Graphical Models Research Articles

Collaborative Synthesis of Patient Records through Multi-Visit Health State Inference

Electronic health records (EHRs) have become the foundation of machine learning applications in healthcare, while the utility of real patient records is often limited by privacy and security concerns. Synthetic EHR generation provides an additional perspective to compensate for this limitation. Most existing methods synthesize new records based on real EHR data, without consideration of different types of events in EHR data, which cannot control the event combinations in line with medical common sense. In this paper, we propose MSIC, a Multi-visit health Status Inference model for Collaborative EHR synthesis to address these limitations. First, we formulate the synthetic EHR generation process as a probabilistic graphical model and tightly connect different types of events by modeling the latent health states. Then, we derive a health state inference method tailored for the multi-visit scenario to effectively utilize previous records to synthesize current and future records. Furthermore, we propose to generate medical reports to add textual descriptions for each medical event, providing broader applications for synthesized EHR data. For generating different paragraphs in each visit, we incorporate a multi-generator deliberation framework to collaborate the message passing of multiple generators and employ a two-phase decoding strategy to generate high-quality reports. Our extensive experiments on the widely used benchmarks, MIMIC-III and MIMIC-IV, demonstrate that MSIC advances state-of-the-art results on the quality of synthetic data while maintaining low privacy risks.

Safe Abductive Learning in the Presence of Inaccurate Rules

Integrating complementary strengths of raw data and logical rules to improve the learning generalization has been recently shown promising and effective, e.g., abductive learning is one generic framework that can learn the perception model from data and reason between rules simultaneously. However, the performance would be seriously decreased when inaccurate logical rules appear, which may be even worse than baselines using only raw data. Efforts on this issue are highly desired while remain to be limited. This paper proposes a simple and effective safe abductive learning method to alleviate the harm caused by inaccurate rules. Unlike the existing methods which directly use all rules without correctness checks, it utilizes them selectively by constructing a graphical model with an adaptive reasoning process to prevent performance hazards. Theoretically, we show that induction and abduction are mutually beneficial, and can be rigorously justified from a classical maximum likelihood estimation perspective. Experiments on diverse tasks show that our method can tolerate at least twice as many inaccurate rules as accurate ones and achieve highly competitive performance while other methods can't. Moreover, the proposal can refine inaccurate rules and works well in extended weakly supervised scenarios.

Abstract 792: Continuous, probabilistic variant interpretation with Bayesian graphical models

Abstract Introduction: Current variant classification (VC) frameworks rely on rules-based approaches that use heuristic weighting of various types of evidence, resulting in > 50% of variants being classified as variants of uncertain significance (VUS), and leaving many patients with uncertainty about their disease risk or diagnosis. We propose a probabilistic and scalable Bayesian approach to model the causal relationships between various types of evidence. A fully quantitative system maximizes the utility and integration of various evidence types, empowering clinicians to make more nuanced management decisions. Methods: Probabilistic graphical models (PGMs) are uniquely suited to the needs of clinical VC. Two different component PGMs were developed to model two of the evidence categories that will ultimately be used in a comprehensive VC system: population allele frequency (Population PGM) and reported phenotype observations (Reported Phenotype PGM). Results: The Population PGM treats population allele frequency observations as a binomial process. By conditioning the model on partial observations, the probabilistic relationships between pathogenicity and allele frequencies can be estimated while stochastic variational inference allows uncertainty to be efficiently propagated. The resulting model performs well across a large number of genes at inferring pathogenicity of a variant from its allele frequency, with an average precision of >99% for benign variants. In the Reported Phenotype PGM, phenotypic features characteristic of a disorder are learned from patients expected to be affected based on genotype. Patient-level predictions are in turn combined at the variant level to derive variant pathogenicity likelihoods. To date, models representing at least 102 inherited conditions and 259 genes have demonstrated high predictive performance (>0.8 AUROC) for both patient-level and variant-level predictions. Finally, as a proof-of-concept, we demonstrate how each component PGM can be combined into a probabilistic Bayesian VC framework that also includes protein structure and stability, evolutionary conservation, and sequence context. This framework has high concordance with known, well-accepted pathogenic and benign variants classified with rules-based systems, and could make high-confidence predictions for many variants currently classified as VUS. Conclusion: We present a Bayesian approach that can integrate diverse types of evidence to achieve high VC accuracy while quantifying uncertainty. Future expansion of this Bayesian framework to all evidence types relevant to VC may allow for more accurate risk management guidelines and further inform medical and genetic counseling recommendations. Citation Format: Wolfgang Michael Korn, Yuya Kobayashi, Flavia M. Facio, Arun Nampally, Keith Nykamp, Robert Nussbaum, Alexandre Colavin, Britt Johnson, Toby Manders. Continuous, probabilistic variant interpretation with Bayesian graphical models [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 792.

Abstract 2493: Prospective evaluation of predictive biomarkers for Palbociclib (P) and endocrine therapy (ET) benefit in hormone receptor (HR)-positive [+]/human epidermal growth factor receptor 2 (HER2)-negative [-] advanced breast cancer (ABC) patients (pts) from PARSIFAL clinical trial: The transFAL sub-study

Abstract BACKGROUND Combining cyclin-dependent kinases 4 and 6 inhibitors (CDK4/6i) with first-line ET is the standard of care for pts with HR[+]/HER2[-] ABC. PARSIFAL was a randomized, open-label, phase II trial that evaluated the efficacy and safety of first line P plus fulvestrant (F) or letrozole (L) in this pts population. Both treatments had comparable efficacy and safety results (JAMAOncol. 2021;7(12):1791-1799). Although multiple CDK4/6i clinical outcomes biomarkers have been reported, the only validated predictive factor for response is HR positivity. TransFAL, a translational study of PARSIFAL, assessed a comprehensive set of biomarkers of sensitivity/resistance to P plus ET. MATERIALS AND METHODS TransFAL employed a sample biorepository from PARSIFAL comprising formalin-fixed paraffin-embedded (FFPE) tissue and blood samples obtained from pts included in either study arm. Tumor samples (N=33) derived from the primary tumor at initial diagnosis or from primary tumor or metastatic lesions at study entry. Blood samples were obtained at baseline (N=73) and disease progression (N=22). Pts were categorized as sensitive or resistant based on progression-free survival (PFS). CDK4/6 pathway activation was assessed by immunohistochemistry for CDK4, CDK6, Rb, pRb, ER, PgR, and Ki67. Proteins extracted from FFPE slides were analyzed using hierarchical clustering, significance analysis of microarrays, and probabilistic graphical models. RNA expression and circulating tumor DNA (ctDNA) analysis were assessed by next-generation sequencing (NGS) and analysis of panel of 56 genes/gene regions, respectively. RESULTS A worse median PFS was found in pts with CDK6 positive (≥ 1% positive cells; p < 0.001) and high Ki67 (≥ 10% positive cells; p = 0.013)at baseline. ctDNA density at baseline was significantly higher for resistant (PFS < 9 months; 17.1 ng/mL) pts compared to sensitive (PFS > 31 months; 11.3 ng/mL) pts (p < 0.001). Most pts (83%) presented a genomic alteration detected in ctDNA, TP53 (28%) and PIK3CA (28%) being the most frequently mutated genes. Pts (9%) who harbored ARID1A mutation had lower PFS (p = 0.052). ESR1 mutations at progression were numerically higher in the L arm (37.5%) compared to F arm (14.3%); (p=0.31), but numbers were low. Proteomic and RNA analyses did not yield confirmable distinctive expression pattern between resistant/sensitive pts… CONCLUSIONS High Ki67 levels and CDK6 expression in the tumor and high ctDNA density, but not proteomic or RNA sequencing data, were associated to poor outcome in this analysis of pts treated with CDK4/6i and ET. This data adds to the growing evidence of poor prognosis biomarkers in this setting, highlighting the need of novel strategies for these pts. Citation Format: Joan Albanell, Angelo Gámez Pozo, Carlos L. Arteaga, Meritxell Bellet, Federico Rojo, Abel González, Beatriz Bellosillo, Violeta Serra, Petra Gener, José Antonio Guerrero, Laura López-Montero, Mario Mancino, Jose Rodríguez-Morató, Leonardo Mina, José Manuel Pérez-García, Javier Cortés, Antonio Llombart-Cussac. Prospective evaluation of predictive biomarkers for Palbociclib (P) and endocrine therapy (ET) benefit in hormone receptor (HR)-positive [+]/human epidermal growth factor receptor 2 (HER2)-negative [-] advanced breast cancer (ABC) patients (pts) from PARSIFAL clinical trial: The transFAL sub-study [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2493.

Abstract 7440: Symptom network analysis and unsupervised clustering of a cohort of oncology patients identifies drivers of symptom burden and patient subgroups with distinct symptom patterns

Abstract Purpose: Inter-individual variability in oncology patients’ symptom experiences poses significant challenges in prioritizing symptoms for targeted intervention(s). In this study, computational approaches were used to unbiasedly characterize the heterogeneity of the symptom experience of oncology patients to elucidate their symptom patterns and drivers of symptom burden. Methods: Severity ratings for 32 symptoms on the Memorial Symptom Assessment Scale from 3088 oncology patients were analyzed. Gaussian Graphical Model symptom networks were constructed for the entire cohort and subgroups identified through unsupervised clustering of symptom co-severity patterns. Symptom network characteristics were analyzed and compared using permutation-based statistical tests. Demographic and clinical characteristic differences between subgroups were assessed using multinomial logistic regression. Results: Network analysis of the entire cohort revealed three core symptom clusters: constitutional, gastrointestinal-epithelial, and psychological. Lack of energy was identified as central to the network, suggesting that it plays a crucial role in driving overall symptom burden and may be the most salient to target clinically. Unsupervised clustering of patients based on shared symptom co-severity patterns identified six patient subgroups with distinct symptom patterns and demographic and clinical characteristics. The first subgroup represented a baseline cohort of patients who are most affected by common constitutional symptoms of cancer care. The second subgroup represented patients with severe overall symptom burden. The third subgroup represented patients primarily experiencing neuropathy. The fourth subgroup represented patients with advanced disease and treatment-related symptoms but relatively high quality of life. The fifth subgroup represented younger patients with pronounced psychological disease burden. The sixth subgroup represented patients with minimal symptom burden. Age, treatment status, and Karnofsky Performance Score were the strongest determinants of subgroup membership, but their interplay (in one case even with ethnicity) reaffirms the complex multi-factorial nature of the symptom experience. For each subgroup, symptoms with the highest network centralities that should be prioritized for management were identified. Conclusions: This study provides a translational research framework for clinicians to better understand and address the symptom care needs of oncology patients. The framework combines unbiased stratification of patients and in-depth modeling of symptom relationships to capture the heterogeneity in their experiences. When validated, the identified core symptoms and associated clinical determinants may inform precision-based symptom management. Citation Format: Brandon H. Bergsneider, Terri S. Armstrong, Yvette P. Conley, Bruce Cooper, Marilyn Hammer, Jon D. Levine, Steven Paul, Christine Miaskowski, Orieta Celiku. Symptom network analysis and unsupervised clustering of a cohort of oncology patients identifies drivers of symptom burden and patient subgroups with distinct symptom patterns [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7440.

Energy Price Inflation, Geopolitical Risk, and Bitcoin Dependence Structure: Evidence from BRICS

ABSTRACT This study examines the co-movement between geopolitical risk (GPR), energy price, and bitcoin (BTC) in BRICS countries, namely Brazil, Russia, India, China, and South Africa. Previous studies have focused on the impact of GPR on the volatility and risk premium of BTC investment. However, very limited studies have focused on integrating BTC as an extension of the mix of GPR on the co-movement with energy price. The analysis is based on monthly data of GPR index for BRICS countries, brent oil futures, natural gas futures and BTCs covering the period between March 2012 and Jun 2021. We employ the Bayesian graphical structural vector autoregressive model and time-varying parameter vector autoregressions-based dynamic connectedness to investigate the network-dependence structure. This research project provides useful empirical evidence for assessing the impact of both BTC and GPR on energy prices. Nonetheless, it will also be informative about the likelihood of co-movements occurring at different stages.

Module-based regularization improves Gaussian graphical models when observing noisy data

AbstractInferring relations from correlational data allows researchers across the sciences to uncover complex connections between variables for insights into the underlying mechanisms. The researchers often represent inferred relations using Gaussian graphical models, requiring regularization to sparsify the models. Acknowledging that the modular structure of these inferred networks is often studied, we suggest module-based regularization to balance under- and overfitting. Compared with the graphical lasso, a standard approach using the Gaussian log-likelihood for estimating the regularization strength, this approach better recovers and infers modular structure in noisy synthetic and real data. The module-based regularization technique improves the usefulness of Gaussian graphical models in the many applications where they are employed.

How are irritability and anhedonia symptoms linked? A network approach.

Anhedonia and irritability are two prevalent symptoms of major depressive disorder (MDD) that predict greater depression severity and poor outcomes, including suicidality. Although both symptoms have been proposed to result from paradoxical reward processing dysfunctions, the interactions between these symptoms remain unclear. Anhedonia is a multifaceted symptom reflecting impairments in multiple dimensions of reward processing (e.g., pleasure, desire, motivation, and effort) across distinct reward types (e.g., food, sensory experiences, social activities, hobbies) that may differentially interact with irritability. This study investigated the complex associations between anhedonia and irritability using network analysis. Participants (N = 448, Mage = 33.29, SD = 14.58) reported their symptoms of irritability on the Brief Irritability Test (Holtzman et al., 2015) and anhedonia (i.e., pleasure, desire, motivation, and effort dimensions across four reward types) on the Dimensional Anhedonia Rating Scale (Rizvi et al., 2015). A regularized Gaussian Graphical Model was built to estimate the network structure between items. Irritability was negatively related to willingness to expand effort to obtain food/drinks (estimate = -0.18), social activities (-0.13), and hobbies (-0.12) rewards. Irritability was positively associated with a desire for food/drinks (0.12). Only a small proportion (5.8%) of our sample was clinical and the study design was cross-sectional. A specific link between irritability and the effort dimension of the hedonic response across three reward types was identified. Investigating effort expenditure deficits with experimental paradigms may help us understand the mechanisms underlying the comorbidity between irritability and anhedonia in the context of MDD.

Gaze-based attention network analysis in a virtual reality classroom

This article provides a step-by-step guideline for measuring and analyzing visual attention in 3D virtual reality (VR) environments based on eye-tracking data. We propose a solution to the challenges of obtaining relevant eye-tracking information in a dynamic 3D virtual environment and calculating interpretable indicators of learning and social behavior. With a method called "gaze-ray casting," we simulated 3D-gaze movements to obtain information about the gazed objects. This information was used to create graphical models of visual attention, establishing attention networks. These networks represented participants' gaze transitions between different entities in the VR environment over time. Measures of centrality, distribution, and interconnectedness of the networks were calculated to describe the network structure. The measures, derived from graph theory, allowed for statistical inference testing and the interpretation of participants' visual attention in 3D VR environments. Our method provides useful insights when analyzing students’ learning in a VR classroom, as reported in a corresponding evaluation article with N = 274 participants.•Guidelines on implementing gaze-ray casting in VR using the Unreal Engine and the HTC VIVE Pro Eye.•Creating gaze-based attention networks and analyzing their network structure.•Implementation tutorials and the Open Source software code are provided via OSF: https://osf.io/pxjrc/?view_only=1b6da45eb93e4f9eb7a138697b941198.

Structural changes in contagion channels: the impact of COVID-19 on the Italian electricity market

AbstractOperating on electricity markets requires accurately identifying, quantifying, and measuring risk coupled with their corresponding return: this appears as a crucial point, particularly during and after the COVID-19 pandemic. The aim of the present paper is twofold. First, we propose a novel econometric approach to identifying relevant market factors that capture several elements of the risk transmission mechanism inherent in energy systems. The proposed model extends Bayesian graphical models with change points to a multiple-layer set-up. Multilayer graphs encompass the two relevant channels of shock transmission: volatility and price contagion effects. The choice of these two layers seems natural because electricity prices and their spiky nature, coupled with inherent volatility, constitute essential influential elements for market players to maximize their profits. The change-point specification allows for detecting relevant changes in the electricity market. Second, we apply the proposed econometric framework to the Italian zonal markets analyzing the effects of returns and volatility contagion in several periods detected by the model. The last time intervals identified by the change-point methodology overlap the COVID-19 pandemic period. The model captures relevant abrupt changes in prices and volatility in the zonal electricity market and provides new evidence of interconnections in the zones of the Italian market related to the risk alone, price process alone, and risk versus price process relationship and their interactions.

Personalized Driver Gene Prediction Using Graph Convolutional Networks with Conditional Random Fields.

Cancer is a complex and evolutionary disease mainly driven by the accumulation of genetic variations in genes. Identifying cancer driver genes is important. However, most related studies have focused on the population level. Cancer is a disease with high heterogeneity. Thus, the discovery of driver genes at the individual level is becoming more valuable but is a great challenge. Although there have been some computational methods proposed to tackle this challenge, few can cover all patient samples well, and there is still room for performance improvement. In this study, to identify individual-level driver genes more efficiently, we propose the PDGCN method. PDGCN integrates multiple types of data features, including mutation, expression, methylation, copy number data, and system-level gene features, along with network structural features extracted using Node2vec in order to construct a sample-gene interaction network. Prediction is performed using a graphical convolutional neural network model with a conditional random field layer, which is able to better combine the network structural features with biological attribute features. Experiments on the ACC (Adrenocortical Cancer) and KICH (Kidney Chromophobe) datasets from TCGA (The Cancer Genome Atlas) demonstrated that the method performs better compared to other similar methods. It can identify not only frequently mutated driver genes, but also rare candidate driver genes and novel biomarker genes. The results of the survival and enrichment analyses of these detected genes demonstrate that the method can identify important driver genes at the individual level.

From programming-to-modeling-to-prompts smart ubiquitous applications

Since its introduction by Mark Weiser, ubiquitous computing has received increased interest in the dawn of technological advancement. Supported by wireless technology advancement, embedded systems, miniaturization, and the integration of various intelligent and communicative devise, context-aware ubiquitous applications actively and intelligently use rich contextual information to assist their users. However, their designs are subject to continuous changes imposed by external factors. Nowadays, software engineering, particularly in the fields of Model-Driven Engineering, displays a strong tendency towards developing applications for pervasive computing. This trend is also fueled by the rise of generative artificial intelligence, paving the way for a new generation of no-code development tools and models specifically trained on open-source code repositories to generate applications from their descriptions. The specificities of our approach lies in starting with a graphical model expressed using a domain-specific language (DSL) composed of symbols and formal notations. This allows for graphically instantiating and editing applications, guiding and assisting experts from various engineering fields in defining ubiquitous applications that are eventually transformed into peculiar models. We believe that creating intelligent models is the best way to promote software development efficiency. We have used and evaluated recurrent neural networks, leveraging the recurrence of processing the same contextual information collected within this model, and enabling iterative adaptation to future evolutions in ubiquitous systems. We propose a prototype instantiated by our meta-model which tracks the movements of individuals who were positive for COVID-19 and confirmed to be contagious. Different deep learning models and classical machine learning techniques are considered and compared for the task of detection/classification of COVID-19. Results obtained from all techniques were evaluated with confusion matrices, accuracy, precision, recall and F1-score. In summary, most of the results are very impressive. Our deep learning approach used a RNN architecture produced up to 92.1% accuracy. With the recent development of OpenAI Codex, optimized for programming languages, we provided the same requirements to the Codex model and asked it to generate the source code for the COVID-19 application, comparing it with the application generated by our workshop.

Structured Variational Approximations with Skew Normal Decomposable Graphical Models and Implicit Copulas

Although there is much recent work developing flexible variational methods for Bayesian computation, Gaussian approximations with structured covariance matrices are often preferred computationally in high-dimensional settings. This article considers approximate inference methods for complex latent variable models where the posterior is close to Gaussian, but with some skewness in the posterior marginals. We consider skew decomposable graphical models (SDGMs), which are based on the closed skew normal family of distributions, as variational approximations. These approximations can reflect the true posterior conditional independence structure and capture posterior skewness. To increase flexibility, implicit copula SDGM approximations are also developed, where elementwise transformations of an approximately standardized SDGM random vector are considered. This implicit copula extension is an important contribution of our work, and improves the accuracy of SDGM approximations for only a modest increase in computational cost. Our parameterization of the copula approximation is novel, even in the Gaussian case. Performance of the methods is examined in a number of real examples involving generalized linear mixed models and state space models. Supplemental materials including code and appendix are available online.

Association between unhealthy lifestyles and hypertension, diabetes and dyslipidemia in old adults in China

Objective: To analyze the individual and cumulative effects of unhealthy lifestyle on the prevalence of hypertension, diabetes and dyslipidemia in old adults in China, and find out the critical lifestyle in the network. Methods: Based on the baseline data of Yunnan Behavior and Disease Surveillance Cohort in 2021, a total of 16 763 older adults aged ≥60 years were included in our study. The unhealthy lifestyle factors including smoking, drinking, unhealthy eating habit, lower physical activity level, abnormal BMI and abnormal waist circumference. We calculated the unhealthy lifestyle score by using the cumulative exposures of each participant. Multiple logistic regression and mixed graphical models were used to describe the association between unhealthy lifestyle and the prevalence of hypertension, diabetes and dyslipidemia. Results: The prevalence of hypertension, diabetes and dyslipidemia were 57.0%, 11.5% and 37.0%, respectively. Most of the unhealthy lifestyles included in the study were risk factors for hypertension, diabetes and dyslipidemia, and the risks of disease increased with the increase of the unhealthy lifestyle score. The participants with the highest score (score: 6) had significantly higher prevalence of hypertension (OR=3.99, 95%CI: 1.81-8.80), diabetes (OR=4.64, 95%CI: 1.64-13.15) and dyslipidemia (OR=4.26, 95%CI: 2.08-8.73) compared with those with lowest score (score: 0). In the network constructed by mixed graphical model, abnormal waist circumference (bridge strength=0.81) and hypertension (bridge strength=0.55) were vital bridge nodes connecting unhealthy lifestyle and hypertension, diabetes and dyslipidemia. Conclusions: The unhealthy lifestyle score was associated with risks for hypertension, diabetes and dyslipidemia. Abnormal waist circumference was the key factor for chronic diseases in old adults.

Staged trees and asymmetry-labeled DAGs

AbstractBayesian networks are a widely-used class of probabilistic graphical models capable of representing symmetric conditional independence between variables of interest using the topology of the underlying graph. For categorical variables, they can be seen as a special case of the much more general class of models called staged trees, which can represent any non-symmetric conditional independence. Here we formalize the relationship between these two models and introduce a minimal Bayesian network representation of a staged tree, which can be used to read conditional independences intuitively. A new labeled graph termed asymmetry-labeled directed acyclic graph is defined, with edges labeled to denote the type of dependence between any two random variables. We also present a novel algorithm to learn staged trees which only enforces a specific subset of non-symmetric independences. Various datasets illustrate the methodology, highlighting the need to construct models that more flexibly encode and represent non-symmetric structures.

A novel PET probe to selectively image heat shock protein 90α/β isoforms in the brain

BackgroundHeat shock proteins (HSPs) are present throughout the brain. They function as molecular chaperones, meaning they help with the folding and unfolding of large protein complexes. These chaperones are vital in the development of neuropathological conditions such as Alzheimer’s disease and Lewy body disease, with HSP90, a specific subtype of HSP, playing a key role. Many studies have shown that drugs that inhibit HSP90 activity have beneficial effects in the neurodegenerative diseases. Therefore, HSP90 PET imaging ligand can be used effectively to study HSP90 in neurodegenerative diseases. Among four HSP90 isoforms, two cytosolic isoforms (HSP90α and HSP90β) thought to be involved in the structural homeostasis of the proteins related to the neurodegenerative diseases. Currently, no useful PET imaging ligands selectively targeting the two cytosolic isoforms of HSP90 have been available yet.ResultsIn this study, we developed a novel positron emission tomography (PET) imaging ligand, [11C]BIIB021, by 11C-radiolabeling (a positron emitter with a half-life of 20.4 min) 6-Chloro-9-[(4-methoxy-3,5-dimethylpyridin-2-yl)methyl]-9H-purin-2-amine (BIIB021), an inhibitor with a high affinity for and selectivity to HSP90α and HSP90β. [11C]BIIB021 was synthesized with a high yield, molar activity and radiochemical purity. [11C]BIIB021 showed a high binding affinity for rat brain homogenate as well as human recombinant HSP90α and HSP90β proteins. Radioactivity was well detected in the rat brain (SUV 1.4). It showed clear specific binding in PET imaging of healthy rats and autoradiography of healthy rat and human brain sections. Radiometabolite was detected in the brain, however, total distribution volume was well quantified using dual-input graphical model. Inhibition of p-glycoprotein increased brain radioactivity concentrations. However, total distribution volume values with and without p-glycoprotein inhibition were nearly the same.ConclusionsWe have developed a new PET imaging agent, [11C]BIIB021, specifically targeting HSP90α/β. We have been successful in synthesizing [11C]BIIB021 and in vitro and in vivo imaging HSP90α/β. However, the quantification of HSP90α/β is complicated by the presence of radiometabolites in the brain and the potential to be a substrate for p-glycoprotein. Further efforts are needed to develop radioligand suitable for imaging of HSP90α/β.

The Role of Anxiety and Depression in Shaping the Sleep-Pain Connection in Patients with Nonspecific Chronic Spinal Pain and Comorbid Insomnia: A Cross-Sectional Analysis.

(1) Background: This exploratory study aims to explore the relationship between nonspecific chronic spinal pain (nCSP) and insomnia symptoms, by examining the interconnections, strengths, and directional dependence of the symptoms. In addition, we aim to identify the key symptoms of the nCSP-insomnia relationship and shed light on the bidirectional nature of this relationship. (2) Methods: This study is a secondary analysis of the baseline data (cross-sectional) from a randomized controlled trial, which examined the added value of Cognitive Behavioral Therapy for Insomnia (CBT-I) combined with cognition-targeted exercise therapy, conducted in collaboration with the Universiteit Gent and Vrije Universiteit Brussel (Belgium). One hundred and twenty-three nCSP patients with comorbid insomnia were recruited through the participating hospitals, advertisements, announcements in local newspapers, pharmacies, publications from support groups, and primary care. To explore the interconnections and directionality between symptoms and the strengths of the relationships, we estimated a regularized Gaussian graphical model and a directed acyclic graph. (3) Results: We found only one direct, but weak, link between sleep and pain, namely, between average pain and difficulties maintaining sleep. (4) Conclusions: Despite the lack of strong direct links between sleep and pain, pain and sleep seem to be indirectly linked via anxiety and depression symptoms, acting as presumable mediators in the network of nCSP and comorbid insomnia. Furthermore, feeling slowed down and fatigue emerged as terminal nodes, implying their role as consequences of the network.

Physical health conditions in young children with profound intellectual and multiple disabilities: The prevalence and associations between these conditions.

It is thought that physical health conditions start at a young age in people with profound intellectual and multiple disabilities (PIMD). Knowledge regarding the prevalence, associations and development of these physical health conditions could be used for purposes of prevention as well as appropriate care and support but is currently lacking. The aim of this study is to gain insight into the prevalence of physical health conditions and associations between these conditions in young children with PIMD. The study used cross-sectional data related to the physical health conditions of children with PIMD (n= 51, aged between 12 and 61 months). Data were collected in Belgium and in the Netherlands through a checklist filled in by primary caregiver(s). Physical health conditions were classified into categories by the 10th revision of the International Classification of Diseases and Related Health Problems (ICD-10) system. The number of physical health conditions and associations between them were analysed. The analysis focused on prevalence rates and associations represented by odds ratios (p< 0.05). A graphical model was estimated to represent dependencies and conditional dependencies between physical health conditions. We found a mean of 3.8 (range 1-8, SD 1.9) physical health conditions per child. Most of the physical health conditions were found in the ICD-10 chapter 'Nervous System', with hypotonia as the most frequent at 70.6%. Five significant large associations were found between spasticity-contractures (OR 9.54); circulatory system-contractures (OR 7.50); scoliosis-contractures (OR 10.25); hearing impairments-skin problems (OR 58.20) and obstipation-hypotonia (OR 19.98). This study shows that at a young age, multiple physical health conditions are present in children with PIMD. In addition, we found five associations between physical health conditions.

Graph modeling of relational structures among functioning variables with low back pain: an exploratory analysis based on International Classification of Functioning, Disability and Health.

Given the complex etiology, multidimensional impact, and widespread prevalence of low back pain (LBP), it is crucial to prioritize intervention targets based on understanding the relationships between functional impairments in patients. This prioritization maximizes the physical and psychological benefits for patients, and graph modeling holds promise in achieving these objectives. The aim of this study was establishing a graphical model of functioning variables for LBP based on the International Classification of Functioning, Disability, and Health (ICF) to identify the most influential items (i.e., functioning variables) on the physical and mental well-being of patients. Exploring feasible intervention measures by understanding the dysfunction correlations among these variables. Cross-sectional survey. Nine hospitals in Jiangsu Province, China. Three hundred and six persons with LBP aged ≥18 years. All patients were assessed using the Comprehensive ICF Core Sets for LBP. The scoring system was converted to dichotomous data, with 1 indicating dysfunction and 0 indicating no dysfunction. In the graphical model, network parameters and the results of Item Response Theory modeling (as detailed in our other article) were used to determine the importance of items, while partial correlations were utilized to estimate the dysfunction correlations between functioning variables. 1) A total of 56 ICF items were located in the backbone structure of LBP, among which d430 (Lifting and carrying objects) occupied the most central position, followed by b126 (Temperament and personality functions). 2) In the main component of backbone structure, d430 has moderate dysfunction correlation with looking after one's health (0.6027), social norms, practices and ideologies (0.597), stability of joint functions (0.5759), and emotional functions (0.4078). b126 has moderate dysfunction correlation with basic interpersonal interactions (0.6595). d430 and b126 significantly impact the physical and mental well-being of LBP patients. To improve d430, maintaining exercise habits, reducing working hours, enhancing lumbar stability, and overcoming fear-related emotions are recommended. Similarly, improving b126 can be achieved through enhancing interpersonal relationships. Through the identification of crucial functioning variables and the associated dysfunctional correlation relationships, graphical model of Comprehensive ICF Core Set for LBP can offer healthcare decision-makers valuable insights into potential treatment targets and pathways aimed at improving the condition of LBP patients.

Assessing electrogenetic activation via a network model of biological signal propagation

Introduction: Molecular communication is the transfer of information encoded by molecular structure and activity. We examine molecular communication within bacterial consortia as cells with diverse biosynthetic capabilities can be assembled for enhanced function. Their coordination, both in terms of engineered genetic circuits within individual cells as well as their population-scale functions, is needed to ensure robust performance. We have suggested that “electrogenetics,” the use of electronics to activate specific genetic circuits, is a means by which electronic devices can mediate molecular communication, ultimately enabling programmable control.Methods: Here, we have developed a graphical network model for dynamically assessing electronic and molecular signal propagation schemes wherein nodes represent individual cells, and their edges represent communication channels by which signaling molecules are transferred. We utilize graph properties such as edge dynamics and graph topology to interrogate the signaling dynamics of specific engineered bacterial consortia.Results: We were able to recapitulate previous experimental systems with our model. In addition, we found that networks with more distinct subpopulations (high network modularity) propagated signals more slowly than randomized networks, while strategic arrangement of subpopulations with respect to the inducer source (an electrode) can increase signal output and outperform otherwise homogeneous networks.Discussion: We developed this model to better understand our previous experimental results, but also to enable future designs wherein subpopulation composition, genetic circuits, and spatial configurations can be varied to tune performance. We suggest that this work may provide insight into the signaling which occurs in synthetically assembled systems as well as native microbial communities.