UNASSIGNED: In recent years, with the rapid advancement of fundamental ultrasonography research, the application of ultrasound in disease treatment has progressively increased. An increasing body of research indicates that microbubbles serve not only as contrast agents but also in conjunction with ultrasound, enhancing cavitation effects and facilitating targeted drug delivery, thereby augmenting therapeutic efficacy. The objective of this study is to explore the current status and prevailing research trends in this field from 2009 to 2023 through bibliometric analysis and to forecast future developmental trajectories.
UNASSIGNED: We selected the Science Citation Index Expanded (SCI-Expanded) from the Web of Science Core Collection (WOSCC) as our primary data source. On 19 January 2024, we conducted a comprehensive search encompassing all articles and reviews published between 2009 and 2023 and utilized the bibliometric online analysis platform, CiteSpace and VOSviewer software to analyze countries/regions, institutions, authors, keywords, and references, used Microsoft Excel 2021 to visualize the trends of the number of articles published by year.
UNASSIGNED: Between 1 January 2009, and 31 December 2023, 3,326 publications on ultrasound combined with microbubble therapy technology were included. There were a total of 2,846 articles (85.6%) and 480 reviews (14.4%) from 13,062 scholars in 68 countries/regions published in 782 journals. China and the United States emerged as the primary contributors in this domain. In terms of publication output and global institutional collaboration, the University of Toronto in Canada has made the most significant contribution to this field. Professor Kullervo Hynynen has achieved remarkable accomplishments in this area. Ultrasound in Medicine and Biology is at the core of the publishing of research on ultrasound combined with microbubble therapy technology. Keywords such as \"sonodynamic therapy,\" \"oxygen,\" \"loaded microbubbles\" and \"Alzheimer\'s disease\" indicate emerging trends in the field and hold the potential to evolve into significant areas of future investigation.
UNASSIGNED: This study provides a summary of the key contributions of ultrasound combined with microbubble therapy to the field\'s development over the past 15 years and delves into the historical underpinnings and contemporary trends of ultrasound combined with microbubble therapy technology, providing valuable guidance for researchers.

UNASSIGNED: Activating mutations in epidermal growth factor receptor (EGFR) have been identified as key predictive biomarkers for the customized treatment with EGFR tyrosine kinase inhibitors (TKIs) in non-small cell lung cancer (NSCLC), aiding in improving patient response rates and survival. However, resistance challenges the efficacy of these treatments, with limited understanding of post-resistance therapeutic strategies. A deep understanding of the biology and resistance mechanisms of EGFR-mutant NSCLC is crucial for developing new treatment approaches. This study, through bibliometric analysis, summarizes the trends in research on resistance to EGFR-TKIs.
UNASSIGNED: Research papers on NSCLC with EGFR inhibitor resistance were collected from the Web of Science Core Collection (WoSCC). The analysis utilized bibliometric tools like CiteSpace, VOSviewer, and other platforms for comprehensive analysis and visualization of the outcomes.
UNASSIGNED: The WoSCC database contains a total of 5866 documents on resistance to EGFR-TKIs treatment, including 4727 articles (93.48%) and 1139 reviews (6.52%), spanning 81 countries and regions, 4792 institutions, with the involvement of 23,594 authors. Since 2016, there has been a significant increase in publications in this field. China has the highest publication output, while the United States has the highest citation count for papers. Harvard University leads in terms of the number of publications. Among the top ten journals with the highest output, Clinical Cancer Research has the highest impact factor at 11.5, with 90% of the journals classified in Q1 or Q2. Rafael Rosell is one of the most influential authors in this field, ranking second in publication volume and fourth in citation count. Research on EGFR-TKIs resistance mainly focuses on genetic testing, resistance mechanisms, and post-resistance treatment strategies.
UNASSIGNED: This study provides researchers with a reliable basis and guidance for finding authoritative references, understanding research trends, and exploring potential directions.

The detection of the amount of aflatoxin M1 (AFM1) in milk is crucial for food safety. Here, we utilize a fiber optic (FO) localized surface plasmon resonance (LSPR) biosensor by constructing gold nanoparticle (AuNP) multimers, in which the nanogaps amplified the LSPR signal by the hot spot effect, and achieved a highly sensitive detection of f AFM1. Through the optimization of parameter conditions for the fabrication of the sensor and detection system, a high performance result from the FO LSPR biosensor was obtained, and the method for AFM1 detection was established, with a wide detection range of 0.05-100 ng/mL and a low limit of detection (LOD) of 0.04 ng/mL, and it has been successfully validated with the actual sample milk. Therefore, it is a good strategy to fabricate highly sensitive FO LSPR sensors for detecting AFM1 by constructing AuNP multimers, and this approach is suitable for developing other biosensors.

Protein-DNA complex interactivity plays a crucial role in biological activities such as gene expression, modification, replication and transcription. Understanding the physiological significance of protein-DNA binding interfacial hot spots, as well as the development of computational biology, depends on the precise identification of these regions. In this paper, a hot spot prediction method called EC-PDH is proposed. First, we extracted features of these hot spots\' solid solvent-accessible surface area (ASA) and secondary structure, and then the mean, variance, energy and autocorrelation function values of the first three intrinsic modal components (IMFs) of these conventional features were extracted as new features via the empirical modal decomposition algorithm (EMD). A total of 218 dimensional features were obtained. For feature selection, we used the maximum correlation minimum redundancy sequence forward selection method (mRMR-SFS) to obtain an optimal 11-dimensional-feature subset. To address the issue of data imbalance, we used the SMOTE-Tomek algorithm to balance positive and negative samples and finally used cat gradient boosting (CatBoost) to construct our hot spot prediction model for protein-DNA binding interfaces. Our method performs well on the test set, with AUC, MCC and F1 score values of 0.847, 0.543 and 0.772, respectively. After a comparative evaluation, EC-PDH outperforms the existing state-of-the-art methods in identifying hot spots.

Electroencephalography (EEG) is a widely used tool in neuroscience. To explore the features of the top 100 cited articles related to EEG and aging over the past decade, we conducted a bibliometric analysis using Web of Science Core Collection (WoSCC) data as of January 21, 2024. The selected top 100 cited papers were analyzed using VOSviewer and Excel. We examined the distribution of publication years, authors, institutions, countries/regions, and journals. Hotspots were identified through keyword analysis. The analyzed articles were published between 2014 and 2021, with the majority being published before 2020 (n=91). Citation counts in WoSCC ranged from 24 to 250, with a median of 40 and a mean of 53. A total of 818 authors from 283 institutions in 35 countries/territories contributed to these top papers. The United States of America (USA) (n=37), Germany (n=14), and Canada (n=11) ranked in the top three in terms of total publications or citations. The predominant journals were in the fields of Neuroscience (n=58), Geriatrics & Gerontology (n=22), Clinical Neurology (n=13), and Anesthesiology (n=9), which published most of the high-quality articles. Key themes included EEG, aging, Alzheimer\'s disease, mild cognitive impairment, functional connectivity, and alpha oscillations. Emerging topics included sleep, machine learning, delirium, postoperative cognitive function, virtual reality, monitoring, resting state, coherence, and transcranial direct current stimulation. In conclusion, this study provides a comprehensive overview of the trends in scientific literature on EEG in aging over the past decade. Authors and institutions from North America, Europe, and East Asia led in contributions. Journals focusing on neuroscience, geriatrics, and anesthesiology published the majority of articles. Degenerative neurological diseases and cognitive impairment were prominent topics, suggesting future studies should explore EEG\'s diagnostic utility for these disorders.

The contagious COVID-19 has recently emerged and evolved into a world-threatening pandemic outbreak. After pursuing rigorous prophylactic measures two years ago, most activities globally reopened despite the emergence of lethal genetic strains. In this context, assessing and mapping activity characteristics-based hot spot regions facilitating infectious transmission is essential. Hence, our research question is: How can the potential hotspots of COVID-19 risk be defined intra-cities based on the spatial planning of commercial activity in particular? In our research, Zayed and October cities, Egypt, characterized by various commercial activities, were selected as testbeds. First, we analyzed each activity\'s spatial and morphological characteristics and potential infection risk based on the Centre for Disease Control and Prevention (CDCP) criteria and the Kriging Interpolation method. Then, using Google Mobility, previous reports, and semi-structured interviews, points of interest and population flow were defined and combined with the last step as interrelated horizontal layers for determining hotspots. A validation study compared the generated activity risk map, spatial COVID-19 cases, and land use distribution using logistic regression (LR) and Pearson coefficients (rxy). Through visual analytics, our findings indicate the central areas of both cities, including incompatible and concentrated commercial activities, have high-risk peaks (LR = 0.903, rxy = 0.78) despite the medium urban density of districts, indicating that urban density alone is insufficient for public health risk reduction. Health perspective-based spatial configuration of activities is advised as a risk assessment tool along with urban density for appropriate decision-making in shaping pandemic-resilient cities.

In silico approaches were employed to examine the characteristics of interactions between human mitochondrial thioredoxin 2 (HsTrx2) and its 38 previously identified mitochondrial protein ligands. All interactions appeared driven mainly by electrostatic forces. The statistically significant residues of HsTrx2 for interactions were characterized as \"contact hot spots\". Since these were identical/adjacent to putative thermodynamic hot spots, an energy network approach identified their neighbors to highlight possible contact interfaces. Three distinct areas for binding emerged: (i) one around the active site for covalent interactions, (ii) another antipodal to the active site for strong non-covalent interactions, and (iii) a third area involved in both kinds of interactions. The contact interfaces of HsTrx2 were projected as respective interfaces for Escherichia coli Trx1 (EcoTrx1), 2, and HsTrx1. Comparison of the interfaces and contact hot spots of HsTrx2 to the contact residues of EcoTx1 and HsTrx1 from existing crystal complexes with protein ligands supported the hypothesis, except for a part of the cleft/groove adjacent to Trp30 preceding the active site. The outcomes of this study raise the possibility for the rational design of selective inhibitors for the interactions of HsTrx2 with specific protein ligands without affecting the entirety of the functions of the Trx system.

This study aimed to improve Knee Osteoarthritis (KOA) therapy by evaluating the knowledge framework and investigating research trends in inflammatory mechanisms. Conducting a thorough search on July 31, 2023, using the Science Citation Index Expanded of the Web of Science Core Collection, we identified 1,083 articles authored by 6,159 individuals from 3,610 institutions across 299 countries. China led in productivity with 377 papers, followed by the United States (253) and Japan (60). The University of California System (20 publications), Guangzhou University of Science and Technology (19), Duke University (18), and Shanghai Jiao Tong University (18) were the top institutions. Notably, the USA and Southern Medical University China held significant centrality in countries and institutions, respectively. Among 1,084 co-occurring keywords, \"expression\", \"rheumatoid arthritis\", \"articular cartilage\", \"F kappa b\", and \"Synovial fluid\" emerged as highly correlated topics. Analyzing inflammatory mechanisms in KOA through visualization tools offers insights into the knowledge framework, aiding in identifying future trends for better pain control. The study employed CiteSpace, VOS Viewer, and Tableau to analyze research hotspots and frontiers in inflammation mechanisms in KOA. It focused on essential signaling pathways in articular cartilage, synovial membrane, subchondral bone, and synovial fluids of OA patients and animal models, along with potential therapeutic reagents. Future exploration of the interaction between mechanisms can elucidate key factors in different pathways and the efficacy of injection therapy on inflammation.

Cancer-associated fibroblasts (CAFs) represent the predominant stromal component within the tumour microenvironment (TME), exhibiting considerable heterogeneity and plasticity that significantly impact immune response and metabolic reprogramming within the TME, thereby influencing tumour progression. Consequently, investigating CAFs is of utmost importance. The objective of this study is to employ bibliometric analysis in order to evaluate the current state of research on CAFs and predict future areas of research and emerging trends.
Conduct a comprehensive search for scholarly publications within the Web of Science Core Collection database, encompassing the time period from January 1, 2001, to December 31, 2022. Apply VOSviewer, CiteSpace, R software and Microsoft Excel for bibliometric analysis and visualisation.
This study involved a comprehensive analysis of 5,925 publications authored by 33,628 individuals affiliated with 4,978 institutions across 79 countries/regions. These publications were published in 908 journals, covering 14,495 keywords and 203,947 references. Notably, there was a significant increase in articles published between 2019 and 2022. China had the highest count of articles, while the United States emerged as the most frequently cited country. The primary research institutions in this field were Shanghai Jiao Tong University, Harvard University, and the University of Texas MD Anderson Cancer Center. Sotgia, Federica and Lisanti, Michael P from the University of Manchester, and Martinet, Wim from the University of Antwerp were the most prolific and highly cited authors. The journal Cancers had the highest number of publications, while Cancer Research was the most frequently cited journal. Molecular, biology, immunology, medicine and genetics were the main research disciplines in the field of CAFs. Key directions in CAFs research encompassed the study of transforming growth factor-β, Fibroblast Activation Protein, breast cancer, as well as growth and metastasis. The findings from the analysis of keyword co-occurrence and literature co-citation have revealed several emerging hotspots and trends within the field of CAFs. These include STAT3, multidrug resistance, pancreatic ductal adenocarcinoma, pan-cancer analysis, preclinical evaluation, ionizing radiation, and gold nanoparticles.
Targeting CAFs is anticipated to be a novel and effective strategy for cancer treatment. This study provides a comprehensive overview of the existing research on CAFs from 2001 to 2022, utilizing bibliometric analysis. The study identified the prominent areas of investigation and anticipated future research directions, with the aim of providing valuable insights and recommendations for future studies in the field of CAFs.

Fragment-based drug discovery (FBDD) is a well-established and effective method for generating diverse and novel hits in drug design. Kinases are suitable targets for FBDD due to their well-defined structure. Water molecules contribute to structure and function of proteins and also influence the environment within the binding pocket. Water molecules form a variety of hydrogen-bonded cyclic water-ring networks, collectively known as topological water networks (TWNs). Analyzing the TWNs in protein binding sites can provide valuable insights into potential locations and shapes for fragments within the binding site. Here, we introduce TWN-based fragment screening (TWN-FS) method, a novel screening method that suggests fragments through grouped TWN analysis within the protein binding site. We used this method to screen known CDK2, CHK1, IGF1R and ERBB4 inhibitors. Our findings suggest that TWN-FS method has the potential to effectively screen fragments. The TWN-FS method package is available on GitHub at https://github.com/pkj0421/TWN-FS.