Bibliometric analysis of hotspots and trends of global myopia research

AIM:To gain insights into the global research hotspots and trends of myopia.METHODS:Articles were downloaded from January 1,2013 to December 31,2022 from the Science Core Database website and were mainly statistically analyzed by bibliometrics software.RESULTS:A total of 444 institutions in 87 countries published 4124 articles.Between 2013 and 2022,China had the highest number of publications(n=1865)and the highest H-index(61).Sun Yat-sen University had the highest number of publications(n=229)and the highest H-index(33).Ophthalmology is the main category in related journals.Citations from 2020 to 2022 highlight keywords of options and reference,child health(pediatrics),myopic traction mechanism,public health,and machine learning,which represent research frontiers.CONCLUSION:Myopia has become a hot research field.China and Chinese institutions have the strongest academic influence in the field from 2013 to 2022.The main driver of myopic research is still medical or ophthalmologists.This study highlights the importance of public health in addressing the global rise in myopia,especially its impact on children’s health.At present,a unified theoretical system is still needed.Accurate surgical and therapeutic solutions must be proposed for people with different characteristics to manage and intervene refractive errors.In addition,the benefits of artificial intelligence(AI)models are also reflected in disease monitoring and prediction.

Trends and hotspots in gastrointestinal neoplasms risk assessment: A bibliometric analysis from 1984 to 2022

BACKGROUND Gastrointestinal neoplasm(GN)significantly impact the global cancer burden and mortality,necessitating early detection and treatment.Understanding the evolution and current state of research in this field is vital.AIM To conducts a comprehensive bibliometric analysis of publications from 1984 to 2022 to elucidate the trends and hotspots in the GN risk assessment research,focusing on key contributors,institutions,and thematic evolution.METHODS This study conducted a bibliometric analysis of data from the Web of Science Core Collection database using the"bibliometrix"R package,VOSviewer,and CiteSpace.The analysis focused on the distribution of publications,contributions by institutions and countries,and trends in keywords.The methods included data synthesis,network analysis,and visualization of international collaboration networks.RESULTS This analysis of 1371 articles on GN risk assessment revealed a notable evolution in terms of research focus and collaboration.It highlights the United States'critical role in advancing this field,with significant contributions from institutions such as Brigham and Women's Hospital and the National Cancer Institute.The last five years,substantial advancements have been made,representing nearly 45%of the examined literature.Publication rates have dramatically increased,from 20 articles in 2002 to 112 in 2022,reflecting intensified research efforts.This study underscores a growing trend toward interdisciplinary and international collaboration,with the Journal of Clinical Oncology standing out as a key publication outlet.This shift toward more comprehensive and collaborative research methods marks a significant step in addressing GN risks.CONCLUSION This study underscores advancements in GN risk assessment through genetic analyses and machine learning and reveals significant geographical disparities in research emphasis.This calls for enhanced global collaboration and integration of artificial intelligence to improve cancer prevention and treatment accuracy,ultimately enhancing worldwide patient care.

Spatial and Temporal Analysis of Maximum and Minimum Temperature Trends in Northern Sudan during (1990-2019)

The study addresses an urgent and globally significant issue of climate change by focusing on the detailed spatial and temporal analysis of temperature trends in Northern Sudan. It fills a critical research gap by providing localized data over a substantial period (1990-2019), which could help in understanding the nuanced impacts of climate change in Sahel regions like Northern Sudan. In addition, the comprehensive coverage of both spatial and temporal dimensions, supported by a substantial dataset from five meteorological stations, provides a thorough understanding of the subject area. The utilization of robust statistical methods (Mann-Kendall test and Sen’s slope analysis) for analyzing temperature trends adds scientific rigor and credibility to the findings. Our results reveal a consistently increasing trend in maximum temperatures across most stations, particularly during the hot season (AMJ). However, the wet season (JAS) shows high maximum temperatures but no significant trend. Moreover, significant increasing trends in minimum temperatures were observed in all stations except Abu Hamed, where the trend, although increasing, did not reach statistical significance during the hot and cold seasons, and the coldest temperatures were observed during the cold season. These findings underscore the complex temperature dynamics in Northern Sudan and highlight the need for continued monitoring and adaptive measures in response to ongoing climate changes in the region.

Unveiling Global Human Trafficking Trends: A Comprehensive Analysis

This paper presents a comprehensive analysis of global human trafficking trends over a twenty-year period, leveraging a robust dataset from the Counter Trafficking Data Collaborative (CTDC). The study unfolds in a systematic manner, beginning with a detailed data collection phase, where ethical and legal standards for data usage and privacy are strictly observed. Following collection, the data undergoes a rigorous preprocessing stage, involving cleaning, integration, transformation, and normalization to ensure accuracy and consistency for analysis. The analytical phase employs time-series analysis to delineate historical trends and utilizes predictive modeling to forecast future trajectories of human trafficking using the advanced analytical capabilities of Power BI. A comparative analysis across regions—Africa, the Americas, Asia, and Europe—is conducted to identify and visualize the distribution of human trafficking, dissecting the data by victim demographics, types of exploitation, and duration of victimization. The findings of this study not only offer a descriptive and predictive outlook on trafficking patterns but also provide insights into the regional nuances that influence these trends. The article underscores the prevalence and persistence of human trafficking, identifies factors contributing to its evolution, and discusses the implications for policy and law enforcement. By integrating a methodological approach with quantitative analysis, this research contributes to the strategic planning and resource allocation for combating human trafficking. It highlights the necessity for continued research and international cooperation to effectively address and mitigate this global issue. The implications of this research are significant, offering actionable insights for policymakers, law enforcement, and advocates in the ongoing battle against human trafficking.

Hotspots and research trends of Piwi-interacting RNAs from 2006 to 2023 based on bibliometric analysis

Background:Piwi-interacting RNAs(piRNAs)are a type of non-coding RNAs,initially identified in germ cells in 2006,known to bind to the Piwi family proteins.Accumulating studies indicate their importance in genome stability,epigenetics regulation,germ cell differentiation,and tumor development.Despite growing interest in piRNA research,there is a lack of comprehensive bibliometric studies on the subject.This study aims to analyze piRNA research trends from 2006 to 2023.Methods:The literature regarding piRNA was sourced from the Web of Science on April 25,2023.VOSviewer,CiteSpace and a bibliometric online website(https://bibliometric.com/app)were employed to perform bibliometric analysis.Network maps were constructed to evaluate the collaborations among countries,institutions,authors,journals,references,keywords,and research hot pots.Results:In this study,2549 literature were published across 464 countries and 6921 institutions,comprising 2010 articles and 539 reviews.The United States led in publication output(n=1011,39.66%),followed by China(635,24.91%).The University of Tokyo had the most publications among all institutions(n=100,3.92%),followed by the Chinese Academy of Sciences(n=86,3.37%).Among 631 published journals,Nucleic Acids Research was the most published journal(n=83,3.26%).Siomi Mikiko C published the most articles(n=58),with Aravin Alexei A as the most co-cited author.Analysis of term co-occurrence unveiled three highly interconnected clusters,including“piRNA biogenesis and function”,“cancer and regulation”,as well as“protein and species”.The research focus has transferred from male reproductive development to tumor progression.Conclusion:This bibliometric analysis offered a thorough overview of the current state of piRNA research,deepening understanding of the progress in this field over the last 17 years and providing a valuable reference for scholars engaged in piRNA studies.

Research trends on artificial intelligence and endoscopy in digestive diseases: A bibliometric analysis from 1990 to 2022

BACKGROUND Recently,artificial intelligence(AI)has been widely used in gastrointestinal endoscopy examinations.AIM To comprehensively evaluate the application of AI-assisted endoscopy in detecting different digestive diseases using bibliometric analysis.METHODS Relevant publications from the Web of Science published from 1990 to 2022 were extracted using a combination of the search terms“AI”and“endoscopy”.The following information was recorded from the included publications:Title,author,institution,country,endoscopy type,disease type,performance of AI,publication,citation,journal and H-index.RESULTS A total of 446 studies were included.The number of articles reached its peak in 2021,and the annual citation numbers increased after 2006.China,the United States and Japan were dominant countries in this field,accounting for 28.7%,16.8%,and 15.7%of publications,respectively.The Tada Tomohiro Institute of Gastroenterology and Proctology was the most influential institution.“Cancer”and“polyps”were the hotspots in this field.Colorectal polyps were the most concerning and researched disease,followed by gastric cancer and gastrointestinal bleeding.Conventional endoscopy was the most common type of examination.The accuracy of AI in detecting Barrett’s esophagus,colorectal polyps and gastric cancer from 2018 to 2022 is 87.6%,93.7%and 88.3%,respectively.The detection rates of adenoma and gastrointestinal bleeding from 2018 to 2022 are 31.3%and 96.2%,respectively.CONCLUSION AI could improve the detection rate of digestive tract diseases and a convolutional neural network-based diagnosis program for endoscopic images shows promising results.

An analysis of agarwood trade patterns,historical perspectives,and species identification challenges:repercussions for importing nations

Background:Agarwood,primarily derived from the Aquilaria and Gyrinops genera,holds significant economic importance.However,there is a lack of comprehensive investigations providing guidance to importing nations regarding cultivation quantities and expected yields of Agarwood from distinct species.This study aims to address this gap by exploring the historical context and trade evolution of Agarwood,highlighting its global importance,and the challenges associated with securing accurate species information.Method:On-site visits to Agarwood cultivation sites were conducted to gain a nuanced understanding of Aquilaria species and their cultivation requirements.Additionally,a thorough analysis of global export and import data for Agarwood products over the last decade was undertaken.Results:China Mainland emerged as the leading exporter of Agarwood,averaging an annual export value of USD 1 million.India’s substantial exports challenge the prevailing notion of limited Agarwood production within its borders.Hong Kong and Singapore are pivotal distribution hubs,while Hong Kong and Taipei feature prominently as import destinations.Our analysis uncovers anomalies in the representation of Agarwood producers from 2001 to 2008,suggesting potential misclassification of Aquilaria Agarwood as Gyrinops in global export information.These findings underscore the urgency of investigating classification and reporting practices in the Agarwood trade.Furthermore,A.filaria emerges as a notable source,while A.malaccensis is decline in prominence.Conclusion:This study provides crucial insights for policymakers,stakeholders,and industry players seeking to make informed decisions in the Agarwood trade landscape.The results highlight the need for accurate species identification,classification,and reporting practices to ensure sustainable cultivation and trade of Agarwood.

A bibliometric analysis and visualization of osteochondral lesions of talus (2004-2021)

Background:Osteochondral lesions of the talus(OLTs)are a significant challenge for foot and ankle specialists,which could cause pain and decrease patient function.Researchers can use the findings of this study to shape future directions for research by exploring global trends and hotspots in OLT.Methods:Web of Science Core Collection was used to retrieve literature related to OLT between 2004 and 2021.This report covers the current state of OLTs,such as publications,journals,trends,hotspots,and the performances of relevant countries,institutions and authors.The co-citation analysis,the coauthorship analysis,the cooccurrence analysis,and the bibliographic coupling analysis were conducted with the Bibliometrix R package,VOSviewer v1.6.10.0,and CiteSpace 5.8.R3.Results:During an 18-year review,717 articles and 76 review articles on OLT published from 2004 to 2021 were reviewed.The USA has made the largest contribution to the OLT-related literature,and a significant contribution has been made by Kennedy JG(48/6.05%)and van Dijk CN(30/3.78%).In terms of total link strength,Foot&Ankle International was the leading journal.Analysis showed that the global research hotspots of OLTs focused on the pathogenesis,diagnosis,clinical research,and surgical treatment of OLT.It would be significant to pay close attention to future research on osteochondral autograft transplantation and management,surgery,multidisciplinary integration and mechanisms of OLT,and its related diseases.Conclusions:The study provides information about the current status and hotspots of research in the domain of OLT over the past 18 years that will assist researchers in identifying potential perspectives on hot topics and research frontiers.

Publication trends of primary angle-closure disease during 1991-2022:a bibliometric analysis

AIM:To perform a bibliometric analysis in the field of primary angle-closure disease(PACD)research to characterize current global trends and compare contributions from different countries,institutions,journals,and authors.METHODS:All PACD-related publications from 1991 to 2022 from the Web of Science Core Collection database were extracted.Microsoft Excel and VOSviewer were used to collect publication data,analyze publication trends,and visualize relevant results.RESULTS:A total of 1721 publications with 34591 citations were identified.China produced the most publications(554)while ranking third in citations(8220 times).The United States contributed the most citations(12315 times)with publications(362)ranking second.The Investigative Ophthalmology Visual Science was the most productive journal concerning PACD,and Aung Tin was the author with the highest number of publications in the field.Keywords were classified into three clusters,epidemiology and pathogenesis research,optical coherence tomography(OCT)and other imaging examinations,and glaucoma surgery treatment.Genome-wide association,susceptibility loci,OCT,and combined phacoemulsification have become new hot research topics in recent years since 2015.CONCLUSION:China,the United States,and Singapore make the most outstanding contributions in the field of PACD research.OCT,combined phacoemulsification,and gene mutation-related study,are considered the potential focus for future research.

Research trends of machine learning in traditional medicine:a big-data based tenyear bibliometric analysis

Background:With the rapid development of the world’s technology,the connection and integration between traditional medicine and modern machine learning technology are increasingly close.In this study,we aimed to analyze publications on machine learning in traditional medicine by using bibliometric methods and explore global trends in the field.Methods:Relevant research on machine learning in traditional medicine extracted from the Web of Science Core Collection database.Bibliometric analysis and visualization were performed using the Bibliometrix package in R software.Global trends,source journals,authorship,and thematic keywords analysis were performed in this study.Results:From 2012 to 2022,a total of 282 publications on machine learning in traditional medicine were identified and analyzed.The average annual growth rate of the publications was 13.35%.China had the largest contribution in this field(53.9%),followed by the United States(32.6%).IEEE Access had the largest number of published articles,with a total of 15 articles.Calvin Yu-Chian Chen,Xiao-juan Hu and Jue Wang were the main researchers in this field.Shanghai University of Traditional Chinese Medicine and University of California,San Francisco were the main research institutions.Conclusion:This study provides information on research trends in machine learning in traditional medicine to better understand research hotspots and future developments in this field.According to current global trends,the number of publications in this field will gradually increase.China currently dominated the field.Applied research of machine learning techniques may be the next hot topic in this field and deserves further attention.

Predicting Bitcoin Trends Through Machine Learning Using Sentiment Analysis with Technical Indicators

Predicting Bitcoin price trends is necessary because they represent the overall trend of the cryptocurrency market.As the history of the Bitcoin market is short and price volatility is high,studies have been conducted on the factors affecting changes in Bitcoin prices.Experiments have been conducted to predict Bitcoin prices using Twitter content.However,the amount of data was limited,and prices were predicted for only a short period(less than two years).In this study,data from Reddit and LexisNexis,covering a period of more than four years,were collected.These data were utilized to estimate and compare the performance of the six machine learning techniques by adding technical and sentiment indicators to the price data along with the volume of posts.An accuracy of 90.57%and an area under the receiver operating characteristic curve value(AUC)of 97.48%were obtained using the extreme gradient boosting(XGBoost).It was shown that the use of both sentiment index using valence aware dictionary and sentiment reasoner(VADER)and 11 technical indicators utilizing moving average,relative strength index(RSI),stochastic oscillators in predicting Bitcoin price trends can produce significant results.Thus,the input features used in the paper can be applied on Bitcoin price prediction.Furthermore,this approach allows investors to make better decisions regarding Bitcoin-related investments.

Review and Development Trends of Dynamic Adaptive Building Skin Research Based on CiteSpace Analysis

Building skin plays an important role in reducing energy consumption,and low-carbon ecology has become the development goal of architecture all over the world.Through the dynamic control of variable components on the surface,the building with dynamic adaptive building skin can better adapt to the climate,thus achieving better energy saving effects.By searching the articles in the web of science database and using CiteSpace software for visualization analysis,this paper analyzes the research process,research hotspot and research trend of dynamic adaptive building skin from the perspectives of time,quantity,distribution domain,representative experts and articles,institutions,keywords,co-citations and main research contents.It is concluded that the development trend of dynamic adaptive building skin includes the application of efficiency simulation,new materials,bionic technology,and the combination of solar photovoltaics.

Comparative Analysis of Climatic Change Trend and Change-Point Analysis for Long-Term Daily Rainfall Annual Maximum Time Series Data in Four Gauging Stations in Niger Delta

The aim of this study is to establish the prevailing conditions of changing climatic trends and change point dates in four selected meteorological stations of Uyo, Benin, Port Harcourt, and Warri in the Niger Delta region of Nigeria. Using daily or 24-hourly annual maximum series (AMS) data with the Indian Meteorological Department (IMD) and the modified Chowdury Indian Meteorological Department (MCIMD) models were adopted to downscale the time series data. Mann-Kendall (MK) trend and Sen’s Slope Estimator (SSE) test showed a statistically significant trend for Uyo and Benin, while Port Harcourt and Warri showed mild trends. The Sen’s Slope magnitude and variation rate were 21.6, 10.8, 6.00 and 4.4 mm/decade, respectively. The trend change-point analysis showed the initial rainfall change-point dates as 2002, 2005, 1988, and 2000 for Uyo, Benin, Port Harcourt, and Warri, respectively. These prove positive changing climatic conditions for rainfall in the study area. Erosion and flood control facilities analysis and design in the Niger Delta will require the application of Non-stationary IDF modelling.

Analysis of the Development Environment and Trend of Cross-Border E-commerce in China

With the conclusion of the novel coronavirus pandemic and the increasingly complex market environment,China’s cross-border e-commerce has entered a new phase of development.The external landscape is evolving rapidly,and there is a gradual improvement in laws and regulations governing cross-border e-commerce,coupled with increased government support.Despite the impact of the COVID-19 pandemic on the market economy,overall development has been steadily improving.The Internet population is expanding,the online retail market is experiencing rapid growth,the consumption structure is undergoing transformation and upgrading,and the e-commerce market is demonstrating significant potential.The advancement of technologies such as big data,artificial intelligence,blockchain,and supply chain has provided more efficient operational support for the cross-border e-commerce industry.Against the backdrop of the emergence of new forms of cross-border e-commerce in China post-pandemic,this paper utilizes the PEST model to analyze the macro environment of cross-border e-commerce in China and project its future development trends.

The application of geostatistics in grain size trend analysis： A case study of eastern Beibu Gulf

There are 71 surface sediment samples collected from the eastern Beibu Gulf. The moment parameters （i.e. mean size, sorting and skewness） were obtained after applying grain size analysis. The geostatistical analysis was then applied to study the spatial autocorrelation for these parameters; while range, a parameter in the semivariogram that meters the scale of spatial autocorrelation, was estimated. The results indicated that the range for sorting coefficient was physically meaningful. The trend vectors calculated from grain size trend analysis model were consistent with the annual ocean circulation patterns and sediment transport rates according to previous studies. Therefore the range derived from the semivariogram of mean size can be used as the characteristic distance in the grain size trend analysis, which may remove the bias caused by the traditional way of basing on experiences or testing methods to get the characteristic distance. Hence the results from geostatistical analysis can also offer useful information for the determination of sediment sampling density in the future field work.

Correlation analysis between the Aral Sea shrinkage and the Amu Darya River

The shrinkage of the Aral Sea,which is closely related to the Amu Darya River,strongly affects the sustainability of the local natural ecosystem,agricultural production,and human well-being.In this study,we used the Bayesian Estimator of Abrupt change,Seasonal change,and Trend(BEAST)model to detect the historical change points in the variation of the Aral Sea and the Amu Darya River and analyse the causes of the Aral Sea shrinkage during the 1950–2016 period.Further,we applied multifractal detrend cross-correlation analysis(MF-DCCA)and quantitative analysis to investigate the responses of the Aral Sea to the runoff in the Amu Darya River,which is the main source of recharge to the Aral Sea.Our results showed that two significant trend change points in the water volume change of the Aral Sea occurred,in 1961 and 1974.Before 1961,the water volume in the Aral Sea was stable,after which it began to shrink,with a shrinkage rate fluctuating around 15.21 km3/a.After 1974,the water volume of the Aral Sea decreased substantially at a rate of up to 48.97 km3/a,which was the highest value recorded in this study.In addition,although the response of the Aral Sea's water volume to its recharge runoff demonstrated a complex non-linear relationship,the replenishment of the Aral Sea by the runoff in the lower reaches of the Amu Darya River was identified as the dominant factor affecting the Aral Sea shrinkage.Based on the scenario analyses,we concluded that it is possible to slow down the retreat of the Aral Sea and restore its ecosystem by increasing the efficiency of agricultural water use,decreasing agricultural water use in the middle and lower reaches,reducing ineffective evaporation from reservoirs and wetlands,and increasing the water coming from the lower reaches of the Amu Darya River to the 1961–1973 level.These measures would maintain and stabilise the water area and water volume of the Aral Sea in a state of ecological restoration.Therefore,this study focuses on how human consumption of recharge runoff affects the Aral Sea and provides scientific perspective on its ecological conservation and sustainable development.

Mapping theme trends and knowledge structures for human neural stem cells:a quantitative and co-word biclustering analysis for the 2013-2018 period

Neural stem cells,which are capable of multi-potential differentiation and self-renewal,have recently been shown to have clinical potential for repairing central nervous system tissue damage.However,the theme trends and knowledge structures for human neural stem cells have not yet been studied bibliometrically.In this study,we retrieved 2742 articles from the PubMed database from 2013 to 2018 using "Neural Stem Cells" as the retrieval word.Co-word analysis was conducted to statistically quantify the characteristics and popular themes of human neural stem cell-related studies.Bibliographic data matrices were generated with the Bibliographic Item Co-Occurrence Matrix Builder.We identified 78 high-frequency Medical Subject Heading(MeSH)terms.A visual matrix was built with the repeated bisection method in gCLUTO software.A social network analysis network was generated with Ucinet 6.0 software and GraphPad Prism 5 software.The analyses demonstrated that in the 6-year period,hot topics were clustered into five categories.As suggested by the constructed strategic diagram,studies related to cytology and physiology were well-developed,whereas those related to neural stem cell applications,tissue engineering,metabolism and cell signaling,and neural stem cell pathology and virology remained immature.Neural stem cell therapy for stroke and Parkinson’s disease,the genetics of microRNAs and brain neoplasms,as well as neuroprotective agents,Zika virus,Notch receptor,neural crest and embryonic stem cells were identified as emerging hot spots.These undeveloped themes and popular topics are potential points of focus for new studies on human neural stem cells.

The reverse sediment transport trend between abandoned Huanghe River(Yellow River) Delta and radial sand ridges along Jiangsu coastline of China——an evidence from grain size analysis

To reveal the sediment transporting mechanism between the abandoned Huanghe River （Yellow River） Delta and radial sand ridges, “End Member” Model and grain size trend analysis have been employed to separate the “dynamic populations” in the surficial sediment particle spectra and to determine the possible sediment transporting pathway. The results reveal four “dynamic subpopulations”（EM1 to EM4） and two reverse sediment transporting directions： a northward transport tend from the radial sand ridges to mud patch, and a southward transport trend in deep water area outside the mud patch. Combined with the published hydrodynamic information, the transporting mechanism of dynamic populations has been discussed, and the main conclusion is that the transporting of finer subpopulations EM1 and EM2 is controlled by the “anticlockwise residual current circulation” forming during tidal cycle, which favor a northward transporting trend and the forming of mud patch on the north of radial sand ridges, while the transporting of coarser EM3 is mainly controlled by wind driven drift in winter, which favors a southward transporting direction.

Sediment transport in the Luanhe River delta:grain size trend analysis

Sediment grain size in the deltaic environment of the Luanhe River(LR),Liaoning,China,contains sediment transport pathway information useful in elucidating the shoreline change and fluvialmarine interaction.In this study,we utilized numerical partitioning of the sedimentary components and geostatistical grain size trend analysis(GSTA)to define the sediment transport pattern in the Luanhe River delta(LRD)and interpolated the sediment transport pattern using content changes of end numbers(EM).EM1(the mean grain size 7.12Ф,fine silt),EM2(2.37Ф,fine sand),and EM3(1.27Ф,medium sand)components were identified by the numerical partitioning by GSTA.Kriging interpolation method was used to interpolate the parameters of the grain size for the regular grid,and the interpolation radius was 0.015 decimal degree.We chose 0.09 decimal degree as the characteristic distance for GSTA in the semivariogram model using the geostatistical method.The FB(-)case(finer,better sorted and more negatively skewed)was adopted in GSTA for its satisfaction in the Global Moran’s I test.The result of the GSTA shows that the sediments in the south barriers(SBs)were transported to the southwest of the study area.The sediments in the north,in the SE direction of sediment transport trend from the river mouth,indicated that the sediments in the north of the study area were transported from the LR to the northern beaches,and to the south and east of the study area.The sediment transport trend that simplified by GSTA as the FB(-)case was approved by the content changes of sedimentary components(i.e.EM1,EM2,and EM3).In addition,the turbulent jet diffusion pattern indicated that the coarse sediments(EM3)were delivered by LR during the flood season,and the EM2 and EM1 were from wave and tide,respectively.

Human brain organoid:trends,evolution,and remaining challenges

Advanced brain organoids provide promising platforms for deciphering the cellular and molecular processes of human neural development and diseases.Although various studies and reviews have described developments and advancements in brain organoids,few studies have comprehensively summarized and analyzed the global trends in this area of neuroscience.To identify and further facilitate the development of cerebral organoids,we utilized bibliometrics and visualization methods to analyze the global trends and evolution of brain organoids in the last 10 years.First,annual publications,countries/regions,organizations,journals,authors,co-citations,and keywords relating to brain organoids were identified.The hotspots in this field were also systematically identified.Subsequently,current applications for brain organoids in neuroscience,including human neural development,neural disorders,infectious diseases,regenerative medicine,drug discovery,and toxicity assessment studies,are comprehensively discussed.Towards that end,several considerations regarding the current challenges in brain organoid research and future strategies to advance neuroscience will be presented to further promote their application in neurological research.