Research Hotspot Research Articles

Amino Acid‐Assisted Synthesis of Zeolites with Improved Catalytic Properties

Conventional zeolites are limited in their ability to catalyze macromolecular reactions due to micropore constraints, resulting in sluggish reactant and product diffusion and subsequently pore clogging and catalyst deactivation. Consequently, the pore and textural refinement of zeolites to meet industrial demands has become a research hotspot. Herein, we review the amino acid‐assisted methods in zeolite synthesis and scrutinize the principle and influential factors governing amino acid involvement in zeolite synthesis. Additionally, we analyze the advantages and challenges associated with the amino acid‐assisted method. Certain amino acids can interact with zeolite precursors or crystal surface, thus altering the crystal growth rate and enabling precise control over the crystal size and shape. On the other hand, amino acids can serve as structure‐directing agents to orchestrate the generation of mesoporous pores. These capabilities enable the production of zeolites with well‐defined pores, particle sizes and/or crystal shapes that satisfy catalytic requirements. Moreover, the unique properties of amino acids allow their complete elimination from the solid product through a simple aqueous washing process, facilitating their recovery for subsequent usage. As result, the amino acid‐assisted synthesis methods offer a convenient, green route to zeolites with modulated textual properties for high‐performance catalysis.

HGNN−BRFE: Heterogeneous Graph Neural Network Model Based on Region Feature Extraction

With the strong capability of heterogeneous graphs in accurately modeling various types of nodes and their interactions, they have gradually become a research hotspot, promoting the rapid development of the field of heterogeneous graph neural networks (HGNNs). However, most existing HGNN models rely on meta−paths for feature extraction, which can only utilize part of the data from the graph for training and learning. This not only limits the data generalization ability of deep learning models but also affects the application effect of data−driven adaptive technologies. In response to this challenge, this study proposes a new model—heterogeneous graph neural network based on regional feature extraction (HGNN−BRFE). This model enhances performance through an “extraction−fusion” strategy in three key aspects: first, it efficiently extracts features of neighboring nodes of the same type according to specific regions; second, it effectively fuses information from different regions and hierarchical neighbors using attention mechanisms; third, it specially designs a process for feature extraction and fusion targeting heterogeneous type nodes, ensuring that the rich semantic and heterogeneity information within the heterogeneous graph is retained while maintaining the node’s own characteristics during the node embedding process to prevent the loss of its own features and potential over−smoothing issues. Experimental results show that HGNN−BRFE achieves a performance improvement of 1–3% over existing methods on classification tasks across multiple real−world datasets.

Emerging trends and hotspots in intestinal microbiota research in sepsis: bibliometric analysis

BackgroundThe association between the gut microbiota and sepsis has garnered attention in the field of intestinal research in sepsis. This study utilizes bibliometric methods to visualize and analyze the literature on gut microbiota research in sepsis from 2011 to 2024, providing a scientific foundation for research directions and key issues in this domain.MethodsOriginal articles and reviews of gut microbiota research in sepsis, which published in English between 2011 and 2024, were obtained from the Web of Science Core Collection on June 21, 2024. Python, VOSviewer, and CiteSpace software were used for the visual analysis of the retrieved data.ResultsA total of 1,031 articles were analyzed, originating from 72 countries or regions, 1,614 research institutions, and 6,541 authors. The articles were published in 434 different journals, covering 89 different research fields. The number of publications and citations in this research area showed a significant growth trend from 2011 to 2024, with China, the United States, and the United Kingdom being the main research forces. Asada Leelahavanichkul from Thailand was identified as the most prolific author, making him the most authoritative expert in this field. “Nutrients” had the highest number of publications, while “Frontiers in Cellular and Infection Microbiology,” “Frontiers in Immunology” and “the International Journal of Molecular Sciences” have shown increasing attention to this field in the past 2 years. Author keywords appearing more than 100 times included “gut microbiota (GM),” “sepsis” and “microbiota.” Finally, this study identified “lipopolysaccharides (LPS),” “short-chain fatty acids (SCFAs),” “probiotics,” “fecal microbiota transplantation (FMT)” and “gut-liver axis” as the research hotspots and potential frontier directions in this field.ConclusionThis bibliometric study summarizes current important perspectives and offers comprehensive guidance between sepsis and intestinal microbiota, which may help researchers choose the most appropriate research directions.

Prevention of early thrombosis in transplanted vein model by encapsulation with tirofiban microneedle drug delivery system

Abstract Early thrombosis following CABG surgery leads to perioperative myocardial infarction, which causes difficulties for clinicians and patients. Moreover, once perioperative myocardial infarction occurs, the mortality rate is extremely high. In recent years, microneedle (MN) drug delivery systems have become a research hotspot with broad clinical application prospects. These systems are capable of achieving sustained, safe, and painless local drug release. In cardiovascular applications, MNs maximize local anticoagulant effects, inhibit endometrial hyperplasia, and reduce systemic side effects. We speculate that a MN drug delivery system can be used to target transplanted veins to inhibit their thrombosis and reduce the incidence of perioperative myocardial infarction after CABG surgery. Therefore, this study developed a hyaluronic acid MN patch loaded with tirofiban and conducted preliminary physicochemical tests. The safety, efficacy, biocompatibility, and targeting of the MN system were evaluated using in vitro and in vivo experiments using a jugular vein transplantation model. The results indicate that the MN system has excellent physical properties, safety, effectiveness, biocompatibility, and strong targeting, which can effectively inhibit early local thrombus formation. In addition, the observation of early postoperative endometrial hyperplasia activation provides a foundation for future research.

Non‐Aqueous Liquid Electrolytes for Li‐O2 Batteries

AbstractLi‐O2 batteries (LOBs) have become a research hotspot of energy storage devices because of its high theoretical energy density. Practical applications require that non‐aqueous LOBs can deliver stable and high reversible capacity, which heavily depends on the appropriate electrolyte system. Therefore, it is very important to select electrolytes that are hard to decompose and conducive to modulating the growth kinetics of discharge products. Herein, we will review the current progress and challenges of non‐aqueous liquid electrolytes for LOBs by analyzing the influence factors on electrolyte stability and introducing the design and modification methods of electrolytes with different solvent types. At last, the possible research tactics have been proposed to develop advanced electrolytes for improving electrochemical performance of LOBs.

Synthesis and Fungicidal Activity Evaluation of Novel Triazole Thione/Thioether Derivatives Containing a Pyridylpyrazole Moiety

Compounds containing N‐pyridylpyrazole motif have aroused interest and brought about research hotspots due to their highly‐efficient insecticidal activity. The fungicidal potential of N‐pyridylpyrazole derivatives has gradually been disclosed in recent years. To discover new agrochemicals with poly‐heterocyclic features, a series of novel triazole thione/thioether derivatives containing pyridylpyrazole motif (8‐11) was synthesized. The new compounds were identified by melting point, 1H NMR, 13C NMR, 19F NMR, HRMS, and elemental analysis. The bioassays showed that most of the pyridylpyrazole‐containing triazole thione Mannich bases possessed favorable in vitro fungicidal activity toward pathogenic fungi, such as Magnaporthe oryzae, Sclerotinia sclerotiorum, Botrytis cinerea and Fusarium verticillioides, and were comparable with those of the contrast compounds A and triadimefon. Some of them exhibited moderate to good in vivo fungicidal activity against S. sclerotiorum at 0.2 mg/mL (e.g. 8f control efficacy: 60.9±3.2%). The SEM observation displayed that 8f might cause disruption of cell membrane and wall of S. sclerotiorum. Compounds 8a, 8c, 8f‐8h, 8p and 9b can serve as promising new fungicidal agents to make further structural optimization. The findings in this article provide useful clue and guidance for the design and development of new poly‐heterocyclic agrochemicals.

Hotspots and future trends of dermatofibrosarcoma protuberans

ObjectiveDermatofibrosarcoma protuberans (DFSP) is a moderately malignant soft tissue sarcoma with localized infiltrative growth. The extensive surgical scope and high recurrence rate of DFSP brings dysfunctional, aesthetic, psychological and economic problems to patients. The aim of this study is to explore the global publication characteristics, research hotspots and future trends of DFSP over the past 32 years via an intuitive visualized way.MethodsThe Web of Science Core Collection (WoSCC) database was searched for papers related to DFSP published from 1990 to 2022. Then bibliometric analysis of these publications was performed, including collaborative networks, co-citation analysis of journals and references, and cluster analysis of keywords.ResultsA total of 1588 papers were retrieved between 1990-2022. The United States was the most prolific country, followed by China. The article Imatinib Mesylate in Advanced Dermatofibrosarcoma Protuberans: Pooled Analysis of Two Phase II Clinical Trials, received most citations. Research hotspots and future trends are mainly focused on disease diagnosis, COL1A1-PDGFB fusion gene, drug and surgical treatment, fibrosarcomatous transformation, and immunotherapy.ConclusionThe research on DFSP faces several clinical challenges. This study provides novel insights into future research directions and scientific decisions for DFSP.

Research hotspots and evolution trends of food safety risk assessment techniques and methods

AbstractFood safety incidents frequently occur in China and other countries. Food safety risk management has evolved from harm‐based to risk‐based approaches to control risks more scientifically and effectively. Risk assessment is at the core of risk control and provides a scientific basis for the development of risk management measures. Therefore, analyzing the research status and development trends of food safety risk assessment technologies and methods is important for improving food safety risk assessment and risk management. In this study, CiteSpace software was used to analyze a knowledge graph of popular research topics in the field of food safety risk assessment over the past 14 years, identify research keywords, and provide centrality statistics. Food safety risk assessments primarily focus on in vitro high‐throughput testing, computational toxicology, predictive microbiology, mechanistic research, and other technical methods. In the future, international coordination, cooperation, and cross‐integration of relevant disciplines should be strengthened, and research on and application of innovative technologies for food safety risk assessment should be promoted. Research on food safety risk assessment technologies and methods is in an active development stage and has good prospects for further exploration and application.

A Survey on Multimodal Large Language Models

Abstract Recently, Multimodal Large Language Model (MLLM) represented by GPT-4V has been a new rising research hotspot, which uses powerful Large Language Models (LLMs) as a brain to perform multimodal tasks. The surprising emergent capabilities of MLLM, such as writing stories based on images and Optical Character Recognition (OCR)-free math reasoning, are rare in traditional multimodal methods, suggesting a potential path to artificial general intelligence. To this end, both academia and industry have endeavored to develop MLLMs that can compete with or even outperform GPT-4V, pushing the limit of research at a surprising speed. In this paper, we aim to trace and summarize the recent progress of MLLMs. First of all, we present the basic formulation of MLLM and delineate its related concepts, including architecture, training strategy and data, as well as evaluation. Then, we introduce research topics about how MLLMs can be extended to support more granularity, modalities, languages, and scenarios. We continue with multimodal hallucination and extended techniques, including Multimodal ICL (M-ICL), Multimodal CoT (M-CoT), and LLM-Aided Visual Reasoning (LAVR). To conclude the paper, we discuss existing challenges and point out promising research directions.

Research Situation, Hotspots, and Global Trends in Laser Treatment of Acne Scars: A Bibliometric Analysis of Related Research From 2014 to 2024

ABSTRACTBackgroundAcne vulgaris is a chronic inflammatory skin condition, commonly resulting in acne scars. Treating acne scars remains a significant challenge in dermatology. With advancements in laser technology, its clinical use for treating acne scars has been increasing annually. However, bibliometric analysis on laser treatment for acne scars is lacking. This study aims to use bibliometrics to comprehensively understand the development trends and research hotspots in laser treatment for acne scars.MethodsUsing “acne scar” and “laser” as search terms, literature on laser treatment of acne scars from 2014 to 2024 was retrieved from the Web of Science Core Collection (WoSCC) database. The literature data were visualized using VOSviewers, CiteSpace, and R software, generating maps of countries, research institutions, authors, journals, references, and keywords.ResultsThe analysis included 536 articles from 46 countries, with the United States and China leading in publications. Publications focusing on laser treatment of acne scars exhibit a consistent growth trend annually. Key research institutions include Mahidol University in Thailand, Cairo University in Egypt, and Hallym University in South Korea. The “Journal of Cosmetic Dermatology” had the highest number of articles in this field, while “Dermatologic Surgery” was the most cited publication. These publications involved contributions from 2135 authors, with Professor Manuskiatti Woraphong from Mahidol University in Thailand being the most prolific author in this field. Research on the efficacy of laser treatment for acne scars is a major focus in this field. Among the different types of lasers, CO2 lasers are the most commonly used. Emerging research focuses include therapies such as platelet‐rich plasma, picosecond laser, trichloroacetic acid, and burn scar.ConclusionsTreatment efficacy is the primary focus of research in the field of laser treatment for acne scars. Platelet‐rich plasma, and novel picosecond lasers, have emerged as hot topics and trends in this research field. However, it is important to note that the impact factors of journals publishing in this field are currently low. Therefore, clinicians must consider and explore strategies for publishing high‐quality clinical research in the future.

Bibliometric analysis of research hotspots and trends on the relationship between the gut microbiota and depression from 2020 to 2024

IntroductionIn recent years, an increasing body of research has illustrated a strong correlation between gut microbiota and depression. However, there has yet to be a comprehensive discussion or summary of the latest advancements and trends in this field.MethodsWe retrieved research articles focused on gut microbiota and depression through the WOS database from 2020 to 2024, using visual text analysis tools such as CiteSpace and VOSviewer.ResultsThe literature on the relationship between gut microbiota and depression surged from 396 papers in 2020 to 711 by 2024. During this period, the journal with the highest publication rate was Nutrients. China led the countries in contributions, while University College Cork topped the institutions. Kenji Hashimoto emerged as the most prolific author. The most cited paper was authored by Cryan JF et al., published in 2019 in Physiol Rev. The keywords “gut microbiota,” “depression,” and “anxiety” appeared most frequently, while recent years saw explosive increases in terms such as “growth performance,” “receptors,” “depression-like phenotypes,” “stress response,” “gastrointestinal symptoms,” “reliability,” and “neurogenesis.”DiscussionOur article displayed the overview of the relationship between the gut microbiome and depression from 2020 to 2024 using bibliometric methods, providing perspectives and research hotspots for studies exploring the correlation between the gut microbiome and depression.

The current state and trends of immunotherapy research in lung cancer: a review and bibliometric analysis

In recent years, the integration of immunotherapy in the treatment of lung cancer has marked a significant evolution in the field. This is evidenced by the surge in the volume of scientific publications, reflecting rapid advances over time. This paper presents a bibliometric analysis of lung cancer and immunotherapy research from January 2012 to December 2022, drawing on the Web of Science literature database and using the citexs data analysis platform to examine the shifts in topic hotspots over the decade. A total of 8,722 publications were retrieved, with annual publication numbers soaring from 79 in 2012 to 2,112 in 2021. The most prolific country in terms of publication volume was China (n = 3,363, 38.56%), with The University of Texas MD Anderson Cancer Center making the most significant institutional contribution (n = 156, 1.79%). Notably, the most productive authors in this domain were Benjamin Besse and Marina Chiara Garassino, who have collectively published 35 articles to date. Predominant research themes include PD1/PDL1, clinical trials, pembrolizumab, nivolumab, and immune checkpoint inhibitors. Moreover, this paper visualizes the analysis of journals, keywords, key genes and targets, and associated diseases, aiming to provide a systematic review and a forward-looking perspective on research in lung cancer and immunotherapy. By exploring current research dynamics and hotspots and identifying areas for improvement, this study seeks to provide valuable insights for future investigations in this burgeoning field.

A bibliometric analysis and visualization of non-suicidal self-injury in adolescents

BackgroundNon-suicidal self-injury(NSSI)is a widespread occurrence among adolescents, and this behavior can bring serious consequences. In recent years, the prevalence of NSSI continues to rise, which has attracted the attention of many researchers. But currently there is no research exploring the overall research distribution of NSSI in adolescents through quantitative analysis. Therefore, it is necessary to understand the status of development and main research hotspots of NSSI in adolescents via bibliometric analysis.MethodsWe searched the relevant studies from the Web of Science Core Collection(WoSCC)from January 1, 2014 to December 31, 2023. Using CiteSpace and VOSviewer visual analysis tools, we analyzed studies from the perspectives of country, region, institution, journal, author, and keywords.ResultA total of 2177 studies related to NSSI in adolescents were included. USA and Harvard University were the leading country and institution in this research field. Penelope Hasking was the most prolific author. Frontiers in Psychiatry and the Journal of Affective Disorders were the most productive journals. The most high-frequency keywords were ‘depression’, ‘mental health’, ‘emotion regulation’ and ‘borderline personality disorder’. ‘mindfully’, ‘intervention’, ‘self-compassion’ and ‘ecological momentary assessment’ were the emerging keywords.ConclusionsExploring the relevant factors and mechanisms of comorbidities, identifying etiology and risk/protective factors, and finding the impact of NSSI on adolescents are the hot topics. Moreover, intervention measures and interdisciplinary collaborative research for NSSI in adolescents will emerge as frontiers in the future.

Mapping the Landscape of Medical Humanities Education: Trends and Insights

ABSTRACTBackgroundMedical humanities education is an integral component of medical education. However, the current landscape of research on medical humanities education remains incomplete. The purpose of this study is to identify the trends in research on medical humanities education and provide a global overview of the field by analysing various aspects such as sources, authors, literature, and keywords. The aim is to offer insights and recommendations for the future development of medical humanities education.MethodsThis research utilised bibliometric tools such as VOSviewer, CiteSpace, SciMAT, and the R software package biblioshiny to analyse literature sources, authors, documents, and keywords. This systematic review methodology allowed for a comprehensive examination of the field. Additionally, the study investigated international collaborations and scientific outputs, shedding light on the global landscape of medical humanities education research.ResultsThe study included a total of 403 articles published between January 1980 and December 2023 from the Web of Science (WoS) database. The field of medical humanities education experienced an annual growth rate of 9.08% in literature output from 1980 to 2023, indicating its increasing prominence and scholarly interest over time. The research topics within this field have evolved in response to societal and medical developments. Core research themes that have consistently garnered attention from researchers include empathy, ethics, and narrative medicine, reflecting their significance and ongoing relevance in the field.ConclusionThese shifts in research hotspots signify the dynamic nature of the field, adapting to evolving contexts and emerging research domains. By embracing cross‐cultural perspectives and fostering interdisciplinary collaborations, stakeholders in medical humanities education can collectively enrich the field, promote inclusivity, and enhance the overall educational experience for healthcare professionals.

The Theoretical Logic, Challenges and Countermeasures of Vocational Education Enabling the Development of New Quality Productive Forces

New quality productive forces are the advanced productive forces that conform to the needs of new economic development and accord with the new development concept. Vocational education has become one of the research hotspots of the development of new quality productivity, and the two two-way empowerments. However, vocational education is faced with problems such as lagging education concept, mismatch of education system, and poor degree of integration of industry and education. Therefore, vocational education needs to change the concept of education, optimize the professional structure, improve the level of teachers, deepen the integration of industry and education.

Reductive Transformation of CO2 to Organic Compounds

AbstractCarbon dioxide is a major greenhouse gas and a safe, abundant, easily accessible, and renewable C1 resource that can be chemically converted into high value‐added chemicals, fuels and materials. The preparation of urea, organic carbonates, salicylic acid, etc. from CO2 through non‐reduction conversion has been used in industrial production, while CO2 reduction transformation has become a research hotspot in recent years due to its involvement in energy storage and product diversification. Designing suitable catalysts to achieve efficient and selective conversion of CO2 is crucial due to its thermodynamic stability and kinetic inertness. From this perspective, the redistribution of charges within CO2 molecules through the interaction of Lewis acid/base or metal complexes with CO2, or the forced transfer of electrons to CO2 through photo‐ or electrocatalysis, is a commonly used effective way to activate CO2. Based on understanding of the activation/reaction mechanism on a molecular level, we have developed metal complexes, metal salts, inorganic/organic salts, ionic liquids, as well as nitrogen rich and porous materials as efficient catalysts for CO2 reductive conversions. The goal of this personal account is to summarize the catalytic processes of CO2 reductive conversion that have been developed in the past 7 years: 1) For the reductive functionalization of CO2, the major challenge lies in accurately adjusting reaction parameters (such as pressure) to achieve high catalytic efficiency and the product selectivity; 2) For photocatalytic or electrocatalytic reduction of CO2, how to suppress competitive hydrogen evolution reactions and improve catalyst stability are key points that requires continuous attention.

Visual analysis of patient safety research hotspots and trends in the context of telemedicine based on Web of Science

To analyze the research status, hotspots, and trends of patient safety in the context of international telemedicine, and to provide reference for future research in various countries. The literature pertaining to patient safety within the realm of telemedicine was systematically retrieved from the Web of Science core collection database, encompassing the period from January 2010 to December 2023. Visual analysis of publication quantity, primary authorship, and keyword trends was conducted using CiteSpace (6.2R6) software. The geographical distribution of research focus was visualized through VOSviewer software and SCImago Graphica software, while research institutions were depicted using VOSviewer software and Highcharts software. Data organization was facilitated by Excel 2019 software. A total of 5356 related articles were included, and the number of published papers showed an overall upward trend, and the countries and institutions with the largest number of papers were the United States and Harvard University, respectively, and a stable core author research population had not yet been formed in this research field. Through keyword analysis, it can be seen that the research hotspots mainly focus on the research on the influencing factors of patient safety in the context of telemedicine, the research on the application value of telemedicine, and the research on coping strategies that affect patient safety. The research on patient safety in the context of telemedicine in foreign countries has a certain depth and breadth, which has important reference significance for improving the medical quality and patient safety of Internet hospitals in various countries.

Technological State and Optimization Analysis of High-Rise Glass Curtain Wall Cleaning Robots

Abstract. With the proliferation of high-rise buildings in urban areas, glass curtain walls are widely used due to their excellent light transmittance and modern appearance. However, these glass curtain walls are prone to accumulating dust, rain stains, and other dirt during use, affecting their aesthetics and transparency, thus requiring regular cleaning. Traditional manual cleaning methods are inefficient and pose significant safety risks. Consequently, high-rise glass curtain wall cleaning robot technology has become a research hotspot. This paper reviews the current research status of high-rise glass curtain wall cleaning robots, which provides a detailed analysis of four main adhesion methods (magnetic adhesion, thrust adhesion, vacuum negative pressure adhesion, and bionic adhesion), four main motion modes (legged, wheeled, tracked, and external assistance), and two main cleaning methods (physical and chemical). For path planning, this paper explores the application status and challenges of regular scanning, random walking, sensor feedback dynamic planning, and bionic path planning. Through the evaluation of the advantages and disadvantages of existing technologies, this paper identifies the main issues faced by each technology and proposes optimization strategies for adhesion technology, motion modes, cleaning efficiency, and path planning. Future directions include improvements in intelligence and automation, multifunctional integration, energy efficiency enhancement, and human-machine collaboration. This paper aims to provide a comprehensive technological review and constructive suggestions to advance high-rise glass curtain wall cleaning robot technology.

A study of machine learning applications in healthcare

Abstract. Machine learning, a sub-field of artificial intelligence, has numerous algorithms with applications in image recognition and classification, natural language processing, data prediction, medical diagnosis, and more, achieving significant results. Nowadays, the application of many algorithms of machine learning in the medical field has become a research hotspot and attracted wide attention from society, which plays a key role in disease screening, disease prediction, and assisted diagnosis of diseases. In this paper, we have developed a new approach to the medical field that can be applied in the healthcare industry by using technologies such as k-nearest neighbor (KNN), recurrent neural network (RNN), plain Bayesian (NB), decision tree (DTT), random forest (RF), deep convolutional adversarial network (DCAN), generative adversarial network (GAN), deep convolutional generative adversarial network (DCGAN),deep convolutional neural network (CNN), and support vector machine (SVM). By referring to the existing related literature and application practice results, this paper provides a systematic introduction to the application and research of machine learning in the medical field. This paper also analyses and describes the challenges and difficulties in the implementation of machine learning in the medical field.

Overview of object detection based on deep learning

Abstract. In recent years, computer vision technology has developed rapidly, as one of its important research directions, object detection has received widespread attention due to its high accuracy. Meanwhile, object detection has many application fields, such as intelligent transportation, medical and health, security systems, etc. Traditional object detection methods have limitations when applied to complex real-world scenarios. To improve the shortcomings of conventional methods, deep learning based object detection algorithms have significantly improved the efficiency of object detection and become a research hotspot in object detection. This article summarizes the algorithms into two-stage and one-stage object detection algorithms based on the technical processes and structural differences in handling object detection tasks. Firstly, several common two-stage and one-stage object detection algorithms and their applications in real-world scenarios are introduced. Then their data sets and algorithm performance are analyzed and compared. Finally, according to the results of the comparison, the existing problems of the two-stage object detection algorithm and the one-stage object detection method are discussed, and their future development directions are pointed out.