A translational genomics approach identifies IL10RB as the top candidate gene target for COVID-19 susceptibility.

Recent efforts have identified genetic loci that are associated with coronavirus disease 2019 (COVID-19) infection rates and disease outcome severity. Translating these genetic findings into druggable genes that reduce COVID-19 host susceptibility is a critical next step. Using a translational genomics approach that integrates COVID-19 genetic susceptibility variants, multi-tissue genetically regulated gene expression (GReX), and perturbagen signatures, we identified IL10RB as the top candidate gene target for COVID-19 host susceptibility. In a series of validation steps, we show that predicted GReX upregulation of IL10RB and higher IL10RB expression in COVID-19 patient blood is associated with worse COVID-19 outcomes and that in vitro IL10RB overexpression is associated with increased viral load and activation of disease-relevant molecular pathways.

IL10RB as a key regulator of COVID-19 host susceptibility and severity.

BACKGROUND: Recent efforts have identified genetic loci that are associated with coronavirus disease 2019 (COVID-19) infection rates and disease outcome severity. Translating these genetic findings into druggable genes and readily available compounds that reduce COVID-19 host susceptibility is a critical next step. METHODS: We integrate COVID-19 genetic susceptibility variants, multi-tissue genetically regulated gene expression (GReX) and perturbargen signatures to identify candidate genes and compounds that reverse the predicted gene expression dysregulation associated with COVID-19 susceptibility. The top candidate gene is validated by testing both its GReX and observed blood transcriptome association with COVID-19 severity, as well as by in vitro perturbation to quantify effects on viral load and molecular pathway dysregulation. We validate the in silico drug repositioning analysis by examining whether the top candidate compounds decrease COVID-19 incidence based on epidemiological evidence. RESULTS: We identify IL10RB as the top key regulator of COVID-19 host susceptibility. Predicted GReX up-regulation of IL10RB and higher IL10RB expression in COVID-19 patient blood is associated with worse COVID-19 outcomes. In vitro IL10RB overexpression is associated with increased viral load and activation of immune-related molecular pathways. Azathioprine and retinol are prioritized as candidate compounds to reduce the likelihood of testing positive for COVID-19. CONCLUSIONS: We establish an integrative data-driven approach for gene target prioritization. We identify and validate IL10RB as a suitable molecular target for modulation of COVID-19 host susceptibility. Finally, we provide evidence for a few readily available medications that would warrant further investigation as drug repositioning candidates.

Automatic identification of recent high impact clinical articles in PubMed to support clinical decision making using time-agnostic features.

OBJECTIVES: Finding recent clinical studies that warrant changes in clinical practice ("high impact" clinical studies) in a timely manner is very challenging. We investigated a machine learning approach to find recent studies with high clinical impact to support clinical decision making and literature surveillance. METHODS: To identify recent studies, we developed our classification model using time-agnostic features that are available as soon as an article is indexed in PubMed®, such as journal impact factor, author count, and study sample size. Using a gold standard of 541 high impact treatment studies referenced in 11 disease management guidelines, we tested the following null hypotheses: (1) the high impact classifier with time-agnostic features (HI-TA) performs equivalently to PubMed's Best Match sort and a MeSH-based Naïve Bayes classifier; and (2) HI-TA performs equivalently to the high impact classifier with both time-agnostic and time-sensitive features (HI-TS) enabled in a previous study. The primary outcome for both hypotheses was mean top 20 precision. RESULTS: The differences in mean top 20 precision between HI-TA and three baselines (PubMed's Best Match, a MeSH-based Naïve Bayes classifier, and HI-TS) were not statistically significant (12% vs. 3%, p = 0.101; 12% vs. 11%, p = 0.720; 12% vs. 25%, p = 0.094, respectively). Recall of HI-TA was low (7%). CONCLUSION: HI-TA had equivalent performance to state-of-the-art approaches that depend on time-sensitive features. With the advantage of relying only on time-agnostic features, the proposed approach can be used as an adjunct to help clinicians identify recent high impact clinical studies to support clinical decision-making. However, low recall limits the use of HI-TA for literature surveillance.

Interactive Visual Displays for Interpreting the Results of Clinical Trials: Formative Evaluation With Case Vignettes.

BACKGROUND: At the point of care, evidence from randomized controlled trials (RCTs) is underutilized in helping clinicians meet their information needs. OBJECTIVE: To design interactive visual displays to help clinicians interpret and compare the results of relevant RCTs for the management of a specific patient, and to conduct a formative evaluation with physicians comparing interactive visual versus narrative displays. METHODS: We followed a user-centered and iterative design process succeeded by development of information display prototypes as a Web-based application. We then used a within-subjects design with 20 participants (8 attendings and 12 residents) to evaluate the usability and problem-solving impact of the information displays. We compared subjects' perceptions of the interactive visual displays versus narrative abstracts. RESULTS: The resulting interactive visual displays present RCT results side-by-side according to the Population, Intervention, Comparison, and Outcome (PICO) framework. Study participants completed 19 usability tasks in 3 to 11 seconds with a success rate of 78% to 100%. Participants favored the interactive visual displays over narrative abstracts according to perceived efficiency, effectiveness, effort, user experience and preference (all P values <.001). CONCLUSIONS: When interpreting and applying RCT findings to case vignettes, physicians preferred interactive graphical and PICO-framework-based information displays that enable direct comparison of the results from multiple RCTs compared to the traditional narrative and study-centered format. Future studies should investigate the use of interactive visual displays to support clinical decision making in care settings and their effect on clinician and patient outcomes.

Automatic identification of high impact articles in PubMed to support clinical decision making.

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 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.

Feasibility of Extracting Key Elements from ClinicalTrials.gov to Support Clinicians' Patient Care Decisions.

Motivation: Clinicians need up-to-date evidence from high quality clinical trials to support clinical decisions. However, applying evidence from the primary literature requires significant effort. Objective: To examine the feasibility of automatically extracting key clinical trial information from ClinicalTrials.gov. Methods: We assessed the coverage of ClinicalTrials.gov for high quality clinical studies that are indexed in PubMed. Using 140 random ClinicalTrials.gov records, we developed and tested rules for the automatic extraction of key information. Results: The rate of high quality clinical trial registration in ClinicalTrials.gov increased from 0.2% in 2005 to 17% in 2015. Trials reporting results increased from 3% in 2005 to 19% in 2015. The accuracy of the automatic extraction algorithm for 10 trial attributes was 90% on average. Future research is needed to improve the algorithm accuracy and to design information displays to optimally present trial information to clinicians.

Ultra-Thin Monocrystalline Silicon Solar Cell with 12.2% Efficiency Using Silicon-On-Insulator Substrate.

Single side heterojunction silicon solar cells were designed and fabricated using Silicon-On-Insulator (SOI) substrate. The TCAD software was used to simulate the effect of silicon layer thickness, doping concentration and the series resistance. A 10.5 µm thick monocrystalline silicon layer was epitaxially grown on the SOI with boron doping concentration of 2 x 10(16) cm(-3) by thermal CVD. Very high Voc of 678 mV was achieved by applying amorphous silicon heterojunction emitter on the front surface. The single cell efficiency of 12.2% was achieved without any light trapping structures. The rear surface recombination and the series resistance are the main limiting factors for the cell efficiency in addition to the c-Si thickness. By integrating an efficient light trapping scheme and further optimizing fabrication process, higher efficiency of 14.0% is expected for this type of cells. It can be applied to integrated circuits on a monolithic chip to meet the requirements of energy autonomous systems.

Text summarization in the biomedical domain: a systematic review of recent research.

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.

An in vivo cardiac assay to determine the functional consequences of putative long QT syndrome mutations.

RATIONALE: Genetic testing for Long QT Syndrome 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 Long QT Syndrome 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%), whereas correctly identifying a disease-causing variant in 39/39 cases (positive predictive value 100%). CONCLUSIONS: 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 long QT syndrome genetic test, the zebrafish cardiac assay provides an additional tool for the final determination of pathogenicity of gene variants identified in long QT syndrome genetic screening.

No association of the C>T polymorphism that is located 1385 upstream from initial code of fibroblast growth factor 1 gene with Alzheimer's disease in Chinese.

Several lines of evidence indicate that fibroblast growth factor 1 (FGF1) confers neuroprotection against excitotocity and contributes to the selective vulnerability of neurons in entorhinal cortex in Alzheimer's disease (AD). Especially, FGF1 is related to Apolipoprotein E (ApoE) expression in reactive astrocytes. Therefore, FGF1 is a promising candidate gene for AD. Two studies reported the association of a polymorphism that is located 1385bp upstream from the initial code of FGF1 gene (FGF1 -1385 C>T) polymorphism with AD. To determine whether this polymorphism could affect AD development, we investigated the association between this polymorphism and AD risk in 372 sporadic AD patients and 349 controls in a Chinese Han population. No significant difference of allele and genotype distributions between the AD cases and the controls was observed in the total samples (for the alleles, chi(2)=0.126; p=0.722; for the genotypes, chi(2)=0.089; p=0.765), neither when the samples were stratified by ApoE epsilon4-carrying status, age/age at onset and gender. Our data suggested no association between the FGF1 -1385 C>T polymorphism and AD risk in Chinese Han population.

A critical role for type I IFN in arthritis development following Borrelia burgdorferi infection of mice.

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-gamma 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-gamma 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.

Association of NAD(P)H:quinone oxidoreductase 1 polymorphism and Alzheimer's disease in Chinese.

Several lines of evidence support a role of oxidative stress in the pathology of Alzheimer's disease (AD). NAD(P)H:quinone oxidoreductase 1 (NQO1) catalyzes the two-electron reduction of quinones, preventing their participation in redox cycling and subsequent generation of reactive oxygen species. We examined association between the NQO1 C609T gene polymorphism and sporadic AD in a Chinese population comprising 311 AD patients and 330 controls. Our results showed a higher T-allele frequency in the AD cases compared with the controls. The difference was close to but did not reach statistically significant level [p = 0.059; odds ratio (OR) T versus C = 1.236; 95% confidence interval (95% CI), 0.992-1.540]. A significantly low C/C genotype frequency in the AD cases compared with the controls was detected (p = 0.025; OR C/C versus C/T + T/T = 0.674; 95% CI, 1.049-2.098) and APOE epsilon4 status analysis revealed significant difference in the APOE epsilon4 non-carriers (p = 0.036; OR = 0.633; 95% CI, 1.027-2.427). In the > or =65 years samples, significantly low C/C frequency in the AD cases in comparison with the controls was observed in the APOE epsilon4 non-carriers (p = 0.045; OR = 0.595; 95% CI, 1.010-2.794). These results indicated that the C/C genotype had a possible protective effect against AD development, and the T allele might be a weak risk factor for late onset AD.

Association analysis of brain-derived neurotrophic factor (BDNF) gene 196 A/G polymorphism with Alzheimer's disease (AD) in mainland Chinese.

A functional polymorphism in the coding region of brain-derived neurotrophic factor (BDNF) gene (196 A/G, Met66Val) has recently been reported to be associated with Alzheimer's disease (AD) and with an overrepresentation of G allele in AD patients, but different results have also been presented. We conducted a case-control study to analyze the association between the BDNF A/G polymorphism and sporadic AD in a sample composed of 203 AD patients and 239 controls from Mainland Chinese Han population. No association between the polymorphism and AD, no association between the polymorphism and age at onset in AD, and no significant interaction between BDNF and apolipoprotein E (APOE) genotype were detected in either the total or the male samples. However, a significantly high frequency of the GG genotype in the female controls compared with the female patients was detected. A postponed age at onset in the female patients with the GG genotype was also observed. These results suggest that the GG genotype has a protection effect from AD development in females. A significant low frequency of AD patients with the BDNF GG genotype in the AD APOEepsilon4 carriers compared with the frequency of the controls with the BDNF GG genotype in the control APOEepsilon4 carriers was also detected in the female individuals, suggesting that the BDNF GG genotype may reduce the effect of APOEepsilon4 on AD risk in females. Additionally, low frequencies of BDNF G allele and GG genotype were revealed in Chinese when compared with that in the other race populations so far reported.