UNASSIGNED: Recurrent ovarian cancer (ROC) presents a dismal prognosis, persistently devoid of efficacious therapeutic strategies. Over the past decade, significant shifts have transpired in ROC management, marked by the identification of novel therapeutic targets and advancements in biomarker research and innovation. Since bibliometrics is an effective method for revealing scientific literature, we conducted a bibliometric analysis of literature pertaining to ROC. Our exploration encompassed identifying emerging research trends and common patterns, analyzing collaborative networks, and anticipating future directions within this clinical context.
UNASSIGNED: We conducted a search in the Web of Science Core Collection (WoSCC) to acquire relevant articles as our dataset, which were then exported using R-Studio-2023.12.0-369 software. The Bibliometrix R package was utilized to perform visual analyses on countries, institutions, journals, authors, landmark articles, and keywords within this research field.
UNASSIGNED: A total of 1538 articles and 173 reviews published between 2014 and 2023 were eventually retrieved. The annual growth rate of scientific production was 4.27%. The USA led the way in the number of published works, total citations, and collaboration. Gynecologic Oncology was the most favoured journal in this research field. Vergote I from the University Hospital Leuven, was the most influential author. At last, the most prominent keywords were \"chemotherapy\" (n = 124), \"bevacizumab\" (n = 87), and \"survival\" (n = 65). Clinical outcomes (prognosis, survival), chemotherapy, bevacizumab, and PARP inhibitors (olaparib, niraparib) represented the basic and transversal themes, while antibody-drug conjugate (ADC) and drug resistance were emerging themes. Cytoreduction surgical procedures and tamoxifen were niche themes, while immunotherapy and biomarkers were motor themes and had high centrality.
UNASSIGNED: The trends in the ROC research field over the past decade were revealed through bibliometric analysis. Platinum resistance, ADC, and immunotherapy have emerged as the current prominent research topics.

UNASSIGNED: The aim of this study was to identify potential causal cytokines in thymic malignancies and benign tumors from the FinnGen database using Mendelian randomization (MR).
UNASSIGNED: In this study, data from genome-wide association studies (GWAS) of 91 cytokines were used as exposure factors, and those of thymic malignant tumors and thymic benign tumors were the outcome variables. Two methods were used to determine the causal relationship between exposure factors and outcome variables: inverse variance weighting (IVW) and MR-Egger regression. Sensitivity analysis was performed using three methods, namely, the heterogeneity test, the pleiotropy test, and the leave-one-out test.
UNASSIGNED: There was a causal relationship between the expression of fibroblast growth factor 5, which is a risk factor for thymic malignant tumors, and thymic malignant tumors. C-C motif chemokine 19 expression, T-cell surface glycoprotein CD5 levels, and interleukin-12 subunit beta levels were causally related to thymic malignant tumors and were protective. Adenosine deaminase levels, interleukin-10 receptor subunit beta expression, tumor necrosis factor (TNF)-related apoptosis-inducing ligand levels, and TNF-related activation-induced cytokine levels showed a causal relationship with thymic benign tumors, which are its risk factors. Caspase 8 levels, C-C motif chemokine 28 levels, interleukin-12 subunit beta levels, latency-associated peptide transforming growth factor beta 1 levels, and programmed cell death 1 ligand 1 expression showed a causal relationship with thymic benign tumors, which are protective factors. Sensitivity analysis showed no heterogeneity.
UNASSIGNED: Cytokines showed a causal relationship with benign and malignant thymic tumors. Interleukin-12 subunit beta is a common cytokine that affects malignant and benign thymic tumors.

A phylogenetic tree can reflect the evolutionary relationships between species or gene families, and they play a critical role in modern biological research. In this review, we summarize common methods for constructing phylogenetic trees, including distance methods, maximum parsimony, maximum likelihood, Bayesian inference, and tree-integration methods (supermatrix and supertree). Here we discuss the advantages, shortcomings, and applications of each method and offer relevant codes to construct phylogenetic trees from molecular data using packages and algorithms in R. This review aims to provide comprehensive guidance and reference for researchers seeking to construct phylogenetic trees while also promoting further development and innovation in this field. By offering a clear and concise overview of the different methods available, we hope to enable researchers to select the most appropriate approach for their specific research questions and datasets.

UNASSIGNED: Cerebral palsy (CP) ranks as a major cause of motor disabilities in children, with spastic CP making up roughly 70-80% of all CP cases. The primary objective of our study is to identify characteristics of Traditional Chinese Medicine(TCM) symptom of spastic CP, thereby establishing correlations between the TCM symptom and the disease, providing a more scientific theoretical foundation for TCM treatments on spastic CP, enabling a deeper comprehension of clinical interventions, ultimately, improving rehabilitation outcomes in TCM treatment for spastic CP.
UNASSIGNED: We conducted a data mining study on TCM symptom of spastic CP children aged 4-14 years old treated at Xi\'an Encephalopathy Hospital Affiliated to Shaanxi University of Chinese Medicine, from October 2021 to March 2023. The medical records of all eligible and complete spastic CP patients were extracted, processed for data cleansing, transformed, and subsequently analyzed to discern distinctive TCM symptom. K-Means Clustering Analysis and Association Rule Analysis were used for data mining.
UNASSIGNED: Core symptoms identified for spastic CP encompassed \"Motor Dysfunction\", \"Impaired Speech\", \"Delayed Development\", \"Limb Stiffness\", \"Rigidity in the limbs\", \"Intellectual Impairment\", \"Timidity and susceptibility to startle responses\", \"Muscle Wasting\", and \"Pale or Dull Complexion\". Among the top-ranking associations of symptom, patterns emerge wherein \"Motor dysfunction\" intertwine with \"Impaired speech\", \"Motor dysfunction\" coexist with \"Delayed development\", and \"Impaired speech\" are accompanied by \"Delayed development\".
UNASSIGNED: This study identified the core symptom of spastic CP and tentatively suggests that the clinical manifestations of spastic CP are essentially consistent with the TCM pattern \"liver exuberance and spleen weakness\". This finding has facilitated the preliminary establishment of correlations between TCM pattern differentiation and the disease in medicine. It is anticipated that this correlation will bring tangible benefits to a larger number of children with spastic CP.

Standardizing and translating species names from different databases is key to the successful integration of data sources in biodiversity research. There are numerous taxonomic name-resolution applications that implement increasingly powerful name-cleaning and matching approaches, allowing the user to resolve species relative to multiple backbones simultaneously. Yet there remains no principled approach for combining information across these underlying taxonomic backbones, complicating efforts to combine and merge species lists with inconsistent and conflicting taxonomic information. Here, we present Treemendous, an open-source software package for the R programming environment that integrates taxonomic relationships across four publicly available backbones to improve the name resolution of tree species. By mapping relationships across the backbones, this package can be used to resolve datasets with conflicting and inconsistent taxonomic origins, while ensuring the resulting species are accepted and consistent with a single reference backbone. The user can chain together different functionalities ranging from simple matching to a single backbone, to graph-based iterative matching using synonym-accepted relations across all backbones in the database. In addition, the package allows users to \'translate\' one tree species list into another, streamlining the assimilation of new data into preexisting datasets or models. The package provides a flexible workflow depending on the use case, and can either be used as a stand-alone name-resolution package or in conjunction with existing packages as a final step in the name-resolution pipeline. The Treemendous package is fast and easy to use, allowing users to quickly merge different data sources by standardizing their species names according to the regularly updated database. By combining taxonomic information across multiple backbones, the package increases matching rates and minimizes data loss, allowing for more efficient translation of tree species datasets to aid research into forest biodiversity and tree ecology.

In response to growing concerns about the efficacy of antibiotic treatment, there has been a significant increase in research on bacteria that are resistant to antibiotics over the past two centuries. Such investigations might bring a spotlight on the field\'s evolution and future prospects. The study was aimed at conducting a measurable bibliometric review of the scientific literature on bacterial persistence and dormancy in the 21st Century. A scientific literature published during 21st Century was analyzed to gain insights into and identify research trends and outputs in persistent bacteria. Bibliometrix (R language package) and the VOS viewer were used to conduct a bibliometric investigation to determine the globally indexed persistent bacteria research output. WoS Core Collection databases were searched for persistent bacteria selected as the subject. A total of 1,160 published documents from 495 sources from the preceding two decades were reviewed. Maximum publications of 112 were observed in 2021 with 860 citations; however, 82 publications appeared in 2015 and were able to get the highest number of citations (4,214), only 43 (3.7%) were single-authored, whereas 1,117 (96.3%) publications are the result of collaborative works. Out of the top 10 countries ranked for publications, the USA took the top spot for the most highly productive country with 435 articles. Dormancy\' appeared 2,351 times, followed by \'Escherichia coli\" (1,744, and \'Growth\' 1,184 times) in research publications on bacterial persistence research. The findings from this study will aid in the creation of strategies and guidelines for regulating and avoiding bacterial persistence status.

[This corrects the article DOI: 10.3389/fphar.2023.1044330.].

Ecological and evolutionary studies often require high quality biodiversity data. This information is readily available through the many online databases that have compiled biodiversity data from herbaria, museums, and human observations. However, the process of preparing this information for analysis is complex and time consuming. In this study, we have developed a protocol in R language to process spatial data (download, merge, clean, and correct) and extract climatic data, using some genera of the ginseng family (Araliaceae) as an example. The protocol provides an automated way to process spatial and climatic data for numerous taxa independently and from multiple online databases. The script uses GBIF, BIEN, and WorldClim as the online data sources, but can be easily adapted to include other online databases. The script also uses genera as the sampling unit but provides a way to use species as the target. The cleaning process includes a filter to remove occurrences outside the natural range of the taxa, gardens, and other human environments, as well as erroneous locations and a spatial correction for misplaced occurrences (i.e., occurrences within a distance buffer from the coastal boundary). Additionally, each step of the protocol can be run independently. Thus, the protocol can begin with data cleaning, if the database has already been compiled, or with climatic data extraction, if the database has already been parsed. Each line of the R script is commented so that it can also be run by users with little knowledge of R.

Advancements in high-throughput technologies, genomics, transcriptomics, and metabolomics play an important role in obtaining biological information about living organisms. The field of computational biology and bioinformatics has experienced significant growth with the advent of high-throughput sequencing technologies and other high-throughput techniques. The resulting large amounts of data present both opportunities and challenges for data analysis. Big data analysis has become essential for extracting meaningful insights from the massive amount of data. In this chapter, we provide an overview of the current status of big data analysis in computational biology and bioinformatics. We discuss the various aspects of big data analysis, including data acquisition, storage, processing, and analysis. We also highlight some of the challenges and opportunities of big data analysis in this area of research. Despite the challenges, big data analysis presents significant opportunities like development of efficient and fast computing algorithms for advancing our understanding of biological processes, identifying novel biomarkers for breeding research and developments, predicting disease, and identifying potential drug targets for drug development programs.

The degree of sexual dimorphism in certain traits between males and females differ from one sample to another. Although trait differences by sex are often reported in bioanthropological research, few studies test for statistical significance or make raw data available. TestDimorph is the first R package dedicated to testing and comparing the degree of sexual dimorphism among different samples by leveraging summary statistics.
We provide two approaches of analysis of inter-sample differences in degree of sexual dimorphism: univariate and multivariate for two or more samples. The methods follow upon publications primarily from the AJBA. Within-sex size variability between samples is compared using one-way ANOVA followed by control for multiple pairwise comparisons. In addition, we compute the overlapping area between the density functions of two normal distributions from the mixture intersection index or the non-overlapping area using the dissimilarity index as well as Hedges\' g with inferential support using the 95% confidence interval. Finally, we use a multivariate analysis of differences in patterning of sexual dimorphism between samples.
We demonstrate various results from applying TestDimorph functions to data supplied with the package.
The package has many features including functionality for working with summary statistics, simulating data from summary statistics, and the extraction of summary statistics from raw data, so that the entire analysis can be performed through the package.