siSPOTR: a tool for designing highly specific and potent siRNAs for human and mouse.

RNA interference (RNAi) serves as a powerful and widely used gene silencing tool for basic biological research and is being developed as a therapeutic avenue to suppress disease-causing genes. However, the specificity and safety of RNAi strategies remains under scrutiny because small inhibitory RNAs (siRNAs) induce off-target silencing. Currently, the tools available for designing siRNAs are biased toward efficacy as opposed to specificity. Prior work from our laboratory and others' supports the potential to design highly specific siRNAs by limiting the promiscuity of their seed sequences (positions 2-8 of the small RNA), the primary determinant of off-targeting. Here, a bioinformatic approach to predict off-targeting potentials was established using publically available siRNA data from more than 50 microarray experiments. With this, we developed a specificity-focused siRNA design algorithm and accompanying online tool which, upon validation, identifies candidate sequences with minimal off-targeting potentials and potent silencing capacities. This tool offers researchers unique functionality and output compared with currently available siRNA design programs. Furthermore, this approach can greatly improve genome-wide RNAi libraries and, most notably, provides the only broadly applicable means to limit off-targeting from RNAi expression vectors.

The (lack of) impact of retraction on citation networks.

Article retraction in research is rising, yet retracted articles continue to be cited at a disturbing rate. This paper presents an analysis of recent retraction patterns, with a unique emphasis on the role author self-cites play, to assist the scientific community in creating counter-strategies. This was accomplished by examining the following: (1) A categorization of retracted articles more complete than previously published work. (2) The relationship between citation counts and after-retraction self-cites from the authors of the work, and the distribution of self-cites across our retraction categories. (3) The distribution of retractions written by both the author and the editor across our retraction categories. (4) The trends for seven of our nine defined retraction categories over a 6-year period. (5) The average journal impact factor by category, and the relationship between impact factor, author self-cites, and overall citations. Our findings indicate new reasons for retractions have emerged in recent years, and more editors are penning retractions. The rates of increase for retraction varies by category, and there is statistically significant difference of average impact factor between many categories. 18 % of authors self-cite retracted work post retraction with only 10 % of those authors also citing the retraction notice. Further, there is a positive correlation between self-cites and after retraction citations.

Increased Incidence of Vestibular Disorders in Patients With SARS-CoV-2.

Objective: Determine the incidence of vestibular disorders in patients with SARS-CoV-2 compared to the control population. Study Design: Retrospective. Setting: Clinical data in the National COVID Cohort Collaborative database (N3C). Methods: Deidentified patient data from the National COVID Cohort Collaborative database (N3C) were queried based on variant peak prevalence (untyped, alpha, delta, omicron 21K, and omicron 23A) from covariants.org to retrospectively analyze the incidence of vestibular disorders in patients with SARS-CoV-2 compared to control population, consisting of patients without documented evidence of COVID infection during the same period. Results: Patients testing positive for COVID-19 were significantly more likely to have a vestibular disorder compared to the control population. Compared to control patients, the odds ratio of vestibular disorders was significantly elevated in patients with untyped (odds ratio [OR], 2.39; confidence intervals [CI], 2.29-2.50; P < 0.001), alpha (OR, 3.63; CI, 3.48-3.78; P < 0.001), delta (OR, 3.03; CI, 2.94-3.12; P < 0.001), omicron 21K variant (OR, 2.97; CI, 2.90-3.04; P < 0.001), and omicron 23A variant (OR, 8.80; CI, 8.35-9.27; P < 0.001). Conclusions: The incidence of vestibular disorders differed between COVID-19 variants and was significantly elevated in COVID-19-positive patients compared to the control population. These findings have implications for patient counseling and further research is needed to discern the long-term effects of these findings.

The National COVID Cohort Collaborative (N3C): Rationale, design, infrastructure, and deployment.

OBJECTIVE: Coronavirus disease 2019 (COVID-19) poses societal challenges that require expeditious data and knowledge sharing. Though organizational clinical data are abundant, these are largely inaccessible to outside researchers. Statistical, machine learning, and causal analyses are most successful with large-scale data beyond what is available in any given organization. Here, we introduce the National COVID Cohort Collaborative (N3C), an open science community focused on analyzing patient-level data from many centers. MATERIALS AND METHODS: The Clinical and Translational Science Award Program and scientific community created N3C to overcome technical, regulatory, policy, and governance barriers to sharing and harmonizing individual-level clinical data. We developed solutions to extract, aggregate, and harmonize data across organizations and data models, and created a secure data enclave to enable efficient, transparent, and reproducible collaborative analytics. RESULTS: Organized in inclusive workstreams, we created legal agreements and governance for organizations and researchers; data extraction scripts to identify and ingest positive, negative, and possible COVID-19 cases; a data quality assurance and harmonization pipeline to create a single harmonized dataset; population of the secure data enclave with data, machine learning, and statistical analytics tools; dissemination mechanisms; and a synthetic data pilot to democratize data access. CONCLUSIONS: The N3C has demonstrated that a multisite collaborative learning health network can overcome barriers to rapidly build a scalable infrastructure incorporating multiorganizational clinical data for COVID-19 analytics. We expect this effort to save lives by enabling rapid collaboration among clinicians, researchers, and data scientists to identify treatments and specialized care and thereby reduce the immediate and long-term impacts of COVID-19.

Research Networking Systems: The State of Adoption at Institutions Aiming to Augment Translational Research Infrastructure.

Fostering collaborations across multiple disciplines within and across institutional boundaries is becoming increasingly important with the growing emphasis on translational research. As a result, Research Networking Systems that facilitate discovery of potential collaborators have received significant attention by institutions aiming to augment their research infrastructure. We have conducted a survey to assess the state of adoption of these new tools at the Clinical and Translational Science Award (CTSA) funded institutions. Survey results demonstrate that most CTSA funded institutions have either already adopted or were planning to adopt one of several available research networking systems. Moreover a good number of these institutions have exposed or plan to expose the data on research expertise using linked open data, an established approach to semantic web services. Preliminary exploration of these publically-available data shows promising utility in assessing cross-institutional collaborations. Further adoption of these technologies and analysis of the data are needed, however, before their impact on cross-institutional collaboration in research can be appreciated and measured.

Personal health records: a randomized trial of effects on elder medication safety.

PURPOSE: To examine the impact of a personal health record (PHR) on medication-use safety among older adults. BACKGROUND: Online PHRs have potential as tools to manage health information. We know little about how to make PHRs accessible for older adults and what effects this will have. METHODS: A PHR was designed and pretested with older adults and tested in a 6-month randomized controlled trial. After completing mailed baseline questionnaires, eligible computer users aged 65 and over were randomized 3:1 to be given access to a PHR (n=802) or serve as a standard care control group (n=273). Follow-up questionnaires measured change from baseline medication use, medication reconciliation behaviors, and medication management problems. RESULTS: Older adults were interested in keeping track of their health and medication information. A majority (55.2%) logged into the PHR and used it, but only 16.1% used it frequently. At follow-up, those randomized to the PHR group were significantly less likely to use multiple non-steroidal anti-inflammatory drugs-the most common warning generated by the system (viewed by 23% of participants). Compared with low/non-users, high users reported significantly more changes in medication use and improved medication reconciliation behaviors, and recognized significantly more side effects, but there was no difference in use of inappropriate medications or adherence measures. CONCLUSIONS: PHRs can engage older adults for better medication self-management; however, features that motivate continued use will be needed. Longer-term studies of continued users will be required to evaluate the impact of these changes in behavior on patient health outcomes.

Adoption of Research Networking Systems in the Clinical and Translational Science Award (CTSA) Consortium.

Research Networking Systems (RNSs) have been of substantial interest for large research organizations as a means of fostering collaboration and increasing the visibility of individual investigators. A survey of the CTSA consortium assessed the state of first generation RNS adoption by member institutions. The survey queried the level of research networking adoption, system diversity, implementation maturity, data sources, use of Linked Open Data (LOD) and the VIVO core ontology, resources required, and individuals and roles profiled. Sixty-one CTSA-funded institutions were contacted and yielded a 75% response rate. The results demonstrate the extent of adoption of Research Networking Recommendations approved by the CTSA Consortium Executive and Steering Committee in October 2011.

Direct2Experts: a pilot national network to demonstrate interoperability among research-networking platforms.

Research-networking tools use data-mining and social networking to enable expertise discovery, matchmaking and collaboration, which are important facets of team science and translational research. Several commercial and academic platforms have been built, and many institutions have deployed these products to help their investigators find local collaborators. Recent studies, though, have shown the growing importance of multiuniversity teams in science. Unfortunately, the lack of a standard data-exchange model and resistance of universities to share information about their faculty have presented barriers to forming an institutionally supported national network. This case report describes an initiative, which, in only 6 months, achieved interoperability among seven major research-networking products at 28 universities by taking an approach that focused on addressing institutional concerns and encouraging their participation. With this necessary groundwork in place, the second phase of this effort can begin, which will expand the network's functionality and focus on the end users.

Synergies and distinctions between computational disciplines in biomedical research: perspective from the Clinical andTranslational Science Award programs.

Clinical and translational research increasingly requires computation. Projects may involve multiple computationally oriented groups including information technology (IT) professionals, computer scientists, and biomedical informaticians. However, many biomedical researchers are not aware of the distinctions among these complementary groups, leading to confusion, delays, and suboptimal results. Although written from the perspective of Clinical and Translational Science Award (CTSA) programs within academic medical centers, this article addresses issues that extend beyond clinical and translational research. The authors describe the complementary but distinct roles of operational IT, research IT, computer science, and biomedical informatics using a clinical data warehouse as a running example. In general, IT professionals focus on technology. The authors distinguish between two types of IT groups within academic medical centers: central or administrative IT (supporting the administrative computing needs of large organizations) and research IT (supporting the computing needs of researchers). Computer scientists focus on general issues of computation such as designing faster computers or more efficient algorithms, rather than specific applications. In contrast, informaticians are concerned with data, information, and knowledge. Biomedical informaticians draw on a variety of tools, including but not limited to computers, to solve information problems in health care and biomedicine. The paper concludes with recommendations regarding administrative structures that can help to maximize the benefit of computation to biomedical research within academic health centers.

Standardized nursing language in the systematized nomenclature of medicine clinical terms: A cross-mapping validation method.

Many standardized healthcare languages have been mapped to the Systematized Nomenclature of Medicine Clinical Terms known as SNOMED CT, which was developed by the College of American Pathologists. This study describes a methodology for detecting misassigned concepts from source systems to SNOMED CT and presents the results of applying this methodology to a subset of concepts from two standardized nursing languages, the Nursing Interventions Classification and the Nursing Outcomes Classification. The methodology is based on comparing the knowledge representations of a set of nursing concepts between source systems (nursing languages) and SNOMED CT. If any nursing concept differs in knowledge representation in the target system compared with the source system, editorial misassignment of the concept was declared and recommendations for target system developers were made. In a total of 75 nursing concepts used to test this method, five misassigned concepts(6.6%) were found in SNOMED CT. This method can be used to validate other healthcare languages.