Search and visualization of gene-drug-disease interactions for pharmacogenomics and precision medicine research using GeneDive.

BACKGROUND: Understanding the relationships between genes, drugs, and disease states is at the core of pharmacogenomics. Two leading approaches for identifying these relationships in medical literature are: human expert led manual curation efforts, and modern data mining based automated approaches. The former generates small amounts of high-quality data, and the latter offers large volumes of mixed quality data. The algorithmically extracted relationships are often accompanied by supporting evidence, such as, confidence scores, source articles, and surrounding contexts (excerpts) from the articles, that can be used as data quality indicators. Tools that can leverage these quality indicators to help the user gain access to larger and high-quality data are needed. APPROACH: We introduce GeneDive, a web application for pharmacogenomics researchers and precision medicine practitioners that makes gene, disease, and drug interactions data easily accessible and usable. GeneDive is designed to meet three key objectives: (1) provide functionality to manage information-overload problem and facilitate easy assimilation of supporting evidence, (2) support longitudinal and exploratory research investigations, and (3) offer integration of user-provided interactions data without requiring data sharing. RESULTS: GeneDive offers multiple search modalities, visualizations, and other features that guide the user efficiently to the information of their interest. To facilitate exploratory research, GeneDive makes the supporting evidence and context for each interaction readily available and allows the data quality threshold to be controlled by the user as per their risk tolerance level. The interactive search-visualization loop enables relationship discoveries between diseases, genes, and drugs that might not be explicitly described in literature but are emergent from the source medical corpus and deductive reasoning. The ability to utilize user's data either in combination with the GeneDive native datasets or in isolation promotes richer data-driven exploration and discovery. These functionalities along with GeneDive's applicability for precision medicine, bringing the knowledge contained in biomedical literature to bear on particular clinical situations and improving patient care, are illustrated through detailed use cases. CONCLUSION: GeneDive is a comprehensive, broad-use biological interactions browser. The GeneDive application and information about its underlying system architecture are available at http://www.genedive.net. GeneDive Docker image is also available for download at this URL, allowing users to (1) import their own interaction data securely and privately; and (2) generate and test hypotheses across their own and other datasets.

PathFXweb: a web application for identifying drug safety and efficacy phenotypes.

SUMMARY: Limited efficacy and intolerable safety limit therapeutic development and identification of potential liabilities earlier in development could significantly improve this process. Computational approaches which aggregate data from multiple sources and consider the drug's pathways effects could add to identification of these liabilities earlier. Such computational methods must be accessible to a variety of users beyond computational scientists, especially regulators and industry scientists, in order to impact the therapeutic development process. We have previously developed and published PathFX, an algorithm for identifying drug networks and phenotypes for understanding drug associations to safety and efficacy. Here we present a streamlined and easy-to-use PathFX web application that allows users to search for drug networks and associated phenotypes. We have also added visualization, and phenotype clustering to improve functionality and interpretability of PathFXweb. AVAILABILITY AND IMPLEMENTATION: https://www.pathfxweb.net/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Classroom sound can be used to classify teaching practices in college science courses.

Active-learning pedagogies have been repeatedly demonstrated to produce superior learning gains with large effect sizes compared with lecture-based pedagogies. Shifting large numbers of college science, technology, engineering, and mathematics (STEM) faculty to include any active learning in their teaching may retain and more effectively educate far more students than having a few faculty completely transform their teaching, but the extent to which STEM faculty are changing their teaching methods is unclear. Here, we describe the development and application of the machine-learning-derived algorithm Decibel Analysis for Research in Teaching (DART), which can analyze thousands of hours of STEM course audio recordings quickly, with minimal costs, and without need for human observers. DART analyzes the volume and variance of classroom recordings to predict the quantity of time spent on single voice (e.g., lecture), multiple voice (e.g., pair discussion), and no voice (e.g., clicker question thinking) activities. Applying DART to 1,486 recordings of class sessions from 67 courses, a total of 1,720 h of audio, revealed varied patterns of lecture (single voice) and nonlecture activity (multiple and no voice) use. We also found that there was significantly more use of multiple and no voice strategies in courses for STEM majors compared with courses for non-STEM majors, indicating that DART can be used to compare teaching strategies in different types of courses. Therefore, DART has the potential to systematically inventory the presence of active learning with ∼90% accuracy across thousands of courses in diverse settings with minimal effort.

Development and validation of a new cognitive screening test: The Hong Kong Brief Cognitive Test (HKBC).

OBJECTIVES: To develop and examine the validity of a new brief cognitive test with less educational bias for screening cognitive impairment. METHODS: A new cognitive test, Hong Kong Brief Cognitive Test (HKBC), was developed based on review of the literature, as well as the views of an expert panel. Three groups of subjects aged 65 or above were recruited after written consent: normal older people recruited in elderly centres, people with mild NCD (neurocognitive disorder), and people with major NCD. The brief cognitive test, Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment Scale (MoCA), were administered to the subjects. The performance of HKBC in differentiating subjects with major NCD, mild NCD, and normal older people were compared with the clinical diagnosis, as well as the MMSE and MoCA scores. RESULTS: In total, 359 subjects were recruited, with 99 normal controls, 132 subjects with major NCD, and 128 with mild NCD. The mean MMSE, MoCA, and HKBC scores showed significant differences among the 3 groups of subjects. In the receiving operating characteristic curve analysis of the HKBC in differentiating normal subjects from those with cognitive impairment (mild NCD + major NCD), the area under the curve was 0.955 with an optimal cut-off score of 21/22. The performances of MMSE and MoCA in differentiating normal from cognitively impaired subjects are slightly inferior to the HKBC. CONCLUSIONS: The HKBC is a brief instrument useful for screening cognitive impairment in older adults and is also useful in populations with low educational level.

A Resident Led Newsletter Is a Powerful Communication Tool.

Background: Communication with stakeholders for a graduate medical education (GME) program depends on shared visual and written content. Residency training programs are embracing social media as a communication channel. However, curated information that may only be viewed by subscribers or followers is difficult to archive and may appear overwhelming to novice users. An electronic, printable newsletter may be a unique communication tool for training programs to share information among residents, faculty, and hospital administration. Methods: We published a monthly electronic newsletter sent to all residents, teaching faculty, and additional stakeholders in our internal medicine residency program. We conducted an electronic anonymous survey and sent it to all residents in February 2023 and January 2024. The survey consisted of 5 questions to assess the satisfaction level of the newsletter. Results: Sixty of 232 (25.9%) residents completed the survey. Of those, 44 (73.3%) residents were very satisfied regarding overall satisfaction, 39 (65.0%) residents were very satisfied with the overall content, and 42 (70.0%) residents were very satisfied with the timeliness of the information presented. Thirty-six (60%) residents reported the importance of having a resident-led newsletter. Conclusion: We found an overall high satisfaction level in a resident-led newsletter based on a survey completed by residents within our program. Most survey respondents deemed resident leadership crucial for the newsletter. We also received strong positive feedback from key stakeholders ranging from hospital administration to residency applicants.

Medication abortion: State of information on Student Health Center websites.

Objective: To document the information on medication abortion on university Student Health Center (SHC) websites. Study Sample: Four-year bachelors granting public universities' Student Health Center websites. Methods: We conduct thematic content analysis for medication abortion-related information on 547 SHC websites in the United States as of August 2022 using computer-assisted software. We validate the software with human annotation. Results: Medication abortion is mentioned on 23 (4%) university SHC websites, and in 13 (57%) of these websites, it is only mentioned to exclude it from definitions of emergency contraception. Only two websites, 6% of the California public schools included in the sample, advertise medication abortion services through their SHC websites. One hundred fifteen (21%) university websites provide links to Planned Parenthood that gives information on how to navigate abortion access across all 50 states. Conclusion: There is inadequate information on SHC websites regarding medication abortion services and how to access them.

Quantifying the Quality of Web-Based Health Information on Student Health Center Websites Using a Software Tool: Design and Development Study.

BACKGROUND: The internet has become a major source of health information, especially for adolescents and young adults. Unfortunately, inaccurate, incomplete, or outdated health information is widespread on the web. Often adolescents and young adults turn to authoritative websites such as the student health center (SHC) website of the university they attend to obtain reliable health information. Although most on-campus SHC clinics comply with the American College Health Association standards, their websites are not subject to any standards or code of conduct. In the absence of quality standards or guidelines, monitoring and compliance processes do not exist for SHC websites. Thus, there is no oversight of the health information published on SHC websites by any central governing body. OBJECTIVE: The aim of this study is to develop, describe, and validate an open-source software that can effectively and efficiently assess the quality of health information on SHC websites in the United States. METHODS: Our cross-functional team designed and developed an open-source software, QMOHI (Quantitative Measures of Online Health Information), that assesses information quality for a specified health topic from all SHC websites belonging to a predetermined list of universities. The tool was designed to compute 8 different quality metrics that quantify various aspects of information quality based on the retrieved text. We conducted and reported results from 3 experiments that assessed the QMOHI tool in terms of its scalability, generalizability in health topics, and robustness to changes in universities' website structure. RESULTS: Empirical evaluation has shown the QMOHI tool to be highly scalable and substantially more efficient than manually assessing web-based information quality. The tool's runtime was dominated by network-related tasks (98%), whereas the metric computations take <2 seconds. QMOHI demonstrated topical versatility, evaluating SHC website information quality for four disparate and broad health topics (COVID, cancer, long-acting reversible contraceptives, and condoms) and two narrowly focused topics (hormonal intrauterine device and copper intrauterine device). The tool exhibited robustness, correctly measuring information quality despite changes in SHC website structure. QMOHI can support longitudinal studies by being robust to such website changes. CONCLUSIONS: QMOHI allows public health researchers and practitioners to conduct large-scale studies of SHC websites that were previously too time- and cost-intensive. The capability to generalize broadly or focus narrowly allows a wide range of applications of QMOHI, allowing researchers to study both mainstream and underexplored health topics. QMOHI's ability to robustly analyze SHC websites periodically promotes longitudinal investigations and allows QMOHI to be used as a monitoring tool. QMOHI serves as a launching pad for our future work that aims to develop a broadly applicable public health tool for web-based health information studies with potential applications far beyond SHC websites.

PhenClust, a standalone tool for identifying trends within sets of biological phenotypes using semantic similarity and the Unified Medical Language System metathesaurus.

OBJECTIVES: We sought to cluster biological phenotypes using semantic similarity and create an easy-to-install, stable, and reproducible tool. MATERIALS AND METHODS: We generated Phenotype Clustering (PhenClust)-a novel application of semantic similarity for interpreting biological phenotype associations-using the Unified Medical Language System (UMLS) metathesaurus, demonstrated the tool's application, and developed Docker containers with stable installations of two UMLS versions. RESULTS: PhenClust identified disease clusters for drug network-associated phenotypes and a meta-analysis of drug target candidates. The Dockerized containers eliminated the requirement that the user install the UMLS metathesaurus. DISCUSSION: Clustering phenotypes summarized all phenotypes associated with a drug network and two drug candidates. Docker containers can support dissemination and reproducibility of tools that are otherwise limited due to insufficient software support. CONCLUSION: PhenClust can improve interpretation of high-throughput biological analyses where many phenotypes are associated with a query and the Dockerized PhenClust achieved our objective of decreasing installation complexity.

Identification of soil heavy metal sources from anthropogenic activities and pollution assessment of Fuyang County, China.

Understanding regional variations of soil heavy metals and their anthropogenic influence are very important for environmental planning. In this study, 286 surface soil samples were collected in Fuyang county, and the 'total' metals for copper (Cu), zinc (Zn), lead (Pb), cadmium (Cd) and nickel (Ni) were measured in 2005. Statistic analysis showed that Cu, Zn, Pb and Cd had been added by exterior factors, and Ni was mainly controlled by natural factors. The combination of multivariate statistical and geostatistical analysis successfully grouped three groups (Cu, Zn and Pb; Cd; and Ni) of heavy metals from different sources. Through pollution evaluation, it was found that 15.76% of the study area for Cu, Zn and Pb, and 46.14% for Cd suffered from moderate or severe pollution. Further spatial analysis identified the limestone mining activities, paper mills, cement factory and metallurgic activities were the main sources for the concentration of Cu, Zn, Pb and Cd in soils, and soil Ni was mainly determined by the parent materials.

Improving the explainability of Random Forest classifier - user centered approach.

Machine Learning (ML) methods are now influencing major decisions about patient care, new medical methods, drug development and their use and importance are rapidly increasing in all areas. However, these ML methods are inherently complex and often difficult to understand and explain resulting in barriers to their adoption and validation. Our work (RFEX) focuses on enhancing Random Forest (RF) classifier explainability by developing easy to interpret explainability summary reports from trained RF classifiers as a way to improve the explainability for (often non-expert) users. RFEX is implemented and extensively tested on Stanford FEATURE data where RF is tasked with predicting functional sites in 3D molecules based on their electrochemical signatures (features). In developing RFEX method we apply user-centered approach driven by explainability questions and requirements collected by discussions with interested practitioners. We performed formal usability testing with 13 expert and non-expert users to verify RFEX usefulness. Analysis of RFEX explainability report and user feedback indicates its usefulness in significantly increasing explainability and user confidence in RF classification on FEATURE data. Notably, RFEX summary reports easily reveal that one needs very few (from 2-6 depending on a model) top ranked features to achieve 90% or better of the accuracy when all 480 features are used.

GeneDive: A gene interaction search and visualization tool to facilitate precision medicine.

Obtaining relevant information about gene interactions is critical for understanding disease processes and treatment. With the rise in text mining approaches, the volume of such biomedical data is rapidly increasing, thereby creating a new problem for the users of this data: information overload. A tool for efficient querying and visualization of biomedical data that helps researchers understand the underlying biological mechanisms for diseases and drug responses, and ultimately helps patients, is sorely needed. To this end we have developed GeneDive, a web-based information retrieval, filtering, and visualization tool for large volumes of gene interaction data. GeneDive offers various features and modalities that guide the user through the search process to efficiently reach the information of their interest. GeneDive currently processes over three million gene-gene interactions with response times within a few seconds. For over half of the curated gene sets sourced from four prominent databases, more than 80% of the gene set members are recovered by GeneDive. In the near future, GeneDive will seamlessly accommodate other interaction types, such as gene-drug and gene-disease interactions, thus enabling full exploration of topics such as precision medicine. The GeneDive application and information about its underlying system architecture are available at http://www.genedive.net.

Reference data on thickness and mechanics of tissue layers and anthropometry of musculoskeletal extremities.

Musculoskeletal extremities exhibit a multi-layer tissue structure that is composed of skin, fat, and muscle. Body composition and anthropometric measurements have been used to assess health status and build anatomically accurate biomechanical models of the limbs. However, comprehensive datasets inclusive of regional tissue anatomy and response under mechanical manipulation are missing. The goal of this study was to acquire and disseminate anatomical and mechanical data collected on extremities of the general population. An ultrasound system, instrumented with a load transducer, was used for in vivo characterization of skin, fat, and muscle thicknesses in the extremities of 100 subjects at unloaded (minimal force) and loaded (through indentation) states. For each subject, the unloaded and loaded state provided anatomic tissue layer measures and tissue indentation response for 48 and 8 regions, respectively. A publicly available web-based system has been used for data management and dissemination. This comprehensive database will provide the foundation for comparative studies in regional musculoskeletal composition and improve visual and haptic realism for computational models of the limbs.

High precision prediction of functional sites in protein structures.

We address the problem of assigning biological function to solved protein structures. Computational tools play a critical role in identifying potential active sites and informing screening decisions for further lab analysis. A critical parameter in the practical application of computational methods is the precision, or positive predictive value. Precision measures the level of confidence the user should have in a particular computed functional assignment. Low precision annotations lead to futile laboratory investigations and waste scarce research resources. In this paper we describe an advanced version of the protein function annotation system FEATURE, which achieved 99% precision and average recall of 95% across 20 representative functional sites. The system uses a Support Vector Machine classifier operating on the microenvironment of physicochemical features around an amino acid. We also compared performance of our method with state-of-the-art sequence-level annotator Pfam in terms of precision, recall and localization. To our knowledge, no other functional site annotator has been rigorously evaluated against these key criteria. The software and predictive models are incorporated into the WebFEATURE service at http://feature.stanford.edu/wf4.0-beta.

The porcelain crab transcriptome and PCAD, the porcelain crab microarray and sequence database.

BACKGROUND: With the emergence of a completed genome sequence of the freshwater crustacean Daphnia pulex, construction of genomic-scale sequence databases for additional crustacean sequences are important for comparative genomics and annotation. Porcelain crabs, genus Petrolisthes, have been powerful crustacean models for environmental and evolutionary physiology with respect to thermal adaptation and understanding responses of marine organisms to climate change. Here, we present a large-scale EST sequencing and cDNA microarray database project for the porcelain crab Petrolisthes cinctipes. METHODOLOGY/PRINCIPAL FINDINGS: A set of approximately 30K unique sequences (UniSeqs) representing approximately 19K clusters were generated from approximately 98K high quality ESTs from a set of tissue specific non-normalized and mixed-tissue normalized cDNA libraries from the porcelain crab Petrolisthes cinctipes. Homology for each UniSeq was assessed using BLAST, InterProScan, GO and KEGG database searches. Approximately 66% of the UniSeqs had homology in at least one of the databases. All EST and UniSeq sequences along with annotation results and coordinated cDNA microarray datasets have been made publicly accessible at the Porcelain Crab Array Database (PCAD), a feature-enriched version of the Stanford and Longhorn Array Databases. CONCLUSIONS/SIGNIFICANCE: The EST project presented here represents the third largest sequencing effort for any crustacean, and the largest effort for any crab species. Our assembly and clustering results suggest that our porcelain crab EST data set is equally diverse to the much larger EST set generated in the Daphnia pulex genome sequencing project, and thus will be an important resource to the Daphnia research community. Our homology results support the pancrustacea hypothesis and suggest that Malacostraca may be ancestral to Branchiopoda and Hexapoda. Our results also suggest that our cDNA microarrays cover as much of the transcriptome as can reasonably be captured in EST library sequencing approaches, and thus represent a rich resource for studies of environmental genomics.

Assessing soil Cu content and anthropogenic influences using decision tree analysis.

Recent enhanced urbanization and industrialization in China have greatly influenced soil Cu content. To better understand the magnitude of Cu contamination in soil, it is essential to understand its spatial distribution and estimate its values at unsampled points. However, Kriging often can not achieve satisfactory estimates when soil Cu data have weak spatial dependence. The proposed classification and regression tree method (CART) simulated Cu content using environmental variables, and it had no special data requirements. The Cu concentration classes estimated by CART had accuracy in attribution to the right classes of 80.5%, this is 29.3% better than ordinary Kriging method. Moreover, CART provides some insight into the sources of current soil Cu contents. In our study, low soil Cu accumulation was driven by terrain characteristic, agriculture land uses, and soil properties; while high Cu concentration resulted from industrial and agricultural land uses.

The prevalence of depressive symptoms among elderly Chinese private nursing home residents in Hong Kong.

BACKGROUND: Privately-owned Nursing homes (PNH) in Hong Kong present a unique setting of institutional care where elderly with the whole spectrum of health status live together. OBJECTIVES: This cross-sectional descriptive study aimed to determine the prevalence of significant depressive symptoms in a group of Cantonese-speaking Chinese private nursing home elderly living in Hong Kong, and to identify associated psychosocial and health factors. METHODOLOGICAL RESULTS: Two hundred and forty five residents fulfilled the inclusion criteria. Using the Chinese version of the Geriatric Depression Scale--Short Form (GDS-SF), we detected significant depressive symptoms in 29% of subjects. Univariate analysis revealed some associated socio-economic risk factors including current non-Comprehensive Social Security Assistance (CSSA) recipients, education levels and low abilities for social activities. Low vision, swallowing difficulties and low levels of basic activities of daily living (BADL) as reflected by the total Modified Barthel Index of less than 61 were important health predictors. Depression was also associated with features of self-perception of financial inadequacy, life dissatisfaction, poor self-perceived health, poor attitudes towards living arrangement and suicidal thoughts. Stepwise logistic regression identified swallowing problems, current non-CSSA recipient and low BADL ability as independent risk factors. CONCLUSION: The high prevalence of depressive symptoms in the nursing home elderly requires the attention of Government authorities, health care and social service providers.