Bone Strain Index predicts fragility fracture in osteoporotic women: an artificial intelligence-based study

Background We applied an artificial intelligence-based model to predict fragility fractures in postmenopausal women, using different dual-energy x-ray absorptiometry (DXA) parameters. Methods One hundred seventy-four postmenopausal women without vertebral fractures (VFs) at baseline (mean age 66.3 ± 9.8) were retrospectively evaluated. Data has been collected from September 2010 to August 2018. All subjects performed a spine x-ray to assess VFs, together with lumbar and femoral DXA for bone mineral density (BMD) and the bone strain index (BSI) evaluation. Follow-up exams were performed after 3.34 ± 1.91 years. Considering the occurrence of new VFs at follow-up, two groups were created: fractured versus not-fractured. We applied an artificial neural network (ANN) analysis with a predictive tool (TWIST system) to select relevant input data from a list of 13 variables including BMD and BSI. A semantic connectivity map was built to analyse the connections among variables within the groups. For group comparisons, an independent-samples t-test was used; variables were expressed as mean ± standard deviation. Results For each patient, we evaluated a total of n = 6 exams. At follow-up, n = 69 (39.6%) women developed a VF. ANNs reached a predictive accuracy of 79.56% within the training testing procedure, with a sensitivity of 80.93% and a specificity of 78.18%. The semantic connectivity map showed that a low BSI at the total femur is connected to the absence of VFs. Conclusion We found a high performance of ANN analysis in predicting the occurrence of VFs. Femoral BSI appears as a useful DXA index to identify patients at lower risk for lumbar VFs.

Screening Novel Drug Candidates for Kidney Renal Clear Cell Carcinoma Treatment: A Study on Differentially Expressed Genes through the Connectivity Map Database

<b><i>Objective:</i></b> Kidney renal clear cell carcinoma (KIRC) is a common cancer with high morbidity and mortality in renal cancer. Thus, the transcriptome data of KIRC patients in The Cancer Genome Atlas (TCGA) database were analyzed and drug candidates for the treatment of KIRC were explored through the connectivity map (CMap) database. <b><i>Methods:</i></b> The transcriptome data of KIRC patients were downloaded from TCGA database, and KIRC-associated hub genes were screened out through differential analysis and protein-protein interaction (PPI) network analysis. Afterward, the CMap database was used to select drug candidates for KIRC treatment, and the drug-targeted genes were obtained through the STITCH database. A PPI network was constructed by combining drug-targeted genes with hub genes that affected the pathogenesis of KIRC to obtain final hub genes. Finally, combining hub genes and KIRC-associated hub genes, the pathways affected by drugs were explored by pathway enrichment analysis. <b><i>Results:</i></b> A total of 2,312 differentially expressed genes were found in patients, which were concentrated in immune cell activity, cytokine, and chemokine secretion pathways. Drug screening disclosed 5 drug candidates for KIRC treatment: fedratinib, Ly344864, geldanamycin, AS-605240, and luminespib. Based on drug-targeted genes and KIRC-associated hub genes, 16 hub genes were screened out. Pathway enrichment analysis revealed that drugs mainly affected pathways such as neuroactive ligand pathways, cell adhesion, and chemokines. <b><i>Conclusion:</i></b> The above results indicated that fedratinib, LY 344864, geldanamycin, AS-605240, and luminespib could be used as candidates for KIRC therapy. The findings from this study will make contributions to the treatment of KIRC in the future.

Trans border recreational tourism complexes (TBRTC) as a result of international collaboration

The paper discusses the notion of a transborder recreational tourism complex (TBRTC) as a geographic and as a wider multidisciplinary concept and describes such complexes within transborder tourism regions in Western Ukraine. A TBRTC is introduced as a spatial formation that arises within tourism-specific transborder collaboration. A TBRTC combines three necessary components: the entirety of all tourism-attracting resources in a specific transborder region; the travel-enabling infrastructure, including transborder-specific features like border-crossing checkpoints; and informational and coordination resources such as tour operating services and defined tourist routes. The paper explains basic terminology and key principles for formation and existence of TBRTCs. The authors start by explaining the unique characteristics of transborder tourism in Europe and in tourism-oriented formations called Euro-zones. A principal model of TBRTC is proposed, including the list of factors necessary for its formation, the key structural modules of a complex; and the causal connectivity map between these modules applied to different types of tourism. The concept of a TBRTC is illustrated using the example of the TBRTC forming in Bukovina-Bessarabia transborder region along part of Ukraine-Romania-Moldova border. This region offers multiple types of tourism activities, which translates into the emergence of a robust transborder tourism recreation complex that started forming in the late 1990s and continues to be shaped until this time.

Different Features of Metabolic Connectivity Map and Granger Causality Method in Revealing Directed Dopamine Pathways: A Pilot Study Based on Integrated PET/MR

Granger causality (GC) analysis and metabolic connectivity map (MCM) are two effective connectivity (EC) methods commonly used in functional brain researches. Although they have a common basis in central neurophysiology, their differences in depicting EC are not clear because of absenting data acquired simultaneously and exactly aligned. Integrated positron emission tomography and magnetic resonance image (PET/MR) technology makes this available. Using the “Monash rs-PET/MR” dataset obtained from the OpenNeuro database, we first conducted GC and MCM analysis of the brain dopamine reward circuit, a well-known system mainly consisting of the bilateral Orbital Frontal Cortex (OFC), Caudate (CAU), Nucleus Accumbens (NAc), Thalamus (THA) and Substantia Nigra (SN). Then, we validated their ability of describing EC to priori knowledge. The significance of each directed pathways within group were tested through one-sample t-test (for MCM) or Wilcoxcon test (for GC), the significance level was set at p<0.05 after FDR correction. Three types of connections were found: the cortico-nucleus (long-range), the nucleus-nucleus (neighborhood) and the symmetrical connections. GC revealed long-range connections including OFC-CAU and OFC-NAc; MCM revealed neighborhood connections including NAc-CAU, SN-THA, and THA-CAU, the symmetrical connections including the bilateral NAc, CAU, THA, as well as OFC-CAU. Thus, different patterns in directional networks of dopamine reward circuit revealed by GC and MCM. GC predominated at aspects of cortico-nucleus bidirected connections, while MCM of directed connections among close regions and symmetrical regions. This study implicates that research involving in effective connections should choose an appropriate analysis method according to the study purpose.

Evaluation of connectivity map shows limited reproducibility in drug repositioning

The Connectivity Map (CMap) is a popular resource designed for data-driven drug repositioning using a large transcriptomic compendium. However, evaluations of its performance are limited. We used two iterations of CMap (CMap 1 and 2) to assess their comparability and reliability. We queried CMap 2 with CMap 1-derived signatures, expecting CMap 2 would highly prioritize the queried compounds; the success rate was 17%. Analysis of previously published prioritizations yielded similar results. Low recall is caused by low differential expression (DE) reproducibility both between CMaps and within each CMap. DE strength was predictive of reproducibility, and is influenced by compound concentration and cell-line responsiveness. Reproducibility of CMap 2 sample expression levels was also lower than expected. We attempted to identify the “better” CMap by comparison with a third dataset, but they were mutually discordant. Our findings have implications for CMap usage and we suggest steps for investigators to limit false positives.

Clinical connectivity map for drug repurposing: using laboratory results to bridge drugs and diseases

Background Drug repurposing, the process of identifying additional therapeutic uses for existing drugs, has attracted increasing attention from both the pharmaceutical industry and the research community. Many existing computational drug repurposing methods rely on preclinical data (e.g., chemical structures, drug targets), resulting in translational problems for clinical trials. Results In this study, we propose a novel framework based on clinical connectivity mapping for drug repurposing to analyze therapeutic effects of drugs on diseases. We firstly establish clinical drug effect vectors (i.e., drug-laboratory results associations) by applying a continuous self-controlled case series model on a longitudinal electronic health record data, then establish clinical disease sign vectors (i.e., disease-laboratory results associations) by applying a Wilcoxon rank sum test on a large-scale national survey data. Eventually, a repurposing possibility score for each drug-disease pair is computed by applying a dot product-based scoring function on clinical disease sign vectors and clinical drug effect vectors. During the experiment, we comprehensively evaluate 392 drugs for 6 important chronic diseases (include asthma, coronary heart disease, congestive heart failure, heart attack, type 2 diabetes, and stroke). The experiment results not only reflect known associations between diseases and drugs, but also include some hidden drug-disease associations. The code for this paper is available at: https://github.com/HoytWen/CCMDR Conclusions The proposed clinical connectivity map framework uses laboratory results found from electronic clinical information to bridge drugs and diseases, which make their relations explainable and has better translational power than existing computational methods. Experimental results demonstrate the effectiveness of our proposed framework, further case analysis also proves our method can be used to repurposing existing drugs opportunities.

Integrating multi-omics data reveals function and therapeutic potential of deubiquitinating enzymes

Deubiquitinating enzymes (DUBs) are proteases that remove ubiquitin conjugates from proteins, thereby regulating protein turnover. Inhibition of DUBs promises to make classically undruggable targets such as the tumor suppressor TP53 and oncogene c-Myc amenable to regulation by small molecules. However, the majority of substrates and pathways regulated by DUBs remain unknown, impeding efforts to prioritize specific enzymes for research and drug development. To assemble a knowledgebase of DUB activities, co-dependent genes, and substrates, we combined targeted experiments using CRISPR libraries and inhibitors with systematic mining of functional genomic databases. Analysis of the Dependency Map, Connectivity Map, Cancer Cell Line Encyclopedia, and protein-protein interaction databases yielded specific hypotheses about DUB function, a subset of which were confirmed in follow-on experiments. The data in this paper, which are browsable online via the DUB Portal, promise to improve understanding of DUBs as a family as well as the activities of specific DUBs such as USP14, UCHL5 and USP7, which have been targeted with investigational cancer therapeutics.

HOXB7 acts as an oncogenic biomarker in head and neck squamous cell carcinoma

Background The homeobox gene Homeobox B7 (HOXB7) is overexpressed across a range of cancers and promotes tumorigenesis through varying effects on proliferation, survival, migration and invasion. However, its expression pattern and oncogenic role of HOXB7 in head and neck squamous cell carcinoma (HNSCC) remain largely unexplored. Here, we aimed to explore the expression pattern of HOXB7, its clinical significance as well as functional roles in HNSCC. Methods HOXB7 mRNA expression in HNSCC was determined by data mining and analyses from TCGA (The Cancer Genome Atlas) and GEO (Gene Expression Omnibus) datasets. The protein abundance of HOXB7 was measured by immunohistochemistry in 119 primary HNSCC samples and associations between its expression and clinicopathological parameters and patient survival were evaluated. The pro-tumorigenic roles of HOXB7 in HNSCC were further delineated in vitro by loss-of-function assay. And a xenograft tumor model was established in nude mice to assess the role of HOXB7 in tumor growth. Connectivity Map (CMap) analysis was performed to identify bioactive small molecules which might be potential inhibitors for HOXB7. Results Bioinformatics analyses showed that HOXB7 mRNA was significantly overexpressed in 8 independent HNSCC datasets from TCGA and GEO databases. HOXB7 protein was markedly upregulated in HNSCC samples as compared to normal counterparts and its overexpression significantly associated with high pathological grade, advanced clinical stage, cervical node metastasis (P = 0.0195, 0.0152, 0.0300) and reduced overall and disease-free survival (P = 0.0014, 0.0007). Univariate and multivariate Cox regression analyses further revealed HOXB7 as an independent prognostic factor for patients’ overall survival. Moreover, HOXB7 knockdown significantly inhibited cell proliferation, migration and invasion and induced cell apoptosis in HNSCC cells, and resulted in compromised tumour growth in vivo. Furthermore, CMap (Connectivity map) analysis has identified three potential bioactive small molecule inhibitors (NU-1025, thiamine, vinburnine) for HOXB7 targeted therapy in HNSCC. Conclusions Our findings revealed that overexpression of HOXB7 was associates with tumour aggressiveness and unfavourable prognosis by serving a novel prognostic biomarker in HNSCC. Moreover, HOXB7 might be involved in the development and progression of HNSCC as an oncogene, and thereby might be a potential therapeutic target for HNSCC.

Mapping the subcortical connectivity of the human default mode network

The default mode network (DMN) mediates self-awareness and introspection, core components of human consciousness. Therapies to restore consciousness in patients with severe brain injuries have historically targeted subcortical sites in the brainstem, thalamus, hypothalamus, basal forebrain, and basal ganglia, with the goal of reactivating cortical DMN nodes. However, the subcortical connectivity of the DMN has not been fully mapped and optimal subcortical targets for therapeutic neuromodulation of consciousness have not been identified. In this work, we created a comprehensive map of DMN subcortical connectivity by combining high-resolution functional and structural datasets with advanced signal processing methods. We analyzed 7 Tesla resting-state functional MRI (rs-fMRI) data from 168 healthy volunteers acquired in the Human Connectome Project. The rs-fMRI blood-oxygen-level-dependent (BOLD) data were temporally synchronized across subjects using the BrainSync algorithm. Cortical and subcortical DMN nodes were jointly analyzed and identified at the group level by applying a novel Nadam-Accelerated SCAlable and Robust (NASCAR) tensor decomposition method to the synchronized dataset. The subcortical connectivity map was then overlaid on a 7 Tesla 100 micron ex vivo MRI dataset for neuroanatomic analysis using automated segmentation of nuclei within the brainstem, thalamus, hypothalamus, basal forebrain, and basal ganglia. We further compared the NASCAR subcortical connectivity map with its counterpart generated from canonical seed-based correlation analyses. The NASCAR method revealed that BOLD signal in the central lateral nucleus of the thalamus and ventral tegmental area of the midbrain is strongly correlated with that of the DMN. In an exploratory analysis, additional subcortical sites in the median and dorsal raphe, lateral hypothalamus, and caudate nuclei were correlated with the cortical DMN. We also found that the putamen and globus pallidus are negatively correlated (i.e., anti-correlated) with the DMN, providing rs-fMRI evidence for the mesocircuit hypothesis of human consciousness, whereby a striatopallidal feedback system modulates anterior forebrain function via disinhibition of the central thalamus. Seed-based analyses yielded similar subcortical DMN connectivity, but the NASCAR result showed stronger contrast and better spatial alignment with dopamine immunostaining data. The DMN subcortical connectivity map identified here advances understanding of the subcortical regions that contribute to human consciousness and can be used to inform the selection of therapeutic targets in clinical trials for patients with disorders of consciousness.