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Introduction: The Coronavirus disease of 2019 (COVID-19) is a catastrophic emerging global health threat caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). COVID-19 has a wide range of complications and sequelae. It is devastating in developing countries, causing serious health and socioeconomic crises as a result of the increasingly overburdened healthcare system. This study was conducted to determine the prevalence of SARS-CoV-2 infection in Ethiopia. Methods: Electronic databases, such as PubMed, Google Scholar, Web of Science, Research Gate, Embase, and Scopus were thoroughly searched from March to April 2022 to identify relevant studies. The quality of the included studies was evaluated using the Newcastle-Ottawa Quality scale for cross-sectional studies. STATA-12 was used for analysis. A random-effects model was used to compute the pooled prevalence of SARS-CoV-2 infection. The heterogeneity was quantified by using the I2 value. Subgroup analysis was done for sex, age of study subjects, population type, diagnostic methods, and publication year. Publication bias was assessed using a funnel plot and Egger's test. A sensitivity analysis was also done. Result: 11 studies consisting of 35,376 study participants (15,759 male and 19,838 female) were included in this systematic review and meta-analysis. The pooled prevalence of SARS-CoV-2 was 8.83%. There was substantial heterogeneity, with an I2 value of 99.3%. The pooled prevalence of SARS-CoV-2 was higher in males (9.27%) than in females (8.8%). According to the publication year, a higher prevalence was obtained in 2021 (12.69%). Similarly, it was higher in the population of specific groups (16.65%) than in the general population (5.75%). Conclusion: the national pooled prevalence of SARS-CoV-2 infection in the Ethiopian population was 8.83%. This indicates that the burden of COVID-19 is still high, which urges routine screening and appropriate treatment. [ FROM AUTHOR] Copyright of COVID is the property of MDPI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
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Background and objective Urology residency match occurs through the American Urological Association (AUA), and hence information about the success of applicants in finding a match is not readily available. The average number of publications a successful urology applicant has when applying for residency is unknown. In light of this, we conducted this study to examine the number of PubMed-indexed research projects involving US senior medical students who successfully matched into the top 50 urology residency programs in the 2021, 2022, and 2023 match cycles. We also assessed these applicants based on their medical schools and gender. Methods Doximity Residency Navigator was used to generate the top 50 residency programs as sorted by reputation. Newly matched residents were found using program Twitter accounts and residency program websites. PubMed was queried for peer-reviewed publications of incoming interns. Results The average number of publications across all incoming interns in the three years was 3.65. The average number of urology-specific publications was 1.86 and that of first-author urology publications was 1.11. The median number of total publications for matched applicants was 2, and applicants with a total of five publications were in the 75th percentile for research productivity. Conclusion A successful applicant had two PubMed-indexed urology papers on average and also had a urology-specific first-author paper in the cycles we surveyed. There has been an increase in publications per applicant when comparing the results to previous application cycles, which can be attributed to post-pandemic changes.
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Large chest X-rays (CXR) datasets have been collected to train deep learning models to detect thorax pathology on CXR. However, most CXR datasets are from single-center studies and the collected pathologies are often imbalanced. The aim of this study was to automatically construct a public, weakly-labeled CXR database from articles in PubMed Central Open Access (PMC-OA) and to assess model performance on CXR pathology classification by using this database as additional training data. Our framework includes text extraction, CXR pathology verification, subfigure separation, and image modality classification. We have extensively validated the utility of the automatically generated image database on thoracic disease detection tasks, including Hernia, Lung Lesion, Pneumonia, and pneumothorax. We pick these diseases due to their historically poor performance in existing datasets: the NIH-CXR dataset (112,120 CXR) and the MIMIC-CXR dataset (243,324 CXR). We find that classifiers fine-tuned with additional PMC-CXR extracted by the proposed framework consistently and significantly achieved better performance than those without (e.g., Hernia: 0.9335 vs 0.9154; Lung Lesion: 0.7394 vs. 0.7207; Pneumonia: 0.7074 vs. 0.6709; Pneumothorax 0.8185 vs. 0.7517, all in AUC with p< 0.0001) for CXR pathology detection. In contrast to previous approaches that manually submit the medical images to the repository, our framework can automatically collect figures and their accompanied figure legends. Compared to previous studies, the proposed framework improved subfigure segmentation and incorporates our advanced self-developed NLP technique for CXR pathology verification. We hope it complements existing resources and improves our ability to make biomedical image data findable, accessible, interoperable, and reusable.
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Pneumonia , Pneumothorax , Thoracic Diseases , Humans , Pneumothorax/diagnostic imaging , Radiography, Thoracic/methods , X-Rays , Access to Information , Pneumonia/diagnostic imagingABSTRACT
COVID-19 has greatly impacted science. It has become a global research front that constitutes a unique phenomenon of interest for the scientometric community. Accordingly, there has been a proliferation of descriptive studies on COVID-19 papers using altmetrics. Social media metrics serve to elucidate how research is shared and discussed, and one of the key points is to determine which factors are well-conditioned altmetric values. The main objective of this study is to analyze whether the altmetric mentions of COVID-19 medical studies are associated with the type of study and its level of evidence. Data were collected from the PubMed and Altmetric.com databases. A total of 16,672 publications by study types (e.g., case reports, clinical trials, or meta-analyses) that were published in the year 2021 and that had at least one altmetric mention were retrieved. The altmetric indicators considered were Altmetric Attention Score (AAS), news mentions, Twitter mentions, and Mendeley readers. Once the dataset of COVID-19 had been created, the first step was to carry out a descriptive study. Then, a normality hypothesis was evaluated by means of the Kolmogorov–Smirnov test, and since this was significant in all cases, the overall comparison of groups was performed using the nonparametric Kruskal–Wallis test. When this test rejected the null hypothesis, pairwise comparisons were performed with the Mann– Whitney U test, and the intensity of the possible association was measured using Cramer's V coefficient. The results suggest that the data do not fit a normal distribution. The Mann–Whitney U test revealed coincidences in five groups of study types: The altmetric indicator with most coincidences was news mentions, and the study types with the most coincidences were the systematic reviews together with the meta-analyses, which coincided with four altmetric indicators. Likewise, between the study types and the altmetric indicators, a weak but significant association was observed through the chi-square and Cramer's V. It can thus be concluded that the positive association between altmetrics and study types in medicine could reflect the level of the "pyramid” of scientific evidence. © 2023, El Profesional de la Informacion. All rights reserved.
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Background: Research publications related to the novel coronavirus disease COVID-19 are rapidly increasing. However, current online literature hubs, even with artificial intelligence, are limited in identifying the complexity of COVID-19 research topics. We developed a comprehensive Latent Dirichlet Allocation (LDA) model with 25 topics using natural language processing (NLP) techniques on PubMed® research articles about "COVID." We propose a novel methodology to develop and visualise temporal trends, and improve existing online literature hubs. Our results for temporal evolution demonstrate interesting trends, for example, the prominence of "Mental Health" and "Socioeconomic Impact" increased, "Genome Sequence" decreased, and "Epidemiology" remained relatively constant. Applying our methodology to LitCovid, a literature hub from the National Center for Biotechnology Information, we improved the breadth and depth of research topics by subdividing their pre-existing categories. Our topic model demonstrates that research on "masks" and "Personal Protective Equipment (PPE)" is skewed toward clinical applications with a lack of population-based epidemiological research.
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This paper presents a computational approach designed to construct and query a literature-based knowledge graph for predicting novel drug therapeutics. The main objective is to offer a platform that discovers drug combinations from FDA-approved drugs and accelerates their investigations by domain scientists. Specifically, the paper introduced the following algorithms: (1) an algorithm for constructing the knowledge graph from drug, gene, and disease mentions in the biomedical literature;(2) an algorithm for vetting the knowledge graph from drug combinations that may pose a risk of drug interaction;(3) and two querying algorithms for searching the knowledge graph by a single drug or a combination of drugs. The resulting knowledge graph had 844 drugs, 306 gene/protein features, and 19 disease mentions. The original number of drug combinations generated was 2,001. We queried the knowledge graph to eliminate noise generated from chemicals that are not drugs. This step resulted in 614 drug combinations. When vetting the knowledge graph to eliminate the potentially risky drug combinations, it resulted in predicting 200 combinations. Our domain expert manually eliminated extra 54 combinations which left only 146 combination candidates. Our three-layered knowledge graph, empowered by our algorithms, offered a tool that predicted drug combination therapeutics for scientists who can further investigate from the viewpoint of drug targets and side effects. © 2022 IEEE.
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Introduction: Research into patients' perception of empathy has revealed that patients with stomas feel unsupported by healthcare professionals, who can lack an understanding of how it feels to live life with a stoma. A literature review was undertaken to explore what is the evidence for best practice for teaching empathy skills to healthcare professionals and how this can be applied to caring for people with a stoma. Search strategy: Included studies were required to explore teaching empathy or measuring levels of empathy in pre- and post-graduate nurses and in healthcare professionals caring for patients with a stoma. Excluded studies were those involving paediatric and mental health nursing, as these domains of nursing were considered to differ in clinical specialism and any other healthcare professional discipline outside the nursing profession such as doctors or allied healthcare professionals. Results: Given the number of articles reporting that empathy is lacking in stoma care, it is remarkable that so little original research has been carried out in this area, specifically the lack of qualitative research. A variety of interventions were used to assess empathy in pre- and post-graduate nurses, from multiple nations with diverse cultures. Conclusions: Results from the data revealed several themes for the best practice of teaching empathy skills to healthcare professionals, including essential nurse attributes, innate nurse characteristics, nurse experience and the contribution of experiential learning.
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Overlay maps of science are global base maps over which subsets of publications can be projected. Such maps can be used to monitor, explore, and study research through its publication output. Most maps of science, including overlay maps, are flat in the sense that they visualize research fields at one single level. Such maps generally fail to provide both overview and detail about the research being analyzed. The aim of this study is to improve overlay maps of science to provide both features in a single visualization. I created a map based on a hierarchical classification of publications, including broad disciplines for overview and more granular levels to incorporate detailed information. The classification was obtained by clustering articles in a citation network of about 17 million publication records in PubMed from 1995 onwards. The map emphasizes the hierarchical structure of the classification by visualizing both disciplines and the underlying specialties. To show how the visualization methodology can help getting both an overview of research and detailed information about its topical structure, I studied two cases: coronavirus/Covid-19 research and the university alliance called Stockholm Trio. © 2022 Peter Sjögårde. Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
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To examine current literature on the role of physical therapy (PT) in management of post COVID-19 related conditions and common symptomatology in adults, as well as potential guidelines for rehabilitation in the outpatient setting. PubMed, CINAHL, Medline, Cochrane. Searches were conducted to examine current data related to PT interventions and their effectiveness for treating post COVID-19 conditions. Articles were evaluated for relevance based on the following criteria: articles in English, original peer reviewed articles, adult population (over 18), relevant PT interventions for rehabilitation, post-acute infection of COVID-19. Consensus agreement confirmed approximately 25% of reviewed articles. Articles were analyzed for relevance to implications regarding post COVID-19 and potential PT rehabilitation interventions. Interventions were assessed in feasibility and applicability to an outpatient clinic setting. Independent Data Extraction followed by consensus discussion was applied. Articles were examined for content regarding the latest updates on disease criteria, manifestations, new classifications, and cohorts emerging as the pandemic progresses as well as management strategies applicable to PT practice. After article analysis, the findings include a key theme that PT services helped improve overall functional mobility and symptom management in patients after an acute infection of COVID-19. An essential consideration is keeping the interventions specific to the patient and their goals while preventing exacerbations of symptoms that could lead to further setbacks. PT has a growing role in the management of post COVID-19 deficits as well as implications related to long COVID sequelae. By choosing the appropriate parameters and having awareness of the varying symptomology amongst patients, physical therapists can improve patients' functional mobility and post COVID-19 disease management. The focus of future studies should include more specific interventions related to managing conditions and finding the most effective treatment strategies. No conflicts to disclose.
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Many studies have assessed the prevalence of alcohol consumption in Iran. In this study, we investigated the prevalence of alcohol consumption in different groups. We searched international and databases including PubMed, Web of Science, Scopus and we searched two main Farsi-language index databases including Scientific Information Database (SID) and the Irandoc. Grey literature search was also performed in Google Scholar, PsycINFO, ProQuest Dissertation and Theses without time limit until June 2020. All studies that reported the prevalence of alcohol consumption among Iranians were included in current study. From 9,038 screened studies, 109 studies with 925,480 participants were included. The pooled prevalence of alcohol consumption was estimated 24% (95% CI: 18.0–31.0), 12% (95%CI: 10.0–14.0), 14% (95%CI: 13.0–15.0), 19% (95%CI: 13.0–26.0), 15% (95%CI: 3.0–28.0) among prisoners, general population, students, drivers, and street children, respectively. This systematic review indicated higher prevalence of alcohol consumption in prisoners, drivers, and street children than the general population and other subgroups. The distribution of the alcohol prevalence studies in different groups and provinces were heterogeneous. The lack of studies among some groups and in regions warrants further, attention.
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Burnout in hospice and palliative care nurses is a growing issue, especially in light of the COVID-19 pandemic. However, few studies have focused specifically on burnout in this population. A scoping review was undertaken to identify what is known about burnout among hospice and palliative care nurses, and to unify disparate findings. Analysis of eight articles revealed three overarching categories: personal factors, organizational/workplace factors, and nursing professional development factors. Each category was then divided into three cross-cutting subcategories: contributory and noncontributory factors, mitigating factors, and workplace issues. Recommendations for individuals include self-care as well as self-awareness of intrinsic characteristics that can predispose one to burnout. Within the workplace, leaders are challenged to support evidence-based practice and ongoing education. Role modeling positive communication skills, effective conflict mitigation, responsiveness, promotion of equity, and workplace commitment also help to create a culture of wellness. Nursing professional development may aid in resilience-building, and promotion of self-efficacy, self-confidence, and assertiveness. Although all identified recommendations were derived from the literature, no interventional studies have been conducted to test the effects of suggested interventions. Future research should include interventional studies as well as qualitative research to capture nuanced experiences of burnout in hospice and palliative care nurses.
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Artificial intelligence (AI) refers to the simulation of human intelligence in machines, using machine learning (ML), deep learning (DL) and neural networks (NNs). AI enables machines to learn from experience and perform human-like tasks. The field of AI research has been developing fast over the past five to ten years, due to the rise of 'big data' and increasing computing power. In the medical area, AI can be used to improve diagnosis, prognosis, treatment, surgery, drug discovery, or for other applications. Therefore, both academia and industry are investing a lot in AI. This review investigates the biomedical literature (in the PubMed and Embase databases) by looking at bibliographical data, observing trends over time and occurrences of keywords. Some observations are made: AI has been growing exponentially over the past few years; it is used mostly for diagnosis; COVID-19 is already in the top-3 of diseases studied using AI; China, the United States, South Korea, the United Kingdom and Canada are publishing the most articles in AI research; Stanford University is the world's leading university in AI research; and convolutional NNs are by far the most popular DL algorithms at this moment. These trends could be studied in more detail, by studying more literature databases or by including patent databases. More advanced analyses could be used to predict in which direction AI will develop over the coming years. The expectation is that AI will keep on growing, in spite of stricter privacy laws, more need for standardization, bias in the data, and the need for building trust.
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Supplemental Digital Content is Available in the Text. Background and Purpose.: A main component of the conceptual model of excellence in physical therapist education, introduced by Jensen et al, is a culture of excellence. A culture of excellence relies on identifying accountable faculty who set high expectations and execute systems toward ongoing improvement. Peer review of teaching (PRT) is an established system that cultivates a culture of collaboration, reflection, and excellence through feedback and collegial discourse. The purpose of this scoping review was to understand PRT implementation by 1) summarizing the program development process, 2) identifying program characteristics, 3) identifying review instruments, and 4) determining program evaluation strategies. Methods.: A scoping review was conducted using a methodological framework. With library scientist counsel, search terms were established, and 3 databases were queried for articles describing PRT programs in health care education. Articles were managed in the Covidence Systematic Review Management Software. Researchers independently screened search results for article inclusion and extracted data from included studies. Descriptive data analysis was conducted. Results.: Thirty-five articles met inclusion criteria. Seven different health care professions have published PRT articles;however, none in Doctor of Physical Therapy (DPT) education. Results indicated that most programs underwent a systematic development process, included faculty input, and sought to ensure consistency between the program purpose and characteristics. A 3-step formative process was most common. Faculty were paired systematically or used self-selection. Evaluative instruments were often program specific, guided by core competencies of teaching excellence or previously published tools. Program outcomes commonly reported positive faculty opinion of PRT and teaching improvement. Only 2 articles evaluated student metrics to assess PRT impact and effectiveness. Discussion and Conclusion.: Peer review of teaching has been successfully adopted by health care education faculty to promote teaching excellence and could be a foundation for creating a culture of excellence in DPT education. These results provide an understanding of the processes for implementing PRT to guide DPT educators establishing PRT programs.
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Presentamos un modelo de tópicos basado en el método asignación latente de Dirichlet (LDA, por sus siglas en inglés) con el objetivo de examinar patrones en la investigación científica del Covid--19 teniendo en cuenta las publicaciones indexadas en la base datos especializada PubMed. Se toman 4928 resumenes científicos publicados durante el primer semestre de 2020. Se ajusta un modelo LDA utilizando dos tópicos. El primer tópico corresponde a factores de riesgo, severidad y mortalidad por infección viral, mientras que el segundo al impacto de las infecciones respiratorias en la salud pública. La clasificación propuesta brinda una visión global sobre las dos tendencias de investigación presentes a la fecha en la que el análisis tiene lugar. Adicionalmente, los resultados señalan que la aplicación de la metodología propuesta provee un camino para direccionar y hacer más eficiente la revisión bibliográfica en el contexto académico.Alternate : We consider a topic modeling approach using latent Dirichlet allocation (LDA) methods aiming to examine patterns in the scientific research of Covid-19 using publications indexed in the PubMed database. A total of 4928 scientific s published during the first semester of 2020 are taken into account. An LDA model is fitted using two topics. The first topic corresponds to risk factors, severity, and mortality due to viral infection, whereas the second is the impact of respiratory illnesses on public health. Our classification provides a global overview of these two research trends from the moment the analysis takes place. Additionally, our findings suggest that the systematic application of the proposed methodology provides a way to address and make more efficient the bibliographic review in academic contexts.
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The contagious disease "Covid-19" pandemic at the end of 2019 has affected the world. The clinicians and researchers are engaged in the disease's control, treatment, and care. In the early phase of the outbreak, the alternative medicine system attracted attention as not much knowledge and remedies were available for the disease. Concurrently, vaccine development research was initiated and is still ongoing. The PubMed public domain database specifically devoted to the health science system, along with the iCite database, is used to undertake metric analysis on the above two subject areas. Bibexcel and VOSviewer software are used for the bibliometric analysis of parameters such as language-wise contribution, top journals, most prolific authors, countries, and co-organisations. It's found that English as a language and the USA as a country are leading across both the subject areas. The inferential analysis reveals that the double-blind peer review system is having a higher positive impact while, among the publication types, observational studies and funded support research studies are significantly influential.
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The interest in wearable wireless monitoring systems has accelerated secondary to the ongoing COVID-19 pandemic. Moreover, the alarmingly high number of infections in the pediatric population underscores a gap in monitoring these vulnerable populations, particularly in the home setting. This systematic review aims to identify and assess currently available wearables used to monitor cardiopulmonary function in infants and neonates. The study, prospectively registered on PROSPERO (CRD42020200642), completed a search of PubMed 1946-, Embase 1947-, Cochrane Library, Scopus 1823-, and IEEE Explore 1872-in June 2020. A total of 2324 unique citations were identified, with 16 studies describing 17 unique devices meeting inclusion criteria. Types of devices included smart clothing, belts, and mechanical adhesives, each with unique battery designs, data collection, and transmission hardware. Only four of the 17 devices underwent rigorous comparative testing, and three demonstrated correlation with the standard of care monitoring systems. Low sensitivity and specificity were reported in two commercially available consumer devices compared to the standard of care monitoring systems. The risk of bias in the entire cohort was highly based on a modified ROBINS-I scale. Further development and rigorous wearable device testing are necessary for neonatal and infant deployment.
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Background: The COVID-19 pandemic has captured the mental health discussion worldwide. Examining countries' representation in this discussion could prove instrumental in identifying potential gaps in terms of ensuring a truly global conversation in times of global crisis. Methods: We collected mental health and COVID-19-related journal articles published in PubMed in 2020. We focused on the corresponding authors' countries of affiliation to explore countries' representation. We also examined these articles' academic impact and correlations with their corresponding authors' countries of affiliation. Additional journals and countries' indicators were collected from the Web of Science and World Bank websites, respectively. Data were analyzed using the IBM SPSS Statistics and the VOSviewer software. Results: In total, 3492 publications were analyzed. Based on the corresponding author, high-income countries produced 61.9% of these publications. Corresponding authors from Africa, Latin America and the Caribbean, and the Middle East combined accounted for 11.8% of the publications. Europe hosted corresponding authors with the most publications and citations, and corresponding authors from North America had the largest mean journal impact factor. Conclusions: The global scientific discussion during the COVID-19 pandemic saw an increased contribution of academics from developing countries. However, authors from high-income countries have continued to shape this discussion. It is imperative to ensure the active participation of low- and middle-income countries in setting up the global mental health research agenda, particularly in situations of global crisis, such as the ongoing pandemic.
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Machine learning, a rapidly evolving field of data analysis, has now become an integral part of life science research. It has been widely utilized for exploring the information encoded by the genome and beyond the genome. In this study, we surveyed the trends of scientific actors and the conceptual structure of machine learning implementation in biomedical research through the published literature retrieved from the PubMed search engine. A longitudinal cohort bibliographic coupling was executed by employing the VOS viewer tool for 4-time periods, 1964–2010, 2011–2015, 2016–2018, and 2019–2020. Scientific actors of machine learning research include 42,629 unique authors, 27,364 organizations with a mean collaboration index of 3.9. Coword analysis revealed that the conceptual framework of machine learning applications in life sciences moved from simple pattern searching to omic science and medical imaging analytic approaches and from Bayes’ theorem to deep learning algorithms. It is observed that presently machine learning is extensively utilized in investigating emerging situations like coronavirus disease. To epitomize, researchers capitalized on advancements in machine learning tools and high-throughput technologies to delve into the intricate and evolving concepts of biology and medicine. © 2022 Vanaja and Yella.