < .0001), respectively. With gadobutrol, the mean dentate nucleus-to-pons and dentate nucleus-to-cerebellar peduncle signal intensity ratios decreased to 1.03 ± 0.03 and 1.02 ± 0.04, respectively (P < .0001). With use of a mixed effects model linear regression allowing for each patient to have a different y intercept, mean dentate nucleus-to-pons and dentate nucleus-to-cerebellar peduncle signal intensity ratios decreased with follow-up time (dentate nucleus-to-pons: slope = -0.2% per month [95% confidence interval: -0.0024, -0.0015], R2 = 0.58, P < .0001 for nonzero slope; dentate nucleus-to-cerebellar peduncle: slope = -0.2% per month [95% confidence interval: -0.0024, -0.0015], R2 = 0.61, P < .0001 for nonzero slope). Conclusion Dentate signal intensity increased with at least six gadopentetate dimeglumine-enhanced MR examinations and decreased after switching from a linear (gadopentetate dimeglumine) to a macrocyclic (gadobutrol) GBCA. © RSNA, 2018 Online supplemental material is available for this article.

Trifunctional PSMA-targeting constructs for prostate cancer with unprecedented localization to LNCaP tumors.

PURPOSE: Treatment of late-stage prostate cancer by targeted radiotherapeutics such as 131I-MIP-1095 and 177Lu-PSMA-617 has shown encouraging early results. Lu-177 is preferred to I-131 in clinical settings, but targeted radioligand therapy (RLT) with 177Lu-PSMA-617 has not reached its full potential due to insufficient dose delivery to the tumor. We recently developed a dual-targeting radioiodinated ligand, RPS-027, that targets PSMA and uses albumin binding to enable good tumor uptake and significantly reduced kidney uptake in a preclinical model. Further development of this ligand is limited by the inability to independently modify PSMA and albumin binding and the requirement of I-131 for therapeutic application. We therefore sought to devise a new class of trifunctional ligands for RLT with (1) a high-affinity PSMA-binding domain, (2) an albumin-binding group (ABG), and (3) a chelator for radiometals such as 68Ga3+, 177Lu3+ and 225Ac3+. METHODS: Ligands incorporating a triazolylphenylurea-containing PSMA-targeting group, an Nε-(2-(4-iodophenyl)acetyl)lysine ABG and the bifunctional chelator p-SCN-Bn-DOTA linked by a PEG-containing polymer containing 0,3,4,6,8 or 12 repeats were prepared. PSMA affinity was determined in LNCaP cells and uptake and tissue distribution was studied in mice bearing LNCaP tumor xenografts and compared to 177Lu-PSMA-617. Imaging studies were performed up to 24 h post-injection (p.i.) using 66Ga3+ and biodistribution studies at 4 h, 24 h and 96 h p.i. with 177Lu3+. RESULTS: PSMA affinity was high (IC50 = 1-10 nM) and inversely proportional to the linker length. Tumor uptake correlated with binding affinity and was significantly greater than for 177Lu-PSMA-617 over 96 h. The highest uptake was achieved with 177Lu-RPS-063 (30.0 ± 6.9 %ID/g; 4 h p.i.). Kidney uptake was generally high, with the exception of the lowest affinity ligand 177Lu-RPS-067. Each of the compounds showed slower blood clearance than 177Lu-PSMA-617, with clearance proportional to linker length. CONCLUSIONS: The high tumor uptake achieved with these trifunctional ligands predicts larger (up to 4×) doses delivered to the tumor than can be achieved with 177Lu-PSMA-617. Although PSMA-mediated kidney uptake was also observed, the exceptional area under the curve (AUC) in the tumor warrants further investigation of these novel ligands as candidates for RLT.

Association networks in a matched case-control design - Co-occurrence patterns of preexisting chronic medical conditions in patients with major depression versus their matched controls.

OBJECTIVE: We present a method for comparing association networks in a matched case-control design, which provides a high-level comparison of co-occurrence patterns of features after adjusting for confounding factors. We demonstrate this approach by examining the differential distribution of chronic medical conditions in patients with major depressive disorder (MDD) compared to the distribution of these conditions in their matched controls. MATERIALS AND METHODS: Newly diagnosed MDD patients were matched to controls based on their demographic characteristics, socioeconomic status, place of residence, and healthcare service utilization in the Korean National Health Insurance Service's National Sample Cohort. Differences in the networks of chronic medical conditions in newly diagnosed MDD cases treated with antidepressants, and their matched controls, were prioritized with a permutation test accounting for the false discovery rate. Sensitivity analyses for the associations between prioritized pairs of chronic medical conditions and new MDD diagnosis were performed with regression modeling. RESULTS: By comparing the association networks of chronic medical conditions in newly diagnosed depression patients and their matched controls, five pairs of such conditions were prioritized among 105 possible pairs after controlling the false discovery rate at 5%. In sensitivity analyses using regression modeling, four out of the five prioritized pairs were statistically significant for the interaction terms. CONCLUSION: Association networks in a matched case-control design can provide a high-level comparison of comorbid features after adjusting for confounding factors, thereby supplementing traditional clinical study approaches. We demonstrate the differential co-occurrence pattern of chronic medical conditions in patients with MDD and prioritize the chronic conditions that have statistically significant interactions in regression models for depression.

Regional vulnerability in Alzheimer's disease: The role of cell-autonomous and transneuronal processes.

INTRODUCTION: The stereotypical progression of Alzheimer's disease (AD) pathology is not fully understood. The selective impact of AD on distinct regions has led the field to question if innate vulnerability exists. This study aims to determine if the causative factors of regional vulnerability are dependent on cell-autonomous or transneuronal (non-cell autonomous) processes. METHODS: Using mathematical and statistical models, we analyzed the contribution of cell-autonomous and non-cell autonomous factors to predictive linear models of AD pathology. RESULTS: Results indicate gene expression as a weak contributor to predictive linear models of AD. Instead, the network diffusion model acts as a strong predictor of observed AD atrophy and hypometabolism. DISCUSSION: We propose a convenient methodology for identifying genes and their role in determining AD topography, in comparison with network spread. Results reinforce the role of transneuronal network spread on disease progression and suggest that innate gene expression plays a secondary role in seeding and subsequent disease progression.

Bayesian network feature finder (BANFF): an R package for gene network feature selection.

MOTIVATION: Network marker selection on genome-scale networks plays an important role in the understanding of biological mechanisms and disease pathologies. Recently, a Bayesian nonparametric mixture model has been developed and successfully applied for selecting genes and gene sub-networks. Hence, extending this method to a unified approach for network-based feature selection on general large-scale networks and creating an easy-to-use software package is on demand. RESULTS: We extended the method and developed an R package, the Bayesian network feature finder (BANFF), providing a package of posterior inference, model comparison and graphical illustration of model fitting. The model was extended to a more general form, and a parallel computing algorithm for the Markov chain Monte Carlo -based posterior inference and an expectation maximization-based algorithm for posterior approximation were added. Based on simulation studies, we demonstrate the use of BANFF on analyzing gene expression on a protein-protein interaction network. AVAILABILITY: https://cran.r-project.org/web/packages/BANFF/index.html CONTACT: jiankang@umich.edu, tianwei.yu@emory.eduSupplementary information: Supplementary data are available at Bioinformatics online.

Knowledge-guided learning methods for integrative analysis of multi-omics data.

Integrative analysis of multi-omics data has the potential to yield valuable and comprehensive insights into the molecular mechanisms underlying complex diseases such as cancer and Alzheimer's disease. However, a number of analytical challenges complicate multi-omics data integration. For instance, -omics data are usually high-dimensional, and sample sizes in multi-omics studies tend to be modest. Furthermore, when genes in an important pathway have relatively weak signal, it can be difficult to detect them individually. There is a growing body of literature on knowledge-guided learning methods that can address these challenges by incorporating biological knowledge such as functional genomics and functional proteomics into multi-omics data analysis. These methods have been shown to outperform their counterparts that do not utilize biological knowledge in tasks including prediction, feature selection, clustering, and dimension reduction. In this review, we survey recently developed methods and applications of knowledge-guided multi-omics data integration methods and discuss future research directions.