1,721,186 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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Integration of benchwork, clinical trials, and real world data to investigate drug interactions
Full text linkReal world data (RWD), data from various sources other than clinical trials, is increasingly being integrated into the research setting. In particular, electronic health records (EHRs), which serve as a clinical record to document a patient’s medical history as well as support administrative functions, have been an invaluable resource rich with patient data. Here we present three projects spanning four chapters where EHRs, in combination with clinical trials and pharmacokinetic and pharmacodynamic (PKPD) modelling, were used to extend and complement studies and findings in the laboratory focusing on transporter-mediated drug interactions. Transporter-mediated drug interactions have the potential to influence both drug efficacy as well as toxicity. During the clinical development of the Janus Kinase 2 (JAK2) inhibitor fedratinib, several patients developed symptoms similar to Wernicke’s encephalopathy, a life-threating disease caused by Vitamin B1 (thiamine) deficiency; subsequent in vitro studies showed that fedratinib is a potent inhibitor of ThTR-2. Motivated by this drug-nutrient interaction (DNI) observed in the fedratinib trial, we investigated if commonly used prescription drugs can inhibit ThTR-2. Using a multifaceted approach, we started with an in vitro high-throughput screen which was further complemented by quantitative structure activity relationship (QSAR) modelling and real world data. Our comprehensive analysis suggested that several marketed drugs inhibit ThTR-2 and may contribute to thiamine deficiency, especially in at-risk populations. In order to further explore the impact of these potential inhibitors in humans, we designed and conducted a clinical study in healthy volunteers. Interestingly, we observed that thiamine concentrations were higher when co-administered with trimethoprim, one of the potent, clinically relevant inhibitors identified in our screen. The maximum concentration achieved (Cmax) and area under the curve from 0 to 24 hours (AUC0-24) were 2.7- and 4.6-fold higher in the combination arm, respectively. We hypothesized that trimethoprim may inhibit OCT1, a hepatic uptake transporter, in addition to ThTR-2, which was supported using EHR data by comparing laboratory values of endogenous OCT1 biomarkers in patients prescribed trimethoprim versus patients not prescribed trimethoprim. Next, we shifted our focus to pharmacogenomics, that is, genetic factors that affect drug response. Response to allopurinol, the first line treatment for gout, is highly variable; the reduced function variant BCRP p.Q141K has been associated with poor response to allopurinol. Thus, we aimed to characterize the relationship between BCRP p.Q141K, allopurinol/oxypurinol, and serum uric acid (SUA) levels by performing a clinical trial, building a PKPD model, and mining EHRs. Our clinical study found that p.Q141K associated with longer half-life of oxypurinol and our PKPD model found that gender affected oxypurinol volume of distribution while BCRP genotype and kidney function were significant covariates for baseline SUA levels. Additionally, using RWD, we found that drugs that were clinical inhibitors of BCRP associated with increased SUA levels, suggesting the potential of these drugs to cause hyperuricemia. Finally, given the ongoing COVID19 pandemic, we conducted extensive in vitro experiments aimed at predicting the potential for 25 small molecule drugs in clinical trials for COVID19 to cause transporter-mediated drug-drug interactions (DDIs). We found that 21 of the drugs were predicted to cause a clinically relevant DDI, and we were able to provide preliminary validation of these in vitro findings using EHR data, including a database representing nearly 120,000 COVID19 patients. Collectively, my dissertation research demonstrates how the integration of benchwork, clinical trials, and real world data provides us a new approach to translational research, bridging findings from the laboratory to patients
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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Integrative Precision Medicine Approach to Dissect Patient Heterogeneity in Systemic Lupus Erythematosus
Full text linkAutoimmune disease arises from dysregulation of the immune system, leading to its attack on the body's own tissues and organs. The clinical heterogeneity of these diseases arises from several sources, such as genetic predisposition, environmental triggers, and aberrant immune responses. One emerging area of interest is the role of transposable elements (TEs) in autoimmune disease pathogenesis because these self-nucleic acids can be mistakenly detected as foreign, which can trigger a chronic immune reaction.There is growing appreciation for the role of TEs in systemic lupus erythematosus (SLE) and studies have found differentially expressed TEs in SLE patients, which suggests a link between TE activity and disease mechanisms. Our work investigated TE expression in four immune cell types from SLE patients, revealing cell-specific and SLE subphenotype-specific differentially expressed TEs, with additional cell-type-specific TE associations in different ancestry groups. TE expression was also associated with host gene expression involved in antiviral and immune responses, supporting the hypothesis that TEs could activate the innate immune system and contribute to chronic inflammation and autoimmunity.
This study underscores the importance of TEs in SLE heterogeneity and highlights the need for further exploration of TE expression in normal immune cells and functional studies to understand their role in SLE pathogenesis. Future work to study whether antiretroviral drugs could reduce expression of TEs and mitigate SLE symptoms is warranted, given the potential involvement of TEs in autoimmune disease pathogenesi
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Leveraging human tissue samples to investigate tumor heterogeneity in the context of cancer models, therapeutics, and patient outcomes
Full text linkCancer is among the leading causes of mortality worldwide and the number of cancer-related deaths is expected to rise to 16.4 million by 2040. Given the wealth of publicly available cancer data that has been generated over the past few decades, it is now possible to investigate cancer at an unprecedented scale using computational approaches. This body of work covers three projects that leverage human tumor samples to evaluate cancer models, predict cancer therapeutics, and investigate the prognostic value of infiltrating B cell repertoires. In the first project, we compared cell lines from the Cancer Cell Line Encyclopedia to primary tumor samples from the Cancer Genome Atlas (TCGA) to evaluate how well each cell lines represents its primary tumors. We predicted subtype classifications for individual cell lines and proposed a new pan-cancer cell line panel with the most representative cell lines across 22 tumor types to facilitate pan-cancer studies. In the second project, we applied a computational drug repositioning approach to identify compounds to sensitize drug resistant breast cancers using patient samples from the I-SPY2 TRIAL and the Connectivity Map drug perturbation dataset. We identified a drug hit, fulvestrant, which we validated experimentally and found that it increased drug response in a paclitaxel-resistant breast cancer cell line. In the third project, we extracted B cell repertoires from TCGA RNA-seq samples and performed diversity and network analysis. We then evaluated the prognostic value of these repertoire features and identified significant associations with survival in a subset of tumor types. Together, this research demonstrates how computational methods can leverage publicly available datasets to extract new insights into cancer biolog
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A Systems Biology Approach to Precision Medicine for Alzheimer’s Disease: Cell-type-directed Network-correcting Therapeutics and Transcriptomic Profiling across Major Risk Factors
Full text linkAlzheimer's disease (AD) is a multifactorial neurodegenerative condition characterized by heterogeneous molecular alterations across various brain cell types, posing significant challenges for the development of effective treatments. To address this complexity, we undertook two complementary projects aimed at advancing precision medicine approaches for AD, each building on the other to create a robust foundation for targeted therapies.The first project serves as a proof of concept for an innovative drug discovery strategy, utilizing a network correction approach grounded in direct human evidence and real-world data. By integrating diverse datasets, including single-cell human transcriptomics, drug perturbations, and electronic clinical records, we identified the combination of letrozole and irinotecan as potential therapeutics designed to correct gene expression alterations across multiple cell types implicated in AD. Rigorous validation in AD mouse models demonstrated that this combination therapy, targeting both neurons and glial cells, significantly ameliorated memory deficits and other AD-related pathologies, outperforming the single-drug treatments targeting either neurons or glial cells alone. The success of this project underscores the potential of cell-type-directed network-correcting therapy, demonstrating that targeting the transcriptomic landscape at a cell-type-specific level may offer a more efficacious approach to treating AD.
Building on this foundation, the second project was designed to expand the knowledge foundation for precision medicine by comprehensively characterizing the molecular influences of major AD risk factors, such as age, apolipoprotein E4 (APOE4), and sex using AD mouse models. By analyzing single-nucleus RNA-sequencing (snRNA-seq) data from the hippocampus of human APOE4 and APOE3 knock-in (KI) female and male mice across different ages, we identified significant variations in cell type abundance and gene expression patterns, particularly driven by sex differences and the interplay between age and APOE genotype. This detailed molecular profiling not only enriches our understanding of the disease but also provides a valuable dataset for future applications of the network correction method. Specifically, it enables the mapping of distinct risk profiles and the identification of tailored therapeutic interventions for individuals based on their unique transcriptomic signatures.
Together, these two projects are highly complementary: the first project validates the efficacy of network correction therapy, while the second project supplies the essential molecular data needed to understand disease heterogeneity, enabling the application of this therapeutic approach in a precision medicine framework. By integrating these insights, we can more accurately tailor treatments to individuals with distinct risk profiles, thereby advancing the development of personalized therapies for AD
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