Transition From Mentored to Independent NIH Funding by Gender and Department.

This study uses National Institutes of Health RePORTER data for mentored K awards and R01-equivalent grants to all departments in US schools of medicine to characterize K-award distribution and K-to-R transition by gender and department between 1997 and 2021.

Basic principles of biobanking: from biological samples to precision medicine for patients.

The term "biobanking" is often misapplied to any collection of human biological materials (biospecimens) regardless of requirements related to ethical and legal issues or the standardization of different processes involved in tissue collection. A proper definition of biobanks is large collections of biospecimens linked to relevant personal and health information (health records, family history, lifestyle, genetic information) that are held predominantly for use in health and medical research. In addition, the International Organization for Standardization, in illustrating the requirements for biobanking (ISO 20387:2018), stresses the concept of biobanks being legal entities driving the process of acquisition and storage together with some or all of the activities related to collection, preparation, preservation, testing, analysing and distributing defined biological material as well as related information and data. In this review article, we aim to discuss the basic principles of biobanking, spanning from definitions to classification systems, standardization processes and documents, sustainability and ethical and legal requirements. We also deal with emerging specimens that are currently being generated and shaping the so-called next-generation biobanking, and we provide pragmatic examples of cancer-associated biobanking by discussing the process behind the construction of a biobank and the infrastructures supporting the implementation of biobanking in scientific research.

Examining "race" in physiology.

Racial disparities in cardiovascular and cerebrovascular health outcomes are well described, and recent research has shed light on the mechanistic underpinnings of those disparities. However, "race" is a social construct that is poorly defined and continually evolving and is historically based on faulty premises. The continued categorization by race in physiological research suggests that there are inherent differences between races, rather than addressing the specific underlying factors that result in health disparities between groups. The purpose of this Perspectives article is to provide a brief history of the genesis of categorization by race, why such categorization should be reconsidered in physiology research, and offer recommendations to more directly investigate the underlying factors that result in group disparities in cardiovascular and cerebrovascular health.

Integrating image caption information into biomedical document classification in support of biocuration.

Gathering information from the scientific literature is essential for biomedical research, as much knowledge is conveyed through publications. However, the large and rapidly increasing publication rate makes it impractical for researchers to quickly identify all and only those documents related to their interest. As such, automated biomedical document classification attracts much interest. Such classification is critical in the curation of biological databases, because biocurators must scan through a vast number of articles to identify pertinent information within documents most relevant to the database. This is a slow, labor-intensive process that can benefit from effective automation.We present a document classification scheme aiming to identify papers containing information relevant to a specific topic, among a large collection of articles, for supporting the biocuration classification task. Our framework is based on a meta-classification scheme we have introduced before; here we incorporate into it features gathered from figure captions, in addition to those obtained from titles and abstracts. We trained and tested our classifier over a large imbalanced dataset, originally curated by the Gene Expression Database (GXD). GXD collects all the gene expression information in the Mouse Genome Informatics (MGI) resource. As part of the MGI literature classification pipeline, GXD curators identify MGI-selected papers that are relevant for GXD. The dataset consists of ~60 000 documents (5469 labeled as relevant; 52 866 as irrelevant), gathered throughout 2012-2016, in which each document is represented by the text of its title, abstract and figure captions. Our classifier attains precision 0.698, recall 0.784, f-measure 0.738 and Matthews correlation coefficient 0.711, demonstrating that the proposed framework effectively addresses the high imbalance in the GXD classification task. Moreover, our classifier's performance is significantly improved by utilizing information from image captions compared to using titles and abstracts alone; this observation clearly demonstrates that image captions provide substantial information for supporting biomedical document classification and curation.Database URL.

La categorización familiar como técnica de apoyo al proceso de análisis que sigue la teoría fundamentada / Family categorization as a technique to support the analysis process that follows the grounded theory

Uno de los aspectos que mayor atención requiere por parte de las personas investigadoras que emplean metodologías cualitativas es la construcción de categorías teóricas, o más en concreto, el paso entre la codificación y la categorización. Una de las propuestas metodológicas más estructuradas y utilizadas en ciencias de la salud en este sentido es la teoría fundamentada. En el presente trabajo se expone una técnica analítica denominada «categorización familiar» que persigue el objetivo de facilitar el proceso de creación de las categorías iniciales, basándose en el análisis intrafamiliar e interfamiliar de los códigos

An important aspect that requires special attention by researchers using qualitative methods is the construction of theoretical categories, or more specifically, the step between coding and categorization. One of the most structured methodological proposals used in health sciences is grounded theory. In this paper an analytical technique called "family categorization" is proposed. Its aim is to facilitate the steps of creating the initial categories, based on intra- and inter-family codes analysis

Exploratory Data Mining for Subgroup Cohort Discoveries and Prioritization.

Finding small homogeneous subgroup cohorts in large heterogeneous populations is a critical process for hypothesis development in biomedical research. Concurrent computational approaches are still lacking in robust answers to the question "what hypotheses are likely to be novel and to produce clinically relevant results with well thought-out study designs?" We have developed a novel subgroup discovery method which employs a deep exploratory mining process to slice and dice thousands of potential subpopulations and prioritize potential cohorts based on their explainable contrast patterns and which may provide interventionable insights. We conducted computational experiments on both synthesized data and a clinical autism data set to assess performance quantitatively for coverage of pre-defined cohorts and qualitatively for novel knowledge discovery, respectively. We also conducted a scaling analysis using a distributed computing environment to suggest computational resource needs for when the subpopulation number increases. This work will provide a robust data-driven framework to automatically tailor potential interventions for precision health.

LSTM-Based End-to-End Framework for Biomedical Event Extraction.

Biomedical event extraction plays an important role in the extraction of biological information from large-scale scientific publications. However, most state-of-the-art systems separate this task into several steps, which leads to cascading errors. In addition, it is complicated to generate features from syntactic and dependency analysis separately. Therefore, in this paper, we propose an end-to-end model based on long short-term memory (LSTM) to optimize biomedical event extraction. Experimental results demonstrate that our approach improves the performance of biomedical event extraction. We achieve average F1-scores of 59.68, 58.23, and 57.39 percent on the BioNLP09, BioNLP11, and BioNLP13's Genia event datasets, respectively. The experimental study has shown our proposed model's potential in biomedical event extraction.

Definition of a systematic review used in overviews of systematic reviews, meta-epidemiological studies and textbooks.

BACKGROUND: A standard or consensus definition of a systematic review does not exist. Therefore, if there is no definition about a systematic review in secondary studies that analyse them or the definition is too broad, inappropriate studies might be included in such evidence synthesis. The aim of this study was to analyse the definition of a systematic review (SR) in health care literature, elements of the definitions that are used and to propose a starting point for an explicit and non-ambiguous SR definition. METHODS: We included overviews of systematic reviews (OSRs), meta-epidemiological studies and epidemiology textbooks. We extracted the definitions of SRs, as well as the inclusion and exclusion criteria that could indicate which definition of a SR the authors used. We extracted individual elements of SR definitions, categorised and quantified them. RESULTS: Among the 535 analysed sources of information, 188 (35%) provided a definition of a SR. The most commonly used reference points for the definitions of SRs were Cochrane and the PRISMA statement. We found 188 different elements of SR definitions and divided them into 14 categories. The highest number of SR definition elements was found in categories related to searching (N = 51), analysis/synthesis (N = 23), overall methods (N = 22), quality/bias/appraisal/validity (N = 22) and aim/question (N = 13). The same five categories were also the most commonly used combination of categories in the SR definitions. CONCLUSION: Currently used definitions of SRs are vague and ambiguous, often using terms such as clear, explicit and systematic, without further elaboration. In this manuscript we propose a more specific definition of a systematic review, with the ultimate aim of motivating the research community to establish a clear and unambiguous definition of this type of research.

Hierarchies of evidence applied to lifestyle Medicine (HEALM): introduction of a strength-of-evidence approach based on a methodological systematic review.

BACKGROUND: Current methods for assessing strength of evidence prioritize the contributions of randomized controlled trials (RCTs). The objective of this study was to characterize strength of evidence (SOE) tools in recent use, identify their application to lifestyle interventions for improved longevity, vitality, or successful aging, and to assess implications of the findings. METHODS: The search strategy was created in PubMed and modified as needed for four additional databases: Embase, AnthropologyPlus, PsycINFO, and Ageline, supplemented by manual searching. Systematic reviews and meta-analyses of intervention trials or observational studies relevant to lifestyle intervention were included if they used a specified SOE tool. Data was collected for each SOE tool. Conditions necessary for assigning the highest SOE grading and treatment of prospective cohort studies within each SOE rating framework were summarized. The expert panel convened to discuss the implications of findings for assessing evidence in the domain of lifestyle medicine. RESULTS AND CONCLUSIONS: A total of 15 unique tools were identified. Ten were tools developed and used by governmental agencies or other equivalent professional bodies and were applicable in a variety of settings. Of these 10, four require consistent results from RCTs of high quality to award the highest rating of evidence. Most SOE tools include prospective cohort studies only to note their secondary contribution to overall SOE as compared to RCTs. We developed a new construct, Hierarchies of Evidence Applied to Lifestyle Medicine (HEALM), to illustrate the feasibility of a tool based on the specific contributions of diverse research methods to understanding lifetime effects of health behaviors. Assessment of evidence relevant to lifestyle medicine requires a potential adaptation of SOE approaches when outcomes and/or exposures obviate exclusive or preferential reliance on RCTs. This systematic review was registered with the International Prospective Register of Systematic Reviews, PROSPERO [CRD42018082148].

La epidemia de las revistas depredadoras / The predatory journals epidemy

La proliferación de las llamadas revistas depredadoras y su desconocimiento por parte de los investigadores son un peligro para la integridad científica. Es una necesidad y deber de los autores conocer y saber detectar este tipo de revistas para no publicar en ellas, pero también para no basar sus investigaciones en los trabajos que publican, y que no han pasado un proceso crítico de calidad apoyado en la revisión por pares. Se introduce el concepto de revista depredadora, algunos de sus elementos diferenciadores y los peligros que entrañan para la ciencia en general y los investigadores en particular

The proliferation of so-called predatory journals and their ignorance by researchers are a danger to scientific integrity. Authors need and must know them and therefore know how to detect these types of journals so as not to publish in them, but also so as not to base their research on the work they publish, which has not undergone a critical quality process supported by peer review. This text introduces the concept of a predatory journal, some of its differentiating elements and the dangers they entail for science in general and the researcher in particular

Regulatory aspects of prospective and retrospective clinical research in France in 2018.

In France, the so-called "Jardé law" (named for its proposer) on human research, implemented since 2016, defines the regulatory and legal framework for "prospective" studies, formerly known as "biomedical research" or "common care". Three categories are distinguished: type 1 is at-risk drug or non-drug interventional research, type 2 is low-risk, low-burden interventional research, and type 3 is non-interventional research. The decrees of April 12, 2018 precisely define a list of research categories for types 2 and 3, thereby clarifying the regulatory procedures. The Sponsor registers the trial on the database of the National Drug Safety Agency (ANSM), or the European EudraCT database for drug studies, to obtain an identification number. Regulatory procedures are undertaken with the IRB and ANSM and then the Data Protection Commission (CNIL). Retrospective research on previously collected data (other than genetic) does not come under the Jardé law, and is governed by the 1978 data protection law, updated by the application decree of December 2016 and the law No. 2018-493 of June 20, 2018 on protection of personal data. This article presents a clarification of the key methodologic and regulatory steps.

Productivity of CNPq Researchers from Different Fields in Biomedical Sciences: The Need for Objective Bibliometric Parameters-A Report from Brazil.

In Brazil, the CNPq (National Council for Scientific and Technological Development) provides grants, funds and fellowships to productive scientists to support their investigations. They are ranked and categorized into four hierarchical levels ranging from PQ 1A (the highest) to PQ 1D (the lowest). Few studies, however, report and analyse scientific productivity in different sub-fields of Biomedical Sciences (BS), e.g., Biochemistry, Pharmacology, Biophysics and Physiology. In fact, systematic comparisons of productivity among the PQ 1 categories within the above sub-fields are lacking in the literature. Here, the scientific productivity of 323 investigators receiving PQ 1 fellowships (A to D levels) in these sub-fields of BS was investigated. The Scopus database was used to compile the total number of articles, citations, h-index values and authorship positions (first-, co- or last-listed author) in the most cited papers by researchers granted CNPq fellowships. We found that researchers from Pharmacology had the best performance for all of the parameters analysed, followed by those in Biochemistry. There was great variability in scientific productivity within the PQ 1A level in all of the sub-fields of BS, but not within the other levels (1B, 1C and 1D). Analysis of the most cited papers of PQ 1(A-D) researchers in Pharmacology revealed that the citations of researchers in the 1C and 1D levels were associated with publications with their senior supervisors, whereas those in the 1B level were less connected with their supervisors in comparison to those in 1A. Taken together, these findings suggest that the scientific performance of PQ 1A researchers in BS is not homogenous. In our opinion, parameters such as the most cited papers without the involvement of Ph.D. and/or post-doctoral supervisors should be used to make decisions regarding any given researcher's fellowship award level.

[Guide for the classification of clinical research articles for the International Journal of Andrology]. / Guía para la clasificación de artículos de investigación clínica para la Revista Internacional de Andrología.

The classification of knowledge is essential for understanding between researchers and the advancement of science. In this article a brief classification of clinical research methods is presented. The advantages that researchers use the same nomenclature are recalled, which enables improved communication among researchers, facilitates replication of studies and searches in databases, and helps to establish the aspects that can affect the different research models. They are grouped into four broad categories: epidemiological articles, articles of qualitative methodology, mixed methodology articles and review articles.

High-impact and transformative science (HITS) metrics: Definition, exemplification, and comparison.

Countries, research institutions, and scholars are interested in identifying and promoting high-impact and transformative scientific research. This paper presents a novel set of text- and citation-based metrics that can be used to identify high-impact and transformative works. The 11 metrics can be grouped into seven types: Radical-Generative, Radical-Destructive, Risky, Multidisciplinary, Wide Impact, Growing Impact, and Impact (overall). The metrics are exemplified, validated, and compared using a set of 10,778,696 MEDLINE articles matched to the Science Citation Index ExpandedTM. Articles are grouped into six 5-year periods (spanning 1983-2012) using publication year and into 6,159 fields constructed using comparable MeSH terms, with which each article is tagged. The analysis is conducted at the level of a field-period pair, of which 15,051 have articles and are used in this study. A factor analysis shows that transformativeness and impact are positively related (ρ = .402), but represent distinct phenomena. Looking at the subcomponents of transformativeness, there is no evidence that transformative work is adopted slowly or that the generation of important new concepts coincides with the obsolescence of existing concepts. We also find that the generation of important new concepts and highly cited work is more risky. Finally, supporting the validity of our metrics, we show that work that draws on a wider range of research fields is used more widely.

Leveraging Wikipedia knowledge to classify multilingual biomedical documents.

This article presents a classifier that leverages Wikipedia knowledge to represent documents as vectors of concepts weights, and analyses its suitability for classifying biomedical documents written in any language when it is trained only with English documents. We propose the cross-language concept matching technique, which relies on Wikipedia interlanguage links to convert concept vectors between languages. The performance of the classifier is compared to a classifier based on machine translation, and two classifiers based on MetaMap. To perform the experiments, we created two multilingual corpus. The first one, Multi-Lingual UVigoMED (ML-UVigoMED) is composed of 23,647 Wikipedia documents about biomedical topics written in English, German, French, Spanish, Italian, Galician, Romanian, and Icelandic. The second one, English-French-Spanish-German UVigoMED (EFSG-UVigoMED) is composed of 19,210 biomedical abstract extracted from MEDLINE written in English, French, Spanish, and German. The performance of the approach proposed is superior to any of the state-of-the art classifier in the benchmark. We conclude that leveraging Wikipedia knowledge is of great advantage in tasks of multilingual classification of biomedical documents.