Biomedical Articles Research Articles

PlasmiR: A Manual Collection of Circulating microRNAs of Prognostic and Diagnostic Value

Simple SummaryOnly recently have the important biomarker capacities of microRNAs (miRNAs) in blood samples during disease been revealed. miRNAs are abundantly detected in circulation, and are less prone to degradation than longer RNA. Details regarding potential discriminatory miRNAs against numerous pathologic conditions are dispersed across articles, while existing resources that catalogue miRNA abundance in blood samples are not tailored to biomarker research. This study presents the meticulous manual curation of more than 200 articles that specifically interrogate the biomarker potential of miRNAs in whole blood, serum, or plasma. This annotation effort resulted in the creation of plasmiR, a database that systematically provides experimental evidence for the diagnostic and prognostic potential of circulating miRNAs against human diseases. plasmiR features 1021 entries, accompanied by rich study-specific meta-information, and an intuitive interface that enables the formation of complex queries and visualizations.Only recently, microRNAs (miRNAs) were found to exist in traceable and distinctive amounts in the human circulatory system, bringing forth the intriguing possibility of using them as minimally invasive biomarkers. miRNAs are short non-coding RNAs that act as potent post-transcriptional regulators of gene expression. Extensive studies in cancer and other disease landscapes investigate the protective/pathogenic functions of dysregulated miRNAs, as well as their biomarker potential. A specialized resource amassing experimentally verified, circulating miRNA biomarkers does not exist. We queried the existing literature to identify articles assessing diagnostic/prognostic roles of miRNAs in blood, serum, or plasma samples. Articles were scrutinized in order to exclude instances lacking sufficient experimental documentation or employing no biomarker assessment methods. We incorporated information from more than 200 biomedical articles, annotating crucial meta-information including cohort sizes, inclusion-exclusion criteria, disease/healthy confirmation methods and quantification details. miRNAs and diseases were systematically characterized using reference resources. Our circulating miRNA biomarker collection is provided as an online database, plasmiR. It consists of 1021 entries regarding 251 miRNAs and 112 diseases. More than half of plasmiR’s entries refer to cancerous and neoplastic conditions, 183 of them (32%) describing prognostic associations. plasmiR facilitates smart queries, emphasizing visualization and exploratory modes for all researchers.

ECO-CollecTF: A Corpus of Annotated Evidence-Based Assertions in Biomedical Manuscripts.

Analysis of high-throughput experiments in the life sciences frequently relies upon standardized information about genes, gene products, and other biological entities. To provide this information, expert curators are increasingly relying on text mining tools to identify, extract and harmonize statements from biomedical journal articles that discuss findings of interest. For determining reliability of the statements, curators need the evidence used by the authors to support their assertions. It is important to annotate the evidence directly used by authors to qualify their findings rather than simply annotating mentions of experimental methods without the context of what findings they support. Text mining tools require tuning and adaptation to achieve accurate performance. Many annotated corpora exist to enable developing and tuning text mining tools; however, none currently provides annotations of evidence based on the extensive and widely used Evidence and Conclusion Ontology. We present the ECO-CollecTF corpus, a novel, freely available, biomedical corpus of 84 documents that captures high-quality, evidence-based statements annotated with the Evidence and Conclusion Ontology.

Uprooting race-based assumptions in biomedical journal articles.

Coronavirus disease 2019 (COVID-19) has exacerbated the structural inequities in healthcare and the challenges of translating research into public discourse. This article highlights key antiracist considerations, presents previously noted core challenges, and provides recommendations for writing and reporting. Importantly, this article contributes to combating racialized science in the biomedical community.

Invention and identity

History of Innovation Members of social groups may be more likely to patent inventions targeted toward their own group's needs and interests. Koning et al. examined US biomedical patents and found that although fewer women engage in commercial patenting compared with men, their patents are more likely to focus on women's health (see the Perspective by Murray). In an evaluation of biomedical articles, the researchers found that women were also more likely to make scientific discoveries that might lead to women's health patents. These findings demonstrate that a lack of representation among inventors translates into a lack of breadth in inventions. Science , aba6990, this issue p. [1345][1]; see also abh3178, p. [1260][2] [1]: /lookup/doi/10.1126/science.aba6990 [2]: /lookup/doi/10.1126/science.abh3178

Who do we invent for? Patents by women focus more on women's health, but few women get to invent.

Women engage in less commercial patenting and invention than do men, which may affect what is invented. Using text analysis of all U.S. biomedical patents filed from 1976 through 2010, we found that patents with all-female inventor teams are 35% more likely than all-male teams to focus on women's health. This effect holds over decades and across research areas. We also found that female researchers are more likely to discover female-focused ideas. These findings suggest that the inventor gender gap is partially responsible for thousands of missing female-focused inventions since 1976. More generally, our findings suggest that who benefits from innovation depends on who gets to invent.

Editors-in-chief perceptions of patients as (co) authors on publications and the acceptability of ICMJE authorship criteria: a cross-sectional survey

BackgroundAccess to, and awareness of, appropriate authorship criteria is an important right for patient partners. Our objective was to measure medical journal Editors-in-Chief’ perceptions of including patients as (co-)authors on research publications and to measure their views on the application of the ICMJE (International Committee of Medical Journals Editors) authorship criteria to patient partners.MethodsWe conducted a cross-sectional survey co-developed with a patient partner. Editors-in-Chief of English-language medical journals were identified via a random sample of journals obtained from the Scopus source list. The key outcome measures were whether Editors-in-Chief believed: 1) patient partners should be (co-)authors and; 2) whether they felt the ICMJE criteria for authorship required modification for use with patient partners. We also measured Editors-in-Chief description of how their journal’s operations incorporate patient partner perspectives.ResultsOne hundred twelve Editors-in-Chief responded to our survey (18.7% response rate; 66.69% male). Participants were able to skip any questions they did not want to answer, so there is missing data for some items. 69.2% (N = 74) of Editors-in-Chief indicated it was acceptable for patient partners to be authors or co-authors on published biomedical research articles, with the remaining 30.8% (N = 33) indicating this would not be appropriate. When asked specifically about the ICMJE authorship criteria, and whether this should be revised to be more inclusive of patient partners, 35.8% (N = 39) indicated it should be revised, 35.8% (N = 39) indicated it should not be revised, and 28.4% (N = 31) were unsure about a revision. 74.1% (N = 80) of Editors-in-Chief did not think patients should be required to have an academic affiliation to published while 16.7% (N = 18) and 9.3% (N = 10) indicated they should or were unsure. 3.6% (N = 4) of Editors-in-Chief indicated their journal had a policy that specifies how patients or patient partners should be considered as authors.ConclusionsOur findings highlight gaps that may act as barriers to patient partner participation in research. A key implication is the need for education and for consensus building within the biomedical community to establish processes that will facilitate equitable patient partners inclusion.

Identification of the features of scientific discoveries in biomedical articles

Este trabalho propõe um método para a identificação de Descobertas Significativas (DI) na área biomédica através da comparação das principais conclusões de artigos nesta área com o conteúdo de uma ontologia pública na Web. Dessa forma, torna-se possível reconhecer o DI relatado no artigo antes mesmo de ser referenciado pela literatura. 75 artigos foram analisados ​​manualmente. Os resultados obtidos indicam que se o conteúdo da conclusão de um artigo estiver mal representado na ontologia, isso pode ser um indício de descoberta significativa. Um indício a favor dessa hipótese é o fato de o artigo que marca a descoberta da enzima telomerase ser de 1985, mas o termo “telomerase” só foi incluído no MeSH após 10 anos.

Sex bias persists in surgical research: A 5-year follow-up study

BackgroundFederal initiatives have recently addressed the sex bias that exists in biomedical and clinical research. However, improvement to the inclusion of sex as a biological variable remains unknown. MethodsWe performed a 5-year follow-up study of all clinical and biomedical research articles published in 5 surgical journals from January 1, 2017, through December 31, 2018. Human, animal, and cell subjects were analyzed for study/subject type, sex of participants, sex matching, and sex-based data reporting, analysis, and discussion. ResultsComparing 2017 to 2018 with 2011 to 2012, slightly more articles reported the sex of the human studied (87% vs 83%; P = .001). Inclusion of both sexes remained high (94% vs 95%; P = .22), but sex-based data reporting (36% vs 38%; P = .17), analysis (35% vs 33%; P = .39), and discussion of results (10% vs 23%; P < .0001) remained unchanged or worsened. Regarding animal research, the number of articles that stated the sex studied remained unchanged (79% vs 78%; P = .67); if stated, slightly more included both sexes (7% vs 3%; P = .002). Regarding cell research, fewer articles reported the sex of the cells studied (5% vs 24%; P = .0001); if stated, more articles included both sexes, but the difference did not reach statistical significance (25% vs 7%; P = .34). Sex matching remained poor with only 50% of human, 4% of animal, and 9% of cell studies matching the inclusion of both sexes by at least 50%. ConclusionSex bias persists in surgical research. The majority of articles failed to report, analyze, or discuss results based on sex, which will negatively affect clinical translatability and outcomes of evidence-based medicine.

Ensemble summarization of bio-medical articles integrating clustering and multi-objective evolutionary algorithms

The rapid exponential growth of scientific literature of bio-medicine, genomics, and other bio-sciences increases difficulties to access useful information timely and efficiently. The medical researchers, physicians and clinicians need to extract the gist of the novel articles effectively to gather knowledge and apply it properly for better treatment of the patients. In the paper, the bio-medical articles available in an open access source, PubMed MEDLINE, are considered for generating their extractive summaries using clustering and multi-objective evolutionary algorithm based ensemble summarization technique. Initially, we have extracted only the medical terms of an article and determined the concepts of all medical terms using Unified Medical Language System (UMLS) to represent the article in terms of a set of concepts. Next, extractive summaries of the article have been generated using various popularly used clustering algorithms and different centrality measures. The generated summaries are referred to as the base summaries of the article. Finally, multi-objective evolutionary algorithm is applied on these base summaries for generating an ensemble summary of the given article. The evolutionary algorithm uses two competent objective functions to measure the fitness value of each chromosome in the population. One objective function is the linear combination of jaccard similarity and Word2Vec similarity between the original article and the ensemble summary generated by the chromosome. The other objective function is defined as the information gain of the chromosome about the article by generating the ensemble summary. The best pareto optimal solution of the final population of the evolutionary algorithm provides the ensemble summary of the given article. The proposed ensemble method is compared with some state-of-art methods to demonstrate that it is both effective and statistically significant.

The effects of long-term extracurricular scientific research on the medical students: Insight from Jinan University Medical School.

The benefits and long-term effects of extracurricular scientific research on undergraduate students in many countries have been intensively investigated, but it remains obscure for Chinese medical students. In this study, we investigated the outcome of 60 medical students who have participated in extracurricular scientific research at Jinan University Medical School over a period of 7 years (2011-2018). The results revealed that these students have contributed to 31 biomedical science articles in reputable academic journals, as first- or co-authors. Furthermore, they also independently procured various funding based on their research achievements, and smaller awards for achievements in conferences and competitions. Assessment of the grade point average score of these students revealed that conducting extracurricular scientific research did not affect their routine medical study and exam grades (P>0.05). The students benefited from participating in extracurricular research, by acquiring the ability to think scientifically and enhancing their communication skills. In addition, the medical students were motivated to enlist for postgraduate studies so that they could further embark in scientific research. In sum, Chinese medical students are capable of participating in scientific research and make a significant contribution to science.

Retracted articles in the biomedical literature from Indian authors.

The aim of the present study is to identify retracted articles in the biomedical literature (co) authored by Indian authors and to examine the features of retracted articles. The PubMed database was searched to find the retracted articles in order to reach the goal. The search yielded 508 records and retrieved for the detailed analysis of: authorships and collaboration type, funding information, who retracts? journals and impact factors, and reasons for retraction. The results show that most of the biomedical articles retracted were published after 2010 and common reasons are plagiarism and fake data for retraction. More than half of the retracted articles were co-authored within the institutions and there is no repeat offender. 25% of retracted articles were published in the top 15 journals and 33% were published in the non-impact factor journals. Average time from publication to retraction is calculated to 2.86 years and retractions due to fake data takes longest period among the reasons. Majority of the funded research was retracted due to fake data whereas it is plagiarism for non-funded.

An improved BM25 algorithm for clinical decision support in Precision Medicine based on co-word analysis and Cuckoo Search

BackgroundRetrieving gene and disease information from a vast collection of biomedical abstracts to provide doctors with clinical decision support is one of the important research directions of Precision Medicine.MethodWe propose a novel article retrieval method based on expanded word and co-word analyses, also conducting Cuckoo Search to optimize parameters of the retrieval function. The main goal is to retrieve the abstracts of biomedical articles that refer to treatments. The methods mentioned in this manuscript adopt the BM25 algorithm to calculate the score of abstracts. We, however, propose an improved version of BM25 that computes the scores of expanded words and co-word leading to a composite retrieval function, which is then optimized using the Cuckoo Search. The proposed method aims to find both disease and gene information in the abstract of the same biomedical article. This is to achieve higher relevance and hence score of articles. Besides, we investigate the influence of different parameters on the retrieval algorithm and summarize how they meet various retrieval needs.ResultsThe data used in this manuscript is sourced from medical articles presented in Text Retrieval Conference (TREC): Clinical Decision Support (CDS) Tracks of 2017, 2018, and 2019 in Precision Medicine. A total of 120 topics are tested. Three indicators are employed for the comparison of utilized methods, which are selected among the ones based only on the BM25 algorithm and its improved version to conduct comparable experiments. The results showed that the proposed algorithm achieves better results.ConclusionThe proposed method, an improved version of the BM25 algorithm, utilizes both co-word implementation and Cuckoo Search, which has been verified achieving better results on a large number of experimental sets. Besides, a relatively simple query expansion method is implemented in this manuscript. Future research will focus on ontology and semantic networks to expand the query vocabulary.

Assessment of transparency indicators across the biomedical literature: How open is open?

Recent concerns about the reproducibility of science have led to several calls for more open and transparent research practices and for the monitoring of potential improvements over time. However, with tens of thousands of new biomedical articles published per week, manually mapping and monitoring changes in transparency is unrealistic. We present an open-source, automated approach to identify 5 indicators of transparency (data sharing, code sharing, conflicts of interest disclosures, funding disclosures, and protocol registration) and apply it across the entire open access biomedical literature of 2.75 million articles on PubMed Central (PMC). Our results indicate remarkable improvements in some (e.g., conflict of interest [COI] disclosures and funding disclosures), but not other (e.g., protocol registration and code sharing) areas of transparency over time, and map transparency across fields of science, countries, journals, and publishers. This work has enabled the creation of a large, integrated, and openly available database to expedite further efforts to monitor, understand, and promote transparency and reproducibility in science.

Biomedical articles share annotations with their citation neighbors

BackgroundNumerous efforts have been poured into annotating the wealth of knowledge contained in biomedical articles. Thanks to such efforts, it is now possible to quantitatively explore relations between these annotations and the citation network at large scale.ResultsWith the aid of several large and small annotation databases, this study shows that articles share annotations with their citation neighborhood to the point that the neighborhood’s most common annotations are likely to be those appearing in the article.ConclusionsThese findings posit that an article’s citation neighborhood defines to a large extent the article’s annotated content. Thus, citations should be considered as a foundation for future knowledge management and annotation of biomedical articles.

Effect size, sample size and power of forced swim test assays in mice: Guidelines for investigators to optimize reproducibility.

A recent flood of publications has documented serious problems in scientific reproducibility, power, and reporting of biomedical articles, yet scientists persist in their usual practices. Why? We examined a popular and important preclinical assay, the Forced Swim Test (FST) in mice used to test putative antidepressants. Whether the mice were assayed in a naïve state vs. in a model of depression or stress, and whether the mice were given test agents vs. known antidepressants regarded as positive controls, the mean effect sizes seen in the experiments were indeed extremely large (1.5-2.5 in Cohen's d units); most of the experiments utilized 7-10 animals per group which did have adequate power to reliably detect effects of this magnitude. We propose that this may at least partially explain why investigators using the FST do not perceive intuitively that their experimental designs fall short-even though proper prospective design would require ~21-26 animals per group to detect, at a minimum, large effects (0.8 in Cohen's d units) when the true effect of a test agent is unknown. Our data provide explicit parameters and guidance for investigators seeking to carry out prospective power estimation for the FST. More generally, altering the real-life behavior of scientists in planning their experiments may require developing educational tools that allow them to actively visualize the inter-relationships among effect size, sample size, statistical power, and replicability in a direct and intuitive manner.

Construcción de un diccionario combinatorio de inglés biomédico

El objetivo de este artículo es presentar el proceso de gestación de SciE-Lex, un diccionario combinatorio de términos generales frecuentemente utilizados en inglés biomédico, que nació con el propósito de ayudar a los científicos españoles a redactar artículos científicos en inglés. Como proyecto lexicográfico se integra plenamente en la corriente léxico-gramatical que subraya la estrecha relación entre el léxico y la gramática. Su desarrollo ha venido marcado por tres etapas. En la primera, SciE-Lex proporciona, además del equivalente en español de cada entrada, información morfosintáctica y combinatoria, ilustrada con ejemplos y con notas de uso. En una segunda fase añadimos expresiones prefabricadas e información explícita acerca de su variabilidad, composición, funcionamiento y distribución en el artículo biomédico. Finalmente, en una tercera etapa, aun en desarrollo, nos proponemos introducir la semántica de marcos para establecer relaciones sintáctico-semánticas entre los distintos términos que pertenecen al mismo marco.

MeSHProbeNet-P

Indexing biomedical research articles with Medical Subject Headings (MeSH) can greatly facilitate biomedical research and information retrieval. Currently MeSH indexing is performed by human experts. To alleviate the time consumption and monetary cost caused by manual indexing, many automatic MeSH indexing models have been developed, such as MeSHProbeNet, DeepMeSH, and NLM’s official model Medical Text Indexer. In this article, we propose an end-to-end framework, MeSHProbeNet-P, which extends MeSHProbeNet with personalizable MeSH probes. In MeSHProbeNet-P, each MeSH probe carries certain aspects of biomedical knowledge and extracts related information from input articles. MeSHProbeNet-P is able to automatically personalize its MeSH probes for different input articles to ensure that the current MeSH probes best fit the current input article and the most informative features can be extracted from the article. We demonstrate the effectiveness of MeSHProbeNet-P in a real-world large-scale MeSH indexing challenge. MeSHProbeNet-P won the first place in the first batch of Task A in the 2019 BioASQ challenge. The result on the first test set of the challenge is reported in this article. We also provide ablation studies to show the advantages of personalizable MeSH probes.

Molluscan Compounds Provide Drug Leads for the Treatment and Prevention of Respiratory Disease.

Respiratory diseases place an immense burden on global health and there is a compelling need for the discovery of new compounds for therapeutic development. Here, we identify research priorities by critically reviewing pre-clinical and clinical studies using extracts and compounds derived from molluscs, as well as traditional molluscan medicines, used in the treatment of respiratory diseases. We reviewed 97 biomedical articles demonstrating the anti-inflammatory, antimicrobial, anticancer, and immunomodulatory properties of >320 molluscan extracts/compounds with direct relevance to respiratory disease, in addition to others with promising bioactivities yet to be tested in the respiratory context. Of pertinent interest are compounds demonstrating biofilm inhibition/disruption and antiviral activity, as well as synergism with approved antimicrobial and chemotherapeutic agents. At least 100 traditional medicines, incorporating over 300 different mollusc species, have been used to treat respiratory-related illness in cultures worldwide for thousands of years. These medicines provide useful clues for the discovery of bioactive components that likely underpin their continued use. There is particular incentive for investigations into anti-inflammatory compounds, given the extensive application of molluscan traditional medicines for symptoms of inflammation, and shells, which are the principal molluscan product used in these preparations. Overall, there is a need to target research toward specific respiratory disease-related hypotheses, purify bioactive compounds and elucidate their chemical structures, and develop an evidence base for the integration of quality-controlled traditional medicines.

Clinical Context-Aware Biomedical Text Summarization Using Deep Neural Network: Model Development and Validation.

BackgroundAutomatic text summarization (ATS) enables users to retrieve meaningful evidence from big data of biomedical repositories to make complex clinical decisions. Deep neural and recurrent networks outperform traditional machine-learning techniques in areas of natural language processing and computer vision; however, they are yet to be explored in the ATS domain, particularly for medical text summarization.ObjectiveTraditional approaches in ATS for biomedical text suffer from fundamental issues such as an inability to capture clinical context, quality of evidence, and purpose-driven selection of passages for the summary. We aimed to circumvent these limitations through achieving precise, succinct, and coherent information extraction from credible published biomedical resources, and to construct a simplified summary containing the most informative content that can offer a review particular to clinical needs.MethodsIn our proposed approach, we introduce a novel framework, termed Biomed-Summarizer, that provides quality-aware Patient/Problem, Intervention, Comparison, and Outcome (PICO)-based intelligent and context-enabled summarization of biomedical text. Biomed-Summarizer integrates the prognosis quality recognition model with a clinical context–aware model to locate text sequences in the body of a biomedical article for use in the final summary. First, we developed a deep neural network binary classifier for quality recognition to acquire scientifically sound studies and filter out others. Second, we developed a bidirectional long-short term memory recurrent neural network as a clinical context–aware classifier, which was trained on semantically enriched features generated using a word-embedding tokenizer for identification of meaningful sentences representing PICO text sequences. Third, we calculated the similarity between query and PICO text sequences using Jaccard similarity with semantic enrichments, where the semantic enrichments are obtained using medical ontologies. Last, we generated a representative summary from the high-scoring PICO sequences aggregated by study type, publication credibility, and freshness score.ResultsEvaluation of the prognosis quality recognition model using a large dataset of biomedical literature related to intracranial aneurysm showed an accuracy of 95.41% (2562/2686) in terms of recognizing quality articles. The clinical context–aware multiclass classifier outperformed the traditional machine-learning algorithms, including support vector machine, gradient boosted tree, linear regression, K-nearest neighbor, and naïve Bayes, by achieving 93% (16127/17341) accuracy for classifying five categories: aim, population, intervention, results, and outcome. The semantic similarity algorithm achieved a significant Pearson correlation coefficient of 0.61 (0-1 scale) on a well-known BIOSSES dataset (with 100 pair sentences) after semantic enrichment, representing an improvement of 8.9% over baseline Jaccard similarity. Finally, we found a highly positive correlation among the evaluations performed by three domain experts concerning different metrics, suggesting that the automated summarization is satisfactory.ConclusionsBy employing the proposed method Biomed-Summarizer, high accuracy in ATS was achieved, enabling seamless curation of research evidence from the biomedical literature to use for clinical decision-making.

Peryton: a manual collection of experimentally supported microbe-disease associations.

We present Peryton (https://dianalab.e-ce.uth.gr/peryton/), a database of experimentally supported microbe-disease associations. Its first version constitutes a novel resource hosting more than 7900 entries linking 43 diseases with 1396 microorganisms. Peryton's content is exclusively sustained by manual curation of biomedical articles. Diseases and microorganisms are provided in a systematic, standardized manner using reference resources to create database dictionaries. Information about the experimental design, study cohorts and the applied high- or low-throughput techniques is meticulously annotated and catered to users. Several functionalities are provided to enhance user experience and enable ingenious use of Peryton. One or more microorganisms and/or diseases can be queried at the same time. Advanced filtering options and direct text-based filtering of results enable refinement of returned information and the conducting of tailored queries suitable to different research questions. Peryton also provides interactive visualizations to effectively capture different aspects of its content and results can be directly downloaded for local storage and downstream analyses. Peryton will serve as a valuable source, enabling scientists of microbe-related disease fields to form novel hypotheses but, equally importantly, to assist in cross-validation of findings.