In this paper we propose an open source application called LDAShiny, which provides a graphical user interface to perform a review of scientific literature using the latent Dirichlet allocation algorithm and machine learning tools in an interactive and easy-to-use way. The procedures implemented are based on familiar approaches to modeling topics such as preprocessing, modeling, and postprocessing. The tool can be used by researchers or analysts who are not familiar with the R environment. We demonstrated the application by reviewing the literature published in the last three decades on the species Oreochromis niloticus. In total we reviewed 6196 abstracts of articles recorded in Scopus. LDAShiny allowed us to create the matrix of terms and documents. In the preprocessing phase it went from 530,143 unique terms to 3268. Thus, with the implemented options the number of unique terms was reduced, as well as the computational needs. The results showed that 14 topics were sufficient to describe the corpus of the example used in the demonstration. We also found that the general research topics on this species were related to growth performance, body weight, heavy metals, genetics and water quality, among others.

Wastewater treatment is a critical process for ensuring water quality and public health, particularly in the context of increasing environmental challenges such as pollution and water scarcity. Artificial intelligence (AI) has emerged as a transformative technology capable of optimizing various wastewater treatment processes, such as contaminant removal, energy consumption, and cost-efficiency. This study presents a comprehensive bibliometric analysis of AI applications in wastewater treatment, utilizing data from Scopus and Web of Science covering 4335 publications from 1985 to 2024. Utilizing machine learning techniques such as neural networks, fuzzy logic, and genetic algorithms, the analysis reveals key trends in the role of the AI in optimizing wastewater treatment processes. The results show that AI has increasingly been applied to solve complex problems like membrane fouling, nutrient removal, and biofouling control. Regional contributions highlight a strong focus on advanced oxidation processes, microbial sludge treatment, and energy optimization. The Latent Dirichlet Allocation (LDA) model further identifies emerging topics such as real-time process monitoring and AI-driven effluent prediction as pivotal areas for future research. The findings provide valuable insights into the current state and future potential of AI technologies in wastewater management, offering a roadmap for researchers exploring the integration of AI to address sustainability challenges in the field.

Background: There is an immense debate about the factors that could limit the adoption of agroforestry systems. However, one of the most important is the generation of scientific information that supports the viability and benefits of the proposed techniques. Statistical analysis: This work used the Latent Dirichlet Allocation (LDA) modeling method to identify and interpret scientific information on topics in relation to existing categories in a set of documents. It also used the HJ-Biplot method to determine the relationship between the analyzed topics, taking into consideration the years under study. Results: A review of the literature was conducted in this study and a total of 9794 abstracts of scientific articles published between 1993 and 2022 were obtained. The United States, India, Brazil, the United Kingdom, and Germany were the five countries that published the largest number of studies about agroforestry, particularly soil organic carbon, which was the most studied case. The five more frequently studied topics were: soil organic carbon, adoption of agroforestry practices, biodiversity, climatic change global policies, and carbon and climatic change. Conclusion: the LDA and HJ-Biplot statistical methods are useful tools for determining topicality in text analysis in agroforestry and related topics.

Background: Neuroendocrine tumors (NETs) are severe and relatively rare and may affect any organ of the human body. The prevalence of NETs has increased in recent years; however, there seem to be more data on particular types, even though, despite the efforts of different guidelines, there is no consensus on how to identify different types of NETs. In this review, we investigated the countries that published the most articles about NETs, the most frequent organs affected, and the most common related topics. Methods: This work used the Latent Dirichlet Allocation (LDA) method to identify and interpret scientific information in relation to the categories in a set of documents. The HJ-Biplot method was also used to determine the relationship between the analyzed topics, by taking into consideration the years under study. Results: In this study, a literature review was conducted, from which a total of 7658 abstracts of scientific articles published between 1981 and 2020 were extracted. The United States, Germany, United Kingdom, France, and Italy published the majority of studies on NETs, of which pancreatic tumors were the most studied. The five most frequent topics were t_21 (clinical benefit), t_11 (pancreatic neuroendocrine tumors), t_13 (patients one year after treatment), t_17 (prognosis of survival before and after resection), and t_3 (markers for carcinomas). Finally, the results were put through a two-way multivariate analysis (HJ-Biplot), which generated a new interpretation: we grouped topics by year and discovered which NETs were the most relevant for which years. Conclusions: The most frequent topics found in our review highlighted the severity of NETs: patients have a poor prognosis of survival and a high probability of tumor recurrence.

The pollution of the oceans by plastic waste is one of the major environmental problems of our time. It is estimated that there are millions of tons of polymers floating on the surface of the oceans, concentrated in several large plastic accumulations so-called plastic islands. The main consequence of marine plastic pollution is the negative impact on marine ecosystems and wildlife. The microplastics can be ingested by marine organisms and transferred through the food chain, potentially having harmful effects on human health. Humanity needs to take immediate action to reduce the high number of plastics that end up in the oceans, otherwise the harmful consequences for our oceans and for humanity will be irreversible. In view of this scenario, the present bibliometric review reflects a growing global commitment to sustainability. Major areas of research, including data-driven models for plastic waste management, technological innovations in the circular economy and the impact of microplastics on ecosystems and human health, exemplify the diverse strategies being employed to promote sustainability. Integrating advanced technologies with sustainable practices is crucial to reduce the environmental footprint of plastic waste and mitigate its adverse effects on both marine ecosystems and human health.

Background: Renal cell carcinoma (RCC) is a common, often lethal kidney cancer that originates in the renal cortex. Its incidence is rising, and major factors include smoking, obesity, and hypertension, though its etiology is uncertain. While surgery is effective for localized RCC, treatments for metastatic RCC have advanced significantly due to better diagnostic, prognostic, and predictive tools. Despite this progress, challenges remain, including long-term drug resistance and the complexity of RCC as a diverse group of diseases rather than a single entity. Objective: The aim of this bibliometric review was a comprehensive analysis of the topics and trends in RCC research, offering a foundation for future investigations. Methods: We used R “Bibliometrix” to conduct a bibliographic search in Scopus and PubMed covering publications from 1975 to 2023 to statistically assess the distribution of publications associated with RCC by year, journal, and country. Topic modeling of RCC research was conducted using latent Dirichlet allocation, a Bayesian network-based probabilistic algorithm that identifies unobserved thematic clusters in a collection of text documents. Trends in the retrieved themes were then characterized by using regression slopes over time, across countries, and in different journals. These trends were visualized as a heatmap, which was then used for hierarchical clustering to group similar topics based on their correlation strengths. Results: A total of 35,228 documents from 3070 sources were found, with a steady yearly growth of 9.86% and 118 participating countries. Thirty topics with the best coherence score were found in 8 crucial domains: treatment and therapies, biomolecular and genetic characteristics, disease characteristics and progression, diagnosis and evaluation, metastasis and dissemination, epidemiology and risk factors, related conditions, and pathological features. The pertinent clustergrams that resulted from the heatmaps mirrored the latent Dirichlet allocation’s algorithm identification of major RCC research subjects. Conclusions: Over 50 years, RCC research’s focus has shifted from diagnosis and assessment to a more thorough understanding of disease characteristics and progression. Because many patients are diagnosed with abdominal imaging studies, an emerging topic in RCC is diagnostic imaging and radiological evolution. The advances in omics technologies and the function of microRNA signature in the progression, diagnosis, therapy targeting, and prognosis of RCC have garnered a lot of attention. The discovery of the genetic background has enhanced our understanding of the growth of RCC. Drug resistance, local RCC ablation, and postoperative surveillance of RCC recurrence following nephrectomy are key future research avenues. The next generation of drug-targeted therapy and immunotherapy will make it possible to successfully treat metastatic RCC following nephrectomy. Neglected topics include the association between ferroptosis and RCC, the long-term assessment of novel treatments, and the application of artificial intelligence on RCC. Our bibliographic review delivered pertinent data for clinical decision-making and the planning of future RCC research.