Fetus in fetu is a rare condition in which a fetiform calcified mass often is present in the abdomen of its host, a newborn or an infant. We report on a case of a 19-month-old girl whose plain abdominal radiograph, ultrasonography, and computed tomography scan revealed a mass in which the contents favor a fetus in fetu rather than a teratoma. The noncalcified vertebral column invisible on the radiographs was identified by the pathologist; therefore, the nonvisualization of the vertebral axis on radiography or on computed tomography scan does not exclude the diagnosis of fetus in fetu.

Health personnel and community workers are at the front line of the COVID-19 emergency response and need to be equipped with adequate knowledge related to epidemics for an effective response. This study aimed to identify the coverage of COVID-19 health information via different sources accessed by health workers and community workers in Vietnam. A cross-sectional study using a web-based survey was carried out from January to February 2020 in Vietnam. Respondent-driven sampling (RDS) was used for recruiting participants. We utilized the exploratory factor analysis (EFA) to examine the construct validity of the questionnaire. A higher percentage of participants knew about “Clinical and pathogen characteristics of COVID-19”, compared to “Regulations and policies related to COVID-19”. The percentage of participants accessing the information on “Guidelines and policies on prevention and control of COVID-19” was the lowest, especially among medical students. “Mass media and peer-educators” channels had a higher score of accessing COVID-19 information, compared to “Organizations/ agencies/ associations” sources. Participants consumed most of their COVID-19 information via “Internet, online newspapers, social networks”. Our findings indicate an urgency to re-design training programs and communication activities for a more effective dissemination of information related to the COVID-19 epidemic or epidemics in general.

An exponential growth of literature about novel coronavirus disease 19 has been observed in the last few months. This textual analysis of 5,780 publications extracted from the Web of Science, Medline, and Scopus databases was performed to explore the current research focuses and propose further research agenda. The Latent Dirichlet allocation was used for topic modeling. Regression analysis was conducted to examine country variations in the research focuses. Results indicated that publications were mainly contributed by the United States, China, and European countries. Guidelines for emergency care and surgical, viral pathogenesis, and global responses in the COVID-19 pandemic were the most common topics. There was variation in the research approaches to mitigate COVID-19 problems in countries with different income and transmission levels. Findings highlighted the need for global research collaboration among highand low/middle-income countries in the different stages of prevention and control the pandemic.

Stigma and discrimination are among the greatest challenges that people living with human immunodeficiency virus (HIV) face, and both are known to negatively affect quality of life as well as treatment outcomes. We analyzed the growing research and current understanding of HIV-related stigma and contextual factors in HIV/AIDS (human Immunodeficiency virus/ acquired immunodeficiency syndrome) bibliography. A total of 5984 publications published from 1991 to 2017 were retrieved from the Web of Science database. The number of papers and their impacts have been considerably grown in recent years. Research landscapes related to stigma and discrimination include clinical, physical and mental health outcomes, risk behaviors of most-at-risk populations, and HIV-related services. We found a lack of empirical studies not only on social, cultural and economic contexts, but also on specific interventions for particular settings and sub-populations. This study highlights certain gaps and provides a basis for future studies and interventions on this critical issue given the changing drivers of HIV epidemics.

The detection of first COVID-19 infected industrial worker in Vietnam on 13 April 2020 prompted timely effort to examine the health problems, behaviors, and health services access of industrial workers to inform effective and appropriate COVID-19 control measures, minimizing the risk of industrial sites becoming the next disease cluster. A search strategy involving search terms corresponding to 'health', 'industrial worker', and 'Vietnam' was applied to search for related papers published in English on Web of Science, PubMed, and Google Scholar. Duplicates were removed, and relevant data were extracted from the full text of remaining publications. Results showed that underlying health problems, including respiratory system problems, were common among industrial workers. Many suffered occupational diseases and/or work-related injuries. Self-treatment (without medication) was the most used method when having health problems (by 28.2-51% of participants), followed by visiting commune health centers (24%) and self-medication (20.3%). Findings suggest a high risk of disease spreading among industrial workers and of them suffering more severe conditions when infected. Economic vulnerabilities may be the reason for workers' reluctance to taking time off work to attend hospital/clinic. These imply a need for involving local pharmacies, commune health centers, traditional health providers or village health collaborators as local health gatekeepers who are the first point of detecting and reporting of suspected COVID-19 cases, as well as a channel where accurate information regarding COVID-19, protective equipment, and intervention packages can be delivered. Having COVID-19 testing centers at or near industrial sites are also recommended.

Depression in people living with HIV (PLWH) has become an urgent issue and has attracted the attention of both physicians and epidemiologists. Currently, 39% of HIV patients are reported to suffer from depression. This population is more likely to experience worsening disease states and, thus, poorer health outcomes. In this study, we analyzed research growth and current understandings of depression among HIV-infected individuals. The number of papers and their impacts have been considerably grown in recent years, and a total of 4872 publications published from 1990–2017 were retrieved from the Web of Science database. Research landscapes related to this research field include risk behaviors and attributable causes of depression in HIV population, effects of depression on health outcomes of PLWH, and interventions and health services for these particular subjects. We identified a lack of empirical studies in countries where PLWH face a high risk of depression, and a modest level of interest in biomedical research. By demonstrating these research patterns, highlighting the research gaps and putting forward implications, this study provides a basis for future studies and interventions in addressing the critical issue of HIV epidemics.

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Artificial intelligence (AI)-based techniques have been widely applied in depression research and treatment. Nonetheless, there is currently no systematic review or bibliometric analysis in the medical literature about the applications of AI in depression. We performed a bibliometric analysis of the current research landscape, which objectively evaluates the productivity of global researchers or institutions in this field, along with exploratory factor analysis (EFA) and latent dirichlet allocation (LDA). From 2010 onwards, the total number of papers and citations on using AI to manage depressive disorder have risen considerably. In terms of global AI research network, researchers from the United States were the major contributors to this field. Exploratory factor analysis showed that the most well-studied application of AI was the utilization of machine learning to identify clinical characteristics in depression, which accounted for more than 60% of all publications. Latent dirichlet allocation identified specific research themes, which include diagnosis accuracy, structural imaging techniques, gene testing, drug development, pattern recognition, and electroencephalography (EEG)-based diagnosis. Although the rapid development and widespread use of AI provide various benefits for both health providers and patients, interventions to enhance privacy and confidentiality issues are still limited and require further research.