Identification of cell surface markers for acute myeloid leukemia prognosis based on multi-model analysis

Given the extremely high inter-patient heterogeneity of acute myeloid leukemia(AML),the identification of biomarkers for prognostic assessment and therapeutic guidance is critical.Cell surface markers(CSMs)have been shown to play an important role in AML leukemogenesis and progression.In the current study,we evaluated the prognostic potential of all human CSMs in 130 AML patients from The Cancer Genome Atlas(TCGA)based on differential gene expression analysis and univariable Cox proportional hazards regression analysis.By using multi-model analysis,including Adaptive LASSO regression,LASSO regression,and Elastic Net,we constructed a 9-CSMs prognostic model for risk stratification of the AML patients.The predictive value of the 9-CSMs risk score was further validated at the transcriptome and proteome levels.Multivariable Cox regression analysis showed that the risk score was an independent prognostic factor for the AML patients.The AML patients with high 9-CSMs risk scores had a shorter overall and event-free survival time than those with low scores.Notably,single-cell RNA-sequencing analysis indicated that patients with high 9-CSMs risk scores exhibited chemotherapy resistance.Furthermore,PI3K inhibitors were identified as potential treatments for these high-risk patients.In conclusion,we constructed a 9-CSMs prognostic model that served as an independent prognostic factor for the survival of AML patients and held the potential for guiding drug therapy.

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.

Global research trends and hotspots in overweight/obese comorbid with depression among children and adolescents: A bibliometric analysis

BACKGROUND Overweight/obesity combined with depression among children and adolescents(ODCA)is a global concern.The bidirectional relationship between depression and overweight/obesity often leads to their comorbidity.Childhood and adolescence represent critical periods for physical and psychological development,during which the comorbidity of overweight/obesity and depression may increase the risk of adverse health outcomes.AIM To evaluate the relationship between ODCA,we conduct a bibliometric analysis to aid in formulating prevention and treatment strategies.METHODS From 2004 to 2023,articles related to ODCA were selected using the Science Citation Index Expanded from the Web of Science Core Collection.Bibliometric analysis of relevant publications,including countries/regions,institutions,authors,journals,references,and keywords,was conducted using the online bibliometric analysis platforms,CiteSpace,VOSviewer,and bibliometrix.RESULTS Between 2004 and 2023,a total of 1573 articles were published on ODCA.The United States has made leading contributions in this field,with Harvard University emerging as the leading contributor in terms of research output,and Tanofsky being the most prolific author.The J Adolescent Health has shown significant activity in this domain.Based on the results of the keyword and reference analyses,inequality,adverse childhood experiences,and comorbidities have become hot topics in ODCA.Moreover,the impact of balancedrelated behavior and exploration of the biological mechanisms,including the potential role of key adipocytokines and lipokines,as well as inflammation in ODCA,have emerged as frontier topics.CONCLUSION The trend of a significant increase in ODCA publications is expected to continue.The research findings will contribute to elucidating the pathogenic mechanisms of ODCA and its prevention and treatment.

Generalized nth-Order Perturbation Method Based on Loop Subdivision Surface Boundary Element Method for Three-Dimensional Broadband Structural Acoustic Uncertainty Analysis

In this paper,a generalized nth-order perturbation method based on the isogeometric boundary element method is proposed for the uncertainty analysis of broadband structural acoustic scattering problems.The Burton-Miller method is employed to solve the problem of non-unique solutions that may be encountered in the external acoustic field,and the nth-order discretization formulation of the boundary integral equation is derived.In addition,the computation of loop subdivision surfaces and the subdivision rules are introduced.In order to confirm the effectiveness of the algorithm,the computed results are contrasted and analyzed with the results under Monte Carlo simulations(MCs)through several numerical examples.

Analysis of Changing Trend of Summer Precipitation in North China

Based on the reanalysis data of the National Center for Environmental Prediction(NCEP)and the precipitation dataset of the U.S.Climate Prediction Center(CPC),the changing trend of summer precipitation in North China(35°-40°N,110°-125°E)during 1979-2020 was studied.By calculating the monthly climatic precipitation in North China,it is found that precipitation was mainly distributed from June to August,so the trend of precipitation in North China from June to August was mainly analyzed.Firstly,the five-point moving average of regional mean precipitation in North China from June to August during 1979-2020 was conducted.It is found that the fitting curve of the five-point sliding average was basically consistent with the changing trend of regional precipitation,and it showed a certain upward trend.Secondly,the cumulative anomaly of regional average summer precipitation in North China showed a significant upward trend after 2005,which was similar to the moving average result,indicating that the precipitation in the later period increased compared with the earlier period.The changing trend of summer precipitation in North China in the past 42 years was analyzed,and the results show that precipitation showed a significant increasing trend in most areas of North China,so that regional average precipitation also tended to increase significantly.By comparing the precipitation in the past five years(2016-2020)and the last 36 years(1979-2015),it is found that the increase of summer precipitation in North China was more obvious,so the reasons for the increase in precipitation were further analyzed.Since the occurrence of precipitation requires favorable thermal dynamic conditions,the one-dimensional linear regression of water vapor content at 850 hPa and meridional wind speed was conduced,and it is found that the two variables tended to increase obviously,which was consistent with the increasing trend of precipitation.Seen from both the results of regional average and the spatial distribution of trends,the lower atmospheric water vapor content and wind speed showed a significant positive trend,which led to the increase of summer precipitation.Therefore,it can be concluded that there was a certain changing trend of summer precipitation in North China in the past 42 years,which can provide certain reference for the future forecast of summer precipitation in North China.

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.

Prediction and sensitivity analysis of the surface quality in Ni60/WC coatings

For laser cladding a large temperature gradient easily weakened the surface quality by generating cracks and irregular coating surfaces,which in turn affected the bearing capacity and corrosion resistance of coatings in the rapid heating and cooling process.The response surface methodology(RSM)was used to predict coating cracks by changing the powder ratio,energy density,and preheating temperature,which obtained the relevant mathematical model.After that,the sensitivity of the crack length to process parameters was analyzed based on the sensitivity analysis method.The effect of Ni60/WC composite powder process parameters on the surface quality was revealed in laser cladding.The crack length first decreased and then increased,and the Smooth decreased with the increased powder ratio.The crack length and Smooth increased with the increased energy density.The crack length decreased and Smooth increased with the increased preheating temperature.Sensitivity analysis showed that the crack length and Smooth were the most sensitive to the powder ratio.Therefore,the process parameters were reasonably selected to control the surface quality.The mathematical model and sensitivity analysis method in the work could improve the surface quality,which provided a theoretical basis for the prediction and control of laser cladding cracks.

Trend surface analysis of forest landscape pattern in Guandishan forest region of Shanxi,China

Landscape pattern is a widely used concept for the demonstration of landscape characteristic features. The integral spatial distribution trend of landscape elements is interested point in the landscape ecological research, especially in those of complex secondary forest regions with confusing mosaics of land cover. Trend surface analysis which used in community and population ecological researches was introduced to reveal the landscape pattern. A reasonable and reliable approach for application of trend surface analysis was provided in detail. As key steps of the approach, uniform grid point sampling method was developed. The efforts were also concentrated at an example of Guandishan forested landscape. Some basic rules of spatial distribution of landscape elements were exclaimed. These will be benefit to the further study in the area to enhance the forest sustainable management and landscape planning.

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.

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.

Remote sensing composite information's trend surface analysis of Nansha Islands coral islets and reefs' top

Based on the remote sensing information feature of Nansha coral islets and reefs that controlled by the Nansha Islands local area's goological structure and growth law, by means of mathematical model and PC, the Nansha Islands coral islets and reefs' top geological data' spatial distribution and local change trend are simulated by using the trend surface system on the remote sensing composite information, and an scientific interpretation and local comparison of Nansha coral islands and islets' spatial distribution feature are made.

Rail Surface Defect Detection Based on Improved UPerNet and Connected Component Analysis

To guarantee the safety of railway operations,the swift detection of rail surface defects becomes imperative.Traditional methods of manual inspection and conventional nondestructive testing prove inefficient,especially when scaling to extensive railway networks.Moreover,the unpredictable and intricate nature of defect edge shapes further complicates detection efforts.Addressing these challenges,this paper introduces an enhanced Unified Perceptual Parsing for Scene Understanding Network(UPerNet)tailored for rail surface defect detection.Notably,the Swin Transformer Tiny version(Swin-T)network,underpinned by the Transformer architecture,is employed for adept feature extraction.This approach capitalizes on the global information present in the image and sidesteps the issue of inductive preference.The model’s efficiency is further amplified by the windowbased self-attention,which minimizes the model’s parameter count.We implement the cross-GPU synchronized batch normalization(SyncBN)for gradient optimization and integrate the Lovász-hinge loss function to leverage pixel dependency relationships.Experimental evaluations underscore the efficacy of our improved UPerNet,with results demonstrating Pixel Accuracy(PA)scores of 91.39%and 93.35%,Intersection over Union(IoU)values of 83.69%and 87.58%,Dice Coefficients of 91.12%and 93.38%,and Precision metrics of 90.85%and 93.41%across two distinct datasets.An increment in detection accuracy was discernible.For further practical applicability,we deploy semantic segmentation of rail surface defects,leveraging connected component processing techniques to distinguish varied defects within the same frame.By computing the actual defect length and area,our deep learning methodology presents results that offer intuitive insights for railway maintenance professionals.

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.

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.

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.

Hydroisomerization of n-Pentane over Zn-Fe-S2O8-2/ZrO2-Al2O3 Superacid Catalyst: Activity, Surface Analysis and the Investigation of Deactivation and Regeneration

The Zn and Fe modified /ZrO2-Al2O3 catalyst (Zn-Fe-SZA) was prepared and mechanisms of deactivation and methods for regeneration of as-prepared catalyst were explored with n-pentane isomerization as a probe reaction. The results indicated that the isopentane yield of the fresh Zn-Fe-SZA-F catalyst was about 57% at the beginning of the run, and declined gradually to 50% within 1500 min, then fell rapidly from 50% to 40% between 1500 and 2500 minutes. The deactivation of Zn-Fe-SZA catalyst may be caused by carbon formation on surface of the catalyst, sulfate group attenuation owing to reduction by hydrogen, removal of sulfur species and the loss of strong acid sites. It was found that the initial catalytic activity over Zn-Fe-SZA-T catalyst was 48%, which recovered by 84.3% as compared to that of fresh catalyst (57%). However, it showed a sharp decrease in isopentane yield from 48% to 29% within 1500 minutes, showing poor stability. This is associated to the loss of acidity caused by removal of sulfur species cannot be basically restored by thermal treatment. Resulfating the calcined catalyst could improve the acidity of catalyst significantly, especially strong acid sites, as compared with the calcined sample. The improved stability of the resulfated catalyst can be explained by: 1) eliminaton of carbon deposition to some extent by calcination process, 2) formation of improved acidic nature by re-sulfation, favoring isomerization on acidic sites, 3) restructuring of the acid and metal sites via the calcination-re-sulfation procedure.