UNASSIGNED: To perform a bibliometric analysis as a comprehensive review of publications associated with catamenial epilepsy and discuss the current state of knowledge in the field.
UNASSIGNED: Publications published between 1956 and 2022 were retrieved from the Scopus database. Bibliometric analysis was performed using the R package and VOSviewer to show the data and network of journals, organizations, authors, countries, and keywords. The analysis conducted on October 15, 2022, yielded a total of 320 refinement studies.
UNASSIGNED: The number of publications has escalated significantly, particularly in the last 20 years. Catamenial epilepsy-related publications originated mostly from medicine and other subject areas, with the United States having the largest publication output. Collaboration is low at the author, organizational, and national levels, especially in the Asian continent. Publications remain scarce, particularly on practice guidelines, risk assessment, and medication-related research. Based on a keyword analysis, a bibliometric analysis identified possible themes for future investigation.
UNASSIGNED: Catamenial epilepsy-related literature is crucial but still insufficient, and further studies are required.

In this essay, I argue that the combination of research synthesis and philosophical methods can fill an important methodological gap in neuroscience. While experimental research and formal modelling have seen their methods progressively increase in rigour and sophistication over the years, the task of analysing and synthesizing the vast literature reporting new results and models has lagged behind. The problem is aggravated because neuroscience has grown and expanded into a vast mosaic of related but partially independent subfields, each with their own literatures. This fragmentation not only makes it difficult to see the full picture emerging from neuroscience research but also limits progress in individual subfields. The current neuroscience literature has the perfect conditions to create what the information scientist Don Swanson called \"undiscovered public knowledge\"-knowledge that exists in the mutual implications of different published pieces of information but that is nonetheless undiscovered because those pieces have not been explicitly connected. Current methods for rigorous research synthesis, such as systematic reviews and meta-analyses, mostly focus on combining similar studies and are not suited for exploring undiscovered public knowledge. To that aim, they need to be adapted and supplemented. I argue that successful exploration of the hidden implications in the neuroscience literature will require the combination of these adapted research synthesis methods with philosophical methods for rigorous (and creative) analysis and synthesis.

BACKGROUND: Research gaps refer to unanswered questions in the existing body of knowledge, either due to a lack of studies or inconclusive results. Research gaps are essential starting points and motivation in scientific research. Traditional methods for identifying research gaps, such as literature reviews and expert opinions, can be time consuming, labor intensive, and prone to bias. They may also fall short when dealing with rapidly evolving or time-sensitive subjects. Thus, innovative scalable approaches are needed to identify research gaps, systematically assess the literature, and prioritize areas for further study in the topic of interest.
OBJECTIVE: In this paper, we propose a machine learning-based approach for identifying research gaps through the analysis of scientific literature. We used the COVID-19 pandemic as a case study.
METHODS: We conducted an analysis to identify research gaps in COVID-19 literature using the COVID-19 Open Research (CORD-19) data set, which comprises 1,121,433 papers related to the COVID-19 pandemic. Our approach is based on the BERTopic topic modeling technique, which leverages transformers and class-based term frequency-inverse document frequency to create dense clusters allowing for easily interpretable topics. Our BERTopic-based approach involves 3 stages: embedding documents, clustering documents (dimension reduction and clustering), and representing topics (generating candidates and maximizing candidate relevance).
RESULTS: After applying the study selection criteria, we included 33,206 abstracts in the analysis of this study. The final list of research gaps identified 21 different areas, which were grouped into 6 principal topics. These topics were: \"virus of COVID-19,\" \"risk factors of COVID-19,\" \"prevention of COVID-19,\" \"treatment of COVID-19,\" \"health care delivery during COVID-19,\" \"and impact of COVID-19.\" The most prominent topic, observed in over half of the analyzed studies, was \"the impact of COVID-19.\"
CONCLUSIONS: The proposed machine learning-based approach has the potential to identify research gaps in scientific literature. This study is not intended to replace individual literature research within a selected topic. Instead, it can serve as a guide to formulate precise literature search queries in specific areas associated with research questions that previous publications have earmarked for future exploration. Future research should leverage an up-to-date list of studies that are retrieved from the most common databases in the target area. When feasible, full texts or, at minimum, discussion sections should be analyzed rather than limiting their analysis to abstracts. Furthermore, future studies could evaluate more efficient modeling algorithms, especially those combining topic modeling with statistical uncertainty quantification, such as conformal prediction.

UNASSIGNED: Artificial intelligence has demonstrated utility in orthopedic research. Algorithmic models derived from machine learning have demonstrated adaptive learning with predictive application towards outcomes, leading to increased traction in the literature. This study aims to identify machine learning arthroplasty research trends and anticipate emerging key terms.
UNASSIGNED: Published literature focused on machine learning in arthroplasty from 1992 to 2023 was selected through the Web of Science Core Collection of Clarivate Analytics. Following that, bibliometric indicators were attained and brought in to perform an additional examination using Bibliometrix and VOSviewer to identify historical and present patterns within the literature.
UNASSIGNED: A total of 235 documents were obtained through bibliometric sourcing based on machine learning applications within the arthroplasty literature. Thirty-four countries published articles on the topic, and the United States was demonstrated to be the largest global contributor. Four hundred-five institutions internationally contributed articles, with Harvard Medical School and the University of California system as the most relevant institutes, with 75 and 44 articles produced, respectively. Kwon YM was the most productive author, while Haeberle HS and Ramkumar PN were the most impactful based on h-index. The Thematic map and Co-occurrence visualization helped identify both major and niche themes present in the scientific databases.
UNASSIGNED: Machine learning in arthroplasty research continues to gain traction with a growing annual production rate and contributions from international authors and institutions. Institutions and authors based in the United States are the leading contributors to machine learning applications within arthroplasty research. This research discerns trends that have occurred, are presently ongoing, and are emerging within this field, aiming to inform future hotspot development.

Benthic foraminifera, single-celled marine organisms, are known for their wide distribution, high abundance and species diversity, test (i.e., shell) preservation in the sedimentary (e.g., historical) record, and sensitivity to environmental changes. Because of these characteristics, they have been widely used as bioindicators in environmental monitoring and, more recently, as Biological Quality Elements (BQEs) in the Ecological Quality Status (EcoQS) evaluation. The global scientific literature on benthic foraminifera as bioindicators was gathered from the Scopus database (overall 966 papers from 1973 to 2022) and explored with scientometric software. The outcomes highlight that the investigation of benthic foraminiferal response to pollutants started over 50 years ago. Indeed, not only the number of published documents has recently peaked (i.e., 2021 and 2022) but there has been also a growth in the percentages of papers falling within the Decision Sciences category that deals with the application of foraminiferal indices for the EcoQS assessment.

Being able to communicate scientifically is an important skill for students graduating with a science degree. Skills used in future graduate school and careers for science majors include oral and written communication, as well as science literacy and being able to create figures to display information. There is a consensus that these skills should be taught throughout an undergraduate science curriculum; however, many instructors have cited insufficient time to cover skills and develop materials to effectively incorporate these skills, especially into lower-level content-focused courses. Here, we present an active curriculum that can easily be incorporated into any content-focused undergraduate Cell Biology course. The curriculum is designed around scientific literature that engages students in a multitude of active learning activities to develop different types of scientific communication skills. This curriculum not only develops student skills and self-efficacy in scientific communication, it also engages them in course content and stimulates their interest in research. While making changes to a course to include scientific communication can be difficult, making small changes, such as addition of this curriculum to an already-existing content-focused course, could make a big difference in the skills and attitudes of early undergraduate science students.

Scientific knowledge is being accumulated in the biomedical literature at an unprecedented pace. The most widely used database with biomedicine-related article abstracts, PubMed, currently contains more than 36 million entries. Users performing searches in this database for a subject of interest face thousands of entries (articles) that are difficult to process manually. In this work, we present an interactive tool for automatically digesting large sets of PubMed articles: PMIDigest (PubMed IDs digester). The system allows for classification/sorting of articles according to different criteria, including the type of article and different citation-related figures. It also calculates the distribution of MeSH (medical subject headings) terms for categories of interest, providing in a picture of the themes addressed in the set. These MeSH terms are highlighted in the article abstracts in different colors depending on the category. An interactive representation of the interarticle citation network is also presented in order to easily locate article \"clusters\" related to particular subjects, as well as their corresponding \"hub\" articles. In addition to PubMed articles, the system can also process a set of Scopus or Web of Science entries. In summary, with this system, the user can have a \"bird\'s eye view\" of a large set of articles and their main thematic tendencies and obtain additional information not evident in a plain list of abstracts.

UNASSIGNED: This study aims to identify determinants of high impact, measured by Impact Factor (IF) and Eigenfactor score, among otolaryngology journals.
UNASSIGNED: Bibliometric data of \"otorhinolaryngology\" journals were collected from the Journal Citation Reports (JCR) database. For the years 2009-2020, we collected normalized Eigenfactor score, 5-year IF, immediacy index, fraction of IF from journal-self citation, proportion and magnitude of published citable articles, and total citation counts. High-IF and -Eigenfactor journals were considered those within the top-quartile of that metric each respective year.
UNASSIGNED: High-IF and -Eigenfactor otolaryngology journals displayed higher 5-year IFs, immediacy indexes, and IF without self-citation (p < .05 for all years) including total citations counts and citable articles when ranked by Eigenfactor (p < .05 for all years). Otolaryngology IF correlated with 5-year IF and immediacy index within the same year (p < .05 for all years) and from previous years (p < .05 for all years; p < .05 for 2017-2018; p > .05 for 2009-2016). Eigenfactor correlated with 5-year IF, total citation counts, and citable articles within the same year (p < .05 for all years) and previous years (p < .05 for 2013-2018). Multilinear regression revealed that 5-year IF (p < .05 for 2009-2018) and immediacy index from the prior 2 years (p < .05 for 2017-2018; p > .05 for 2009-2016) predicted 2019 IF. Similarly, 5-year IF, total citation counts, and citable articles (p < .05 for 2013-2018) predicted 2019 Eigenfactor score.
UNASSIGNED: Sustained publication of impactful articles is the dominant driver of high IF and Eigenfactor score. Eigenfactor score reflects a unique evaluation of otolaryngology journals; ranking otolaryngology journals by their Eigenfactor scores significantly alters journal ranking compared to ranking by IF.
UNASSIGNED: NA.

Neuroblastoma is a childhood neurological tumor which affects hundreds of thousands of children worldwide, and information about its prognosis can be pivotal for patients, their families, and clinicians. One of the main goals in the related bioinformatics analyses is to provide stable genetic signatures able to include genes whose expression levels can be effective to predict the prognosis of the patients. In this study, we collected the prognostic signatures for neuroblastoma published in the biomedical literature, and noticed that the most frequent genes present among them were three: AHCY, DPYLS3, and NME1. We therefore investigated the prognostic power of these three genes by performing a survival analysis and a binary classification on multiple gene expression datasets of different groups of patients diagnosed with neuroblastoma. Finally, we discussed the main studies in the literature associating these three genes with neuroblastoma. Our results, in each of these three steps of validation, confirm the prognostic capability of AHCY, DPYLS3, and NME1, and highlight their key role in neuroblastoma prognosis. Our results can have an impact on neuroblastoma genetics research: biologists and medical researchers can pay more attention to the regulation and expression of these three genes in patients having neuroblastoma, and therefore can develop better cures and treatments which can save patients\' lives.

Information pertaining to mechanisms, management and treatment of disease-causing pathogens including viruses and bacteria is readily available from research publications indexed in MEDLINE. However, identifying the literature that specifically characterises these pathogens and their properties based on experimental research, important for understanding of the molecular basis of diseases caused by these agents, requires sifting through a large number of articles to exclude incidental mentions of the pathogens, or references to pathogens in other non-experimental contexts such as public health.
In this work, we lay the foundations for the development of automatic methods for characterising mentions of pathogens in scientific literature, focusing on the task of identifying research that involves the experimental study of a pathogen in an experimental context. There are no manually annotated pathogen corpora available for this purpose, while such resources are necessary to support the development of machine learning-based models. We therefore aim to fill this gap, producing a large data set automatically from MEDLINE under some simplifying assumptions for the task definition, and using it to explore automatic methods that specifically support the detection of experimentally studied pathogen mentions in research publications.
We developed a pathogen mention characterisation literature data set -READBiomed-Pathogens- automatically using NCBI resources, which we make available. Resources such as the NCBI Taxonomy, MeSH and GenBank can be used effectively to identify relevant literature about experimentally researched pathogens, more specifically using MeSH to link to MEDLINE citations including titles and abstracts with experimentally researched pathogens. We experiment with several machine learning-based natural language processing (NLP) algorithms leveraging this data set as training data, to model the task of detecting papers that specifically describe experimental study of a pathogen.
We show that our data set READBiomed-Pathogens can be used to explore natural language processing configurations for experimental pathogen mention characterisation. READBiomed-Pathogens includes citations related to organisms including bacteria, viruses, and a small number of toxins and other disease-causing agents.
We studied the characterisation of experimentally studied pathogens in scientific literature, developing several natural language processing methods supported by an automatically developed data set. As a core contribution of the work, we presented a methodology to automatically construct a data set for pathogen identification using existing biomedical resources. The data set and the annotation code are made publicly available. Performance of the pathogen mention identification and characterisation algorithms were additionally evaluated on a small manually annotated data set shows that the data set that we have generated allows characterising pathogens of interest.
N/A.