pVACview: an interactive visualization tool for efficient neoantigen prioritization and selection.

Background: 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. Results: 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. Conclusions: 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.

Bibliometric analysis of research in epilepsy and comorbid depression from 2014 to 2023.

BACKGROUND: Epilepsy and depression have complicated bidirectional relationships. Our study aimed to explore the field of epilepsy comorbid with depression in a bibliometric perspective from 2014-2023. AIM: To improve our understanding of epilepsy and depression by evaluating the relationship between epilepsy and depression, bibliometric analyses were performed. METHODS: Epilepsy and depression-related publications from the last decade were retrieved from the Web of Science Core Collection. We conducted bibliometric and visual analysis using VOSviewer and CiteSpace, examining authorships, countries, institutions, journals of publication, co-citations of references, connections between keywords, clusters of keywords, and keywords with citation bursts. RESULTS: Over the past ten years, we collected 1045 research papers focusing on the field of epilepsy and comorbid depression. Publications on epilepsy and depression have shown a general upward trend over time, though with some fluctuations. The United States, with 287 articles, and the University of Melbourne, contributing 34 articles, were the top countries and institutions, respectively. In addition, in the field of epilepsy and depression, Professor Lee, who has published 30 articles, was the most contributing author. The hot topics pay attention to the quality of life in patients with epilepsy and depression. CONCLUSION: We reported that quality of life and stigma in patients with epilepsy comorbid with depression are possible future hot topics and directions in the field of epilepsy and depression research.

Proceedings of the EuBIC-MS developers meeting 2023.

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.

Random survival forest for predicting the combined effects of multiple physiological risk factors on all-cause mortality.

Understanding the combined effects of risk factors on all-cause mortality is crucial for implementing effective risk stratification and designing targeted interventions, but such combined effects are understudied. We aim to use survival-tree based machine learning models as more flexible nonparametric techniques to examine the combined effects of multiple physiological risk factors on mortality. More specifically, we (1) study the combined effects between multiple physiological factors and all-cause mortality, (2) identify the five most influential factors and visualize their combined influence on all-cause mortality, and (3) compare the mortality cut-offs with the current clinical thresholds. Data from the 1999-2014 NHANES Survey were linked to National Death Index data with follow-up through 2015 for 17,790 adults. We observed that the five most influential factors affecting mortality are the tobacco smoking biomarker cotinine, glomerular filtration rate (GFR), plasma glucose, sex, and white blood cell count. Specifically, high mortality risk is associated with being male, active smoking, low GFR, elevated plasma glucose levels, and high white blood cell count. The identified mortality-based cutoffs for these factors are mostly consistent with relevant studies and current clinical thresholds. This approach enabled us to identify important cutoffs and provide enhanced risk prediction as an important basis to inform clinical practice and develop new strategies for precision medicine.

The evolving landscape and research trend of calcitonin gene-related peptide in migraine: A bibliometric analysis and visualization.

Background: Migraine is a global public health concern, affecting both social and individual well-being. Calcitonin gene-related peptide (CGRP), a crucial neuropeptide, holds important research value in understanding migraine pathogenesis. CGRP receptor antagonists and monoclonal antibodies that target CGRP or its receptors have shown efficacy in reducing migraine frequency and severity, presenting a promising therapeutic approach. This study aimed to conduct a comprehensive bibliometric analysis to analyze the current state, research trends, and future directions of CGRP in migraine. Methods: Bibliometric tools including CiteSpace, VOSviewer, etc., were utilized to extract and summarize publications related to CGRP in migraine from the Web of Science Core Collection Database (WOSCC) between 2004 and 2023, as of December 31, 2023. The analysis focused on trends in annual publications, leading countries/regions and institutions, prominent journals and references, influential authors, and high-frequency keywords in the field. Results: A total of 1,821 articles and reviews involving 5,180 authors from 1,315 organizations across 64 countries were included in the study. These publications were distributed across 362 journals and accumulated 56,999 citations by December 31, 2023. An increasing trend was observed in annual publications on CGRP in migraine. The United States emerged as the leading nation in both publications and citations, with academic Peter Goadsby contributing the highest number of publications. The University of Copenhagen stood out as the institution with the most publications, and Cephalalgia emerged as the most influential journal. The most cited paper identified was "Calcitonin gene-related peptide receptor antagonist BIBN4096BS for the acute treatment of migraine" by Jes Olesen, published in the New Engl Med. Keyword frequency analysis revealed prevalent terms such as "migraine," "CGRP," and "episodic migraine," along with emerging topics represented by keywords including "trial," "monoclonal antibodies," "preventive treatment," and "safety." Conclusion: CGRP is pivotal in migraine pathogenesis, and there is a robust research foundation exploring its role. The US leads in research output on CGRP in migraine. Investigating the mechanism of CGRP and its receptor in migraine remains a key area of interest, particularly focusing on signaling pathways. Future research should target identifying critical therapeutic targets in CGRP antagonist pathways for migraine treatment.

Assessing Seed Germination Response of Parasitic Plant Striga hermonthica with Small-Molecule Probes.

Seed germination of a parasitic plant Striga hermonthica is elicited by strigolactones which are exuded from roots of host plants. Here, we describe a high-throughput germination assay and a method for visualizing in vivo strigolactone receptor functions with a fluorogenic probe.

Recent advancements in machine learning for bone marrow cell morphology analysis.

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.

Bibliometric analysis of T cells in allergic rhinitis.

By reviewing the relevant literature in the field of T cell and allergic rhinitis, we determined the development status, study hotspots, and research frontiers viewpoints of this field to provide a reference for researchers and clinical workers. METHODS: Web of Science Core Collection (WoSCC) was applied to obtain the studies related to T cells and allergic rhinitis (AR) from 2003 to 2023, and the information extracted from these studies was analyzed using CiteSpace 6.1. R6 and VOSviewer 1.6.18. RESULTS: In total, 1585 articles were collected from WoSCC, with the time set between 2003 and 2023. Overall, a growing number of articles are being published annually. The countries and institutions with the maximum publications volume are China (370, 23.34 %) and Sun Yat-sen University (34, 2.15 %). The biggest contributor to the field was Durham, Stephen R. from the UK (22, 1.39 %). The Journal of Allergy and Clinical Immunology published the most related papers in the field (88, 5.54 %). Immunotherapy, Th cells, and inflammation were found to be the research hotspots in this area of T cells and allergic rhinitis in recent years. Pathway, model, Regulatory T cells (Treg cells), regulatory B cells, immunoglobulin E,and innate lymphoid cells were the current research hotspots in this field. CONCLUSION: The field of T cell and allergic rhinitis is developing rapidly, and many countries significantly contributed to this field. Most researchers in this field mainly focused on immunotherapy, Th cell, and inflammation. Pathway, model, Treg cell, regulatory B cell, immunoglobulin E,and innate lymphoid cells were the main subject of current research, and future development is expected to occur in this field.

Global trends of single cell sequence associated in cancer from 2011 to 2024: A bibliometric analysis.

Objective: Exploring the different molecular and clinicopathological features of nodal cancer based on single cell sequencing can reveal the intertumoral heterogeneity in cancer, and provide new ideas for early diagnosis, treatment and prognosis analysis of cancer. Methods: The hotspots, the features of worldwide scientific output, and the frontiers concerning single cell sequence related to cancer from 2011 to 2024 were determined using our bibliometric analysis. Web of Science Core Collection (WOSCC) database was searched for publications on single cell sequence associated with cancer that were published between 2011 and 2024. According to the journals, keywords, number of records, affiliations, citations, and countries, we conducted a bibliometric analysis. With the use of the data gathered from the WOSCC, geographic distribution was visualized, keyword, affiliation, and author cluster analyses were conducted, and co-cited references were reviewed and a descriptive analysis was also performed. Results: From the analysis, it was concluded that 6189 articles that were published between 2011 and 2024 in total were identified. Frontiers in immunology is the leading journal with the most publications in field of the research. The five clusters that were identified for hotspots included immunotherapy, single-cell RNA sequencing, hepatocellular carcinoma, proliferation, gene expression appeared the most frequently. Journals, nations, organizations, scholars with most contribution and most referenced publications globally were extracted. Studies have mostly concentrated on the spatial transcriptomics, pan-cancer analysis, hepatocellular carcinoma et al. Conclusion: Single-cell sequencing plays a significant role in tumor diagnosis, treatment and prognosis.

IMGT/mAb-KG: the knowledge graph for therapeutic monoclonal antibodies.

Introduction: Therapeutic monoclonal antibodies (mAbs) have demonstrated promising outcomes in diverse clinical indications, including but not limited to graft rejection, cancer, and autoimmune diseases lately.Recognizing the crucial need for the scientific community to quickly and easily access dependable information on monoclonal antibodies (mAbs), IMGT®, the international ImMunoGeneTics information system®, provides a unique and invaluable resource: IMGT/mAb-DB, a comprehensive database of therapeutic mAbs, accessible via a user-friendly web interface. However, this approach restricts more sophisticated queries and segregates information from other databases. Methods: To connect IMGT/mAb-DB with the rest of the IMGT databases, we created IMGT/mAb-KG, a knowledge graph for therapeutic monoclonal antibodies connected to IMGT structures and genomics databases. IMGT/mAb-KG is developed using the most effective methodologies and standards of semantic web and acquires data from IMGT/mAb-DB. Concerning interoperability, IMGT/mAb-KG reuses terms from biomedical resources and is connected to related resources. Results and discussion: In February 2024, IMGT/mAb-KG, encompassing a total of 139,629 triplets, provides access to 1,489 mAbs, approximately 500 targets, and over 500 clinical indications. It offers detailed insights into the mechanisms of action of mAbs, their construction, and their various products and associated studies. Linked to other resources such as Thera-SAbDab (Therapeutic Structural Antibody Database), PharmGKB (a comprehensive resource curating knowledge on the impact of genetic variation on drug response), PubMed, and HGNC (HUGO Gene Nomenclature Committee), IMGT/mAb-KG is an essential resource for mAb development. A user-friendly web interface facilitates the exploration and analyse of the content of IMGT/mAb-KG.

Analysis and Visualization of Protein Channels, Tunnels, and Pores with MOLEonline and ChannelsDB 2.0.

Channels, tunnels, and pores serve as pathways for the transport of molecules and ions through protein structures, thus participating to their functions. MOLEonline ( https://mole.upol.cz ) is an interactive web-based tool with enhanced capabilities for detecting and characterizing channels, tunnels, and pores within protein structures. MOLEonline has two distinct calculation modes for analysis of channel and tunnels or transmembrane pores. This application gives researchers rich analytical insights into channel detection, structural characterization, and physicochemical properties. ChannelsDB 2.0 ( https://channelsdb2.biodata.ceitec.cz/ ) is a comprehensive database that offers information on the location, geometry, and physicochemical characteristics of tunnels and pores within macromolecular structures deposited in Protein Data Bank and AlphaFill databases. These tunnels are sourced from manual deposition from literature and automatic detection using software tools MOLE and CAVER. MOLEonline and ChannelsDB visualization is powered by the LiteMol Viewer and Mol* viewer, ensuring a user-friendly workspace. This chapter provides an overview of user applications and usage.

Research hotspots and trends of spinal cord stimulation for neuropathic pain: a bibliometric analysis from 2004 to 2023.

The purpose of this study is to systematically analyze the development trend, research hotspots, and future development direction on the treatment of neuropathic pain (NP) with spinal cord stimulation through bibliometric method. We extracted the literature related to the treatment of NP with spinal cord stimulation from January 2004 to December 2023 from the Web of Science database. As a result, a total of 264 articles were retrieved. By analyzing the annual published articles, authors, countries, institutions, journals, co-cited literature, and keywords, we found that the count of publication in this field has been experiencing an overall growth, and the publications within the past 5 years accounted for 42% of the total output. Experts from the United States and the UK have made significant contributions in this field and established a stable collaborative team, initially establishing an international cooperation network. Pain is the frequently cited journal in this field. The study on spinal cord stimulation therapy for NP especially the study on spinal cord stimulation therapy for back surgery failure syndrome (FBSS) and its potential mechanisms are the research hotspots in this field, while the study on novel paradigms such as high-frequency spinal cord stimulation and spinal cord burst stimulation represents the future development directions. In short, spinal cord stimulation has been an effective treatment method for NP. The novel paradigms of spinal cord stimulation are the key point of future research in this field.

Points2Regions: Fast, interactive clustering of imaging-based spatial transcriptomics data.

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.

Mapping the global research landscape on nonalcoholic fatty liver disease and insulin resistance: A visualization and bibliometric study.

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. AIM: 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". CONCLUSION: 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.

An examination of global research trends for exploring the associations between the gut microbiota and nonalcoholic fatty liver disease through bibliometric and visualization analysis.

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.

"Rigid-Flexible" Dual-Ferrocene Chimeric Nanonetwork for Simultaneous Tumor-Targeted Tracing and Photothermal/Photodynamic Therapy.

There is an urgent need to develop phototherapeutic agents with imaging capabilities to assess the treatment process and efficacy in real-time during cancer phototherapy for precision cancer therapy. The safe near-infrared (NIR) fluorescent dyes have garnered significant attention and are desirable for theranostics agents. However, until now, achieving excellent photostability and fluorescence (FL) imaging capability in aggregation-caused quenching (ACQ) dyes remains a big challenge. Here, for the only FDA-approved NIR dye, indocyanine green (ICG), we developed a dual-ferrocene (Fc) chimeric nanonetwork ICG@HFFC based on the rigid-flexible strategy through one-step self-assembly, which uses rigid Fc-modified hyaluronic acid (HA) copolymer (HA-Fc) and flexible octadecylamine (ODA) bonded Fc (Fc-C18) as the delivery system. HA-Fc reserved the ability of HA to target the CD44 receptor of the tumor cell surface, and the dual-Fc region provided a rigid space for securely binding ICG through metal-ligand interaction and π-π conjugation, ensuring excellent photostability. Additionally, the alkyl chain provided flexible confinement for the remaining ICG through hydrophobic forces, preserving its FL. Thereby, a balance is achieved between outstanding photostability and FL imaging capability. In vitro studies showed improved photobleaching resistance, enhanced FL stability, and increased singlet oxygen (1O2) production efficiency in ICG@HFFC. Further in vivo results display that ICG@HFFC had good tumor tracing ability and significant tumor inhibition which also exhibited good biocompatibility.. Therefore, ICG@HFFC provides an encouraging strategy to realize simultaneous enhanced tumor tracing and photothermal/photodynamic therapy (PTT/PDT) and offers a novel approach to address the limitations of ACQ dyes.

Electronic Health Record-Oriented Knowledge Graph System for Collaborative Clinical Decision Support Using Multicenter Fragmented Medical Data: Design and Application Study.

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.

Ultrasound wavelet spectra enable direct tissue recognition and full-color visualization.

Traditional brightness-mode ultrasound imaging is primarily constrained by the low specificity among tissues and the inconsistency among sonographers. The major cause is the imaging method that represents the amplitude of echoes as brightness and ignores other detailed information, leaving sonographers to interpret based on organ contours that depend highly on specific imaging planes. Other ultrasound imaging modalities, color Doppler imaging or shear wave elastography, overlay motion or stiffness information to brightness-mode images. However, tissue-specific scattering properties and spectral patterns remain unknown in ultrasound imaging. Here we demonstrate that the distribution (size and average distance) of scattering particles leads to characteristic wavelet spectral patterns, which enables tissue recognition and high-contrast ultrasound imaging. Ultrasonic wavelet spectra from similar particle distributions tend to cluster in the eigenspace according to principal component analysis, whereas those with different distributions tend to be distinguishable from one another. For each distribution, a few wavelet spectra are unique and act as a fingerprint to recognize the corresponding tissue. Illumination of specific tissues and organs with designated colors according to the recognition results yields high-contrast ultrasound imaging. The fully-colorized tissue-specific ultrasound imaging potentially simplifies the interpretation and promotes consistency among sonographers, or even enables the applicability for non-professionals.