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.

Improved Image Caption Rating - Datasets, Game, and Model.

How well a caption fits an image can be difficult to assess due to the subjective nature of caption quality. What is a good caption? We investigate this problem by focusing on image-caption ratings and by generating high quality datasets from human feedback with gamification. We validate the datasets by showing a higher level of inter-rater agreement, and by using them to train custom machine learning models to predict new ratings. Our approach outperforms previous metrics - the resulting datasets are more easily learned and are of higher quality than other currently available datasets for image-caption rating.

Ten simple rules for designing and running a computing minor for bio/chem students.

Science students increasingly need programming and data science skills to be competitive in the modern workforce. However, at our university (San Francisco State University), until recently, almost no biology, biochemistry, and chemistry students (from here bio/chem students) completed a minor in computer science. To change this, a new minor in computing applications, which is informally known as the Promoting Inclusivity in Computing (PINC) minor, was established in 2016. Here, we present the lessons we learned from our experience in a set of 10 rules. The first 3 rules focus on setting up the program so that it interests students in biology, chemistry, and biochemistry. Rules 4 through 8 focus on how the classes of the program are taught to make them interesting for our students and to provide the students with the support they need. The last 2 rules are about what happens "behind the scenes" of running a program with many people from several departments involved.

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.

Contraception information on the websites of student health centers in the United States.

OBJECTIVE: As University Student Health Centers are considered reputable sources of information by many young adults, we evaluate the presence of contraceptive information on their websites. STUDY DESIGN: We used a software tool (Quantitative Measures of Online Health Information), designed for public health research to examine online information access on four broad categories of contraception and reproductive health (LARC/injectables, Contraception, Condom, Pap test) on student health center websites from all (591) public four-year universities across the United States between July to September 2020. Using a logistic regression model, we documented factors that are associated with information disparities. RESULTS: Our sample consisted of 545 public universities after excluding those for which information was unavailable. In 357 (66%) of the universities in our sample, we found evidence of some information related to contraception. A one percentage point increase in the student population that are Pell grant recipients, an indicator of the proportion of low-income students enrolled, is associated with a 3% to 6% (0.01

A Very Rare Case of Megalencephalic Leukoencephalopathy With Subcortical Cysts in a Child Born of Non-Consanguineous Marriage in a Non-Predisposed Community.

Megalencephalic leukoencephalopathy (MLC) with subcortical cysts is a very rare white matter disorder characterized predominantly by motor developmental delay and seizures in a child with macrocephaly. Extrapyramidal symptoms, ataxia and mental retardation may also occur. Only a few cases of the disease have been reported worldwide with most of them showing an autosomal recessive pattern of inheritance. In India, most cases have been reported in Agrawal community. Here, we present an interesting case of MLC in a child born in non-Agrawal community to a non-consanguineous marriage. By reporting this case we intend to increase the research horizon and increase the published literature for atypical cases of MLC.

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.

ARtPM: Article Retrieval for Precision Medicine.

BACKGROUND: Information curation and literature surveillance efforts that synthesize the current knowledge about the impact of genetic variability on disease states and drug responses are vitally important for the practise of evidence-based precision medicine. For these efforts, finding the relevant and comprehensive set of articles from the ever growing scientific literature is a challenge. METHODS: We have designed and developed Article Retrieval for Precision Medicine (ARtPM), an end-to-end article retrieval system that employs multi-stage architecture to retrieve and rank relevant articles for a given medical case summary (genetic variants, disease, demographic, and other medical conditions). We compared ARtPM with five baselines, including PubMed Best Match, the improved search functionality recently introduced by PubMed. RESULTS: The differences in the performance of ARtPM and five baselines were statistically significant for four metrics that quantify different aspects of search effectiveness (P-values for P@10, R-prec, infNDCG, Recall@1000 were <.001, <.001,.003,.009, respectively). Pairwise systems' comparisons show that ARtPM is comparable or better than the best performing baseline on three metrics (R-prec: 0.324 vs 0.299, P-value=.06; infNDCG: 0.556 vs 0.465, P-value=.08; R@1000: 0.665 vs 0.572, P-value=.007), but performance in P@10 (0.603 vs 0.630, P-value:.64) needs to improve. CONCLUSION: The recall-focused phase of the ARtPM is effective at retrieving more relevant articles. The precision-focused ranking phase performs well at deeper ranks but needs further work on early ranks (e.g., richer feature set). Overall, the ARtPM system effectively facilitates evidence-based precision medicine practice, and provides a robust search framework for further work in this direction.

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.

Human Rad51 mediated DNA unwinding is facilitated by conditions that favour Rad51-dsDNA aggregation.

BACKGROUND: Human Rad51 (RAD51), analogous to its bacterial homolog, RecA, binds and unwinds double stranded DNA (dsDNA) in the presence of certain nucleotide cofactors. ATP hydrolysis is not required for this process, because even ATP non hydrolysable analogs like AMP-PNP and ATPgammaS, support DNA unwinding. Even ADP, the product of ATP hydrolysis, feebly supports DNA unwinding. RESULTS: We find that human Rad52 (RAD52) stimulates RAD51 mediated DNA unwinding in the presence of all Adenine nucleotide cofactors, (except in AMP and no nucleotide conditions that intrinsically fail to support unwinding reaction) while enhancing aggregation of RAD51-dsDNA complexes in parallel. Interestingly, salt at low concentration can substitute the role of RAD52, in facilitating aggregation of RAD51-dsDNA complexes, that concomitantly also leads to better unwinding. CONCLUSION: RAD52 itself being a highly aggregated protein perhaps acts as scaffold to bring together RAD51 and DNA molecules into large co-aggregates of RAD52-RAD51-DNA complexes to promote RAD51 mediated DNA unwinding reaction, when appropriate nucleotide cofactors are available, presumably through macromolecular crowding effects. Our work highlights the functional link between aggregation of protein-DNA complexes and DNA unwinding in RAD51 system.