Multilingual Framework for Risk Assessment and Symptom Tracking (MRAST).

The importance and value of real-world data in healthcare cannot be overstated because it offers a valuable source of insights into patient experiences. Traditional patient-reported experience and outcomes measures (PREMs/PROMs) often fall short in addressing the complexities of these experiences due to subjectivity and their inability to precisely target the questions asked. In contrast, diary recordings offer a promising solution. They can provide a comprehensive picture of psychological well-being, encompassing both psychological and physiological symptoms. This study explores how using advanced digital technologies, i.e., automatic speech recognition and natural language processing, can efficiently capture patient insights in oncology settings. We introduce the MRAST framework, a simplified way to collect, structure, and understand patient data using questionnaires and diary recordings. The framework was validated in a prospective study with 81 colorectal and 85 breast cancer survivors, of whom 37 were male and 129 were female. Overall, the patients evaluated the solution as well made; they found it easy to use and integrate into their daily routine. The majority (75.3%) of the cancer survivors participating in the study were willing to engage in health monitoring activities using digital wearable devices daily for an extended period. Throughout the study, there was a noticeable increase in the number of participants who perceived the system as having excellent usability. Despite some negative feedback, 44.44% of patients still rated the app's usability as above satisfactory (i.e., 7.9 on 1-10 scale) and the experience with diary recording as above satisfactory (i.e., 7.0 on 1-10 scale). Overall, these findings also underscore the significance of user testing and continuous improvement in enhancing the usability and user acceptance of solutions like the MRAST framework. Overall, the automated extraction of information from diaries represents a pivotal step toward a more patient-centered approach, where healthcare decisions are based on real-world experiences and tailored to individual needs. The potential usefulness of such data is enormous, as it enables better measurement of everyday experiences and opens new avenues for patient-centered care.

Turning Digital Trails into a Telehealth Competitive Edge.

Digital trails, data collections of individuals' traceable digital activities online or on digital devices, have been utilized by many industries to provide valuable insights to enhance customer experience, improve operation efficiency, and increase revenues. Despite the abundance of digital trails among health care data, health care has lagged behind other industries in extracting their values. Recently, telehealth's accelerated adoption due to the COVID-19 pandemic provides an unprecedented opportunity for health care providers to take advantage of digital trails. In this study, we describe digital trails generated from the telehealth workflow and discuss a few use cases to demonstrate how telehealth digital trails can be used to improve clinical service quality, streamline patient care workflow, and enhance the patient experience.

Graph embedding on biomedical networks: methods, applications and evaluations.

MOTIVATION: Graph embedding learning that aims to automatically learn low-dimensional node representations, has drawn increasing attention in recent years. To date, most recent graph embedding methods are evaluated on social and information networks and are not comprehensively studied on biomedical networks under systematic experiments and analyses. On the other hand, for a variety of biomedical network analysis tasks, traditional techniques such as matrix factorization (which can be seen as a type of graph embedding methods) have shown promising results, and hence there is a need to systematically evaluate the more recent graph embedding methods (e.g. random walk-based and neural network-based) in terms of their usability and potential to further the state-of-the-art. RESULTS: We select 11 representative graph embedding methods and conduct a systematic comparison on 3 important biomedical link prediction tasks: drug-disease association (DDA) prediction, drug-drug interaction (DDI) prediction, protein-protein interaction (PPI) prediction; and 2 node classification tasks: medical term semantic type classification, protein function prediction. Our experimental results demonstrate that the recent graph embedding methods achieve promising results and deserve more attention in the future biomedical graph analysis. Compared with three state-of-the-art methods for DDAs, DDIs and protein function predictions, the recent graph embedding methods achieve competitive performance without using any biological features and the learned embeddings can be treated as complementary representations for the biological features. By summarizing the experimental results, we provide general guidelines for properly selecting graph embedding methods and setting their hyper-parameters for different biomedical tasks. AVAILABILITY AND IMPLEMENTATION: As part of our contributions in the paper, we develop an easy-to-use Python package with detailed instructions, BioNEV, available at: https://github.com/xiangyue9607/BioNEV, including all source code and datasets, to facilitate studying various graph embedding methods on biomedical tasks. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Patients-centered SurvivorShIp care plan after Cancer treatments based on Big Data and Artificial Intelligence technologies (PERSIST): a multicenter study protocol to evaluate efficacy of digital tools supporting cancer survivors.

BACKGROUND: It is encouraging to see a substantial increase in individuals surviving cancer. Even more so since most of them will have a positive effect on society by returning to work. However, many cancer survivors have unmet needs, especially when it comes to improving their quality of life (QoL). Only few survivors are able to meet all of the recommendations regarding well-being and there is a body of evidence that cancer survivors' needs often remain neglected from health policy and national cancer control plans. This increases the impact of inequalities in cancer care and adds a dangerous component to it. The inequalities affect the individual survivor, their career, along with their relatives and society as a whole. The current study will evaluate the impact of the use of big data analytics and artificial intelligence on the self-efficacy of participants following intervention supported by digital tools. The secondary endpoints include evaluation of the impact of patient trajectories (from retrospective data) and patient gathered health data on prediction and improved intervention against possible secondary disease or negative outcomes (e.g. late toxicities, fatal events). METHODS/DESIGN: The study is designed as a single-case experimental prospective study where each individual serves as its own control group with basal measurements obtained at the recruitment and subsequent measurements performed every 6 months during follow ups. The measurement will involve CASE-cancer, Patient Activation Measure and System Usability Scale. The study will involve 160 survivors (80 survivors of Breast Cancer and 80 survivors of Colorectal Cancer) from four countries, Belgium, Latvia, Slovenia, and Spain. The intervention will be implemented via a digital tool (mHealthApplication), collecting objective biomarkers (vital signs) and subjective biomarkers (PROs) with the support of a (embodied) conversational agent. Additionally, the Clinical Decision Support system (CDSS), including visualization of cohorts and trajectories will enable oncologists to personalize treatment for an efficient care plan and follow-up management. DISCUSSION: We expect that cancer survivors will significantly increase their self-efficacy following the personalized intervention supported by the m-HealthApplication compared to control measurements at recruitment. We expect to observe improvement in healthy habits, disease self-management and self-perceived QoL. Trial registration ISRCTN97617326. https://doi.org/10.1186/ISRCTN97617326 . Original Registration Date: 26/03/2021.

Documented Reasons of Cancellation and Rescheduling of Telehealth Appointments During the Pandemic.

Background and Objective: The COVID-19 pandemic increased the use of telehealth around the world. The aim is to minimize health care service disruption as well as reducing COVID-19 exposure. However, one of the major operational concerns is cancellations and rescheduling (C/Rs). C/Rs may create additional burden and cost to the patient, provider, and the health system. Our aim is to understand the reasons for C/Rs of the telehealth session after the scheduled start time. Materials and Methods: We reviewed electronic health records (EHRs) to identify the C/R reasons for behavioral health and speech language pathology departments. Documented C/Rs in the medical charts were identified from EHR by using a keyword-based and Natural Language Processing (NLP)-supported EHR search engine. From the search results, we randomly selected 200 notes and conducted a thematic analysis. Results: We identified four themes explaining C/R reasons. Most frequent theme was "technicality" (47, 36%), followed by "engagement" (34, 25%), "scheduling" (31, 24%), and "unspecified" (20, 15%). The findings showed that technical reasons are the leading cause of C/Rs, constituting 36% of the cases (95% confidence interval [CI]: 29-43%). Notably, "engagement" constituted a sizeable 25% (95% CI: 19-31%) of C/Rs, as a result of the inability to engage a patient to complete the telehealth session. Conclusions: The study shows that engagement is one of the new challenges to the pediatric telehealth visits. Future studies of new engagement models are needed for the success of telehealth. Our findings will help fill the literature gaps and may help with enhancing the digital experience for both caregivers and providers, reducing wasted time and resources due to preventable C/Rs, improving clinical operation efficiency, and treatment adherence.

Inhibition of Sphingosine-1-Phosphate Receptor 2 by JTE013 Promoted Osteogenesis by Increasing Vesicle Trafficking, Wnt/Ca2+, and BMP/Smad Signaling.

Sphingosine-1-phosphate receptor 2 (S1PR2) is a G protein-coupled receptor that regulates various immune responses. Herein, we determine the effects of a S1PR2 antagonist (JTE013) or a S1PR2 shRNA on osteogenesis by culturing murine bone marrow stromal cells (BMSCs) in osteogenic media with JTE013, dimethylsulfoxide (DMSO), a S1PR2 shRNA, or a control shRNA. Treatment with JTE013 or the S1PR2 shRNA increased alkaline phosphatase and alizarin red s staining, and enhanced alkaline phosphatase, RUNX2, osteocalcin, and osterix mRNA levels in BMSCs compared with the controls. Protein analysis revealed that a high dose of JTE013 (4 or 8 µM) increased vesicle trafficking-associated proteins (F-actin, clathrin, Early Endosome Antigen 1 (EEA1), and syntaxin 6) and Wnt/Ca2+ signaling. On the other hand, a low dose of JTE013 (1 to 2 µM) increased BMP/Smad signaling. In contrast, the S1PR2 shRNA reduced vesicle trafficking-associated proteins and attenuated Wnts and BMP/Smad signaling, but enhanced p-CaMKII compared with the control, suggesting that the S1PR2 shRNA influenced osteogenesis via different signaling pathways. Moreover, inhibiting protein trafficking by brefeldin A in BMSCs suppressed Wnts and BMPRs expressions. These data supported that enhanced osteogenesis in JTE013-treated BMSCs is associated with increased vesicle trafficking, which promotes the synthesis and transport of osteogenic protein and matrix vesicles and enhances matrix mineralization.

Pyridine alkaloids with activity in the central nervous system.

This review discusses all pyridine alkaloids with CNS activity, their therapeutic potential, and the interesting array of sources whence they originate.

Capturing At-Home Health and Care Information for Children With Medical Complexity Using Voice Interactive Technologies: Multi-Stakeholder Viewpoint.

Digital health tools and technologies are transforming health care and making significant impacts on how health and care information are collected, used, and shared to achieve best outcomes. As most of the efforts are still focused on clinical settings, the wealth of health information generated outside of clinical settings is not being fully tapped. This is especially true for children with medical complexity (CMC) and their families, as they frequently spend significant hours providing hands-on medical care within the home setting and coordinating activities among multiple providers and other caregivers. In this paper, a multidisciplinary team of stakeholders discusses the value of health information generated at home, how technology can enhance care coordination, and challenges of technology adoption from a patient-centered perspective. Voice interactive technology has been identified to have the potential to transform care coordination for CMC. This paper shares opinions on the promises, limitations, recommended approaches, and challenges of adopting voice technology in health care, especially for the targeted patient population of CMC.

Mapping out bare-nosed wombat (Vombatus ursinus) burrows with the use of a drone.

BACKGROUND: Wombats are large, nocturnal herbivores that build burrows in a variety of habitats, including grassland communities, and can come into conflict with people. Counting the number of active burrows provides information on the local distribution and abundance of wombats and could prove to be an important management tool to monitor population numbers over time. We compared traditional ground surveys and a new method employing drones, to determine if drones could be used to effectively identify and monitor bare-nosed wombat burrows. RESULTS: We surveyed burrows using both methods in eight 5-ha transects in grassland, that was interspersed with patches of tussock grassland. Ground surveys were conducted by systematically walking transects and searching for burrows. Drone surveys involved programming flights over transects to capture multiple images, from which an orthomosaic image of each transect was produced. These were subsequently viewed using ArcMap to detect burrows. A total of 204 individual burrows were recorded by drone and/or ground survey methods. In grassland, the methods were equally effective in terms of the numbers of burrows detected in transects. In the smaller areas of tussock grassland, ground surveys detected significantly more burrows, because burrow openings were obscured in orthomosaic images by overhanging grasses. There was agreement between the methods as to whether burrows were potentially active or inactive for most burrows in both vegetation communities. However, image interpretation tended to classify grassland burrows as potentially active. Overall time taken to conduct surveys was similar for both methods, but ground surveys utilised three observers and more time in the field. CONCLUSIONS: Drones provide an effective means to survey bare-nosed wombat burrows that are visible from the air, particularly in areas not accessible to observers and vehicles. Furthermore, drones provide alternative options for monitoring burrows at the landscape level, and for monitoring wombat populations based on observable changes in burrow appearance over time.

Transcending Dimensions: a Comparative Analysis of Cloaca Imaging in Advancing the Surgeon's Understanding of Complex Anatomy.

Surgeons have a steep learning capacity to understand 2-D images provided by conventional cloacagrams. Imaging advances now allow for 3-D reconstruction and 3-D models; but no evaluation of the value of these techniques exists in the literature. Therefore, we sought to determine if advances in 3-D imaging would benefit surgeons, lead to accelerated learning, and improve understanding for operative planning of a cloaca reconstruction. Questionnaires were used to assess the understanding of 2-D and 3-D images by pediatric surgical faculty and trainees. For the same case of a cloacal malformation, a 2D contrast study cloacagram, a 3D model rotatable CT scan reconstruction, a software enhanced 3D video animation (which allowed the observer to manipulate the structure in any orientation), and a printed physical 3D cloaca model that could be held in the observer's hand were employed. Logistic mixed effect models assessed whether the proportion of questions about the case that were answered correctly differed by imaging modality, and whether the proportion answered correctly differed between trainee and attending surgeons for any particular modality. Twenty-nine pediatric surgery trainees (27 pediatric general surgery and 2 pediatric urology surgery trainees) and 30 pediatric surgery and urology faculty participated. For trainees, the percentage of questions answered correctly was: 2-D 10.5%, 3-D PACS 46.7%, 3-D Enhanced 67.1%, and 3-D Printed 73.8%. For faculty, the total percentage of questions answered correctly was: 2-D 22.2%, 3-D PACS 54.8%, 3D Enhanced 66.2%, and 3-D printed 74.0%. The differences in rates of correctness across all four modalities were significant in both fellows and attendings (p < 0.001), with performance being lowest for the 2-D modality, and with increasing percentage of correct answers with each subsequent modality. The difference between trainees and attendings in correctness rate was significant only for the 2-D modality, with attendings answering correctly more often. The 2-D cloacagram, as the least complex model, was the most difficult to interpret. The more complex the modality, the more correct were the responses obtained from both groups. Trainees and attendings had similar levels of correct answers and understanding of the cloacagram for the more advanced modalities. Mental visualization skills of anatomy and complex 3-D spatial arrangements traditionally have taken years of experience to master. Now with novel surgical education resources of a 3-D cloacagram, a more quickly advancing skill is possible.

Comorbidities in Childhood Celiac Disease: A Phenome Wide Association Study Using the Electronic Health Record.

OBJECTIVES: Celiac disease (CD) is associated with a variety of extraintestinal autoimmune and inflammatory findings that manifest clinically as symptoms and comorbidities. Understanding these comorbidities may improve identification of the disease and prevent sequelae. In this study, we use an unbiased electronic health record (EHR)-based Phenome Wide Association Study (PheWAS) method to confirm known comorbidities, discover novel associations and enhance characterization of the clinical presentation of CD in children. METHODS: Data were extracted from the Nationwide Children's Hospital EHR. Confirmed CD cases (n = 433) were matched with 4330 randomly selected controls. Utilizing an EHR-based PheWAS method to analyze associations of phenotypes with CD, we conducted an unbiased screening of all International Classification of Diseases, 10th revision diagnostic codes and examined significance by performing Fisher's Exact tests. We further tested for the association between CD and 14 previously identified comorbidities in an a priori fashion. RESULTS: We found 45 International Classification of Diseases, 10th revision codes significantly associated with CD. Thirteen are known comorbidities and nine are expected symptoms of CD, thus validating our study methods. Further investigation found symptoms that characterized CD clinically and discovered a significant association between eosinophilic disorders of the esophagus and CD. Of 14 previously identified comorbidities, 8 were significantly associated with CD. CONCLUSIONS: An EHR-based PheWAS method is a powerful, efficient, and cost-effective method to screen for possible CD comorbidities and validate associations at the population level. Ours is the first PheWAS of CD to confirm a significant association of eosinophilic disorders of the esophagus with CD in a controlled study.

Proposing an Ecosystem of Digital Health Solutions for Teens With Chronic Conditions Transitioning to Self-Management and Independence: Exploratory Qualitative Study.

BACKGROUND: Chronic disease management is critical to quality of life for both teen patients with chronic conditions and their caregivers. However, current literature is largely limited to a specific digital health tool, method, or approach to manage a specific disease. Guiding principles on how to use digital tools to support the transition to independence are rare. Considering the physiological, psychological, and environmental changes that teens experience, the issues surrounding the transition to independence are worth investigating to develop a deeper understanding to inform future strategies for digital interventions. OBJECTIVE: The purpose of this study was to inform the design of digital health solutions by systematically identifying common challenges among teens and caregivers living with chronic diseases. METHODS: Chronically ill teens (n=13) and their caregivers (n=13) were interviewed individually and together as a team. Verbal and projective techniques were used to examine teens' and caregivers' concerns in-depth. The recorded and transcribed responses were thematically analyzed to identify and organize the identified patterns. RESULTS: Teens and their caregivers identified 10 challenges and suggested technological solutions. Recognized needs for social support, access to medical education, symptom monitoring, access to health care providers, and medical supply management were the predominant issues. The envisioned ideal transition included a 5-component solution ecosystem in the transition to independence for teens. CONCLUSIONS: This novel study systematically summarizes the challenges, barriers, and technological solutions for teens with chronic conditions and their caregivers as teens transition to independence. A new solution ecosystem based on the 10 identified challenges would guide the design of future implementations to test and validate the effectiveness of the proposed 5-component ecosystem.

Simplified Readability Metric Drives Improvement of Radiology Reports: an Experiment on Ultrasound Reports at a Pediatric Hospital.

Highly complex medical documents, including ultrasound reports, are greatly mismatched with patient literacy levels. While improving radiology reports for readability is a longstanding concern, few articles objectively measure the effectiveness of physician training for readability improvement. We hypothesized that writing styles may be evaluated using an objective two-dimensional measure and writing training could improve the writing styles of radiologists. To test it, a simplified "grade vs. length" readability metric is developed based on results from factor analysis of ten readability metrics applied to more than 500,000 radiology reports. To test the short-term effectiveness of a writing workshop, we measured the writing style improvement before and after the training. Statistically significant writing style improvement occurred as a result of the training. Although the degree of improvement varied for different measures, it is evident that targeted training could provide potential benefits to improve readability due to our statistically significant results. The simplified grade vs. length metric enables future clinical decision support systems to quantitatively guide physicians to improve writing styles through writing workshops.

Application of clinical text data for phenome-wide association studies (PheWASs).

MOTIVATION: Genome-wide association studies (GWASs) are effective for describing genetic complexities of common diseases. Phenome-wide association studies (PheWASs) offer an alternative and complementary approach to GWAS using data embedded in the electronic health record (EHR) to define the phenome. International Classification of Disease version 9 (ICD9) codes are used frequently to define the phenome, but using ICD9 codes alone misses other clinically relevant information from the EHR that can be used for PheWAS analyses and discovery. RESULTS: As an alternative to ICD9 coding, a text-based phenome was defined by 23 384 clinically relevant terms extracted from Marshfield Clinic's EHR. Five single nucleotide polymorphisms (SNPs) with known phenotypic associations were genotyped in 4235 individuals and associated across the text-based phenome. All five SNPs genotyped were associated with expected terms (P<0.02), most at or near the top of their respective PheWAS ranking. Raw association results indicate that text data performed equivalently to ICD9 coding and demonstrate the utility of information beyond ICD9 coding for application in PheWAS.

Patient-centered design criteria for wearable seizure detection devices.

INTRODUCTION: Epilepsy is a common neurological condition. Seizure diary reports and patient- or caregiver-reported seizure counts are often inaccurate and underestimated. Many caregivers express stress and anxiety about the patient with epilepsy having seizures when they are not present. Therefore, a need exists for the ability to recognize and/or detect a seizure in the home setting. However, few studies have inquired on detection device features that are important to patients and their caregivers. METHODS: A survey instrument utilizing a population of patients and caregivers was created to obtain information on the design criteria most desired for patients with epilepsy in regard to wearable devices. RESULTS: One thousand one hundred sixty-eight responses were collected. Respondents thought that sensors for muscle signal (61.4%) and heart rate (58.0%) would be most helpful followed by the O2 sensor (41.4%). There was more interest in these three sensor types than for an accelerometer (25.5%). There was very little interest in a microphone (8.9%), galvanic skin response sensor (8.0%), or a barometer (4.9%). Based on a rating scale of 1-5 with 5 being the most important, respondents felt that "detecting all seizures" (4.73) is the most important device feature followed by "text/email alerts" (4.53), "comfort" (4.46), and "battery life" (4.43) as an equally important group of features. Respondents felt that "not knowing device is for seizures" (2.60) and "multiple uses" (2.57) were equally the least important device features. Average ratings differed significantly across age groups for the following features: button, multiuse, not knowing device is for seizures, alarm, style, and text ability. The p-values were all<0.002. Eighty-two point five percent of respondents [95% confidence interval: 80.0%, 84.7%] were willing to pay more than $100 for a wearable seizure detection device, and 42.8% of respondents [95% confidence interval: 39.8%, 45.9%] were willing to pay more than $200. CONCLUSIONS: Our survey results demonstrated that patients and caregivers have design features that are important to them in regard to a wearable seizure detection device. Overall, the ability to detect all seizures rated highest among respondents which continues to be an unmet need in the community with epilepsy in regard to seizure detection. Additional uses for a wearable were not as important. Based on our results, it is important that an alert (via test and/or email) for events be a portion of the system. A reasonable price point appears to be around $200 to $300. An accelerometer was less important to those surveyed when compared with the use of heart rate, oxygen saturation, or muscle twitches/signals. As further products become developed for use in other health arenas, it will be important to consider patient and caregiver desires in order to meet the need and address the gap in devices that currently exist.

SeqHBase: a big data toolset for family based sequencing data analysis.

BACKGROUND: Whole-genome sequencing (WGS) and whole-exome sequencing (WES) technologies are increasingly used to identify disease-contributing mutations in human genomic studies. It can be a significant challenge to process such data, especially when a large family or cohort is sequenced. Our objective was to develop a big data toolset to efficiently manipulate genome-wide variants, functional annotations and coverage, together with conducting family based sequencing data analysis. METHODS: Hadoop is a framework for reliable, scalable, distributed processing of large data sets using MapReduce programming models. Based on Hadoop and HBase, we developed SeqHBase, a big data-based toolset for analysing family based sequencing data to detect de novo, inherited homozygous, or compound heterozygous mutations that may contribute to disease manifestations. SeqHBase takes as input BAM files (for coverage at every site), variant call format (VCF) files (for variant calls) and functional annotations (for variant prioritisation). RESULTS: We applied SeqHBase to a 5-member nuclear family and a 10-member 3-generation family with WGS data, as well as a 4-member nuclear family with WES data. Analysis times were almost linearly scalable with number of data nodes. With 20 data nodes, SeqHBase took about 5 secs to analyse WES familial data and approximately 1 min to analyse WGS familial data. CONCLUSIONS: These results demonstrate SeqHBase's high efficiency and scalability, which is necessary as WGS and WES are rapidly becoming standard methods to study the genetics of familial disorders.

Associations of blood mercury, inorganic mercury, methyl mercury and bisphenol A with dental surface restorations in the U.S. population, NHANES 2003-2004 and 2010-2012.

The potential adverse health effects of mercury from amalgam and bisphenol A (BPA) from composite resin have been significant concerns. It is unclear whether dental restorative materials significantly contribute to mercury or BPA levels. The purpose of this study is to use NHANES data including 14,703 subjects (2003-2004: n=7514; 2011-2012: n=7189) to examine the association between Dental Surface Restorations (DSR) and blood total mercury (THg), inorganic mercury (IHg), methyl mercury (MeHg) and urinary BPA through the stratification of covariates and multivariate analysis. Subjects were divided into three groups based on the number of dental surface restorations (DSRs, 0, 1-8, >8). Blood THg and IHg in 2003-2004 were significantly higher in the subjects with DSR (geometric mean of 0.48, 0.69 and 1.17µg/l for THg; 0.32, 0.33 and 0.39µg/l for IHg with DSR 0, 1-8 and >8). Similarly, increases of THg, IHg and MeHg were also observed in 2013-2014 (geometric mean of 0.51, 0.69 and 0.99µg/l for THg; 0.40, 0.49 and 0.66µg/l for MeHg; 0.20, 0.22 and 0.29µg/l for IHg with DSR 0, 1-8 and >8). Linear regression model analysis revealed blood THg and IHg in 2003-2004 and THg, IHg and MeHg in 2011-2012 were quantitatively associated with the number of DSRs. A dramatic decrease in urinary BPA from 2003 to 2004-2011-2012 was observed, but no significant increase with DSRs in either period of study. In conclusion, significant increases in blood THg, IHg, and MeHg in the subjects with DSRs are confirmed in a nationally representative population, a critical step in assessing the potential risk of adverse effects from dental restorative materials, but no association between dental fillings and urinary BPA was found.

The Disease and Gene Annotations (DGA): an annotation resource for human disease.

Disease and Gene Annotations database (DGA, http://dga.nubic.northwestern.edu) is a collaborative effort aiming to provide a comprehensive and integrative annotation of the human genes in disease network context by integrating computable controlled vocabulary of the Disease Ontology (DO version 3 revision 2510, which has 8043 inherited, developmental and acquired human diseases), NCBI Gene Reference Into Function (GeneRIF) and molecular interaction network (MIN). DGA integrates these resources together using semantic mappings to build an integrative set of disease-to-gene and gene-to-gene relationships with excellent coverage based on current knowledge. DGA is kept current by periodically reparsing DO, GeneRIF, and MINs. DGA provides a user-friendly and interactive web interface system enabling users to efficiently query, download and visualize the DO tree structure and annotations as a tree, a network graph or a tabular list. To facilitate integrative analysis, DGA provides a web service Application Programming Interface for integration with external analytic tools.

Meiosis-induced alterations in transcript architecture and noncoding RNA expression in S. cerevisiae.

Changes in transcript architecture can have powerful effects on protein expression. Regulation of the transcriptome is often dramatically revealed during dynamic conditions such as development. To examine changes in transcript architecture we analyzed the expression and transcript boundaries of protein-coding and noncoding RNAs over the developmental process of meiosis in Saccharomyces cerevisiae. Custom-designed, high-resolution tiling arrays were used to define the time-resolved transcriptome of cells undergoing meiosis and sporulation. These arrays were specifically designed for the S. cerevisiae strain SK1 that sporulates with high efficiency and synchrony. In addition, new methods were created to define transcript boundaries and to identify dynamic changes in transcript expression and architecture over time. Of 8407 total segments, 699 (8.3%) were identified by our algorithm as regions containing potential transcript architecture changes. Our analyses reveal extensive changes to both the coding and noncoding transcriptome, including altered 5' ends, 3' ends, and splice sites. Additionally, 3910 (46.5%) unannotated expressed segments were identified. Interestingly, subsets of unannotated RNAs are located across from introns (anti-introns) or across from the junction between two genes (anti-intergenic junctions). Many of these unannotated RNAs are abundant and exhibit sporulation-specific changes in expression patterns. All work, including heat maps of the tiling array, annotation for the SK1 strain, and phastCONS conservation analysis, is available at http://groups.molbiosci.northwestern.edu/sontheimer/sk1meiosis.php. Our high-resolution transcriptome analyses reveal that coding and noncoding transcript architectures are exceptionally dynamic in S. cerevisiae and suggest a vast array of novel transcriptional and post-transcriptional control mechanisms that are activated upon meiosis and sporulation.

Rodriguez syndrome with SF3B4 mutation: a severe form of Nager syndrome?

We report on the findings of a novel heterozygous de novo SF3B4 mutation in a long-surviving patient with clinical features of Rodriguez syndrome including severe acrofacial dysostosis, phocomelia with pre- and post-axial limb defects, fibular agenesis, rib, and shoulder girdle anomalies. Since SF3B4 mutations have been recently associated with Nager syndrome, this suggests that at least some cases of Rodriguez syndrome are either allelic to or represent unusually severe manifestations of Nager syndrome. Although clinical overlap is obvious, this is somewhat surprising given the presumed autosomal recessive inheritance of Rodriguez syndrome. Investigation of other Rodriguez syndrome patients is needed to clarify the genetic mechanism and possible heterogeneity in patients with clinical features of Rodriguez syndrome.