Gene expression analysis previously revealed a robust IFN-responsive gene induction profile that was selectively up-regulated in Borrelia burgdorferi-infected C3H mice at 1 wk postinfection. This profile was correlated with arthritis development, as it was absent from infected, mildly arthritic C57BL/6 mice. In this report we now demonstrate that profile induction in infected C3H scid mice occurs independently of B or T lymphocyte infiltration in the joint tissue. Additionally, type I IFN receptor-blocking Abs, but not anti-IFN-γ Abs, dramatically reduced arthritis, revealing a critical but previously unappreciated role for type I IFN in Lyme arthritis development. Certain examined IFN-inducible transcripts were also significantly diminished within joint tissue of mice treated with anti-IFNAR1, whereas expression of other IFN-responsive genes was more markedly altered by anti-IFN-γ treatment. These data indicate that induction of the entire IFN profile is not necessary for arthritis development. These findings further tie early type I IFN induction to Lyme arthritis development, a connection not previously made. Bone marrow-derived macrophages readily induced IFN-responsive genes following B. burgdorferi stimulation, and this expression required a functional type I IFN receptor. Strikingly, induction of these genes was independent of TLRs 2,4, and 9 and of the adapter molecule MyD88. These data demonstrate that the extracellular pathogen B. burgdorferi uses a previously unidentified receptor and a pathway traditionally associated with viruses and intracellular bacteria to initiate transcription of type I IFN and IFN-responsive genes and to initiate arthritis development.

Objective The amount of information for clinicians and clinical researchers is growing exponentially. Text summarization reduces information as an attempt to enable users to find and understand relevant source texts more quickly and effortlessly. In recent years, substantial research has been conducted to develop and evaluate various summarization techniques in the biomedical domain. The goal of this study was to systematically review recent published research on summarization of textual documents in the biomedical domain. Materials and methods MEDLINE (2000 to October 2013), IEEE Digital Library, and the ACM Digital library were searched. Investigators independently screened and abstracted studies that examined text summarization techniques in the biomedical domain. Information is derived from selected articles on five dimensions: input, purpose, output, method and evaluation. Results Of 10,786 studies retrieved, 34 (0.3%) met the inclusion criteria. Natural Language processing (17; 50%) and a Hybrid technique comprising of statistical, Natural language processing and machine learning (15; 44%) were the most common summarization approaches. Most studies (28; 82%) conducted an intrinsic evaluation. Discussion This is the first systematic review of text summarization in the biomedical domain. The study identified research gaps and provides recommendations for guiding future research on biomedical text summarization. conclusion Recent research has focused on a Hybrid technique comprising statistical, language processing and machine learning techniques. Further research is needed on the application and evaluation of text summarization in real research or patient care settings.

Objectives The practice of evidence-based medicine involves integrating the latest best available evidence into patient care decisions. Yet, critical barriers exist for clinicians’ retrieval of evidence that is relevant for a particular patient from primary sources such as randomized controlled trials and meta-analyses. To help address those barriers, we investigated machine learning algorithms that find clinical studies with high clinical impact from PubMed®. Methods Our machine learning algorithms use a variety of features including bibliometric features (e.g., citation count), social media attention, journal impact factors, and citation metadata. The algorithms were developed and evaluated with a gold standard composed of 502 high impact clinical studies that are referenced in 11 clinical evidence-based guidelines on the treatment of various diseases. We tested the following hypotheses: 1) our high impact classifier outperforms a state-of-the-art classifier based on citation metadata and citation terms, and PubMed’s® relevance sort algorithm; and 2) the performance of our high impact classifier does not decrease significantly after removing proprietary features such as citation count. Results The mean top 20 precision of our high impact classifier was 34% versus 11% for the state-of-the-art classifier and 4% for PubMed’s® relevance sort (p = 0.009); and 2) the performance of our high impact classifier did not decrease significantly after removing proprietary features (mean top 20 precision = 34% vs. 36%; p = 0.085). Conclusion The high impact classifier, using features such as bibliometrics, social media attention and MEDLINE® metadata, outperformed previous approaches and is a promising alternative to identifying high impact studies for clinical decision support.

Rationale Genetic testing for Long QT Syndrome (LQTS) is now a standard and integral component of clinical cardiology. A major obstacle to the interpretation of genetic findings is the lack of robust functional assays to determine the pathogenicity of identified gene variants in a high throughput manner. Objective The goal of this study was to design and test a high throughput in vivo cardiac assay to distinguish between disease-causing and benign KCNH2 (hERG1) variants, using the zebrafish as a model organism. Methods and Results We tested the ability of previously characterized LQTS hERG1 mutations and polymorphisms to restore normal repolarization in the kcnh2-knockdown embryonic zebrafish. The cardiac assay correctly identified a benign variant in 9 of 10 cases (negative predictive value 90%) while correctly identifying a disease-causing variant in 39/39 cases (positive predictive value 100%). Conclusion The in vivo zebrafish cardiac assay approaches the accuracy of the current benchmark in vitro assay for the detection of disease-causing mutations and is far superior in terms of throughput rate. Together with emerging algorithms for interpreting a positive LQTS genetic test, the zebrafish cardiac assay provides an additional tool for the final determination of pathogenicity of gene variants identified in LQTS genetic screening.