A bibliometric analysis of 23,492 publications on rectal cancer by machine learning: basic medical research is needed

Multi-Algorithm Analysis Reveals Pyroptosis-Linked Genes as Pancreatic Cancer Biomarkers

Pancreatic ductal adenocarcinoma (PDAC) is often diagnosed at late stages, limiting treatment options and survival rates. Pyroptosis-related gene signatures hold promise as PDAC prognostic markers, but limited gene pools and small sample sizes hinder their utility. We aimed to enhance PDAC prognosis with a comprehensive multi-algorithm analysis. Using R, we employed natural language processing and latent Dirichlet allocation on PubMed publications to identify pyroptosis-related genes. We collected PDAC transcriptome data (n = 1273) from various databases, conducted a meta-analysis, and performed differential gene expression analysis on tumour and non-cancerous tissues. Cox and LASSO algorithms were used for survival modelling, resulting in a pyroptosis-related gene expression-based prognostic index. Laboratory and external validations were conducted. Bibliometric analysis revealed that pyroptosis publications focus on signalling pathways, disease correlation, and prognosis. We identified 357 pyroptosis-related genes, validating the significance of BHLHE40, IL18, BIRC3, and APOL1. Elevated expression of these genes strongly correlated with poor PDAC prognosis and guided treatment strategies. Our accessible nomogram model aids in PDAC prognosis and treatment decisions. We established an improved gene signature for pyroptosis-related genes, offering a novel model and nomogram for enhanced PDAC prognosis.

A comprehensive overview of psoriatic research over the past 20 years: machine learning-based bibliometric analysis

Background

The surge in the number of publications on psoriasis has posed significant challenges for researchers in effectively managing the vast amount of information. However, due to the lack of tools to process metadata, no comprehensive bibliometric analysis has been conducted.

Objectives

This study is to evaluate the trends and current hotspots of psoriatic research from a macroscopic perspective through a bibliometric analysis assisted by machine learning based semantic analysis.

Methods

Publications indexed under the Medical Subject Headings (MeSH) term "Psoriasis" from 2003 to 2022 were extracted from PubMed. The generative statistical algorithm latent Dirichlet allocation (LDA) was applied to identify specific topics and trends based on abstracts. The unsupervised Louvain algorithm was used to establish a network identifying relationships between topics.

Results

A total of 28,178 publications were identified. The publications were derived from 176 countries, with United States, China, and Italy being the top three countries. For the term "psoriasis", 9,183 MeSH terms appeared 337,545 times. Among them, MeSH term "Severity of illness index", "Treatment outcome", "Dermatologic agents" occur most frequently. A total of 21,928 publications were included in LDA algorithm, which identified three main areas and 50 branched topics, with "Molecular pathogenesis", "Clinical trials", and "Skin inflammation" being the most increased topics. LDA networks identified "Skin inflammation" was tightly associated with "Molecular pathogenesis" and "Biological agents". "Nail psoriasis" and "Epidemiological study" have presented as new research hotspots, and attention on topics of comorbidities, including "Cardiovascular comorbidities", "Psoriatic arthritis", "Obesity" and "Psychological disorders" have increased gradually.

Conclusions

Research on psoriasis is flourishing, with molecular pathogenesis, skin inflammation, and clinical trials being the current hotspots. The strong association between skin inflammation and biologic agents indicated the effective translation between basic research and clinical application in psoriasis. Besides, nail psoriasis, epidemiological study and comorbidities of psoriasis also draw increased attention.

Bibliometric analysis of the global scientific production on machine learning applied to different cancer types

Cancer disease is one of the main causes of death in the world, with million annual cases in the last decades. The need to find a cure has stimulated the search for efficient treatments and diagnostic procedures. One of the most promising tools that has emerged against cancer in recent years is machine learning (ML), which has raised a huge number of scientific papers published in a relatively short period of time. The present study analyzes global scientific production on ML applied to the most relevant cancer types through various bibliometric indicators. We find that over 30,000 studies have been published so far and observe that cancers with the highest number of published studies using ML (breast, lung, and colon cancer) are those with the highest incidence, being the USA and China the main scientific producers on the subject. Interestingly, the role of China and Japan in stomach cancer is correlated with the number of cases of this cancer type in Asia (78% of the worldwide cases). Knowing the countries and institutions that most study each area can be of great help for improving international collaborations between research groups and countries. Our analysis shows that medical and computer science journals lead the number of publications on the subject and could be useful for researchers in the field. Finally, keyword co-occurrence analysis suggests that ML-cancer research trends are focused not only on the use of ML as an effective diagnostic method, but also for the improvement of radiotherapy- and chemotherapy-based treatments.

A systematic review of progress on hepatocellular carcinoma research over the past 30 years: a machine-learning-based bibliometric analysis

Introduction

Research on hepatocellular carcinoma (HCC) has grown significantly, and researchers cannot access the vast amount of literature. This study aimed to explore the research progress in studying HCC over the past 30 years using a machine learning-based bibliometric analysis and to suggest future research directions.

Methods

Comprehensive research was conducted between 1991 and 2020 in the public version of the PubMed database using the MeSH term "hepatocellular carcinoma." The complete records of the collected results were downloaded in Extensible Markup Language format, and the metadata of each publication, such as the publication year, the type of research, the corresponding author's country, the title, the abstract, and the MeSH terms, were analyzed. We adopted a latent Dirichlet allocation topic modeling method on the Python platform to analyze the research topics of the scientific publications.

Results

In the last 30 years, there has been significant and constant growth in the annual publications about HCC (annual percentage growth rate: 7.34%). Overall, 62,856 articles related to HCC from the past 30 years were searched and finally included in this study. Among the diagnosis-related terms, "Liver Cirrhosis" was the most studied. However, in the 2010s, "Biomarkers, Tumor" began to outpace "Liver Cirrhosis." Regarding the treatment-related MeSH terms, "Hepatectomy" was the most studied; however, recent studies related to "Antineoplastic Agents" showed a tendency to supersede hepatectomy. Regarding basic research, the study of "Cell Lines, Tumors,'' appeared after 2000 and has been the most studied among these terms.

Conclusion

This was the first machine learning-based bibliometric study to analyze more than 60,000 publications about HCC over the past 30 years. Despite significant efforts in analyzing the literature on basic research, its connection with the clinical field is still lacking. Therefore, more efforts are needed to convert and apply basic research results to clinical treatment. Additionally, it was found that microRNAs have potential as diagnostic and therapeutic targets for HCC.

Artificial intelligence in colorectal surgery: an AI-powered systematic review

Artificial intelligence (AI) has the potential to revolutionize surgery in the coming years. Still, it is essential to clarify what the meaningful current applications are and what can be reasonably expected. This AI-powered review assessed the role of AI in colorectal surgery. A Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-compliant systematic search of PubMed, Embase, Scopus, Cochrane Library databases, and gray literature was conducted on all available articles on AI in colorectal surgery (from January 1 1997 to March 1 2021), aiming to define the perioperative applications of AI. Potentially eligible studies were identified using novel software powered by natural language processing (NLP) and machine learning (ML) technologies dedicated to systematic reviews. Out of 1238 articles identified, 115 were included in the final analysis. Available articles addressed the role of AI in several areas of interest. In the preoperative phase, AI can be used to define tailored treatment algorithms, support clinical decision-making, assess the risk of complications, and predict surgical outcomes and survival. Intraoperatively, AI-enhanced surgery and integration of AI in robotic platforms have been suggested. After surgery, AI can be implemented in the Enhanced Recovery after Surgery (ERAS) pathway. Additional areas of applications included the assessment of patient-reported outcomes, automated pathology assessment, and research. Available data on these aspects are limited, and AI in colorectal surgery is still in its infancy. However, the rapid evolution of technologies makes it likely that it will increasingly be incorporated into everyday practice.

Cinacalcet use in secondary hyperparathyroidism: a machine learning-based systematic review

Introduction

This study aimed to systematically review research on cinacalcet and secondary hyperparathyroidism (SHPT) using machine learning-based statistical analyses.

Methods

Publications indexed in the Web of Science Core Collection database on Cinacalcet and SHPT published between 2000 and 2022 were retrieved. The R package "Bibliometrix," VOSviewer, CiteSpace, meta, and latent Dirichlet allocation (LDA) in Python were used to generate bibliometric and meta-analytical results.

Results

A total of 959 articles were included in our bibliometric analysis. In total, 3753 scholars from 54 countries contributed to this field of research. The United States, Japan, and China were found to be among the three most productive countries worldwide. Three Japanese institutions (Showa University, Tokai University, and Kobe University) published the most articles on Cinacalcet and SHPT. Fukagawa, M.; Chertow, G.M.; Goodman W.G. were the three authors who published the most articles in this field. Most articles were published in Nephrology Dialysis Transplantation, Kidney International, and Therapeutic Apheresis and Dialysis. Research on Cinacalcet and SHPT has mainly included three topics: 1) comparative effects of various treatments, 2) the safety and efficacy of cinacalcet, and 3) fibroblast growth factor-23 (FGF-23). Integrated treatments, cinacalcet use in pediatric chronic kidney disease, and new therapeutic targets are emerging research hotspots. Through a meta-analysis, we confirmed the effects of Cinacalcet on reducing serum PTH (SMD = -0.56, 95% CI = -0.76 to -0.37, p = 0.001) and calcium (SMD = -0.93, 95% CI = -1.21to -0.64, p = 0.001) and improving phosphate (SMD = 0.17, 95% CI = -0.33 to -0.01, p = 0.033) and calcium-phosphate product levels (SMD = -0.49, 95% CI = -0.71 to -0.28, p = 0.001); we found no difference in all-cause mortality (RR = 0.97, 95% CI = 0.90 to 1.05, p = 0.47), cardiovascular mortality (RR = 0.69, 95% CI = 0.36 to 1.31, p = 0.25), and parathyroidectomy (RR = 0.36, 95% CI = 0.09 to 1.35, p = 0.13) between the Cinacalcet and non-Cinacalcet users. Moreover, Cinacalcet was associated with an increased risk of nausea (RR = 2.29, 95% CI = 1.73 to 3.05, p = 0.001), hypocalcemia (RR = 4.05, 95% CI = 2.33 to 7.04, p = 0.001), and vomiting (RR = 1.90, 95% CI = 1.70 to 2.11, p = 0.001).

Discussion

The number of publications indexed to Cinacalcet and SHPT has increased rapidly over the past 22 years. Literature distribution, research topics, and emerging trends in publications on Cinacalcet and SHPT were analyzed using a machine learning-based bibliometric review. The findings of this meta-analysis provide valuable insights into the efficacy and safety of cinacalcet for the treatment of SHPT, which will be of interest to both clinical and researchers.

A bibliometric analysis of 16,826 triple-negative breast cancer publications using multiple machine learning algorithms: Progress in the past 17 years

Background

Triple-negative breast cancer (TNBC) is proposed at the beginning of this century, which is still the most challenging breast cancer subtype due to its aggressive behavior, including early relapse, metastatic spread, and poor survival. This study uses machine learning methods to explore the current research status and deficiencies from a macro perspective on TNBC publications.

Methods

PubMed publications under "triple-negative breast cancer" were searched and downloaded between January 2005 and 2022. R and Python extracted MeSH terms, geographic information, and other abstracts from metadata. The Latent Dirichlet Allocation (LDA) algorithm was applied to identify specific research topics. The Louvain algorithm established a topic network, identifying the topic's relationship.

Results

A total of 16,826 publications were identified, with an average annual growth rate of 74.7%. Ninety-eight countries and regions in the world participated in TNBC research. Molecular pathogenesis and medication are most studied in TNBC research. The publications mainly focused on three aspects: Therapeutic target research, Prognostic research, and Mechanism research. The algorithm and citation suggested that TNBC research is based on technology that advances TNBC subtyping, new drug development, and clinical trials.

Conclusion

This study quantitatively analyzes the current status of TNBC research from a macro perspective and will aid in redirecting basic and clinical research toward a better outcome for TNBC. Therapeutic target research and Nanoparticle research are the present research focus. There may be a lack of research on TNBC from a patient perspective, health economics, and end-of-life care perspectives. The research direction of TNBC may require the intervention of new technologies.

A comprehensive overview of cellular senescence from 1990 to 2021: A machine learning-based bibliometric analysis

Background

As a cellular process, senescence functions to prevent the proliferation of damaged, old and tumor-like cells, as well as participate in embryonic development, tissue repair, etc. This study aimed to analyze the themes and topics of the scientific publications related to cellular senescence in the past three decades by machine learning.

Methods

The MeSH term "cellular senescence" was used for searching publications from 1990 to 2021 on the PubMed database, while the R platform was adopted to obtain associated data. A topic network was constructed by latent Dirichlet allocation (LDA) and the Louvain algorithm.

Results

A total of 21,910 publications were finally recruited in this article. Basic studies (15,382, 70.21%) accounted for the most proportion of publications over the past three decades. Physiology, drug effects, and genetics were the most concerned MeSH terms, while cell proliferation was the leading term since 2010. Three senolytics were indexed by MeSH terms, including quercetin, curcumin, and dasatinib, with the accumulated occurrence of 35, 26, and 22, separately. Three clusters were recognized by LDA and network analyses. Telomere length was the top studied topic in the cluster of physiological function, while cancer cell had been a hot topic in the cluster of pathological function, and protein kinase pathway was the most popular topic in the cluster of molecular mechanism. Notably, the cluster of physiological function showed a poor connection with other clusters.

Conclusion

Cellular senescence has obtained increasing attention over the past three decades. While most of the studies focus on the pathological function and molecular mechanism, more researches should be conducted on the physiological function and the clinical translation of cellular senescence, especially the development and application of senotherapeutics.

Research topics and hotspot trends of lumbar spondylolisthesis: A text-mining study with machine learning

Objectives

The study aimed to conduct a bibliometric analysis of publications concerning lumbar spondylolisthesis, as well as summarize its research topics and hotspot trends with machine-learning based text mining.

Methods

The data were extracted from the Web of Science Core Collection (WoSCC) database and then analyzed in Rstudio1.3.1 and CiteSpace5.8. Annual publication production and the top-20 productive authors over time were obtained. Additionally, top-20 productive journals and top-20 influential journals were compared by spine-subspecialty or not. Similarly, top-20 productive countries/regions and top-20 influential countries/regions were compared by they were developed countries/regions or not. The collaborative relationship among countries and institutions were presented. The main topics of lumbar spondylolisthesis were classified by Latent Dirichlet allocation (LDA) analysis, and the hotspot trends were indicated by keywords with strongest citation bursts.

Results

Up to 2021, a total number of 4,245 articles concerning lumbar spondylolisthesis were finally included for bibliometric analysis. Spine-subspecialty journals were found to be dominant in the productivity and the impact of the field, and SPINE, EUROPEAN SPINE JOURNAL and JOURNAL OF NEUROSURGERY-SPINE were the top-3 productive and the top-3 influential journals in this field. USA, Japan and China have contributed to over half of the publication productivity, but European countries seemed to publish more influential articles. It seemed that developed countries/regions tended to produce more articles and more influential articles, and international collaborations mainly occurred among USA, Europe and eastern Asia. Publications concerning surgical management was the major topic, followed by radiographic assessment and epidemiology for this field. Surgical management especially minimally invasive technique for lumbar spondylolisthesis were the recent hotspots over the past 5 years.

Conclusions

The study successfully summarized the productivity and impact of different entities, which should benefit the journal selection and pursuit of international collaboration for researcher who were interested in the field of lumbar spondylolisthesis. Additionally, the current study may encourage more researchers joining in the field and somewhat inform their research direction in the future.

Exploring the landscape, hot topics, and trends of bariatric metabolic surgery with machine learning and bibliometric analysis

Background

This study aimed to analyze the landscape of publications on bariatric metabolic surgery through machine learning and help experts and scholars from various disciplines better understand bariatric metabolic surgery's hot topics and trends.

Methods

In January 2021, publications indexed in PubMed under the Medical Subject Headings (MeSH) term 'Bariatric Surgery' from 1946 to 2020 were downloaded. Python was used to extract publication dates, abstracts, and research topics from the metadata of publications for bibliometric evaluation. Descriptive statistical analysis, social network analysis (SNA), and topic modeling with latent Dirichlet allocation (LDA) were used to reveal bariatric metabolic surgery publication growth trends, landscape, and research topics.

Results

A total of 21,798 records of bariatric metabolic surgery-related literature data were collected from PubMed. The number of publications indexed to bariatric metabolic surgery had expanded rapidly. Obesity Surgery and Surgery for Obesity and Related Diseases are currently the most published journals in bariatric metabolic surgery. The bariatric metabolic surgery research mainly included five topics: bariatric surgery intervention, clinical case management, basic research, body contour, and surgical risk study.

Conclusion

Despite a rapid increase in bariatric metabolic surgery-related publications, few studies were still on quality of life, psychological status, and long-term follow-up. In addition, basic research has gradually increased, but the mechanism of bariatric metabolic surgery remains to be further studied. It is predicted that the above research fields may become potential hot topics in the future.

Connections and Biases in Health Equity and Culture Research: A Semantic Network Analysis

Health equity is a rather complex issue. Social context and economical disparities, are known to be determining factors. Cultural and educational constrains however, are also important contributors to the establishment and development of health inequities. As an important starting point for a comprehensive discussion, a detailed analysis of the literature corpus is thus desirable: we need to recognize what has been done, under what circumstances, even what possible sources of bias exist in our current discussion on this relevant issue. By finding these trends and biases we will be better equipped to modulate them and find avenues that may lead us to a more integrated view of health inequity, potentially enhancing our capabilities to intervene to ameliorate it. In this study, we characterized at a large scale, the social and cultural determinants most frequently reported in current global research of health inequity and the interrelationships among them in different populations under diverse contexts. We used a data/literature mining approach to the current literature followed by a semantic network analysis of the interrelationships discovered. The analyzed structured corpus consisted in circa 950 articles categorized by means of the Medical Subheadings (MeSH) content-descriptor from 2014 to 2021. Further analyses involved systematic searches in the LILACS and DOAJ databases, as additional sources. The use of data analytics techniques allowed us to find a number of non-trivial connections, pointed out to existing biases and under-represented issues and let us discuss what are the most relevant concepts that are (and are not) being discussed in the context of Health Equity and Culture.

Machine-Learning-Based Bibliometric Analysis of Pancreatic Cancer Research Over the Past 25 Years

Machine learning and semantic analysis are computer-based methods to evaluate complex relationships and predict future perspectives. We used these technologies to define recent, current and future topics in pancreatic cancer research. Publications indexed under the Medical Subject Headings (MeSH) term 'Pancreatic Neoplasms' from January 1996 to October 2021 were downloaded from PubMed. Using the statistical computing language R and the interpreted, high-level, general-purpose programming language Python, we extracted publication dates, geographic information, and abstracts from each publication's metadata for bibliometric analyses. The generative statistical algorithm "latent Dirichlet allocation" (LDA) was applied to identify specific research topics and trends. The unsupervised "Louvain algorithm" was used to establish a network to identify relationships between single topics. A total of 60,296 publications were identified and analyzed. The publications were derived from 133 countries, mostly from the Northern Hemisphere. For the term "pancreatic cancer research", 12,058 MeSH terms appeared 1,395,060 times. Among them, we identified the four main topics "Clinical Manifestation and Diagnosis", "Review and Management", "Treatment Studies", and "Basic Research". The number of publications has increased rapidly during the past 25 years. Based on the number of publications, the algorithm predicted that "Immunotherapy", Prognostic research", "Protein expression", "Case reports", "Gemcitabine and mechanism", "Clinical study of gemcitabine", "Operation and postoperation", "Chemotherapy and resection", and "Review and management" as current research topics. To our knowledge, this is the first study on this subject of pancreatic cancer research, which has become possible due to the improvement of algorithms and hardware.

Exploring changes in depression and radiology-related publications research focus: A bibliometrics and content analysis based on natural language processing

OBJECTIVE

This study aims to construct and use natural language processing and other methods to analyze major depressive disorder (MDD) and radiology studies' publications in the PubMed database to understand the historical growth, current state, and potential expansion trend.

METHODS

All MDD radiology studies publications from January 2002 to January 2022 were downloaded from PubMed using R, a statistical computing language. R and the interpretive general-purpose programming language Python were used to extract publication dates, geographic information, and abstracts from each publication's metadata for bibliometric analysis. The generative statistical algorithm "Latent Dirichlet allocation" (LDA) was applied to identify specific research focus and trends. The unsupervised Leuven algorithm was used to build a network to identify relationships between research focus.

RESULTS

A total of 5,566 publications on MDD and radiology research were identified, and there is a rapid upward trend. The top-cited publications were 11,042, and the highly-cited publications focused on improving diagnostic performance and establishing imaging standards. Publications came from 76 countries, with the most from research institutions in the United States and China. Hospitals and radiology departments take the lead in research and have an advantage. The extensive field of study contains 12,058 Medical Subject Heading (MeSH) terms. Based on the LDA algorithm, three areas were identified that have become the focus of research in recent years, "Symptoms and treatment," "Brain structure and imaging," and "Comorbidities research."

CONCLUSION

Latent Dirichlet allocation analysis methods can be well used to analyze many texts and discover recent research trends and focus. In the past 20 years, the research on MDD and radiology has focused on exploring MDD mechanisms, establishing standards, and constructing imaging methods. Recent research focuses are "Symptoms and sleep," "Brain structure study," and "functional connectivity." New progress may be made in studies on MDD complications and the combination of brain structure and metabolism.

A Bibliometric Analysis of 34,692 Publications on Thyroid Cancer by Machine Learning: How Much Has Been Done in the Past Three Decades?

Introduction

Thyroid cancer (TC) is the most common neck malignancy. However, a large number of publications of TC have not been well summarized and discussed with more comprehensive methods. The purpose of this bibliometric study is to summarize scientific publications during the past three decades in the field of TC using a machine learning method.

Material and Methods

Scientific publications focusing on TC from 1990 to 2020 were searched in PubMed using the MeSH term "thyroid neoplasms". Full associated data were downloaded in the format of PubMed, and extracted in the R platform. Latent Dirichlet allocation (LDA) was adopted to identify the research topics from the abstract of each publication using Python.

Results

A total of 34,692 publications related to TC from the last three decades were found and included in this study with an average of 1,119.1 publications per year. Clinical studies and experimental studies shared the most proportion of publications, while the proportion of clinical trials remained at a relatively small level (5.87% as the highest in 2004). Thyroidectomy was the lead MeSH term, followed by prognosis, differential diagnosis, and fine-needle biopsy. The LDA analyses showed the study topics were divided into four clusters, including treatment management, basic research, diagnosis research, epidemiology, and cancer risk. However, a relatively weak connection was shown between treatment managements and basic researches. Top 10 most cited publications in recent years particularly highlighted the applications of active surveillance in TC.

Conclusion

Thyroidectomy, differential diagnosis, genomic analysis, active surveillance are the most concerning topics in TC researches. Although the BRAF-targeted therapy is under development with promising results, there is still an urgent need for conversions from basic studies to clinical practice.

Treatment in locally advanced rectal cancer: a machine learning bibliometric analysis

A bibliometric analysis was performed using a machine learning bibliometric methodology in order to evaluate the research trends in locally advanced rectal cancer treatment between 2000 and 2020. Information regarding publication outputs, countries, institutions, journals, keywords, funding, and citation counts was retrieved from Scopus database. During the search process, a total of 2370 publications were identified. The vast majority of papers originated from the United States of America, reflecting also its research drive in the collaboration network. Neoadjuvant treatment was the topic most studied in the highly cited studies. New keywords, including neoadjuvant chemotherapy, multiparametric magnetic resonance imaging, circulating tumor DNA, and genetic heterogeneity, appeared in the last 2 years. The quantity of publications on locally advanced rectal cancer treatment since 2000 showed an evolving research field. The 'new' keywords explain where research is presently heading.

Psycho-Oncology: A Bibliometric Review of the 100 Most-Cited Articles

(1) Background: A bibliometric review of psycho-oncology research is overdue. (2) Methods: The 100 most-cited journal articles were compiled and ranked according to Scopus. (3) Results: The total citation count for the results ranged from 488-8509 (Mean = 940.27; SD = 1015.69). A significant correlation was found between years since publication and number of citations (p = 0.039). The majority of research originated from the United States (66%). The vast majority of research publications were original articles (80%). Observational research study designs represented the majority of studies (37%). Mixed cancer population research studies represented the largest cancer research population (36%). Positive psychology topics represented the most prolific proportion of studies (30%). Findings were reported in line with PRISMA-ScR guidelines. (4) Conclusions: This analysis offers a comprehensive account of seminal journal articles in psycho-oncology, identifying landmark contributions and areas for future research developments within the field, namely highlighting a need for more RCT studies. This analysis serves as an educational tool for interdisciplinary researchers and clinicians to support compassionate cancer care.

A Bibliometric Analysis of 14,822 Researches on Myocardial Reperfusion Injury by Machine Learning

Myocardial ischemia is the major cause of death worldwide, and reperfusion is the standard intervention for myocardial ischemia. However, reperfusion may cause additional damage, known as myocardial reperfusion injury, for which there is still no effective therapy. This study aims to analyze the landscape of researches concerning myocardial reperfusion injury over the past three decades by machine learning. PubMed was searched for publications from 1990 to 2020 indexed under the Medical Subject Headings (MeSH) term “myocardial reperfusion injury” on 13 April 2021. MeSH analysis and Latent Dirichlet allocation (LDA) analyses were applied to reveal research hotspots. In total, 14,822 publications were collected and analyzed in this study. MeSH analyses revealed that time factors and apoptosis were the leading terms of the pathogenesis and treatment of myocardial reperfusion injury, respectively. In LDA analyses, research topics were classified into three clusters. Complex correlations were observed between topics of different clusters, and the prognosis is the most concerned field of the researchers. In conclusion, the number of publications on myocardial reperfusion injury increases during the past three decades, which mainly focused on prognosis, mechanism, and treatment. Prognosis is the most concerned field, whereas studies on mechanism and treatment are relatively lacking.

Evolutionary Overview and Prediction of Themes in the Field of Land Degradation

Land degradation has become one of the major global environmental problems threatening human well-being. Whether degraded land can be restored has a profound effect on the achievement of the 2030 UN Sustainable Development Goals. Therefore, the ways by which to identify the current research status and potential research topics in the massive scientific literature data in the field of land degradation is a crucial issue for scientific research institutions in various countries. In view of the shortcomings in the current research on the thematic evolution and thematic and thematic prediction, such as the ignorance of random features during scientific innovation, the defects of manual classification, and the difficulty of identifying technical terms, this research proposes a new combined method. First, the Latent Dirichlet Allocation (LDA) algorithm in machine learning is used to capture the potential clustering of themes in the literature sample set of land degradation research. The distribution characteristics and evolution of themes in each period are then analyzed. The method is combined with the Hidden Markov Model (HMM), which contains double stochastic process to quantitatively predict the trend of future thematic evolution. Finally, the above-mentioned combined method is used to analyze the evolution characteristics and future development trends of the themes in the field of land degradation. Comparative experiments show that the method in this study is effective and practical. The research results show that rangeland degradation, surface temperature, island, soil degradation, water quality, crop productivity and restoration are important research topics in the field of land degradation in the future. In addition, based on the advantages of this model, this model can be widely used in the thematic evolution and prediction analysis of different research fields in land use science.