Scientometric analysis of trends in global research on acne treatment.

Acne or acne vulgaris is the most common chronic inflammatory disease of the sebaceous follicles. Objectives: The present study aims to identify the main lines of research in the field of acne treatment using reproducible scientometric methods. In this article, we reviewed the following research trends: facial acne, different antibiotics, retinoids, anti-inflammatory drugs, epidermal growth factor receptor inhibitors therapy, and associated diseases. Methods: The analysis of publications from the PubMed collection was carried out from 1871 to 2022. All data were analyzed using Microsoft Excel. The evolution of the terminological portrait of the disease is shown. Results: Trends in the use of various groups of antibiotics, retinoids, anti-inflammatory drugs, and photodynamic therapy for acne treatment have been found. There is a growing interest in clindamycin and doxycycline (polynomial and exponential growth, respectively). The effects of isotretinoin are also being studied more frequently (active linear growth). The publication of studies on spironolactone is increasing (linear growth). There is also a steady interest in the use of epidermal growth factor receptor inhibitors in the recent years. There is active research on acne and polycystic ovary syndrome (exponential growth). Limitations: Only articles in English were selected. The most frequent terms were considered. Conclusions: The dynamics of publication activity in the field of acne was considered. The aim of the current scientometric study was to analyze the global trends in acne treatments. The trend analysis made it possible to identify the most explored areas of research, as well as indicate those areas in dermatology in which interest is declining.

How Do COVID-19 Risk, Life-Safety Risk, Job Insecurity, and Work-Family Conflict Affect Miner Performance? Health-Anxiety and Job-Anxiety Perspectives.

The coronavirus pandemic (COVID-19) has created challenging working conditions in coal-production activities. In addition to the massive loss of resources for miners, it has had a devastating impact on these individuals' mental health. Based on the conservation of resources (COR) theory and a resource-loss perspective, this study examined the impact of COVID-19 risk, life-safety risk, perceived job insecurity, and work-family conflict on miners' job performance. Moreover, this study investigated the mediating role of job anxiety (JA) and health anxiety (HA). The study data were collected through online structured questionnaires disseminated to 629 employees working in a coal mine in China. The data analysis and hypothesis generation were conducted using the structural equation modeling (partial least squares) method. The results demonstrated that the perception of COVID-19 risk, life-safety risk, job insecurity, and work-family conflict negatively and significantly impacted miners' job performance. In addition, JA and HA negatively mediated the relationships between the perception of COVID-19 risk, life-safety risk, perceived job insecurity, work-family conflict, and job performance. The findings of this study can give coal-mining companies and their staff useful insights into how to minimize the pandemic's effects on their operations.

Global interest in vaccines during the COVID-19 pandemic: Evidence from Google Trends.

COVID-19 (coronavirus disease 2019) vaccines have become available; now, everyone has the opportunity to get vaccinated. We used Google Trends (GT) data to assess the global public interest in COVID-19 vaccines during the pandemic. For the analysis, a period of 17 months was chosen (from Jan 19, 2020, to Jul 04, 2021). Interest in user queries was tracked by keywords (corona vaccine, COVID-19 vaccine development, Sputnik v, Pfizer vaccine, AstraZeneca vaccine, etc.). The geographic analysis of queries was also carried out. The interest of users in the vaccine is significantly increasing. It is focused on the side effects of vaccines, and users pay attention to vaccines' developers from different countries. The correlation between the scientific publications devoted to vaccine development and such requests of users on the internet is absent. This study shows that internet search patterns can be used to gauge public attitudes towards coronavirus vaccination. Safety concerns consistently high follow an interest in vaccine side effects. This data can be used to track and predict attitudes towards vaccination of populations from COVID-19 in different countries before global vaccination becomes available to help mitigate the adverse effects of the pandemic.

An Adaptive Deep Ensemble Learning Method for Dynamic Evolving Diagnostic Task Scenarios.

Increasingly, machine learning methods have been applied to aid in diagnosis with good results. However, some complex models can confuse physicians because they are difficult to understand, while data differences across diagnostic tasks and institutions can cause model performance fluctuations. To address this challenge, we combined the Deep Ensemble Model (DEM) and tree-structured Parzen Estimator (TPE) and proposed an adaptive deep ensemble learning method (TPE-DEM) for dynamic evolving diagnostic task scenarios. Different from previous research that focuses on achieving better performance with a fixed structure model, our proposed model uses TPE to efficiently aggregate simple models more easily understood by physicians and require less training data. In addition, our proposed model can choose the optimal number of layers for the model and the type and number of basic learners to achieve the best performance in different diagnostic task scenarios based on the data distribution and characteristics of the current diagnostic task. We tested our model on one dataset constructed with a partner hospital and five UCI public datasets with different characteristics and volumes based on various diagnostic tasks. Our performance evaluation results show that our proposed model outperforms other baseline models on different datasets. Our study provides a novel approach for simple and understandable machine learning models in tasks with variable datasets and feature sets, and the findings have important implications for the application of machine learning models in computer-aided diagnosis.

Global Scientific Research on SARS-CoV-2 Vaccines: A Bibliometric Analysis.

OBJECTIVE: We performed this bibliometric analysis to identify global scientific research on the SARS-CoV-2 vaccines. MATERIALS AND METHODS: This bibliometric analysis study inclusive search of English-language publications related to the SARS-CoV-2 vaccines was conducted in the Scopus, PubMed, and Dimensions databases without year limitations. The results of bibliometric analysis comprised a time-dependent citation density trend, the name of the journal, journal impact factor (IF), year of publication, type of article, category, subscription or affiliation, co-authorship, and cooccurrence network. RESULTS: A study of the scientific literature from three databases (Scopus, PubMed, Dimensions) shows that investigators have focused more on studying the structure of the coronavirus at different levels (organismic, cellular, and molecular). In addition, the method of virus penetration into the cell and features of the influence of coronavirus on animals are well-studied. Various methods and strategies are being used to develop the vaccines, including both animal-tested methods and computer models. The Dimensions database is the most representative in terms of coverage of research on development of the SARS-CoV-2 vaccines. CONCLUSION: This research is a scientific investigation based on bibliometric analysis of papers related to the SARS-CoV-2 vaccines. The Dimensions database provides the most representative research coverage on the creation of a vaccine against coronavirus. It is characterized by a large number of formed verbose terms (length of more than four words) related to coronavirus, which makes it possible to track trends in the development of methods for creating a vaccine.

A Smart Healthcare Knowledge Service Framework for Hierarchical Medical Treatment System.

This paper reveals the research hotspots and development directions of case-based reasoning in the field of health care, and proposes the framework and key technologies of medical knowledge service systems based on case-based reasoning (CBR) in the big data environment. The 2124 articles on medical CBR in the Web of Science were visualized and analyzed using a bibliometrics method, and a CBR-based knowledge service system framework was constructed in the medical Internet of all people, things and data resources environment. An intelligent construction method for the clinical medical case base and the gray case knowledge reasoning model were proposed. A cloud-edge collaboration knowledge service system was developed and applied in a pilot project. Compared with other diagnostic tools, the system provides case-based explanations for its predicted results, making it easier for physicians to understand and accept, so that they can make better decisions. The results show that the system has good interpretability, has better acceptance than the common intelligent decision support system, and strongly supports physician auxiliary diagnosis and treatment as well as clinical teaching.

Tracking Knowledge Evolution Based on the Terminology Dynamics in 4P-Medicine.

The accelerating evolution of scientific terms connected with 4P-medicine terminology and a need to track this process has led to the development of new methods of analysis and visualization of unstructured information. We built a collection of terms especially extracted from the PubMed database. Statistical analysis showed the temporal dynamics of the formation of derivatives and significant collocations of medical terms. We proposed special linguistic constructs such as megatokens for combining cross-lingual terms into a common semantic field. To build a cyberspace of terms, we used modern visualization technologies. The proposed approaches can help solve the problem of structuring multilingual heterogeneous information. The purpose of the article is to identify trends in the development of terminology in 4P-medicine.