Association of Prenatal Exposure to Antiseizure Medications With Creative and Executive Function at Age 4.5 Years.

BACKGROUND AND OBJECTIVES: Neurodevelopmental effects of fetal antiseizure medication (ASM) exposure on creativity and executive functions are poorly understood. We previously found fetal valproate exposure to adversely affect measures of creativity and executive functions. In this study, we examine fetal exposure of newer ASMs on these functions in children of women with epilepsy (WWE) compared with children of healthy women (HW). METHODS: The Maternal Outcomes and Neurodevelopmental Effects of Antiepileptic Drugs study is a multicenter NIH-funded prospective observational cohort study of WWE and HW enrolled in pregnancy and their offsprings. This report examines blindly assessed creativity and executive functions in 4.5-year-old children of WWE vs HW. In addition, exposure-dependent ASM effects during the third trimester were examined in children of WWE, using a ratio of maximum observed ASM concentrations and ratio of defined daily dose (ratio DDD). For polytherapy, ratios were summed across ASMs. Linear regression models adjusted for multiple potential confounding factors were conducted for all analyses. The primary outcome for 4.5-year-old children was the Torrance Test of Creative Thinking-Figural Creativity Index. Secondary outcomes included the Global Executive Composite Score from the Behavior Rating Inventory of Executive Function-Preschool Version and subscales and other indexes of both measures. RESULTS: The primary analysis included 251 children of WWE and 73 of HW. No differences in creativity or executive function were found between children of WWE vs HW. No ASM exposure-dependent effects were found for the creativity measures, but exposure-dependent effects for executive function were present for ratio ASM concentration and ratio DDD. DISCUSSION: Our findings at 4.5 years show no differences in creative thinking between children of WWE vs HW (-3.2 [-9.0 to 2.7], p = 0.286) or associations with fetal exposure to ASMs (-2.6 [-11.0 to 5.7], p = 0.530). Secondary analyses revealed fetal exposure-dependent effects for executive function in children of WWE (7.0 [2.9-11.2], p = 0.001), which are most marked for levetiracetam (12.9 [4.2-21.6], p = 0.004). Our findings suggest that even for relatively safe ASMs, dosing needs to be adjusted to concentrations that prevent seizures, but balance risks to the fetus that high concentrations may pose. TRIAL REGISTRATION INFORMATION: The study is registered at ClinicalTrials.gov as NCT01730170.

Initiation and Duration of Breastfeeding in the Maternal Outcomes and Neurodevelopmental Effects of Antiepileptic Drugs Study.

BACKGROUND AND OBJECTIVES: Breastfeeding has important health benefits for both mother and child. We characterize breastfeeding initiation and duration in mothers with epilepsy relative to control mothers in a large prospective cohort. METHODS: The Maternal Outcomes and Neurodevelopmental Effects of Antiepileptic Drugs study is a prospective, multicenter observational, US cohort study. Pregnant individuals with and without epilepsy, aged 14-45 years, were enrolled between December 19, 2012, and February 11, 2016. Exclusion criteria included intelligence quotient (IQ) <70, and gestational age >20 weeks at enrollment. Breastfeeding was assessed through electronic diary and at study visits until 2 years postpartum. Odds of initiating breastfeeding was compared between cohorts using unadjusted and adjusted logistic regression models. Duration of breastfeeding was compared between cohorts using the log-rank test. RESULTS: Three hundred fifty-one pregnant individuals with epilepsy and 105 pregnant controls were enrolled. Breastfeeding data were available for 325 mothers with epilepsy and 98 controls. Study cohorts were similar demographically except race (p = 0.008); 84.9% of mothers with epilepsy and 71.4% of controls were White. The mean IQ was lower in mothers with epilepsy compared with that in controls (97.7 vs 104.2, p < 0.001). Breastfeeding was initiated by 74.8% mothers with epilepsy and 88.8% controls; this difference was significant in unadjusted logistic regression (odds ratio [OR] 0.4 [95% CI 0.2, 0.7], p = 0.004), but not in adjusted model (OR 0.5 [95% CI 0.2, 1.0], p = 0.051). Factors associated with breastfeeding were higher maternal education and IQ. There was no difference in duration of breastfeeding between mothers with and without epilepsy (median duration 8.5 months vs 9.9 months, p = 0.793). Among mothers with epilepsy, both convulsive seizures and all seizures that impair awareness during pregnancy were associated with lower breastfeeding initiation (OR 0.4 [95% CI 0.2, 0.8], p = 0.013) and (OR 0.4 [95% CI 0.2, 0.8], p = 0.003, respectively). Any peripartum seizures were associated with shorter breastfeeding duration (median 6 months vs 9.7 months, [p = 0.040]). DISCUSSION: Mothers with epilepsy were less likely to initiate breastfeeding compared with controls; however, this difference was not significant when controlling for maternal IQ and education level. Continuation of breastfeeding once initiated was not different between mothers with and without epilepsy. Seizure control was associated with breastfeeding initiation and duration in mothers with epilepsy. TRIAL REGISTRATION INFORMATION: ClinicalTrials.gov identifier NCT01730170.

Validation of Gait Measurements on Short-Distance Walkways Using Azure Kinect DK in Patients Receiving Chronic Hemodialysis.

Muscle dysfunction, skeletal muscle fibrosis, and disability are associated with weakness in patients with end-stage renal disease. The main purpose of this study was to validate the effectiveness of a proposed system for gait monitoring on short-distance 1.5 m walkways in a dialysis center. Gaits with reduced speed and stride length, long sit-to-stand time (SST), two forward angles, and two unbalanced gait regions are defined in the proposed Kinect v3 gait measurement and analysis system (K3S) and have been considered clinical features in end-stage renal disease (ESRD) associated with poor dialysis outcomes. The stride and pace calibrations of the Kinect v3 system are based on the Zeno Walkway. Its single rating intraclass correlation (ICC) for the stride is 0.990, and its single rating ICC for the pace is 0.920. The SST calibration of Kinect v3 is based on a pressure insole; its single rating ICC for the SST is 0.871. A total of 75 patients on chronic dialysis underwent gait measurement and analysis during walking and weighing actions. After dialysis, patients demonstrated a smaller stride (p < 0.001) and longer SST (p < 0.001). The results demonstrate that patients' physical fitness was greatly reduced after dialysis. This study ensures patients' adequate physical gait strength to cope with the dialysis-associated physical exhaustion risk by tracing gait outliers. As decreased stride and pace are associated with an increased risk of falls, further studies are warranted to evaluate the clinical benefits of monitoring gait with the proposed reliable and valid system in order to reduce fall risk in hemodialysis patients.

Association between hyperlipidemia and trigger finger: A nationwide population-based cohort study.

The cause of trigger fingers remains uncertain. High lipid levels in the blood may reduce blood supply to the distal fingers and promote inflammation. We aimed to explore the association between hyperlipidemia and trigger finger. A nationwide population-based cohort study using longitudinal data from 2000 to 2013, 41,421 patients were included in the hyperlipidemia cohort and 82,842 age- and sex-matched patients were included in the control cohort. The mean age was 49.90 ± 14.73 years in the hyperlipidemia cohort and 49.79 ± 14.71 years in the control cohort. After adjusting for possible comorbidities, the hazard ratio of trigger finger in the hyperlipidemia cohort was 4.03 (95% confidence interval [CI], 3.57-4.55), with values of 4.59 (95% CI, 3.67-5.73) and 3.77 (95% CI, 3.26-4.36) among male and female patients, respectively. This large-scale population-based study demonstrated that hyperlipidemia is correlated to trigger finger.

Long-term ambient hydrocarbon exposure and incidence of urinary bladder cancer.

Particulate matter and volatile organic compounds, including total hydrocarbons (THCs), are major ambient air pollutants. Primary nonmethane hydrocarbons (NMHCs) originate from vehicle emissions. The association between air pollution and urinary bladder cancer (UBC) is debatable. We investigated whether long-term exposure to ambient hydrocarbons increases UBC risk among people aged ≥ 20 years in Taiwan. Linkage dataset research with longitudinal design was conducted among 589,135 initially cancer-free individuals during 2000-2013; 12 airborne pollutants were identified. Several Cox models considering potential confounders were employed. The study outcomes were invasive or in situ UBC incidence over time. The targeted pollutant concentration was divided into three tertiles: T1/T2/T3. The mean age of individuals at risk was 42.5 (SD 15.7), and 50.5% of the individuals were men. The mean daily average over 10 years of airborne THC concentration was 2.25 ppm (SD 0.13), and NMHC was 0.29 ppm (SD 0.09). Both pollutants show long-term monotonic downward trend over time using the Mann-Kendall test. There was a dose-dependent increase in UBC at follow-up. UBC incidence per 100,000 enrollees according to T1/T2/T3 exposure to THC was 60.9, 221.2, and 651.8, respectively; it was 170.0/349.5/426.7 per 100,000 enrollees, corresponding to T1/T2/T3 exposure to NMHC, respectively. Without controlling for confounding air pollutants, the adjusted hazard ratio (adj.HR) was 1.83 (95% CI 1.75-1.91) per 0.13-ppm increase in THC; after controlling for PM2.5, adj.HR was even higher at 2.09 (95% CI 1.99-2.19). The adj.HR was 1.37 (95% CI 1.32-1.43) per 0.09-ppm increase in ambient NMHC concentration. After controlling for SO2 and CH4, the adj.HR was 1.10 (95% CI 1.06-1.15). Sensitivity analyses showed that UBC development risk was not sex-specific or influenced by diabetes status. Long-term exposure to THC and NMHC may be a risk factor for UBC development. Acknowledging pollutant sources can inform risk management strategies.

Re-engineering a Clinical Trial Management System Using Blockchain Technology: System Design, Development, and Case Studies.

BACKGROUND: A clinical trial management system (CTMS) is a suite of specialized productivity tools that manage clinical trial processes from study planning to closeout. Using CTMSs has shown remarkable benefits in delivering efficient, auditable, and visualizable clinical trials. However, the current CTMS market is fragmented, and most CTMSs fail to meet expectations because of their inability to support key functions, such as inconsistencies in data captured across multiple sites. Blockchain technology, an emerging distributed ledger technology, is considered to potentially provide a holistic solution to current CTMS challenges by using its unique features, such as transparency, traceability, immutability, and security. OBJECTIVE: This study aimed to re-engineer the traditional CTMS by leveraging the unique properties of blockchain technology to create a secure, auditable, efficient, and generalizable CTMS. METHODS: A comprehensive, blockchain-based CTMS that spans all stages of clinical trials, including a sharable trial master file system; a fast recruitment and simplified enrollment system; a timely, secure, and consistent electronic data capture system; a reproducible data analytics system; and an efficient, traceable payment and reimbursement system, was designed and implemented using the Quorum blockchain. Compared with traditional blockchain technologies, such as Ethereum, Quorum blockchain offers higher transaction throughput and lowers transaction latency. Case studies on each application of the CTMS were conducted to assess the feasibility, scalability, stability, and efficiency of the proposed blockchain-based CTMS. RESULTS: A total of 21.6 million electronic data capture transactions were generated and successfully processed through blockchain, with an average of 335.4 transactions per second. Of the 6000 patients, 1145 were matched in 1.39 seconds using 10 recruitment criteria with an automated matching mechanism implemented by the smart contract. Key features, such as immutability, traceability, and stability, were also tested and empirically proven through case studies. CONCLUSIONS: This study proposed a comprehensive blockchain-based CTMS that covers all stages of the clinical trial process. Compared with our previous research, the proposed system showed an overall better performance. Our system design, implementation, and case studies demonstrated the potential of blockchain technology as a potential solution to CTMS challenges and its ability to perform more health care tasks.

Enhanced Risk of Osteoporotic Fracture in Patients with Sarcopenia: A National Population-Based Study in Taiwan.

Sarcopenia is a progressive and generalized skeletal muscle disorder associated with poor health outcomes in older adults. However, its association with the risk of fracture risk is yet to be clarified. Therefore, this study aimed to assess the incidence and consequence of osteoporosis-related fractures among patients with sarcopenia in Taiwan. A retrospective, population-based study on 616 patients with sarcopenia, aged >40 years, and 1232 individuals without sarcopenia was conducted to evaluate claims data from Taiwan's National Health Insurance Research Database collected in the period January 2000−December 2013. The incidence rate of osteoporosis-related fracture was 18.13 and 14.61 per 1000 person years in the patients with sarcopenia and comparison cohort, respectively. Patients with sarcopenia had a greater osteoporotic fracture risk (adjusted hazard ratio [HR] 2.11; 95% confidence interval [CI] 1.47−3.04) after correcting for possible confounding. Additionally, females showed statistically significant correlations of sarcopenia with osteoporosis-related fracture risk (HR 1.53; CI 0.83−2.8 for males and HR 2.40, CI 1.51−3.81 for females). During this retrospective study on the fracture risk in Taiwan, an adverse impact of sarcopenia was observed, which substantiates the need to work toward sarcopenia prevention and interventions to reverse fracture susceptibility in patients with sarcopenia.

Network subgraph-based approach for analyzing and comparing molecular networks.

Molecular networks are built up from genetic elements that exhibit feedback interactions. Here, we studied the problem of measuring the similarity of directed networks by proposing a novel alignment-free approach: the network subgraph-based approach. Our approach does not make use of randomized networks to determine modular patterns embedded in a network, and this method differs from the network motif and graphlet methods. Network similarity was quantified by gauging the difference between the subgraph frequency distributions of two networks using Jensen-Shannon entropy. We applied the subgraph approach to study three types of molecular networks, i.e., cancer networks, signal transduction networks, and cellular process networks, which exhibit diverse molecular functions. We compared the performance of our subgraph detection algorithm with other algorithms, and the results were consistent, but other algorithms could not address the issue of subgraphs/motifs embedded within a subgraph/motif. To evaluate the effectiveness of the subgraph-based method, we applied the method along with the Jensen-Shannon entropy to classify six network models, and it achieves a 100% accuracy of classification. The proposed information-theoretic approach allows us to determine the structural similarity of two networks regardless of node identity and network size. We demonstrated the effectiveness of the subgraph approach to cluster molecular networks that exhibit similar regulatory interaction topologies. As an illustration, our method can identify (i) common subgraph-mediated signal transduction and/or cellular processes in AML and pancreatic cancer, and (ii) scaffold proteins in gastric cancer and hepatocellular carcinoma; thus, the results suggested that there are common regulation modules for cancer formation. We also found that the underlying substructures of the molecular networks are dominated by irreducible subgraphs; this feature is valid for the three classes of molecular networks we studied. The subgraph-based approach provides a systematic scenario for analyzing, compare and classifying molecular networks with diverse functionalities.

Ethanolic extract of Puhuang (Pollen Typhae) modulates lipopolysaccharide-induced inflammatory response through inducible nitric oxide synthase / cyclooxygenase-2 signaling in RAW 264.7 macrophage.

OBJECTIVE: To evaluate the immune modulatory response of Puhuang (Pollen Typhae), ethanolic extract of dried pollens (TP-E) and charcoal activated pollens (CTP-E) were used for their phytochemical evaluation and their modulatory response against lipopolysaccharide (LPS) induced inflammatory activity on RAW264.7 macrophage cells. METHODS: Biochemical assays were carried out to quantify the 1,1-Diphenyl-2-picrylhydrazyl Radical Scavenging Activity, Reducing Power, Ferrous ion chelating ability and total polyphenol content and flavonoids. Non-toxic dose of the extract (TP-E and CTP-E) was chosen based on 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assay. Effect of TP-E and CTP-E on lipopolysaccharides-induced inducible nitric oxide synthase (iNOS) and cyclooxygenase-2 (COX-2) expression was measured by Western blot and quantitative PCR (qRT-PCR). Expression of inflammatory cytokines, such as interleukins (IL-1ß and IL-6) and tumor necrosis factor α (TNF-α), was quantified using qRT-PCR. Mitogen-activated protein kinase pathway was analyzed using Western blot. RESULTS: Phytochemical analysis revealed that both TP-E and CTP-E have strong antioxidant activities and high flavonoid and phenolic contents. TP-E and CTP-E effectively inhibit the expression of iNOS and COX-2, thereby inhibiting its downstream proinflammatory regulators, the extracellular signal-related kinase-1/2, that decreases the expression of IL-1ß, IL-6 and TNF-α. CONCLUSION: Phytochemical constituents present in Typha angustifolia Linn could be used for treating inflammation-related diseases.

LASSO and Bioinformatics Analysis in the Identification of Key Genes for Prognostic Genes of Gynecologic Cancer.

The aim of this study is to identify potential biomarkers for early diagnosis of gynecologic cancer in order to improve survival. Cervical cancer (CC) and endometrial cancer (EC) are the most common malignant tumors of gynecologic cancer among women in the world. As the underlying molecular mechanisms in both cervical and endometrial cancer remain unclear, a comprehensive and systematic bioinformatics analysis is required. In our study, gene expression profiles of GSE9750, GES7803, GES63514, GES17025, GES115810, and GES36389 downloaded from Gene Expression Omnibus (GEO) were utilized to analyze differential gene expression between cancer and normal tissues. A total of 78 differentially expressed genes (DEGs) common to CC and EC were identified to perform the functional enrichment analyses, including gene ontology and pathway analysis. KEGG pathway analysis of 78 DEGs indicated that three main types of pathway participate in the mechanism of gynecologic cancer such as drug metabolism, signal transduction, and tumorigenesis and development. Furthermore, 20 diagnostic signatures were confirmed using the least absolute shrink and selection operator (LASSO) regression with 10-fold cross validation. Finally, we used the GEPIA2 online tool to verify the expression of 20 genes selected by the LASSO regression model. Among them, the expression of PAMR1 and SLC24A3 in tumor tissues was downregulated significantly compared to the normal tissue, and found to be statistically significant in survival rates between the CC and EC of patients (p < 0.05). The two genes have their function: (1.) PAMR1 is a tumor suppressor gene, and many studies have proven that overexpression of the gene markedly suppresses cell growth, especially in breast cancer and polycystic ovary syndrome; (2.) SLC24A3 is a sodium-calcium regulator of cells, and high SLC24A3 levels are associated with poor prognosis. In our study, the gene signatures can be used to predict CC and EC prognosis, which could provide novel clinical evidence to serve as a potential biomarker for future diagnosis and treatment.

Two-Year-Old Cognitive Outcomes in Children of Pregnant Women With Epilepsy in the Maternal Outcomes and Neurodevelopmental Effects of Antiepileptic Drugs Study.

Importance: The neurodevelopmental risks of fetal exposure are uncertain for many antiseizure medications (ASMs). Objective: To compare children at 2 years of age who were born to women with epilepsy (WWE) vs healthy women and assess the association of maximum ASM exposure in the third trimester and subsequent cognitive abilities among children of WWE. Design, Setting, and Participants: The Maternal Outcomes and Neurodevelopmental Effects of Antiepileptic Drugs (MONEAD) study is a prospective, observational, multicenter investigation of pregnancy outcomes that enrolled women from December 19, 2012, to January 13, 2016, at 20 US epilepsy centers. Children are followed up from birth to 6 years of age, with assessment at 2 years of age for this study. Of 1123 pregnant women assessed, 456 were enrolled; 426 did not meet criteria, and 241 chose not to participate. Data were analyzed from February 20 to December 4, 2020. Main Outcomes and Measures: Language domain score according to the Bayley Scales of Infant and Toddler Development, Third Edition (BSID-III), which incorporates 5 domain scores (language, motor, cognitive, social-emotional, and general adaptive), and association between BSID-III language domain and ASM blood levels in the third trimester in children of WWE. Analyses were adjusted for multiple potential confounding factors, and measures of ASM exposure were assessed. Results: The BSID-III assessments were analyzed in 292 children of WWE (median age, 2.1 [range, 1.9-2.5] years; 155 female [53.1%] and 137 male [46.9%]) and 90 children of healthy women (median age, 2.1 [range, 2.0-2.4] years; 43 female [47.8%] and 47 male [52.2%]). No differences were found between groups on the primary outcome of language domain (-0.5; 95% CI, -4.1 to 3.2). None of the other 4 BSID-III domains differed between children of WWE vs healthy women. Most WWE were taking lamotrigine and/or levetiracetam. Exposure to ASMs in children of WWE showed no association with the language domain. However, secondary analyses revealed that higher maximum observed ASM levels in the third trimester were associated with lower BSID-III scores for the motor domain (-5.6; 95% CI, -10.7 to -0.5), and higher maximum ASM doses in the third trimester were associated with lower scores in the general adaptive domain (-1.4; 95% CI, -2.8 to -0.05). Conclusions and Relevance: Outcomes of children at 2 years of age did not differ between children of WWE taking ASMs and children of healthy women. Trial Registration: ClinicalTrials.gov Identifier: NCT01730170.

A novel miRNA-based classification model of risks and stages for clear cell renal cell carcinoma patients.

BACKGROUND: Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal carcinoma and patients at advanced stage showed poor survival rate. Despite microRNAs (miRNAs) are used as potential biomarkers in many cancers, miRNA biomarkers for predicting the tumor stage of ccRCC are still limitedly identified. Therefore, we proposed a new integrated machine learning (ML) strategy to identify a novel miRNA signature related to tumor stage and prognosis of ccRCC patients using miRNA expression profiles. A multivariate Cox regression model with three hybrid penalties including Least absolute shrinkage and selection operator (Lasso), Adaptive lasso and Elastic net algorithms was used to screen relevant prognostic related miRNAs. The best subset regression (BSR) model was used to identify optimal prognostic model. Five ML algorithms were used to develop stage classification models. The biological significance of the miRNA signature was analyzed by utilizing DIANA-mirPath. RESULTS: A four-miRNA signature associated with survival was identified and the expression of this signature was strongly correlated with high risk patients. The high risk patients had unfavorable overall survival compared with the low risk group (HR = 4.523, P-value = 2.86e-08). Univariate and multivariate analyses confirmed independent and translational value of this predictive model. A combined ML algorithm identified six miRNA signatures for cancer staging prediction. After using the data balancing algorithm SMOTE, the Support Vector Machine (SVM) algorithm achieved the best classification performance (accuracy = 0.923, sensitivity = 0.927, specificity = 0.919, MCC = 0.843) when compared with other classifiers. Furthermore, enrichment analysis indicated that the identified miRNA signature involved in cancer-associated pathways. CONCLUSIONS: A novel miRNA classification model using the identified prognostic and tumor stage associated miRNA signature will be useful for risk and stage stratification for clinical practice, and the identified miRNA signature can provide promising insight to understand the progression mechanism of ccRCC.

Construction and Validation of a Prognostic Gene-Based Model for Overall Survival Prediction in Hepatocellular Carcinoma Using an Integrated Statistical and Bioinformatic Approach.

Hepatocellular carcinoma (HCC) is one of the most common lethal cancers worldwide and is often related to late diagnosis and poor survival outcome. More evidence is demonstrating that gene-based prognostic models can be used to predict high-risk HCC patients. Therefore, our study aimed to construct a novel prognostic model for predicting the prognosis of HCC patients. We used multivariate Cox regression model with three hybrid penalties approach including least absolute shrinkage and selection operator (Lasso), adaptive lasso and elastic net algorithms for informative prognostic-related genes selection. Then, the best subset regression was used to identify the best prognostic gene signature. The prognostic gene-based risk score was constructed using the Cox coefficient of the prognostic gene signature. The model was evaluated by Kaplan-Meier (KM) and receiver operating characteristic curve (ROC) analyses. A novel four-gene signature associated with prognosis was identified and the risk score was constructed based on the four-gene signature. The risk score efficiently distinguished the patients into a high-risk group with poor prognosis. The time-dependent ROC analysis revealed that the risk model had a good performance with an area under the curve (AUC) of 0.780, 0.732, 0.733 in 1-, 2- and 3-year prognosis prediction in The Cancer Genome Atlas (TCGA) dataset. Moreover, the risk score revealed a high diagnostic performance to classify HCC from normal samples. The prognosis and diagnosis prediction performances of risk scores were verified in external validation datasets. Functional enrichment analysis of the four-gene signature and its co-expressed genes involved in the metabolic and cell cycle pathways was constructed. Overall, we developed a novel-gene-based prognostic model to predict high-risk HCC patients and we hope that our findings can provide promising insight to explore the role of the four-gene signature in HCC patients and aid risk classification.

Dissecting molecular network structures using a network subgraph approach.

Biological processes are based on molecular networks, which exhibit biological functions through interactions of genetic elements or proteins. This study presents a graph-based method to characterize molecular networks by decomposing the networks into directed multigraphs: network subgraphs. Spectral graph theory, reciprocity and complexity measures were used to quantify the network subgraphs. Graph energy, reciprocity and cyclomatic complexity can optimally specify network subgraphs with some degree of degeneracy. Seventy-one molecular networks were analyzed from three network types: cancer networks, signal transduction networks, and cellular processes. Molecular networks are built from a finite number of subgraph patterns and subgraphs with large graph energies are not present, which implies a graph energy cutoff. In addition, certain subgraph patterns are absent from the three network types. Thus, the Shannon entropy of the subgraph frequency distribution is not maximal. Furthermore, frequently-observed subgraphs are irreducible graphs. These novel findings warrant further investigation and may lead to important applications. Finally, we observed that cancer-related cellular processes are enriched with subgraph-associated driver genes. Our study provides a systematic approach for dissecting biological networks and supports the conclusion that there are organizational principles underlying molecular networks.

Identification of most influential co-occurring gene suites for gastrointestinal cancer using biomedical literature mining and graph-based influence maximization.

BACKGROUND: Gastrointestinal (GI) cancer including colorectal cancer, gastric cancer, pancreatic cancer, etc., are among the most frequent malignancies diagnosed annually and represent a major public health problem worldwide. METHODS: This paper reports an aided curation pipeline to identify potential influential genes for gastrointestinal cancer. The curation pipeline integrates biomedical literature to identify named entities by Bi-LSTM-CNN-CRF methods. The entities and their associations can be used to construct a graph, and from which we can compute the sets of co-occurring genes that are the most influential based on an influence maximization algorithm. RESULTS: The sets of co-occurring genes that are the most influential that we discover include RARA - CRBP1, CASP3 - BCL2, BCL2 - CASP3 - CRBP1, RARA - CASP3 - CRBP1, FOXJ1 - RASSF3 - ESR1, FOXJ1 - RASSF1A - ESR1, FOXJ1 - RASSF1A - TNFAIP8 - ESR1. With TCGA and functional and pathway enrichment analysis, we prove the proposed approach works well in the context of gastrointestinal cancer. CONCLUSIONS: Our pipeline that uses text mining to identify objects and relationships to construct a graph and uses graph-based influence maximization to discover the most influential co-occurring genes presents a viable direction to assist knowledge discovery for clinical applications.

Identification of Hub Genes Associated With Development of Head and Neck Squamous Cell Carcinoma by Integrated Bioinformatics Analysis.

Improved insight into the molecular mechanisms of head and neck squamous cell carcinoma (HNSCC) is required to predict prognosis and develop a new therapeutic strategy for targeted genes. The aim of this study is to identify significant genes associated with HNSCC and to further analyze its prognostic significance. In our study, the cancer genome atlas (TCGA) HNSCC database and the gene expression profiles of GSE6631 from the Gene Expression Omnibus (GEO) were used to explore the differential co-expression genes in HNSCC compared with normal tissues. A total of 29 differential co-expression genes were screened out by Weighted Gene Co-expression Network Analysis (WGCNA) and differential gene expression analysis methods. As suggested in functional annotation analysis using the R clusterProfiler package, these genes were mainly enriched in epidermis development and differentiation (biological process), apical plasma membrane and cell-cell junction (cellular component), and enzyme inhibitor activity (molecular function). Furthermore, in a protein-protein interaction (PPI) network containing 21 nodes and 25 edges, the ten hub genes (S100A8, S100A9, IL1RN, CSTA, ANXA1, KRT4, TGM3, SCEL, PPL, and PSCA) were identified using the CytoHubba plugin of Cytoscape. The expression of the ten hub genes were all downregulated in HNSCC tissues compared with normal tissues. Based on survival analysis, the lower expression of CSTA was associated with worse overall survival (OS) in patients with HNSCC. Finally, the protein level of CSTA, which was validated by the Human Protein Atlas (HPA) database, was down-regulated consistently with mRNA levels in head and neck cancer samples. In summary, our study demonstrated that a survival-related gene is highly correlated with head and neck cancer development. Thus, CSTA may play important roles in the progression of head and neck cancer and serve as a potential biomarker for future diagnosis and treatment.

A Patient-Centric Health Information Exchange Framework Using Blockchain Technology.

Health Information Exchange (HIE) exhibits remarkable benefits for patient care such as improving healthcare quality and expediting coordinated care. The Office of the National Coordinator (ONC) for Health Information Technology is seeking patient-centric HIE designs that shift data ownership from providers to patients. There are multiple barriers to patient-centric HIE in the current system, such as security and privacy concerns, data inconsistency, timely access to the right records across multiple healthcare facilities. After investigating the current workflow of HIE, this paper provides a feasible solution to these challenges by utilizing the unique features of blockchain, a distributed ledger technology which is considered "unhackable". Utilizing the smart contract feature, which is a programmable self-executing protocol running on a blockchain, we developed a blockchain model to protect data security and patients' privacy, ensure data provenance, and provide patients full control of their health records. By personalizing data segmentation and an "allowed list" for clinicians to access their data, this design achieves patient-centric HIE. We conducted a large-scale simulation of this patient-centric HIE process and quantitatively evaluated the model's feasibility, stability, security, and robustness.

Text mining in a literature review of urothelial cancer using topic model.

BACKGROUND: Urothelial cancer (UC) includes carcinomas of the bladder, ureters, and renal pelvis. New treatments and biomarkers of UC emerged in this decade. To identify the key information in a vast amount of literature can be challenging. In this study, we use text mining to explore UC publications to identify important information that may lead to new research directions. METHOD: We used topic modeling to analyze the titles and abstracts of 29,883 articles of UC from Pubmed, Web of Science, and Embase in Mar 2020. We applied latent Dirichlet allocation modeling to extract 15 topics and conducted trend analysis. Gene ontology term enrichment analysis and Kyoto encyclopedia of genes and genomes pathway analysis were performed to identify UC related pathways. RESULTS: There was a growing trend regarding UC treatment especially immune checkpoint therapy but not the staging of UC. The risk factors of UC carried in different countries such as cigarette smoking in the United State and aristolochic acid in Taiwan and China. GMCSF, IL-5, Syndecan-1, ErbB receptor, integrin, c-Met, and TRAIL signaling pathways are the most relevant biological pathway associated with UC. CONCLUSIONS: The risk factors of UC may be dependent on the countries and GMCSF, IL-5, Syndecan-1, ErbB receptor, integrin, c-Met, and TRAIL signaling pathways are the most relevant biological pathway associated with UC. These findings may provide further UC research directions.

Development of A Blockchain Framework for Virtual Clinical Trials.

Clinical trials are essential for discovering new treatments, but there are multiple challenges to patient recruitment, patient engagement, and cost containment. Virtual clinical trials (VCT) are an innovative approach that provides potential solutions by conducting home-based, rather than site-based, clinical trials. Virtual clinical trials are still the exception rather than general practice due to technical barriers. "Blockchain," a distributed ledger technology, is a perfect match for virtual clinical trials. Its peer-to-peer design, security settings, and data transparency meet the needs of many healthcare applications. The programmable "Smart Contract" feature makes blockchain more suitable and feasible for VCT by solving computational issues. Our previous work has shown the power of applying blockchain to clinical trial recruitment. This work develops a comprehensive blockchain framework, with simulations and case studies, including patient recruitment, patient engagement, and persistent monitoring modules.