A significant amount of effort has been devoted to the utilization of biochar-based catalysts in the treatment of wastewater. By virtue of its abundant functional groups and high specific surface area, biochar holds significant promise as a catalyst. This article presents a comprehensive systematic review and bibliometric analysis covering the period from 2009 to 2024, focusing on the restoration of wastewater through biochar catalysis. The production, activation, and functionalization techniques employed for biochar are thoroughly examined. In addition, the application of advanced technologies such as advanced oxidation processes (AOPs), catalytic reduction reactions, and biochemically driven processes based on biochar are discussed, with a focus on elucidating the underlying mechanisms and how surface functionalities influence the catalytic performance of biochar. Furthermore, the potential drawbacks of utilizing biochar are also brought to light. To emphasize the progress being made in this research field and provide valuable insights for future researchers, a scientometric analysis was conducted using CiteSpace and VOSviewer software on 595 articles. Hopefully, this review will enhance understanding of the catalytic performance and mechanisms pertaining to biochar-based catalysts in pollutant treatment while providing a perspective and guidelines for future research and development efforts in this area.

BACKGROUND: The medical knowledge graph provides explainable decision support, helping clinicians with prompt diagnosis and treatment suggestions. However, in real-world clinical practice, patients visit different hospitals seeking various medical services, resulting in fragmented patient data across hospitals. With data security issues, data fragmentation limits the application of knowledge graphs because single-hospital data cannot provide complete evidence for generating precise decision support and comprehensive explanations. It is important to study new methods for knowledge graph systems to integrate into multicenter, information-sensitive medical environments, using fragmented patient records for decision support while maintaining data privacy and security.
OBJECTIVE: This study aims to propose an electronic health record (EHR)-oriented knowledge graph system for collaborative reasoning with multicenter fragmented patient medical data, all the while preserving data privacy.
METHODS: The study introduced an EHR knowledge graph framework and a novel collaborative reasoning process for utilizing multicenter fragmented information. The system was deployed in each hospital and used a unified semantic structure and Observational Medical Outcomes Partnership (OMOP) vocabulary to standardize the local EHR data set. The system transforms local EHR data into semantic formats and performs semantic reasoning to generate intermediate reasoning findings. The generated intermediate findings used hypernym concepts to isolate original medical data. The intermediate findings and hash-encrypted patient identities were synchronized through a blockchain network. The multicenter intermediate findings were collaborated for final reasoning and clinical decision support without gathering original EHR data.
RESULTS: The system underwent evaluation through an application study involving the utilization of multicenter fragmented EHR data to alert non-nephrology clinicians about overlooked patients with chronic kidney disease (CKD). The study covered 1185 patients in nonnephrology departments from 3 hospitals. The patients visited at least two of the hospitals. Of these, 124 patients were identified as meeting CKD diagnosis criteria through collaborative reasoning using multicenter EHR data, whereas the data from individual hospitals alone could not facilitate the identification of CKD in these patients. The assessment by clinicians indicated that 78/91 (86%) patients were CKD positive.
CONCLUSIONS: The proposed system was able to effectively utilize multicenter fragmented EHR data for clinical application. The application study showed the clinical benefits of the system with prompt and comprehensive decision support.

The 2023 European Bioinformatics Community for Mass Spectrometry (EuBIC-MS) Developers Meeting was held from January 15th to January 20th, 2023, in Congressi Stefano Franscin at Monte Verità in Ticino, Switzerland. The participants were scientists and developers working in computational mass spectrometry (MS), metabolomics, and proteomics. The 5-day program was split between introductory keynote lectures and parallel hackathon sessions focusing on \"Artificial Intelligence in proteomics\" to stimulate future directions in the MS-driven omics areas. During the latter, the participants developed bioinformatics tools and resources addressing outstanding needs in the community. The hackathons allowed less experienced participants to learn from more advanced computational MS experts and actively contribute to highly relevant research projects. We successfully produced several new tools applicable to the proteomics community by improving data analysis and facilitating future research.

BACKGROUND: Nonalcoholic fatty liver disease (NAFLD) is increasingly recognized as a significant health issue. Emerging research has focused on the role of the gut microbiota in NAFLD, emphasizing the gut-liver axis. This study aimed to identify key research trends and guide future investigations in this evolving area.
METHODS: This bibliometric study utilized Scopus to analyze global research on the link between the gut microbiota and NAFLD. The method involved a search strategy focusing on relevant keywords in article titles, refined by including only peer-reviewed journal articles. The data analysis included bibliometric indicators such as publication counts and trends, which were visualized using VOSviewer software version 1.6.20 for network and co-occurrence analysis, highlighting key research clusters and emerging topics.
RESULTS: Among the 479 publications on the gut microbiota and NAFLD, the majority were original articles (n = 338; 70.56%), followed by reviews (n = 119; 24.84%). The annual publication count increased from 1 in 2010 to 118 in 2022, with a significant growth phase starting in 2017 (R2 = 0.9025, p < 0.001). The research was globally distributed and dominated by China (n = 231; 48.23%) and the United States (n = 90; 18.79%). The University of California, San Diego, led institutional contributions (n = 18; 3.76%). Funding was prominent, with 62.8% of the articles supported, especially by the National Natural Science Foundation of China (n = 118; 24.63%). The average citation count was 43.23, with an h-index of 70 and a citation range of 0 to 1058 per article. Research hotspots shifted their focus post-2020 toward the impact of high-fat diets on NAFLD incidence.
CONCLUSIONS: This study has effectively mapped the growing body of research on the gut microbiota-NAFLD relationship, revealing a significant increase in publications since 2017. There is significant interest in gut microbiota and NAFLD research, mainly led by China and the United States, with diverse areas of focus. Recently, the field has moved toward exploring the interconnections among diet, lifestyle, and the gut-liver axis. We hypothesize that with advanced technologies, new opportunities for personalized medicine and a holistic understanding of NAFLD will emerge.

Imaging-based spatial transcriptomics techniques generate data in the form of spatial points belonging to different mRNA classes. A crucial part of analyzing the data involves the identification of regions with similar composition of mRNA classes. These biologically interesting regions can manifest at different spatial scales. For example, the composition of mRNA classes on a cellular scale corresponds to cell types, whereas compositions on a millimeter scale correspond to tissue-level structures. Traditional techniques for identifying such regions often rely on complementary data, such as pre-segmented cells, or lengthy optimization. This limits their applicability to tasks on a particular scale, restricting their capabilities in exploratory analysis. This article introduces \"Points2Regions,\" a computational tool for identifying regions with similar mRNA compositions. The tool\'s novelty lies in its rapid feature extraction by rasterizing points (representing mRNAs) onto a pyramidal grid and its efficient clustering using a combination of hierarchical and k $$ k $$ -means clustering. This enables fast and efficient region discovery across multiple scales without relying on additional data, making it a valuable resource for exploratory analysis. Points2Regions has demonstrated performance similar to state-of-the-art methods on two simulated datasets, without relying on segmented cells, while being several times faster. Experiments on real-world datasets show that regions identified by Points2Regions are similar to those identified in other studies, confirming that Points2Regions can be used to extract biologically relevant regions. The tool is shared as a Python package integrated into TissUUmaps and a Napari plugin, offering interactive clustering and visualization, significantly enhancing user experience in data exploration.

BACKGROUND: Nonalcoholic fatty liver disease (NAFLD) is a liver condition that is prevalent worldwide and associated with significant health risks and economic burdens. As it has been linked to insulin resistance (IR), this study aimed to perform a bibliometric analysis and visually represent the scientific literature on IR and NAFLD.
OBJECTIVE: To map the research landscape to underscore critical areas of focus, influential studies, and future directions of NAFLD and IR.
METHODS: This study conducted a bibliometric analysis of the literature on IR and NAFLD indexed in the SciVerse Scopus database from 1999 to 2022. The search strategy used terms from the literature and medical subject headings, focusing on terms related to IR and NAFLD. VOSviewer software was used to visualize research trends, collaborations, and key thematic areas. The analysis examined publication type, annual research output, contributing countries and institutions, funding agencies, journal impact factors, citation patterns, and highly cited references.
RESULTS: This analysis identified 23124 documents on NAFLD, revealing a significant increase in the number of publications between 1999 and 2022. The search retrieved 715 papers on IR and NAFLD, including 573 (80.14%) articles and 88 (12.31%) reviews. The most productive countries were China (n = 134; 18.74%), the United States (n = 122; 17.06%), Italy (n = 97; 13.57%), and Japan (n = 41; 5.73%). The leading institutions included the Università degli Studi di Torino, Italy (n = 29; 4.06%), and the Consiglio Nazionale delle Ricerche, Italy (n = 19; 2.66%). The top funding agencies were the National Institute of Diabetes and Digestive and Kidney Diseases in the United States (n = 48; 6.71%), and the National Natural Science Foundation of China (n = 37; 5.17%). The most active journals in this field were Hepatology (27 publications), the Journal of Hepatology (17 publications), and the Journal of Clinical Endocrinology and Metabolism (13 publications). The main research hotspots were \"therapeutic approaches for IR and NAFLD\" and \"inflammatory and high-fat diet impacts on NAFLD\".
CONCLUSIONS: This is the first bibliometric analysis to examine the relationship between IR and NAFLD. In response to the escalating global health challenge of NAFLD, this research highlights an urgent need for a better understanding of this condition and for the development of intervention strategies. Policymakers need to prioritize and address the increasing prevalence of NAFLD.

UNASSIGNED: Neoantigen targeting therapies including personalized vaccines have shown promise in the treatment of cancers, particularly when used in combination with checkpoint blockade therapy. At least 100 clinical trials involving these therapies are underway globally. Accurate identification and prioritization of neoantigens is highly relevant to designing these trials, predicting treatment response, and understanding mechanisms of resistance. With the advent of massively parallel DNA and RNA sequencing technologies, it is now possible to computationally predict neoantigens based on patient-specific variant information. However, numerous factors must be considered when prioritizing neoantigens for use in personalized therapies. Complexities such as alternative transcript annotations, various binding, presentation and immunogenicity prediction algorithms, and variable peptide lengths/registers all potentially impact the neoantigen selection process. There has been a rapid development of computational tools that attempt to account for these complexities. While these tools generate numerous algorithmic predictions for neoantigen characterization, results from these pipelines are difficult to navigate and require extensive knowledge of the underlying tools for accurate interpretation. This often leads to over-simplification of pipeline outputs to make them tractable, for example limiting prediction to a single RNA isoform or only summarizing the top ranked of many possible peptide candidates. In addition to variant detection, gene expression and predicted peptide binding affinities, recent studies have also demonstrated the importance of mutation location, allele-specific anchor locations, and variation of T-cell response to long versus short peptides. Due to the intricate nature and number of salient neoantigen features, presenting all relevant information to facilitate candidate selection for downstream applications is a difficult challenge that current tools fail to address.
UNASSIGNED: We have created pVACview, the first interactive tool designed to aid in the prioritization and selection of neoantigen candidates for personalized neoantigen therapies including cancer vaccines. pVACview has a user-friendly and intuitive interface where users can upload, explore, select and export their neoantigen candidates. The tool allows users to visualize candidates across three different levels, including variant, transcript and peptide information.
UNASSIGNED: pVACview will allow researchers to analyze and prioritize neoantigen candidates with greater efficiency and accuracy in basic and translational settings The application is available as part of the pVACtools pipeline at pvactools.org and as an online server at pvacview.org.

As machine learning progresses, techniques such as neural networks, decision trees, and support vector machines are being increasingly applied in the medical domain, especially for tasks involving large datasets, such as cell detection, recognition, classification, and visualization. Within the domain of bone marrow cell morphology analysis, deep learning offers substantial benefits due to its robustness, ability for automatic feature learning, and strong image characterization capabilities. Deep neural networks are a machine learning paradigm specifically tailored for image processing applications. Artificial intelligence serves as a potent tool in supporting the diagnostic process of clinical bone marrow cell morphology. Despite the potential of artificial intelligence to augment clinical diagnostics in this domain, manual analysis of bone marrow cell morphology remains the gold standard and an indispensable tool for identifying, diagnosing, and assessing the efficacy of hematologic disorders. However, the traditional manual approach is not without limitations and shortcomings, necessitating, the exploration of automated solutions for examining and analyzing bone marrow cytomorphology. This review provides a multidimensional account of six bone marrow cell morphology processes: automated bone marrow cell morphology detection, automated bone marrow cell morphology segmentation, automated bone marrow cell morphology identification, automated bone marrow cell morphology classification, automated bone marrow cell morphology enumeration, and automated bone marrow cell morphology diagnosis. Highlighting the attractiveness and potential of machine learning systems based on bone marrow cell morphology, the review synthesizes current research and recent advances in the application of machine learning in this field. The objective of this review is to offer recommendations to hematologists for selecting the most suitable machine learning algorithms to automate bone marrow cell morphology examinations, enabling swift and precise analysis of bone marrow cytopathic trends for early disease identification and diagnosis. Furthermore, the review endeavors to delineate potential future research avenues for machine learning-based applications in bone marrow cell morphology analysis.

BACKGROUND: Age-related hearing loss (ARHL) - also termed presbycusis - is prevalent among older adults, leading to a range of issues. Although considerable progress in the understanding of ARHL over the decades, available reports lack data from recent years and do not comprehensively reflect the latest advancements and trends. Therefore, our study sought to assess research hotspots and trends in ARHL over the past 5 years to provide the basis for future research.
METHODS: The Web of Science Core Collection database was searched and screened from January 1, 2019 to October 21, 2023, according to the inclusion criteria. CiteSpace (5.8.R3), VOSviewer (1.6.19), and Microsoft Excel 2019 were employed for bibliometric analysis and visualization.
RESULTS: 3084 articles from 92 countries led by the United States and China were included. There has been a steady upward trend in the number of publications from 2019 to 2023. The most productive institutions, authors, and journals are Johns Hopkins University (n = 113), Lin FR (n = 66), and Ear and Hearing (n = 135), respectively. Trend topic analyses revealed that \"cochlear synaptopathy\" and \"dementia\" were the predominant foci. Keywords, including \"individuals\" and \"national health\", began to appear.
CONCLUSIONS: Over the past 5 years, the annual number of publications has increased significantly and will continue to do so. Research on the mechanism of ARHL, represented by \"oxidative stress\", is a continuing focus. Emerging topics such as \"individual differences\" and \"national health\" may be potential future hotspots in this field.

Sensitivity analysis answers questions about the influence of parameters on the simulation results and plays a significant role in the development of environmental models by helping to understand the relations within the model and test its adequacy. Comparison of various sensitivity analysis approaches is often also quite useful because different methods employ different measures for ranking model parameters and their unconformities and disagreements provide additional information on model behavior. The visual representation of numerical results is crucial for their correct interpretation, and at first sight, the visualizations for the sensitivity analysis should be quite universal because in most cases an outcome of sensitivity analysis is the same: a set of indices measuring the significance of model inputs for the selected output. Surprisingly, it is not so straightforward. This paper compares visualization types suitable for the graphical representation of the sensitivity indices and demonstrates their benefits and caveats in different cases.