Given the significant impact on public health and drug development, drug safety has been a focal point and research emphasis across multiple disciplines in addition to scientific investigation, including consumer advocates, drug developers and regulators. Such a concern and effort has led numerous databases with drug safety information available in the public domain and the majority of them contain substantial textual data. Text mining offers an opportunity to leverage the hidden knowledge within these textual data for the enhanced understanding of drug safety and thus improving public health.

Surgical injury can frequently lead to chronic pain. Despite the obvious importance of this problem, the first publications on chronic pain after surgery as a general topic appeared only a decade ago. This study tests the hypothesis that chronic postsurgical pain was, and still is, represented insufficiently.

Alignment-free k-mer-based algorithms in whole genome sequence comparisons remainan ongoing challenge. Here, we explore the possibility to use Topic Modeling for organismwhole-genome comparisons. We analyzed 30 complete genomes from three bacterial families bytopic modeling. For this, each genome was considered as a document and 13-mer nucleotiderepresentations as words. Latent Dirichlet allocation was used as the probabilistic modeling of thecorpus. We where able to identify the topic distribution among analyzed genomes, which is highlyconsistent with traditional hierarchical classification. It is possible that topic modeling may be appliedto establish relationships between genome's composition and biological phenomena.

Annotation of a set of genes is often accomplished through comparison to a library of labelled gene sets such as biological processes or canonical pathways. However, this approach might fail if the employed libraries are not up to date with the latest research, don't capture relevant biological themes or are curated at a different level of granularity than is required to appropriately analyze the input gene set. At the same time, the vast biomedical literature offers an unstructured repository of the latest research findings that can be tapped to provide thematic sub-groupings for any input gene set.

All biological processes are inherently dynamic. Biological systems evolve transiently or sustainably according to sequential time points after perturbation by environment insults, drugs and chemicals. Investigating the temporal behavior of molecular events has been an important subject to understand the underlying mechanisms governing the biological system in response to, such as, drug treatment. The intrinsic complexity of time series data requires appropriate computational algorithms for data interpretation. In this study, we propose, for the first time, the application of dynamic topic models (DTM) for analyzing time-series gene expression data.

Fear of falling (FoF) is a major challenge for the quality of life among older adults. Despite extensive work in previous scoping and systematic reviews on separate domains of FoF and interventions related to FoF, very little attention has been devoted to a comprehensive scoping review mapping the range and scope of this burgeoning area of study, with only a few exceptions. This scoping review aims to provide an overarching review mapping FoF research by identifying main topics, gaps in the literature and potential opportunities for bridging different strains of research on FoF. Such a comprehensive scoping review will allow the subsequent creation of an interdisciplinary theoretical and empirical framework, which may help push forward policy and practice innovations for people living with FoF.

Cell-cell interactions are vital for numerous biological processes including development, differentiation, and response to inflammation. Currently, most methods for studying interactions on scRNA-seq level are based on curated databases of ligands and receptors. While those methods are useful, they are limited to our current biological knowledge. Recent advances in single cell protocols have allowed for physically interacting cells to be captured, and as such we have the potential to study interactions in a complemantary way without relying on prior knowledge. We introduce a new method based on Latent Dirichlet Allocation (LDA) for detecting genes that change as a result of interaction. We apply our method to synthetic datasets to demonstrate its ability to detect genes that change in an interacting population compared to a reference population. Next, we apply our approach to two datasets of physically interacting cells to identify the genes that change as a result of interaction, examples include adhesion and co-stimulatory molecules which confirm physical interaction between cells. For each dataset we produce a ranking of genes that are changing in subpopulations of the interacting cells. In addition to the genes discussed in the original publications, we highlight further candidates for interaction in the top 100 and 300 ranked genes. Lastly, we apply our method to a dataset generated by a standard droplet-based protocol not designed to capture interacting cells, and discuss its suitability for analysing interactions. We present a method that streamlines detection of interactions and does not require prior clustering and generation of synthetic reference profiles to detect changes in expression.

Neuroimaging text mining extracts knowledge from neuroimaging texts and has received widespread attention. Topic learning is an important research focus of neuroimaging text mining. However, current neuroimaging topic learning researches mainly used traditional probability topic models to extract topics from literature and cannot obtain high-quality neuroimaging topics. The existing topic learning methods also cannot meet the requirements of topic learning oriented to full-text neuroimaging literature. In this paper, three types of neuroimaging research topic events are defined to describe the process and result of neuroimaging researches. An event based topic learning pipeline, called neuroimaging Event-BTM, is proposed to realize topic learning from full-text neuroimaging literature. The experimental results on the PLoS One data set show that the accuracy and completeness of the proposed method are significantly better than the existing main topic learning methods.

The gut microbiota is composed of a large number of different bacteria, that play a key role in the construction of a metabolic signaling network. Deepening the link between metabolic pathways of the gut microbiota and human health, it seems increasingly essential to evolutionarily define the principal technologies applied in the field and their future trends. We use a topic analysis tool, Latent Dirichlet Allocation, to extract themes as a probabilistic distribution of latent topics from literature dataset. We also use the Prophet neural network prediction tool to predict future trend of this area of study. A total of 1,271 abstracts (from 2006 to 2020) were retrieved from MEDLINE with the query on "gut microbiota" and "metabolic pathway." Our study found 10 topics covering current research types: dietary health, inflammation and liver cancer, fatty and diabetes, microbiota community, hepatic metabolism, metabolomics-based approach and SFCAs, allergic and immune disorders, gut dysbiosis, obesity, brain reaction, and cardiovascular disease. The analysis indicates that, with the rapid development of gut microbiota research, the metabolomics-based approach and SCFAs (topic 6) and dietary health (topic 1) have more studies being reported in the last 15 years. We also conclude from the data that, three other topics could be heavily focused in the future: metabolomics-based approach and SCFAs (topic 6), obesity (topic 8) and brain reaction and cardiovascular disease (topic 10), to unravel microbial affecting human health.

Patients increasingly use online health communities to exchange health information and peer support. During the progression of health discussions, a change of topic-topic drift-can occur. Topic drift is a frequent phenomenon linked to incoherence and frustration in online communities and other forms of computer-mediated communication. For sensitive topics, such as health, such drift could have life-altering repercussions, yet topic drift has not been studied in these contexts.

The COVID-19 outbreak was designated a global pandemic on March 11, 2020. The relationship between vaping and contracting COVID-19 is unclear, and information on the internet is conflicting. There is some scientific evidence that vaping cannabidiol (CBD), an active ingredient in cannabis that is obtained from the hemp plant, or other substances is associated with more severe manifestations of COVID-19. However, there is also inaccurate information that vaping can aid COVID-19 treatment, as well as expert opinion that CBD, possibly administered through vaping, can mitigate COVID-19 symptoms. Thus, it is necessary to study the spread of inaccurate information to better understand how to promote scientific knowledge and curb inaccurate information, which is critical to the health of vapers. Inaccurate information about vaping and COVID-19 may affect COVID-19 treatment outcomes.

Fear of falling (FoF) is a major concern among older adults and is associated with negative outcomes, such as decreased quality of life and increased risk of falls. Despite several systematic reviews conducted on various specific domains of FoF and its related interventions, the research area has only been minimally covered by scoping reviews, and a comprehensive scoping review mapping the range and scope of the research area is still lacking. This review aims to provide such a comprehensive investigation of the existing literature and identify main topics, gaps in the literature, and potential opportunities for bridging different strains of research. Using the PRISMA-ScR guidelines, we searched the Cochrane Database of Systematic Reviews, CINAHL, Embase, MEDLINE, PsycInfo, Scopus, and Web of Science databases. Following the screening process, 969 titles and abstracts were chosen for the review. Pre-processing steps included stop word removal, stemming, and term frequency-inverse document frequency vectorization. Using the Non-negative Matrix Factorization algorithm, we identified seven main topics and created a conceptual mapping of FoF research. The analysis also revealed that most studies focused on physical health-related factors, particularly balance and gait, with less attention paid to cognitive, psychological, social, and environmental factors. Moreover, more research could be done on demographic factors beyond gender and age with an interdisciplinary collaboration with social sciences. The review highlights the need for more nuanced and comprehensive understanding of FoF and calls for more research on less studied areas.

This descriptive study analyzed 1849 international and 212 Korean studies to explore the main topics of nursing research on accidental falls. We extracted only nouns from each abstract, and four topics were identified through topic modeling, which were divided into aspects of fall prevention and its consequences. "Fall prevention program and scale" is popular among studies on the validity of fall risk assessment tools and the development of exercise and education programs. "Nursing strategy for fall prevention" is common in studies on nurse education programs and practice guidelines to improve the quality of patient safety care. "Hospitalization by fall injury" is used in studies about delayed discharge, increased costs, and deaths of subjects with fall risk factors hospitalized at medical institutions due to fall-related injuries. "Long-term care facility falls" is popular in studies about interventions to prevent fall injuries that occur in conjunction with dementia in long-term care facilities. It is necessary to establish a system and policy for fall prevention in Korean medical institutions. This study confirms the trends in domestic and international fall-related research, suggesting the need for studies to address insufficient fall-related policies and systems and translational research to be applied in clinical trials.

The composition of the maternal vaginal microbiome influences the duration of pregnancy, onset of labor, and even neonatal outcomes. Maternal microbiome research in sub-Saharan Africa has focused on non-pregnant and postpartum composition of the vaginal microbiome. Here we aimed to illustrate the relationship between the vaginal microbiome of 99 laboring Ugandan women and intrapartum fever using routine microbiology and 16S ribosomal RNA gene sequencing from two hypervariable regions (V1-V2 and V3-V4). To describe the vaginal microbes associated with vaginal microbial communities, we pursued two approaches: hierarchical clustering methods and a novel Grades of Membership (GoM) modeling approach for vaginal microbiome characterization. Leveraging GoM models, we created a basis composed of a preassigned number of microbial topics whose linear combination optimally represents each patient yielding more comprehensive associations and characterization between maternal clinical features and the microbial communities. Using a random forest model, we showed that by including microbial topic models we improved upon clinical variables to predict maternal fever. Overall, we found a higher prevalence of Granulicatella, Streptococcus, Fusobacterium, Anaerococcus, Sneathia, Clostridium, Gemella, Mobiluncus, and Veillonella genera in febrile mothers, and higher prevalence of Lactobacillus genera (in particular L. crispatus and L. jensenii), Acinobacter, Aerococcus, and Prevotella species in afebrile mothers. By including clinical variables with microbial topics in this model, we observed young maternal age, fever reported earlier in the pregnancy, longer labor duration, and microbial communities with reduced Lactobacillus diversity were associated with intrapartum fever. These results better defined relationships between the presence or absence of intrapartum fever, demographics, peripartum course, and vaginal microbial topics, and expanded our understanding of the impact of the microbiome on maternal and potentially neonatal outcome risk.

Biology is generating more data than ever. As a result, there is an ever increasing number of publicly available databases that analyse, integrate and summarize the available data, providing an invaluable resource for the biological community. As this trend continues, there is a pressing need to organize, catalogue and rate these resources, so that the information they contain can be most effectively exploited. MetaBase (MB) (http://MetaDatabase.Org) is a community-curated database containing more than 2000 commonly used biological databases. Each entry is structured using templates and can carry various user comments and annotations. Entries can be searched, listed, browsed or queried. The database was created using the same MediaWiki technology that powers Wikipedia, allowing users to contribute on many different levels. The initial release of MB was derived from the content of the 2007 Nucleic Acids Research (NAR) Database Issue. Since then, approximately 100 databases have been manually collected from the literature, and users have added information for over 240 databases. MB is synchronized annually with the static Molecular Biology Database Collection provided by NAR. To date, there have been 19 significant contributors to the project; each one is listed as an author here to highlight the community aspect of the project.

Bioinformatics is an interdisciplinary field at the intersection of molecular biology and computing technology. To characterize the field as convergent domain, researchers have used bibliometrics, augmented with text-mining techniques for content analysis. In previous studies, Latent Dirichlet Allocation (LDA) was the most representative topic modeling technique for identifying topic structure of subject areas. However, as opposed to revealing the topic structure in relation to metadata such as authors, publication date, and journals, LDA only displays the simple topic structure.

Urothelial cancer (UC) includes carcinomas of the bladder, ureters, and renal pelvis. New treatments and biomarkers of UC emerged in this decade. To identify the key information in a vast amount of literature can be challenging. In this study, we use text mining to explore UC publications to identify important information that may lead to new research directions.

In the absence of readily assessed and clinically validated predictors of treatment response, pharmacologic management of major depressive disorder often relies on trial and error.

Medicinal herbs databases have become a crucial part of organizing new scientific literature generated in medicinal herbs field, as well as new drug discoveries in the information era. The aim of this review was to track the current status of medicinal herbs databases. Search for finding medicinal herbs databases was carried out via Google and PubMed. PubMed was searched for papers introducing medicinal herbs databases by the recruited search strategy. Papers with an active database on the web were included in the review. Google was also searched for medicinal herbs databases. Both retrieved papers and databases were reviewed by the authors. In this review, the current status of 25 medicinal herbs databases was reviewed, and the important characteristics of databases were mentioned. The reviewed databases had a great variety in terms of characteristics and functions. Finally, some recommendations for the efficient development of medicinal herbs databases were suggested. Although contemporary medicinal herbs databases represent much useful information, adding some features to these databases could assist them to have better functionality. This work may not cover all the necessary information, but we hope that our review can provide readers with fundamental concepts, perspectives and suggestions for constructing more useful databases.

Renewable energy policies have been recognized as a cornerstone in the transition toward low-emission energy systems. Media reports are an important variable in the policy-making process, interrelating politicians and the public. To understand the changes in media framing of a pioneering renewable energy support act, we collected 6,645 articles from five Germany-wide newspapers between 2000 and 2017 on the German Renewable Energy Act. We developed a structural topic model based on a change-point analysis to assess the temporal patterns of newspaper coverage. We introduced the notion of topic sentiment to elucidate the emotional content of topics. The results show that after its enactment, optimism about renewable energies dominated the media agenda. After 2012, however, the Renewable Energy Act was more associated with its costs. Such shifts in renewable energy policy framing may limit political leverage to reach ambitious climate and energy targets.