Use of biorelevant dissolution media in dissolution tests as a predictive method of oral bioavailability

Abstract Dissolution is a key step in the uptake of oral drugs. In order to compare the behaviour of the dissolution of two formulations, the dissolution profile test was used. This assay must be discriminative and should mimic in vivo conditions. Many dissolution media described in pharmacopoeias are not predictive of bioavailability. Due to this, biorelevant media are used as an alternative to solve this problem. The objective of this work is to evaluate the relevance of biorelevant dissolution media to predict in vivo drug dissolution. For this, a bibliographic search was carried out in scientific databases. The search was first performed for articles verifying the physicochemical properties of human gastrointestinal fluids. Subsequently, a comparison was made between the properties of gastrointestinal fluids and those of biorelevant and pharmacopoeial media. Finally, the results of bioequivalence studies and dissolution profile tests in biorelevant media described in the literature were compared. The results revealed that there are a few publications that have analysed some physicochemical properties of gastrointestinal fluids. In addition, high variability was observed for some properties. Regarding the comparison of these properties with pharmacopoeial media and biorelevant media, the analysis showed that the biorelevant media are more similar to gastrointestinal fluids than the pharmacopoeial media. Finally, the in vitro dissolution profile results were similar to the results obtained in vivo. Thus, biorelevant media may be useful for analysing dissolution profiles.

Article-level classification of scientific publications: A comparison of deep learning, direct citation and bibliographic coupling.

Classification schemes for scientific activity and publications underpin a large swath of research evaluation practices at the organizational, governmental, and national levels. Several research classifications are currently in use, and they require continuous work as new classification techniques becomes available and as new research topics emerge. Convolutional neural networks, a subset of "deep learning" approaches, have recently offered novel and highly performant methods for classifying voluminous corpora of text. This article benchmarks a deep learning classification technique on more than 40 million scientific articles and on tens of thousands of scholarly journals. The comparison is performed against bibliographic coupling-, direct citation-, and manual-based classifications-the established and most widely used approaches in the field of bibliometrics, and by extension, in many science and innovation policy activities such as grant competition management. The results reveal that the performance of this first iteration of a deep learning approach is equivalent to the graph-based bibliometric approaches. All methods presented are also on par with manual classification. Somewhat surprisingly, no machine learning approaches were found to clearly outperform the simple label propagation approach that is direct citation. In conclusion, deep learning is promising because it performed just as well as the other approaches but has more flexibility to be further improved. For example, a deep neural network incorporating information from the citation network is likely to hold the key to an even better classification algorithm.

Publication Categories in Toxicologic Pathology.

Toxicologic Pathology is the official journal of the Society of Toxicologic Pathology (STP), the British Society of Toxicological Pathology, and the European STP (ESTP). Toxicologic Pathology publishes articles related to topics in various aspects of toxicologic pathology such as anatomic pathology, clinical pathology, experimental pathology, and biomarker research. Publications include society-endorsed Best Practice/Position and Points to Consider publications and ESTP Expert Workshop articles that are relevant to toxicologic pathology and scientific regulatory processes, Opinion articles under the banner of the STP Toxicologic Pathology Forum, Original Articles, Review Articles (unsolicited/contributed, mini, and invited), Brief Communications, Letters to the Editor, Meeting Reports, and Book Reviews. This article provides details on the various publication categories in Toxicologic Pathology and will serve as a reference for authors and readers.

Predicting article citations using data of 100 top-cited publications in the journal Medicine since 2011: A bibliometric analysis.

BACKGROUND: Publications regarding the 100 top-cited articles in a given discipline are common, but studies reporting the association between article topics and their citations are lacking. Whether or not reviews and original articles have a higher impact factor than case reports is a point for verification in this study. In addition, article topics that can be used for predicting citations have not been analyzed. Thus, this study aims to METHODS:: We searched PubMed Central and downloaded 100 top-cited abstracts in the journal Medicine (Baltimore) since 2011. Four article types and 7 topic categories (denoted by MeSH terms) were extracted from abstracts. Contributors to these 100 top-cited articles were analyzed. Social network analysis and Sankey diagram analysis were performed to identify influential article types and topic categories. MeSH terms were applied to predict the number of article citations. We then examined the prediction power with the correlation coefficients between MeSH weights and article citations. RESULTS: The citation counts for the 100 articles ranged from 24 to 127, with an average of 39.1 citations. The most frequent article types were journal articles (82%) and comparative studies (10%), and the most frequent topics were epidemiology (48%) and blood and immunology (36%). The most productive countries were the United States (24%) and China (23%). The most cited article (PDID = 27258521) with a count of 135 was written by Dr Shang from Shandong Provincial Hospital Affiliated to Shandong University (China) in 2016. MeSH terms were evident in the prediction power of the number of article citations (correlation coefficients  = 0.49, t = 5.62). CONCLUSION: The breakthrough was made by developing dashboards showing the overall concept of the 100 top-cited articles using the Sankey diagram. MeSH terms can be used for predicting article citations. Analyzing the 100 top-cited articles could help future academic pursuits and applications in other academic disciplines.

Quantifying and contextualizing the impact of bioRxiv preprints through automated social media audience segmentation.

Engagement with scientific manuscripts is frequently facilitated by Twitter and other social media platforms. As such, the demographics of a paper's social media audience provide a wealth of information about how scholarly research is transmitted, consumed, and interpreted by online communities. By paying attention to public perceptions of their publications, scientists can learn whether their research is stimulating positive scholarly and public thought. They can also become aware of potentially negative patterns of interest from groups that misinterpret their work in harmful ways, either willfully or unintentionally, and devise strategies for altering their messaging to mitigate these impacts. In this study, we collected 331,696 Twitter posts referencing 1,800 highly tweeted bioRxiv preprints and leveraged topic modeling to infer the characteristics of various communities engaging with each preprint on Twitter. We agnostically learned the characteristics of these audience sectors from keywords each user's followers provide in their Twitter biographies. We estimate that 96% of the preprints analyzed are dominated by academic audiences on Twitter, suggesting that social media attention does not always correspond to greater public exposure. We further demonstrate how our audience segmentation method can quantify the level of interest from nonspecialist audience sectors such as mental health advocates, dog lovers, video game developers, vegans, bitcoin investors, conspiracy theorists, journalists, religious groups, and political constituencies. Surprisingly, we also found that 10% of the preprints analyzed have sizable (>5%) audience sectors that are associated with right-wing white nationalist communities. Although none of these preprints appear to intentionally espouse any right-wing extremist messages, cases exist in which extremist appropriation comprises more than 50% of the tweets referencing a given preprint. These results present unique opportunities for improving and contextualizing the public discourse surrounding scientific research.

The articles.ELM resource: simplifying access to protein linear motif literature by annotation, text-mining and classification.

Modern biology produces data at a staggering rate. Yet, much of these biological data is still isolated in the text, figures, tables and supplementary materials of articles. As a result, biological information created at great expense is significantly underutilised. The protein motif biology field does not have sufficient resources to curate the corpus of motif-related literature and, to date, only a fraction of the available articles have been curated. In this study, we develop a set of tools and a web resource, 'articles.ELM', to rapidly identify the motif literature articles pertinent to a researcher's interest. At the core of the resource is a manually curated set of about 8000 motif-related articles. These articles are automatically annotated with a range of relevant biological data allowing in-depth search functionality. Machine-learning article classification is used to group articles based on their similarity to manually curated motif classes in the Eukaryotic Linear Motif resource. Articles can also be manually classified within the resource. The 'articles.ELM' resource permits the rapid and accurate discovery of relevant motif articles thereby improving the visibility of motif literature and simplifying the recovery of valuable biological insights sequestered within scientific articles. Consequently, this web resource removes a critical bottleneck in scientific productivity for the motif biology field. Database URL: http://slim.icr.ac.uk/articles/.

Examining influential factors for acknowledgements classification using supervised learning.

Acknowledgements have been examined as important elements in measuring the contributions to and intellectual debts of a scientific publication. Unlike previous studies that were limited in the scope of analysis and manual examination. The present study aimed to conduct the automatic classification of acknowledgements on a large scale of data. To this end, we first created a training dataset for acknowledgements classification by sampling the acknowledgements sections from the entire PubMed Central database. Second, we adopted various supervised learning algorithms to examine which algorithm performed best in what condition. In addition, we observed the factors affecting classification performance. We investigated the effects of the following three main aspects: classification algorithms, categories, and text representations. The CNN+Doc2Vec algorithm achieved the highest performance of 93.58% accuracy in the original dataset and 87.93% in the converted dataset. The experimental results indicated that the characteristics of categories and sentence patterns influenced the performance of classification. Most of the classifiers performed better on the categories of financial, peer interactive communication, and technical support compared to other classes.

Scientific papers: Which type would you prefer?

It's very important to enhance the quality of scientific papers produced by postgraduates and scholars from academic institutions. To encourage their academic and professional development, these young scientists should be encouraged to compose nonresearch articles, in addition to original research articles, including short essays, perspectives and reviews.

El plagio en las publicaciones académicas / Plagiarism in academic publication

No dipsonible

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.

A review on medicinal plants used for treating ototoxicity and acoustic trauma induced hearing loss

Hearing loss induced by chemotherapy and acoustic trauma is mainly associated with two factors, free radical formation and apoptosis pathway activation. Despite numerous efforts on reducing the effects of these factors, no definite strategy is still determined to interfere with and control these processes. In recent studies, various protective agents, including antioxidants have been used on animal models, to inhibit the formation of free radicals thus improving hearing loss.In this review article we will discuss the role of traditional herbal medicine in treatment of noise/drug induced hearing loss, focusing on medicinal plants' active substances,as well as their mechanisms of action in reducing or preventing the formation of free radicals thus increasing the rate of survival of cochlea cells. Data have been gathered since year 2000, from scientific publications including the following keywords: deafness, drug toxicity, acute trauma, medicinal herbs and oxidative stress. The study includes all herbs and medicinal plants that have been experimentally used in studies on animal models and clinical trials. The results from these studies indicate the effectiveness of most of these herbs and their active substances through their antioxidative properties. Medicinal plants reported in this review can thus be considered as effective remedies intreating noise/drug induced hearing loss,yet further studies need to be done.

Systematic review or scoping review? Guidance for authors when choosing between a systematic or scoping review approach.

BACKGROUND: Scoping reviews are a relatively new approach to evidence synthesis and currently there exists little guidance regarding the decision to choose between a systematic review or scoping review approach when synthesising evidence. The purpose of this article is to clearly describe the differences in indications between scoping reviews and systematic reviews and to provide guidance for when a scoping review is (and is not) appropriate. RESULTS: Researchers may conduct scoping reviews instead of systematic reviews where the purpose of the review is to identify knowledge gaps, scope a body of literature, clarify concepts or to investigate research conduct. While useful in their own right, scoping reviews may also be helpful precursors to systematic reviews and can be used to confirm the relevance of inclusion criteria and potential questions. CONCLUSIONS: Scoping reviews are a useful tool in the ever increasing arsenal of evidence synthesis approaches. Although conducted for different purposes compared to systematic reviews, scoping reviews still require rigorous and transparent methods in their conduct to ensure that the results are trustworthy. Our hope is that with clear guidance available regarding whether to conduct a scoping review or a systematic review, there will be less scoping reviews being performed for inappropriate indications better served by a systematic review, and vice-versa.

Dissemination of novel biostatistics methods: Impact of programming code availability and other characteristics on article citations.

BACKGROUND: As statisticians develop new methodological approaches, there are many factors that influence whether others will utilize their work. This paper is a bibliometric study that identifies and quantifies associations between characteristics of new biostatistics methods and their citation counts. Of primary interest was the association between numbers of citations and whether software code was available to the reader. METHODS: Statistics journal articles published in 2010 from 35 statistical journals were reviewed by two biostatisticians. Generalized linear mixed models were used to determine which characteristics (author, article, and journal) were independently associated with citation counts (as of April 1, 2017) in other peer-reviewed articles. RESULTS: Of 722 articles reviewed, 428 were classified as new biostatistics methods. In a multivariable model, for articles that were not freely accessible on the journal's website, having code available appeared to offer no boost to the number of citations (adjusted rate ratio = 0.96, 95% CI = 0.74 to 1.24, p = 0.74); however, for articles that were freely accessible on the journal's website, having code available was associated with a 2-fold increase in the number of citations (adjusted rate ratio = 2.01, 95% CI = 1.30 to 3.10, p = 0.002). Higher citation rates were also associated with higher numbers of references, longer articles, SCImago Journal Rank indicator (SJR), and total numbers of publications among authors, with the strongest impact on citation rates coming from SJR (rate ratio = 1.21 for a 1-unit increase in SJR; 95% CI = 1.11 to 1.32). CONCLUSION: These analyses shed new insight into factors associated with citation rates of articles on new biostatistical methods. Making computer code available to readers is a goal worth striving for that may enhance biostatistics knowledge translation.

Sesión - Indización de documentos según Metodología LILACS - Uso de Tipos de Publicación [4 de 10]

Presenta los tipos de Publicación, su objectivo y aplicación; lista todos y explica como identificar; Tipos de Publicación más frecuentes y tipos especiales; Tipos de Publicación X Descriptores de asuntos, mostra Ejemplo de indización de un articulo.

The 100 most influential publications in asthma from 1960 to 2017: A bibliometric analysis.

BACKGROUND: The area of asthma medicine has produced a large volume of important clinical and scientific papers that can be found in those most influential journals. The purpose of our study was to identify the 100 most cited papers in asthma research and to analyze their characteristics. METHODS: We used the Institute for Scientific Information Web of Knowledge Database to identify the most frequently cited articles published from 1960 to December 2017. Original articles and reviews were included in the study. The 100 top-cited articles were then analyzed with regard to number of citations, publication year, journals, institution, research type and field, authors and countries of authors of publications. RESULTS: The 100 top-cited articles in asthma were published between 1960 and 2011 with a median of 933 citations per article (range, 701-2947). The number of citations per article was greatest for articles published in the 1990s. The United States of America contributed most of the classic articles, followed by England. The leading institutions were Imperial College London, McMaster University, Erasmus University Rotterdam. The 100 top-cited articles were published in twenty-five journals, led by The New England Journal of Medicine (21 articles), followed by American Journal of Respiratory and Critical Care Medicine (19 articles), Lancet (11 articles), respectively. Among the 100 classics, 50% articles were clinical research articles. CONCLUSIONS: Our study provides a historical perspective on the progress of research on asthma. Studies conducted in well-developed European countries and North America, published in high-impact journals had the highest citations.

Cancer Hallmarks Analytics Tool (CHAT): a text mining approach to organize and evaluate scientific literature on cancer.

MOTIVATION: To understand the molecular mechanisms involved in cancer development, significant efforts are being invested in cancer research. This has resulted in millions of scientific articles. An efficient and thorough review of the existing literature is crucially important to drive new research. This time-demanding task can be supported by emerging computational approaches based on text mining which offer a great opportunity to organize and retrieve the desired information efficiently from sizable databases. One way to organize existing knowledge on cancer is to utilize the widely accepted framework of the Hallmarks of Cancer. These hallmarks refer to the alterations in cell behaviour that characterize the cancer cell. RESULTS: We created an extensive Hallmarks of Cancer taxonomy and developed automatic text mining methodology and a tool (CHAT) capable of retrieving and organizing millions of cancer-related references from PubMed into the taxonomy. The efficiency and accuracy of the tool was evaluated intrinsically as well as extrinsically by case studies. The correlations identified by the tool show that it offers a great potential to organize and correctly classify cancer-related literature. Furthermore, the tool can be useful, for example, in identifying hallmarks associated with extrinsic factors, biomarkers and therapeutics targets. AVAILABILITY AND IMPLEMENTATION: CHAT can be accessed at: http://chat.lionproject.net. The corpus of hallmark-annotated PubMed abstracts and the software are available at: http://chat.lionproject.net/about. CONTACT: simon.baker@cl.cam.ac.uk. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Article-level assessment of influence and translation in biomedical research.

Given the vast scale of the modern scientific enterprise, it can be difficult for scientists to make judgments about the work of others through careful analysis of the entirety of the relevant literature. This has led to a reliance on metrics that are mathematically flawed and insufficiently diverse to account for the variety of ways in which investigators contribute to scientific progress. An urgent, critical first step in solving this problem is replacing the Journal Impact Factor with an article-level alternative. The Relative Citation Ratio (RCR), a metric that was designed to serve in that capacity, measures the influence of each publication on its respective area of research. RCR can serve as one component of a multifaceted metric that provides an effective data-driven supplement to expert opinion. Developing validated methods that quantify scientific progress can help to optimize the management of research investments and accelerate the acquisition of knowledge that improves human health.

Machine Learning-Based Classification of 38 Years of Spine-Related Literature Into 100 Research Topics.

STUDY DESIGN: Retrospective review. OBJECTIVE: To identify the top 100 spine research topics. SUMMARY OF BACKGROUND DATA: Recent advances in "machine learning," or computers learning without explicit instructions, have yielded broad technological advances. Topic modeling algorithms can be applied to large volumes of text to discover quantifiable themes and trends. METHODS: Abstracts were extracted from the National Library of Medicine PubMed database from five prominent peer-reviewed spine journals (European Spine Journal [ESJ], The Spine Journal [SpineJ], Spine, Journal of Spinal Disorders and Techniques [JSDT], Journal of Neurosurgery: Spine [JNS]). Each abstract was entered into a latent Dirichlet allocation model specified to discover 100 topics, resulting in each abstract being assigned a probability of belonging in a topic. Topics were named using the five most frequently appearing terms within that topic. Significance of increasing ("hot") or decreasing ("cold") topic popularity over time was evaluated with simple linear regression. RESULTS: From 1978 to 2015, 25,805 spine-related research articles were extracted and classified into 100 topics. Top two most published topics included "clinical, surgeons, guidelines, information, care" (n = 496 articles) and "pain, back, low, treatment, chronic" (424). Top two hot trends included "disc, cervical, replacement, level, arthroplasty" (+0.05%/yr, P < 0.001), and "minimally, invasive, approach, technique" (+0.05%/yr, P < 0.001). By journal, the most published topics were ESJ-"operative, surgery, postoperative, underwent, preoperative"; SpineJ-"clinical, surgeons, guidelines, information, care"; Spine-"pain, back, low, treatment, chronic"; JNS- "tumor, lesions, rare, present, diagnosis"; JSDT-"cervical, anterior, plate, fusion, ACDF." CONCLUSION: Topics discovered through latent Dirichlet allocation modeling represent unbiased meaningful themes relevant to spine care. Topic dynamics can provide historical context and direction for future research for aspiring investigators and trainees interested in spine careers. Please explore https://singdc.shinyapps.io/spinetopics. LEVEL OF EVIDENCE: N A.

Structure and dynamics of European sports science textual contents: Analysis of ECSS abstracts (1996-2014).

The article discusses general structure and dynamics of the sports science research content as obtained from the analysis of 21998 European College of Sport Science abstracts belonging to 12 science topics. The structural analysis showed intertwined multidisciplinary and unifying tendencies structured along horizontal (scope) and vertical (level) axes. Methodological (instrumental and mode of inquiry) integrative tendencies are dominant. Theoretical integrative tendencies are much less detectable along both horizontal and vertical axes. The dynamic analysis of written abstracts text content over the 19 years reveals the contextualizing and guiding role of thematic skeletons of each sports science topic in forming more detailed contingent research ideas and the role of the latter in stabilizing and procreating the former. This circular causality between both hierarchical levels and functioning on separate characteristic time scales is crucial for understanding how stable research traditions self-maintain and self-procreate through innovative contingencies. The structure of sports science continuously rebuilds itself through use and re-use of contingent research ideas. The thematic skeleton ensures its identity and the contingent conceptual sets its flexibility and adaptability to different research or applicative problems.