Identifying the barriers and facilitators to culturally responsive HIV and PrEP screening for racial, ethnic, sexual, and gender minoritized patients: A scoping review protocol.

INTRODUCTION: While mainstream messaging about human immunodeficiency virus (HIV) disparities continues to highlight individual risk-taking behavior among historically marginalized groups, including racial, ethnic, sexual, and gender minoritized patients, the effect of structural factors and social determinants of health (SDOH) on morbidity and mortality remain underestimated. Systemic barriers, including a failure of adequate and acceptable screening, play a significant role in the disparate rates of disease. Primary care practitioner (PCP) competency in culturally responsive screening practices is key to reducing the impact of structural factors on HIV rates and outcomes. To address this issue, a scoping review will be performed to inform the development of a training series and social marketing campaign to improve the competency of PCPs in this area. OBJECTIVES: This scoping review aims to analyze what recent literature identify as facilitators and barriers of culturally responsive HIV and pre-exposure prophylaxis (PrEP) screening practices for historically marginalized populations, specifically racial, ethnic, sexual, and gender minoritized groups. A secondary aim is to identify themes and gaps in the literature to help guide future opportunities for research. METHODS: This scoping review will be performed following the framework set forth by Arksey and O'Malley and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews (PRISMA-ScR). Relevant studies between the years 2019-2022 will be identified using a rigorous search strategy across four databases: MEDLINE (via PubMed), Scopus, Cochrane (CENTRAL; via Wiley), and CINAHL (via EBSCO), using Boolean and Medical Subject Headings (MeSH) search terms. Studies will be uploaded to the data extraction tool Covidence to remove duplicates and perform a title/abstract screening, followed by a full-text screening and data extraction. RESULTS: Data will be extracted and analyzed for themes related to culturally responsive HIV and PrEP screening practices in clinical encounters with the identified target populations. Results will be reported according to PRISMA-ScR guidelines. DISCUSSION: To our knowledge, this is the first study to use scoping methods to investigate barriers and facilitators to culturally responsive HIV and PrEP screening practices for racial, ethnic, sexual, and gender minoritized populations. The limitations of this study include the analysis restrictions of a scoping review and the timeframe of this review. We anticipate that this study's findings will interest PCPs, public health professionals, community activists, patient populations, and researchers interested in culturally responsive care. The results of this scoping review will inform a practitioner-level intervention that will support culturally sensitive quality improvement of HIV-related prevention and care for patients from minoritized groups. Additionally, the themes and gaps found during analysis will guide future avenues of research related to this topic.

Chemical identification and indexing in full-text articles: an overview of the NLM-Chem track at BioCreative VII.

The BioCreative National Library of Medicine (NLM)-Chem track calls for a community effort to fine-tune automated recognition of chemical names in the biomedical literature. Chemicals are one of the most searched biomedical entities in PubMed, and-as highlighted during the coronavirus disease 2019 pandemic-their identification may significantly advance research in multiple biomedical subfields. While previous community challenges focused on identifying chemical names mentioned in titles and abstracts, the full text contains valuable additional detail. We, therefore, organized the BioCreative NLM-Chem track as a community effort to address automated chemical entity recognition in full-text articles. The track consisted of two tasks: (i) chemical identification and (ii) chemical indexing. The chemical identification task required predicting all chemicals mentioned in recently published full-text articles, both span [i.e. named entity recognition (NER)] and normalization (i.e. entity linking), using Medical Subject Headings (MeSH). The chemical indexing task required identifying which chemicals reflect topics for each article and should therefore appear in the listing of MeSH terms for the document in the MEDLINE article indexing. This manuscript summarizes the BioCreative NLM-Chem track and post-challenge experiments. We received a total of 85 submissions from 17 teams worldwide. The highest performance achieved for the chemical identification task was 0.8672 F-score (0.8759 precision and 0.8587 recall) for strict NER performance and 0.8136 F-score (0.8621 precision and 0.7702 recall) for strict normalization performance. The highest performance achieved for the chemical indexing task was 0.6073 F-score (0.7417 precision and 0.5141 recall). This community challenge demonstrated that (i) the current substantial achievements in deep learning technologies can be utilized to improve automated prediction accuracy further and (ii) the chemical indexing task is substantially more challenging. We look forward to further developing biomedical text-mining methods to respond to the rapid growth of biomedical literature. The NLM-Chem track dataset and other challenge materials are publicly available at https://ftp.ncbi.nlm.nih.gov/pub/lu/BC7-NLM-Chem-track/. Database URL https://ftp.ncbi.nlm.nih.gov/pub/lu/BC7-NLM-Chem-track/.

Classifying literature mentions of biological pathogens as experimentally studied using natural language processing.

BACKGROUND: Information pertaining to mechanisms, management and treatment of disease-causing pathogens including viruses and bacteria is readily available from research publications indexed in MEDLINE. However, identifying the literature that specifically characterises these pathogens and their properties based on experimental research, important for understanding of the molecular basis of diseases caused by these agents, requires sifting through a large number of articles to exclude incidental mentions of the pathogens, or references to pathogens in other non-experimental contexts such as public health. OBJECTIVE: In this work, we lay the foundations for the development of automatic methods for characterising mentions of pathogens in scientific literature, focusing on the task of identifying research that involves the experimental study of a pathogen in an experimental context. There are no manually annotated pathogen corpora available for this purpose, while such resources are necessary to support the development of machine learning-based models. We therefore aim to fill this gap, producing a large data set automatically from MEDLINE under some simplifying assumptions for the task definition, and using it to explore automatic methods that specifically support the detection of experimentally studied pathogen mentions in research publications. METHODS: We developed a pathogen mention characterisation literature data set -READBiomed-Pathogens- automatically using NCBI resources, which we make available. Resources such as the NCBI Taxonomy, MeSH and GenBank can be used effectively to identify relevant literature about experimentally researched pathogens, more specifically using MeSH to link to MEDLINE citations including titles and abstracts with experimentally researched pathogens. We experiment with several machine learning-based natural language processing (NLP) algorithms leveraging this data set as training data, to model the task of detecting papers that specifically describe experimental study of a pathogen. RESULTS: We show that our data set READBiomed-Pathogens can be used to explore natural language processing configurations for experimental pathogen mention characterisation. READBiomed-Pathogens includes citations related to organisms including bacteria, viruses, and a small number of toxins and other disease-causing agents. CONCLUSIONS: We studied the characterisation of experimentally studied pathogens in scientific literature, developing several natural language processing methods supported by an automatically developed data set. As a core contribution of the work, we presented a methodology to automatically construct a data set for pathogen identification using existing biomedical resources. The data set and the annotation code are made publicly available. Performance of the pathogen mention identification and characterisation algorithms were additionally evaluated on a small manually annotated data set shows that the data set that we have generated allows characterising pathogens of interest. TRIAL REGISTRATION: N/A.

Best Papers in Human Factors and Sociotechnical Development.

OBJECTIVES: To select the best papers that made original and high impact contributions in human factors and organizational issues in biomedical informatics in 2021. METHODS: A rigorous extraction process based on queries from Web of Science® and PubMed/Medline was conducted to identify the scientific contributions published in 2021 that address human factors and organizational issues in biomedical informatics. The screening of papers on titles and abstracts independently by the two section editors led to a total of 3,206 papers. These papers were discussed for a selection of 12 finalist papers, which were then reviewed by the two section editors, two chief editors, and by three external reviewers from internationally renowned research teams. RESULTS: The query process resulted in 12 papers that reveal interesting and rigorous methods and important studies in human factors that move the field forward, particularly in clinical informatics and emerging technologies such as brain-computer interfaces and mobile health. This year three papers were clearly outstanding and help advance in the field. They provide examples of examining novel and important topics such as the nature of human-machine interaction behavior and norms, use of social-media based design for an electronic health record, and emerging topics such as brain-computer interfaces. thematic development of electronic health records and usability techniques, and condition-focused patient facing tools. Those concerning the Corona Virus Disease 2019 (COVID-19) were included as part of that section. CONCLUSION: The selected papers make important contributions to human factors and organizational issues, expanding and deepening our knowledge of how to apply theory and applications of new technologies in health.

Association of COVID-19 with diabetes: a systematic review and meta-analysis.

Emerging evidence suggests that coronavirus disease-2019 (COVID-19) may lead to a wide range of post-acute sequelae outcomes, including new onset of diabetes. The aim of this meta-analysis was to estimate the incidence of newly diagnosed diabetes in survivors of COVID-19. We searched MEDLINE, Scopus, Cochrane Central Register of Controlled Trials and the World Health Organization Global Literature on Coronavirus Disease and clinical trial registries for studies reporting the association of COVID-19 and diabetes. Search dates were December 2019-October 16, 2022. Two investigators independently assessed studies for inclusion. Risk of bias was assessed using the Newcastle-Ottawa Scale. We estimated the effect of COVID-19 on incident diabetes by random-effects meta-analyses using the generic inverse variance method. We identified 8 eligible studies consisting of 4,270,747 COVID-19 patients and 43,203,759 controls. Median age was 43 years (interquartile range, IQR 35-49), and 50% were female. COVID-19 was associated with a 66% higher risk of incident diabetes (risk ratio, 1.66; 95% CI 1.38; 2.00). The risk was not modified by age, sex, or study quality. The median risk of bias assessment was 7. In this systematic review and meta-analysis, COVID-19 was associated with higher risk for developing new onset diabetes among survivors. Active monitoring of glucose dysregulation after recovery from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is warranted.

Considerations for conducting systematic reviews: evaluating the performance of different methods for de-duplicating references.

BACKGROUND: Systematic reviews involve searching multiple bibliographic databases to identify eligible studies. As this type of evidence synthesis is increasingly pursued, the use of various electronic platforms can help researchers improve the efficiency and quality of their research. We examined the accuracy and efficiency of commonly used electronic methods for flagging and removing duplicate references during this process. METHODS: A heterogeneous sample of references was obtained by conducting a similar topical search in MEDLINE, Embase, Cochrane Central Register of Controlled Trials, and PsycINFO databases. References were de-duplicated via manual abstraction to create a benchmark set. The default settings were then used in Ovid multifile search, EndNote desktop, Mendeley, Zotero, Covidence, and Rayyan to de-duplicate the sample of references independently. Using the benchmark set as reference, the number of false-negative and false-positive duplicate references for each method was identified, and accuracy, sensitivity, and specificity were determined. RESULTS: We found that the most accurate methods for identifying duplicate references were Ovid, Covidence, and Rayyan. Ovid and Covidence possessed the highest specificity for identifying duplicate references, while Rayyan demonstrated the highest sensitivity. CONCLUSION: This study reveals the strengths and weaknesses of commonly used de-duplication methods and provides strategies for improving their performance to avoid unintentionally removing eligible studies and introducing bias into systematic reviews. Along with availability, ease-of-use, functionality, and capability, these findings are important to consider when researchers are selecting database platforms and supporting software programs for conducting systematic reviews.

Publishing in pandemic times: A bibliometric analysis of early medical publications on Kawasaki-like disease (MIS-C, PIMS-TS) related to SARS-CoV-2.

INTRODUCTION: At the end of April 2020, three European pediatric societies published an alert on a new hyperinflammatory disorder linked to SARS-CoV-2. This disease has alternatively been called Kawasaki-like disease, pediatric inflammatory multisystem syndrome temporally associated with SARS-CoV-2 infection (PIMS-TS), and multisystem inflammatory syndrome in children (MIS-C). These alerts provide a clear starting point from which to study the early response of the medical and scientific community to a new disease in terms of scientific publications, and to compare the timeline of this response with levels of general public interest. To this aim, we conducted a bibliometric analysis of articles on this disease published between 1 April and 5 July 2020. METHOD: A literature search was performed using PubMed and in three preprint repositories. For each article, the name used for the disease in the title, the number of authors, the number of patients, the citations according to Google Scholar, the journal impact factor, and the Altmetric score were retrieved. Google search trends for the terms "Kawasaki" and "COVID," "COVID-19," and "coronavirus" were also retrieved, as was the number of Reuters news articles published on the topic. These data were compared longitudinally on a weekly basis. The quality of the reporting of the study was evaluated using the STROBE guidelines for observational studies with more than three patients and using the CARE guidelines for case reports of three or fewer patients. RESULTS: Eighty-six articles were included, among which ten were preprints (three of which were subsequently published) and 49 were clinical articles (57%). A total of 857 patients were described. The median number of authors per article was five (range, 1-45), the median number of patients was four (1-186), the median number of citations was one (0-170), the median Altmetric score was 12 (0-7242), and the median journal impact factor was 3.7 (1-74.7). For the clinical articles, the median percentage of STROBE or CARE checklist items satisfied was 70% (IQR, 56.75-79.25; range, 40-90). Guideline adherence was significantly higher for observational studies than for case reports (median percentage of checklist items satisfied, 78.5% vs 61.5%; P<0.001); however, guideline adherence did not differ significantly between peer-reviewed and preprint articles (median percentage of checklist items satisfied, 57% vs 72%; P=0.205). The only statistically significant difference between clinical articles and other types of articles was the number of authors (median, 7 vs 2; P=2.53E-9). Fifty-seven of the 86 articles were authored by researchers from just three countries (the USA, 31; France, 14; and the UK, 12). The names most frequently used in the title were Kawasaki-like disease (n=37), followed by MIS-C (n=27), PIM-TS (n=14), and other names involving the term "inflammatory" (n=12). Google searches for related terms peaked between weeks 18 and 21, following the initial alerts and decreased rapidly thereafter. The number of Reuters articles on the subject was correlated with Google search trends (ρ: 0.86, 95% CI [0.59; 0.96]; P=0.00016), but the number of medical articles published was not (ρ: -0.54, 95% CI [-0.87; 0.14]; P=0.11). The first small case series was published less than 2 weeks after the initial alert; however, if all articles had been deposited as preprints when they were submitted to journals, the cumulative number of reported cases would have been 300% higher in week 18 (3 vs 1), 400% higher in week 19 (44 vs 11), 70% higher in week 20 (124 vs 73), and 54% higher in week 21 (129 vs 84). CONCLUSION: In a period of 9 weeks after the initial alerts from European pediatric societies, 85 medical articles were published, involving 856 patients (one case report was published before the alerts), allowing rapid dissemination of research information. However, general public interest followed the news cycle rather than scientific releases. The quality of the reporting, as assessed by adherence to STROBE or CARE guidelines, was adequate with more than two-thirds of checklist items satisfied. Learned societies play an important role in the early dissemination of up-to-date peer-reviewed information. Preprint deposition should be encouraged to accelerate the dissemination of research information.

COVID-19 vaccine research focusses on safety, efficacy, immunoinformatics, and vaccine production and delivery: a bibliometric analysis based on VOSviewer.

Coronavirus Disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), has affected tens of millions of people globally since it was declared a pandemic by the World Health Organization (WHO) on March 11, 2020. There is an urgent need for safe and effective preventive vaccines to curb this pandemic. A growing amount of related research has been published. This study aimed to provide the current status of COVID-19 vaccine using bibliometric analysis. We searched Embase.com and MEDLINE comprehensively and included articles, articles in press, reviews, short surveys, conference abstracts and conference papers about COVID-19 vaccine. VOSviewer1.6.11 (Leiden University, Leiden, Netherlands) was applied to perform the bibliometric analysis of these papers. A total of 1,312 papers were finally included. The BMJ has been the most popular journal in this field. The United States maintained a top position worldwide and has provided a pivotal influence, followed by China, India and United Kingdom. Among all the institutions, Harvard University was regarded as a leader for research collaboration. We analyzed the keywords and identified seven COVID-19 vaccine research hotspot clusters. COVID-19 vaccine research hotspots focus on clinical trials on vaccine safety and efficacy, research on vaccine immunology and immunoinformatics, and vaccine hesitancy. Our analysis results demonstrated that cooperation between countries, institutions, and authors were insufficient. The results suggested that clinical trials on vaccine safety, efficacy, immunology, immunoinformatics, production and delivery are research hotspots. Furthermore, we can predict that there will be a lot of research focusing on vaccine adverse reactions.

Overexpression of angiotensin-converting enzyme 2 by renin-angiotensin system inhibitors. Truth or myth? A systematic review of animal studies.

Angiotensin-converting enzyme 2 (ACE2) protects against organ damage in hypertension and cardiovascular diseases by counter regulating the renin-angiotensin system (RAS). ACE2 is also the receptor for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Based on the claim that RAS inhibitors (RASIs) cause ACE2 overexpression in some animal experiments, concerns have arisen that RASIs may aggravate SARS-CoV-2 infection and coronavirus disease-2019 severity in RASI-treated patients. To achieve a comprehensive review, a systematic search of MEDLINE/PubMed was conducted regarding the effects of RASIs on tissue ACE2 mRNA/protein expression in healthy animals and animal models of human diseases. We identified 88 eligible articles involving 168 experiments in the heart, kidneys, lungs, and other organs. Three of 38 experiments involving healthy animals showed ACE2 expression greater than twice that of the control (overexpression). Among 102 disease models (130 experiments), baseline ACE2 was overexpressed in 16 models (18 experiments) and less than half the control level (repression) in 28 models (40 experiments). In 72 experiments, RASIs did not change ACE2 levels from the baseline levels of disease models. RASIs caused ACE2 overexpression compared to control levels in seven experiments, some of which were unsupported by other experiments under similar conditions. In 36 experiments, RASIs reversed or prevented disease-induced ACE2 repression, yielding no or marginal changes. Therefore, ACE2 overexpression appears to be a rare rather than common consequence of RASI treatment in healthy animals and disease models. Future studies should clarify the pathophysiological significance of RASI-induced reversal or prevention of ACE2 repression in disease models.

NewsMeSH: A new classifier designed to annotate health news with MeSH headings.

MOTIVATION: In the age of big data, the amount of scientific information available online dwarfs the ability of current tools to support researchers in locating and securing access to the necessary materials. Well-structured open data and the smart systems that make the appropriate use of it are invaluable and can help health researchers and professionals to find the appropriate information by, e.g., configuring the monitoring of information or refining a specific query on a disease. METHODS: We present an automated text classifier approach based on the MEDLINE/MeSH thesaurus, trained on the manual annotation of more than 26 million expert-annotated scientific abstracts. The classifier was developed tailor-fit to the public health and health research domain experts, in the light of their specific challenges and needs. We have applied the proposed methodology on three specific health domains: the Coronavirus, Mental Health and Diabetes, considering the pertinence of the first, and the known relations with the other two health topics. RESULTS: A classifier is trained on the MEDLINE dataset that can automatically annotate text, such as scientific articles, news articles or medical reports with relevant concepts from the MeSH thesaurus. CONCLUSIONS: The proposed text classifier shows promising results in the evaluation of health-related news. The application of the developed classifier enables the exploration of news and extraction of health-related insights, based on the MeSH thesaurus, through a similar workflow as in the usage of PubMed, with which most health researchers are familiar.

Coronavirus disease 2019 (COVID-19) outcomes in HIV/AIDS patients: a systematic review.

OBJECTIVES: The aim of the study was to systematically review current studies reporting on clinical outcomes in people living with HIV (PLHIV) infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). METHODS: We conducted a systematic review using the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines. A comprehensive literature search was conducted in Global Health, SCOPUS, Medline and EMBASE using pertinent key words and Medical Subject Headings (MeSH) terms relating to coronavirus disease 2019 (COVID-19) and HIV. A narrative synthesis was undertaken. Articles are summarized in relevant sections. RESULTS: Two hundred and eighty-five articles were identified after duplicates had been removed. After screening, eight studies were analysed, totalling 70 HIV-infected patients (57 without AIDS and 13 with AIDS). Three themes were identified: (1) controlled HIV infection does not appear to result in poorer COVID-19 outcomes, (2) more data are needed to determine COVID-19 outcomes in patients with AIDS and (3) HIV-infected patients presenting with COVID-19 symptoms should be investigated for superinfections. CONCLUSIONS: Our findings suggest that PLHIV with well-controlled disease are not at risk of poorer COVID-19 disease outcomes than the general population. It is not clear whether those with poorly controlled HIV disease and AIDS have poorer outcomes. Superimposed bacterial pneumonia may be a risk factor for more severe COVID-19 but further research is urgently needed to elucidate whether PLHIV are more at risk than the general population.

Diabetes mellitus is associated with increased mortality and severity of disease in COVID-19 pneumonia - A systematic review, meta-analysis, and meta-regression.

BACKGROUND AND AIMS: Diabetes Mellitus (DM) is chronic conditions with devastating multi-systemic complication and may be associated with severe form of Coronavirus Disease 2019 (COVID-19). We conducted a systematic review and meta-analysis in order to investigate the association between DM and poor outcome in patients with COVID-19 pneumonia. METHODS: Systematic literature search was performed from several electronic databases on subjects that assess DM and outcome in COVID-19 pneumonia. The outcome of interest was composite poor outcome, including mortality, severe COVID-19, acute respiratory distress syndrome (ARDS), need for intensive care unit (ICU) care, and disease progression. RESULTS: There were a total of 6452 patients from 30 studies. Meta-analysis showed that DM was associated with composite poor outcome (RR 2.38 [1.88, 3.03], p < 0.001; I2: 62%) and its subgroup which comprised of mortality (RR 2.12 [1.44, 3.11], p < 0.001; I2: 72%), severe COVID-19 (RR 2.45 [1.79, 3.35], p < 0.001; I2: 45%), ARDS (RR 4.64 [1.86, 11.58], p = 0.001; I2: 9%), and disease progression (RR 3.31 [1.08, 10.14], p = 0.04; I2: 0%). Meta-regression showed that the association with composite poor outcome was influenced by age (p = 0.003) and hypertension (p < 0.001). Subgroup analysis showed that the association was weaker in studies with median age ≥55 years-old (RR 1.92) compared to <55 years-old (RR 3.48), and in prevalence of hypertension ≥25% (RR 1.93) compared to <25% (RR 3.06). Subgroup analysis on median age <55 years-old and prevalence of hypertension <25% showed strong association (RR 3.33) CONCLUSION: DM was associated with mortality, severe COVID-19, ARDS, and disease progression in patients with COVID-19.