bluuebunny/crossref_metadata_2025_split huggingface dataset:
Bibliometric analysis of evolutionary trends and hotspots of super-enhancers in cancer
Authors: Tang Zhen-Chu , Qu Qiang , Teng Xin-Qi , Zhuang Hai-Hui , Xu Wei-Xin , Qu Jian 
Published: July 2023

Introduction: In the past decade, super-enhancer (SE) has become a research hotspot with increasing attention on cancer occurrence, development, and prognosis. To illustrate the hotspots of SE in cancer research and its evolutionary tendency, bibliometric analysis was carried out for this topic.Methods: Literature published before Dec 31, 2022, in WOSCC, was systematically classified, and Citespace, bibliometric.com/app, and GraphPad Prism analyzed the data.Results: After screening out inappropriate documents and duplicate data, 911 publications were selected for further bibliometric analysis. The top five research areas were Oncology (257, 28.211%), Cell Biology (210, 23.052%), Biochemistry Molecular Biology (209, 22.942%), Science Technology Other Topics (138, 15.148%), and Genetics Heredity (132, 14.490%). The United States of America (United States) has the highest number of documents (462, 50.71%), followed by China (303, 33.26%). Among the most productive institutions, four of which are from the United States and one from Singapore, the National University of Singapore. Harvard Medical School (7.68%) has the highest percentage of articles. Young, Richard A, with 32 publications, ranks first in the number of articles. The top three authors came from Whitehead Institute for Biomedical Research as a research team. More than two-thirds of the research are supported by the National Institutes of Health of the United States (337, 37.654%) and the United States Department of Health Human Services (337, 37.654%). And “super enhancer” (525), “cell identity” (258), “expression” (223), “cancer” (205), and “transcription factor” (193) account for the top 5 occurrence keywords.Discussion: Since 2013, SE and cancer related publications have shown a rapid growth trend. The United States continues to play a leading role in this field, as the top literature numbers, affiliations, funding agencies, and authors were all from the United States, followed by China and European countries. A high degree of active cooperation is evident among a multitude of countries. The role of SEs in cell identity, gene transcription, expression, and inhibition, as well as the relationship between SEs and TFs, and the selective inhibition of SEs, have received much attention, suggesting that they are hot issues for research.
The mediating role of diabetes stigma and self-efficacy in relieving diabetes distress among patients with type 2 diabetes mellitus: a multicenter cross-sectional study
Authors: Xing Shuping , Liu Yeling , Zhang Hua , Li Bin , Jiang Xinjun 
Published: July 2023

BackgroundPatients with diabetes mellitus often suffer from diabetes distress. Social support and certain psychological factors potentially influence diabetes distress, but studies exploring the mechanisms underlying these relationships are scarce.ObjectivesTo reveal the associations between social support, diabetes stigma, diabetes self-efficacy, and diabetes distress among patients with type 2 diabetes and the underlying mechanisms linking these variables.Design and methodsA multicenter cross-sectional study was adopted and a sample of 431 patients with type 2 diabetes was investigated. Social support, diabetes stigma, diabetes self-efficacy, and diabetes distress were surveyed with the Perceived Social Support Scale, Type 2 Diabetes Stigma Assessment Scale, Self-Efficacy for Diabetes Scale, and Diabetes Distress Scale, respectively. The hypothesized model was verified using structural equation modeling.ResultsSocial support and diabetes stigma had direct associations with diabetes distress. Diabetes stigma mediated the association between social support and diabetes distress, and the association between diabetes self-efficacy and diabetes distress. Diabetes stigma and self-efficacy exerted a chain mediation effect on the association between social support and diabetes distress.ConclusionSocial support and diabetes stigma were significant predictors of diabetes distress. Diabetes stigma and self-efficacy play essential mediating roles in relieving diabetes distress. This can provide guidance for the development of evidence- and theory-based interventions. Culturally sensitive interventions that aim to provide ongoing social support, decrease diabetes stigma, and enhance self-efficacy have the potential to relieve diabetes distress.
Hope, optimism, and pessimism as predictors of positive and negative psychological changes related to the COVID-19 pandemic in Slovak adults
Authors: Jurišová Erika , Pivková Lucia , Ráczová Lucia , Sollár Tomáš , Romanová Martina 
Published: July 2023

Background and objectivesPositive and negative changes in outlook represent psychological changes that are the results of the cognitive processing of stressful and traumatic events by an individual. The objectives of the study were (1) to determine the level of occurrence and types of positive and negative changes in connection with the COVID-19 pandemic among adults in Slovakia and (2) to study the role of personality factors such as hope (dispositional and perceived) and life orientation (optimism and pessimism) in the prediction of positive and negative changes in adults during the fourth pandemic wave.MethodsA Short Form of the Changes in Outlook Questionnaire (CiOQ-S), the Dispositional Hope Scale (DHS), the Perceived Hope Scale (PHS), and the Life Orientation Test (LOT-R) were administered. The research sample consisted of 102 participants, whose ages ranged from 20 to 65 years (Mage = 38.90, SD = 14.28). The research design was quantitative, exploratory, and confirmatory.ResultsIn total, 95% of participants reported positive changes related to COVID-19. Concurrently, up to 70% of these participants also reported negative changes from the impact of the pandemic. Only 25% of participants reported positive changes without noticing any negative perception of the consequences of the pandemic. Overall, 68% of participants reported negative changes related to COVID-19. Only 29% of participants reported negative changes without noticing any positive perception of the consequences of the pandemic. In total, up to 86% of participants agreed with experienced psychological changes (positive or negative) as a result of the COVID-19 pandemic. The high prevalence of positive changes along with the relatively high prevalence of negative changes related to the COVID-19 pandemic outline the question of whether reported positive changes represent real or illusory growth. Optimism and pessimism were found to be significant independent predictors of positive changes related to the COVID-19 pandemic. Hope was identified as a significant independent predictor of negative changes related to the COVID-19 pandemic.
A theory-based and data-driven approach to promoting physical activity through message-based interventions
Authors: Catellani Patrizia , Biella Marco , Carfora Valentina , Nardone Antonio , Brischigiaro Luca , Manera Marina Rita , Piastra Marco 
Published: July 2023

ObjectiveWe investigated how physical activity can be effectively promoted with a message-based intervention, by combining the explanatory power of theory-based structural equation modeling with the predictive power of data-driven artificial intelligence.MethodsA sample of 564 participants took part in a two-week message intervention via a mobile app. We measured participants’ regulatory focus, attitude, perceived behavioral control, social norm, and intention to engage in physical activity. We then randomly assigned participants to four message conditions (gain, non-loss, non-gain, loss). After the intervention ended, we measured emotions triggered by the messages, involvement, deep processing, and any change in intention to engage in physical activity.ResultsData analysis confirmed the soundness of our theory-based structural equation model (SEM) and how the emotions triggered by the messages mediated the influence of regulatory focus on involvement, deep processing of the messages, and intention. We then developed a Dynamic Bayesian Network (DBN) that incorporated the SEM model and the message frame intervention as a structural backbone to obtain the best combination of in-sample explanatory power and out-of-sample predictive power. Using a Deep Reinforcement Learning (DRL) approach, we then developed an automated, fast-profiling strategy to quickly select the best message strategy, based on the characteristics of each potential respondent. Finally, the fast-profiling method was integrated into an AI-based chatbot.ConclusionCombining the explanatory power of theory-driven structural equation modeling with the predictive power of data-driven artificial intelligence is a promising strategy to effectively promote physical activity with message-based interventions.
Effects of exercise/physical activity on fear of movement in people with spine-related pain: a systematic review
Authors: Jadhakhan Ferozkhan , Sobeih Raghip , Falla Deborah 
Published: July 2023

BackgroundKinesiophobia (i.e., fear of movement) can be an important contributor for ongoing pain and disability in people with spine-related pain. It remains unclear whether physical activity interventions/exercise influence kinesiophobia in this population. A systematic review was therefore conducted to synthesize the available evidence on whether physical activity interventions/exercise influence kinesiophobia in people with chronic non-specific spine-related pain.MethodsThe study protocol was registered prospectively with PROSPERO (CRD42021295755). The following databases were systematically searched from inception to 31 January 2022 and updated on 22 June 2023: PubMed, MEDLINE, Embase, CINAHL, Web of Science, PsycINFO, ZETOC, PROSPERO and Google Scholar. Inclusion criteria were randomized or non-randomized controlled studies investigating adults aged ≥18 years, reporting the effect of exercise or physical activity on kinesiophobia in individuals with chronic non-specific spine-related pain. Two reviewers independently extracted data and assessed the quality of the included studies. Bias was assessed using the Cochrane ROB2 tool and evidence certainty via Grading of Recommendations Assessment, Development and Evaluation (GRADE).ResultsSeventeen studies from seven countries involving a total of 1,354 individuals were selected for inclusion. The majority of studies (n = 13) involved participants with chronic low back pain (LBP), and Pilates was the most common form of exercise evaluated. Most of the studies reported a positive direction of effect in favor of exercise reducing kinesiophobia when compared to a control group. There was moderate to high risk of bias among the studies and the overall certainty of the evidence was very low.ConclusionThis review supports the use of exercise for reducing kinesiophobia in people with chronic LBP albeit with very low certainty of evidence; Pilates (especially equipment-based) was shown to be effective as were strengthening training programmes. There was limited evidence available on the effects of exercise on kinesiophobia for people with chronic neck or thoracic pain and further research is required.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?RecordID=295755
Technologically assisted intensive home treatment: feasibility study
Authors: Caspi Asaf , Tzur Bitan Dana , Halaly Ofir , Hallaly Ofri , Friedlander Avraham , Barkai Galia , Zimlichman Eyal , Stein Orit , Shani Mordechai , Amitai Ziv , Ansbacher Tsofiya , Weiser Mark 
Published: July 2023

IntroductionIn recent year, many attempts have been made to provide patients with alternatives to psychiatric hospitalization during acute distress. Although several hospitalization alternatives have been offered, most of them still require patients to be distanced from their families, friends, and the social environment.MethodsIn this report we describe the implementation of a novel approach to psychiatric care termed “Technologically assisted Intensive Home Treatment”, where patients arriving to emergency settings are directed to home care with technological aids that enable close monitoring and ongoing contact with their therapists.ResultsWe describe the rationale and treatment principles of the treatment, and provide an elaborative description of the implementation process during the first year of implementation.DiscussionAdditional attention is given to factors associated with early dropout from the program, in order to inform readers of predictors to optimal care. Limitations and directions for future research and practice are discussed.Clinical Trial Registration: The study was registered in the database of clinical trials (registration number SHEBA-19-6555-MW-CTIL) and in the Ministry of Health (registration number MOH_2022-08-22_011992).
Factors associated with COVID-19 vaccine uptake in a US/Mexico border community: demographics, previous influenza vaccination, and trusted sources of health information
Authors: Lomeli Angel , Escoto Arleth A. , Reyes Breanna , Burola Maria Linda M. , Tinoco-Calvillo Stephenie , Villegas Isabel , Cohen Ariel S. , Laurent Louise C. , Salgin Linda , Stadnick Nicole A. , Rabin Borsika , Seifert Marva 
Published: July 2023

BackgroundCOVID-19 vaccine uptake has been uneven, particularly across racial/ethnic and age groups. This study seeks to understand factors associated with COVID-19 vaccine uptake in a large cross-sectional sample of predominantly Latinos/Latinas individuals living near the US/Mexico border.MethodsData are extracted from a 176-item survey conducted as part of a parent study focused on the co-creation of a COVID-19 testing program for underserved communities developed through a partnership between an academic institution and a Federally Qualified Health Center. The following participant variables were examined: health history, COVID-19 symptoms, COVID-19 testing and vaccine experiences, and perceptions of sources of health information. Participant characteristics were compared using chi-square tests. Multivariate logistic regressions were used for the final statistical model.ResultsFrom 1 May 2021 to 30 April 2022, 4,964 adults, 66% of whom were identified as women, completed the survey. Approximately 80% of participants reported having received at least one COVID-19 vaccine. Female sex, older age, Hispanic/Latino(a) ethnicity, previous influenza vaccination, advanced education, and perceived elevated risk of COVID-19 were significantly (p &lt; 0.05) associated with having received a COVID-19 vaccine. Regarding sources of health information, individuals who indicated they trust their doctor, healthcare provider, or the US government “a great deal” were more likely to have received a COVID-19 vaccine compared to individuals who indicated that they trusted these sources “not at all.” In contrast, those who reported having “a great deal” of trust in their faith leader or their social media contacts were significantly less likely to have received a COVID-19 vaccine than those who reported that they trusted these sources “not at all.”ConclusionSex, education, past influenza vaccination, perceived risk of COVID-19 infection, and trust in specific sources of information were correlated with the uptake of COVID-19 vaccination. Additional research is needed to better understand why this confluence of factors, particularly the unique findings about trusted sources of information, are associated with vaccine uptake. Understanding these associations, specifically within underserved, Latino/Hispanic communities, is an important first step to inform efforts aimed at increasing and sustaining COVID-19 vaccine uptake and adoption of other public health interventions.
The demographic features of fatigue in the general population worldwide: a systematic review and meta-analysis
Authors: Yoon Ji-Hae , Park Na-Hyun , Kang Ye-Eun , Ahn Yo-Chan , Lee Eun-Jung , Son Chang-Gue 
Published: July 2023

BackgroundFatigue is one of the most common subjective symptoms that impairs daily life and predict health-related events. This study aimed to estimate the prevalence of fatigue in the global population.MethodsPubMed and the Cochrane Library were used to search for relevant articles from inception to December 31, 2021. Studies with prevalence data of fatigue in the general population were selected and reviewed by three authors independently and cross-checked. Regarding subgroups, adults (≥18 years), minors (&lt;18 years), and specific occupation population (participants in each study being limited to a specific occupational group), and fatigue types and severity, meta-analysis was conducted to produce point estimates and 95% confidence intervals (95% CI).ResultsFrom the initial 3,432 studies, 91 studies accounting for 115 prevalence data points (623,624 participants) were finally selected. The prevalence of general fatigue (fatigue lasting &lt; 6 months, or fatigue of unspecified duration) was 20.4% (95% CI, 16.7–25.0) in adults, 11.7% (95% CI, 5.2–26.6) in minors, and 42.3% (95% CI, 33.0–54.2) in specific occupations. Chronic fatigue (fatigue lasting more than 6 months) affected 10.1% (95% CI, 8.2–12.5) of adults, 1.5% (95% CI, 0.5–4.7) of minors, and 5.5% (95% CI, 1.4–21.6) of subjects in specific occupations. There was an overall female-predominant prevalence for all subgroup analyses, with a total odds ratio of 1.4 (95% CI, 1.3–1.6). Regarding the severity and presence of medical causes, the total prevalence of moderate fatigue [14.6% (95% CI, 9.8–21.8)] was 2.4-fold that of severe fatigue [6.1% (95% CI, 3.4–11.0)], while unexplained fatigue (fatigue experienced by individuals without any underlying medical condition that can explain the fatigue) was ~2.7-fold that of explained fatigue (fatigue experienced by individuals with a medical condition that can explain the fatigue); as proportion of 40.0% of physical, 8.6% of mental, and 28.4% of mixed cause.ConclusionsThis study has produced the first comprehensive picture of global fatigue prevalence in the general population, which will provide vital reference data contributing to fatigue-related research, including the prevention of diseases.Systematic review registrationIdentifier: CRD42021270498.
Mental health and cognitive function among medical students after the COVID-19 pandemic in China
Authors: Cheng Junzhe , Liao Mei , He Ziping , Xiong Rui , Ju Yumeng , Liu Jin , Liu Bangshan , Wu Bei , Zhang Yan 
Published: July 2023

BackgroundChinese people experienced a nationwide coronavirus disease 2019 (COVID-19) pandemic after the adjustment of epidemic response policies from December 2022 to January 2023. This study aims to explore the prevalence of mental and cognitive symptoms and their associated factors among medical students after the COVID-19 pandemic.MethodsA cross-sectional study was conducted between February 27th and March 8th, 2023. The symptoms of anxiety, depression, insomnia, post-traumatic stress disorder (PTSD), and cognitive function among medical students were examined using the Generalized Anxiety Disorder-7 (GAD-7), the Patient Health Questionnaire-9 (PHQ-9), the Athens Insomnia Scale (AIS), the Impact of Event Scale-6 (IES-6), and the Perceived Deficits Questionnaire-Depression-5 (PDQ-D-5). Data on demographic information was also collected. Statistical analyses were conducted to describe the prevalence and explore the associated factors of mental and cognitive symptoms.ResultsAmong 947 participants, the proportion of students experiencing anxiety, depression, insomnia, and PTSD symptoms was 37.8, 39.3, 28.3, and 29.5%, respectively. The self-reported COVID-19 infection rate was 72.2%. Higher grades, childhood, and current rural residence were identified as potential risk factors for mental and cognitive symptoms. Gender, age, average monthly household income, and COVID-19 diagnosis were not associated with mental and cognitive symptoms among medical students.ConclusionOur findings revealed a high prevalence of mental and cognitive symptoms among Chinese medical students after the COVID-19 pandemic. Special attention should be paid to the mental health of higher-grade students and those residing in rural areas.
Machine learning-based prediction of hospital prolonged length of stay admission at emergency department: a Gradient Boosting algorithm analysis
Authors: Zeleke Addisu Jember , Palumbo Pierpaolo , Tubertini Paolo , Miglio Rossella , Chiari Lorenzo 
Published: July 2023

ObjectiveThis study aims to develop and compare different models to predict the Length of Stay (LoS) and the Prolonged Length of Stay (PLoS) of inpatients admitted through the emergency department (ED) in general patient settings. This aim is not only to promote any specific model but rather to suggest a decision-supporting tool (i.e., a prediction framework).MethodsWe analyzed a dataset of patients admitted through the ED to the “Sant”Orsola Malpighi University Hospital of Bologna, Italy, between January 1 and October 26, 2022. PLoS was defined as any hospitalization with LoS longer than 6 days. We deployed six classification algorithms for predicting PLoS: Random Forest (RF), Support Vector Machines (SVM), Gradient Boosting (GB), AdaBoost, K-Nearest Neighbors (KNN), and logistic regression (LoR). We evaluated the performance of these models with the Brier score, the area under the ROC curve (AUC), accuracy, sensitivity (recall), specificity, precision, and F1-score. We further developed eight regression models for LoS prediction: Linear Regression (LR), including the penalized linear models Least Absolute Shrinkage and Selection Operator (LASSO), Ridge and Elastic-net regression, Support vector regression, RF regression, KNN, and eXtreme Gradient Boosting (XGBoost) regression. The model performances were measured by their mean square error, mean absolute error, and mean relative error. The dataset was randomly split into a training set (70%) and a validation set (30%).ResultsA total of 12,858 eligible patients were included in our study, of whom 60.88% had a PloS. The GB classifier best predicted PloS (accuracy 75%, AUC 75.4%, Brier score 0.181), followed by LoR classifier (accuracy 75%, AUC 75.2%, Brier score 0.182). These models also showed to be adequately calibrated. Ridge and XGBoost regressions best predicted LoS, with the smallest total prediction error. The overall prediction error is between 6 and 7 days, meaning there is a 6–7 day mean difference between actual and predicted LoS.ConclusionOur results demonstrate the potential of machine learning-based methods to predict LoS and provide valuable insights into the risks behind prolonged hospitalizations. In addition to physicians' clinical expertise, the results of these models can be utilized as input to make informed decisions, such as predicting hospitalizations and enhancing the overall performance of a public healthcare system.