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.

UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.\"
UNASSIGNED: 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.

In this study, the goal was to develop a method for detecting and classifying organophosphorus pesticides (OPPs) in bodies of water. Sixty-five samples with different concentrations were prepared for each of the organophosphorus pesticides, namely chlorpyrifos, acephate, parathion-methyl, trichlorphon, dichlorvos, profenofos, malathion, dimethoate, fenthion, and phoxim, respectively. Firstly, the spectral data of all the samples was obtained using a UV-visible spectrometer. Secondly, five preprocessing methods, six manifold learning methods, and five machine learning algorithms were utilized to build detection models for identifying OPPs in water bodies. The findings indicate that the accuracy of machine learning models trained on data preprocessed using convolutional smoothing + first-order derivatives (SG + FD) outperforms that of models trained on data preprocessed using other methods. The backpropagation neural network (BPNN) model exhibited the highest accuracy rate at 99.95%, followed by the support vector machine (SVM) and convolutional neural network (CNN) models, both at 99.92%. The extreme learning machine (ELM) and K-nearest neighbors (KNN) models demonstrated accuracy rates of 99.84% and 99.81%, respectively. Following the application of a manifold learning algorithm to the full-wavelength data set for the purpose of dimensionality reduction, the data was then visualized in the first three dimensions. The results demonstrate that the t-distributed domain embedding (t-SNE) algorithm is superior, exhibiting dense clustering of similar clusters and clear classification of dissimilar ones. SG + FD-t-SNE-SVM ranks highest among the feature extraction models in terms of performance. The feature extraction dimension was set to 4, and the average classification accuracy was 99.98%, which slightly improved the prediction performance over the full-wavelength model. As shown in this study, the ultraviolet-visible (UV-visible) spectroscopy system combined with the t-SNE and SVM algorithms can effectively identify and classify OPPs in waterbodies.

UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.

UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: Single-cell sequencing plays a significant role in tumor diagnosis, treatment and prognosis.

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.
CONCLUSIONS: 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.

OBJECTIVE: To describe how the knowledge from standard imaging practices can be translated into three-dimensional visualization techniques and utilized in the surgical planning and management of endometriosis.
METHODS: Tertiary care academic centre.
METHODS: Two case studies of patients with endometriosis are described.
METHODS: Transvaginal ultrasound (1), magnetic resonance imaging, three-dimensional printing (2), and three-dimensional virtual reality modeling (3) were utilized during patient workup and preparation. Three-dimensional modeling was performed by a virtual reality technician and verified for accuracy by a fellowship trained radiologist. Surgical management for endometriosis was performed.
CONCLUSIONS: While expert transvaginal ultrasound and/or magnetic resonance imaging suffice for the majority of cases, three-dimensional printing and virtual reality modeling are a novel adjunct to standard imaging modalities. Rendering two-dimensional images into a three-dimensional representation allows users to interact with the anatomy and is particularly useful when distorted by complex pathology. These techniques contributed to improved patient understanding and experience, and helped medical learners better grasp regular imaging techniques and its translation to pelvic anatomy. Last, it augmented surgeon comprehension of the relationship between the pelvic structures, allowing for enhanced surgical planning and intraoperative decision making. Further study is being performed to quantify these effects.

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.