bluuebunny/crossref_metadata_2025_split huggingface dataset:
A qualitative study exploring the diagnostic and treatment journeys of children and young people with gastroduodenal disorders of gut-brain interaction, their families, and the clinicians who care for them
Authors: Humphrey Gayl , Law Mikaela , Keane Celia , Andrews Christopher N , Gharibans Armen , O’Grady Greg 
Published: October 2024

AbstractBackgroundGastroduodenal disorders of gut-brain interaction (DGBI) are prevalent in the paediatric population. Diagnostic pathways and subsequent treatment management approaches for children and young people can be highly variable, leading to diverse patient and clinical experiences. This study explores the DGBI diagnostic experiences of children and their families and the perspectives of clinicians in the New Zealand context.MethodsSemi-structured interviews were conducted with 12 children with gastroduodenal DGBIs and their families and clinicians who care for children with DGBIs. Interviews were recorded, transcribed, and narratively analysed.ResultsFive children and family themes emerged: 1) how it all started, 2) the impacts symptoms had on child and family life, 3) their experiences with testing and investigations, 4) the perceptions and impacts of challenging clinical relationships, and 5) the uncertainness of trial and error treatments. Clinicians also identified five key themes: 1) navigating the complexity of presenting symptomology, 2) the challenging diagnostic investigation decision-making process, 3) navigating management and treatment approaches, 4) a lack of standardised clinical pathways, and 5) establishing therapeutic relationships with patients and families.ConclusionChildren, their families, and clinicians confirmed the clinical complexity of DGBIs, the challenges of diagnosis and management, and the stress this places on therapeutic relationships. Clearer diagnostic pathways and new investigations that could provide improved identification and discrimination of DGBIs are needed to minimise the treat-test repeat cycle of care and improve health outcomes.
Predicting 30-Day and 1-Year Mortality in Heart Failure with Preserved Ejection Fraction (HFpEF)
Authors: Shin Ikgyu , Bhatt Nilay , Alashi Alaa , Kandala Keervani , Murugiah Karthik 
Published: October 2024

ABSTRACTObjectivesTo develop and compare prediction models for 30-day and 1-year mortality in Heart failure with preserved ejection fraction (HFpEF) using EHR data, utilizing both traditional and machine learning (ML) techniques.BackgroundHFpEF represents 1 in 2 heart failure patients. Predictive models in HFpEF, specifically those derived from electronic health record (EHR) data, are less established.MethodsUsing MIMIC-IV EHR data from 2008-2019, patients aged ≥ 18 years admitted with a primary diagnosis of HFpEF were identified using ICD-9 and 10 codes. Demographics, vital signs, prior diagnoses, and lab data were extracted. Data was partitioned into 80% training, 20% test sets. Prediction models from seven model classes (Support Vector Classifier (SVC), Logistic Regression, Lasso Regression, Elastic Net, Random Forest, Histogram-based Gradient Boosting Classifier (HGBC), and XGBoost) were developed using various imputation and oversampling techniques with 5-fold cross-validation. Model performance was compared using several metrics, and individual feature importance assessed using SHapley Additive exPlanations (SHAP) analysis.ResultsAmong 3910 hospitalizations for HFpEF, 30-day mortality was 6.3%, and 1-year mortality was 29.2%. Logistic regression performed well for 30-day mortality (Area Under the Receiver operating characteristic curve (AUC) 0.83), whereas Random Forest (AUC 0.79) and HGBC (AUC 0.78) for 1-year mortality. Age and NT-proBNP were the strongest predictors in SHAP analyses for both outcomes.ConclusionModels derived from EHR data can predict mortality after HFpEF hospitalization with comparable performance to models derived from registry or trial data, highlighting the potential for clinical implementation.
Protocol for the Feasibility and Acceptability of Mentalization-based Treatment for Early Adolescents With Depression: A Short-Term Psychotherapy Approach for Patients and Families
Authors: Moran-Kneer Javier , Duarte Javiera , Alkabes-Esquenazi Louise , Solervicens Paula , García Francisca , Arancibia Marcelo , Díaz Francisca , Avalos Nicole 
Published: October 2024

AbstractBackgroundApproximately 13.4% of children and adolescents worldwide suffer from depression, with early onset often manifesting before the age of 14 and disproportionately affecting vulnerable groups. The prevalence of adolescent depression has increased since the COVID-19 pandemic, with significant long-term impacts on development, social functioning, and academic performance. Mentalization-based treatment (MBT), particularly the MBT-A model tailored for adolescents, has shown promise in addressing depression by increasing mentalizing capacity. This study explores the feasibility and acceptability of a short-term MBT-A intervention that incorporates both individual and family components to address depression in early adolescents.Methods:This feasibility pilot study will recruit 15 adolescents aged 10--14 years who are diagnosed with mild to moderate depression from primary healthcare centers and psychological care clinics in Valparaíso, Chile. The participants will undergo a 12-session MBT-A intervention, which is structured into three phases: joint understanding of the problem, intensive promotion of mentalization, and evaluation of the process. Outcomes will be assessed through mixed methods, including quantitative measures (e.g., recruitment rate, adherence rate, effect size on depressive symptoms) and qualitative interviews with patients, families, and therapists. Discussion: Fostering mentalization in adolescents with depression may reduce depressive symptoms and enhance emotional regulation, benefiting both individuals and their families. This study aims to provide preliminary data to inform the planning of a larger randomized controlled trial (RCT). By incorporating family components, the intervention not only addresses individual symptoms but also promotes supportive family dynamics, which are crucial for sustaining therapeutic gains. This pilot study will identify practical aspects of implementing MBT-A and gather essential data on its feasibility and potential efficacy in a real-world setting.
Prevalence and predictors of microvascular and macrovascular diabetes complications in adult Ugandans: a systematic review and meta-analysis
Authors: Kibirige Davis , Olum Ronald , Turyamureeba William , Morgan Bethan , Kyazze Andrew Peter , Nsubuga Yakobo , Okot Jerom , Lumu William , Bongomin Felix 
Published: October 2024

ABSTRACTIntroductionThere is a growing prevalence of diabetes and related chronic complications in Uganda. We conducted a systematic review and meta-analysis to document the prevalence and predictors of five microvascular and macrovascular diabetes complications in adult Ugandans with diabetes.Materials and MethodsWe searched Medline, EMBASE, CINAHL, Cochrane Library, and Africa Journal Online databases. We included studies on the prevalence and predictors of any chronic microvascular or macrovascular diabetes complications of interest. We conducted a random effect meta-analysis to determine the pooled prevalence of each diabetes complication. A narrative review was used to describe the significant predictors.ResultsA total of 20 studies involving 11,400 participants were included. The pooled mean (standard deviation) age of the participants was 54.8 (3.6) years, with the majority being female (pooled proportion of 61.1%, 95% confidence interval [CI] 57.1-65.2). For the microvascular diabetes complications, the pooled prevalence of diabetic neuropathy, retinopathy, and nephropathy was 56.8% (95% CI 44.9-68.7, I2= 98.56%, p<0.001), 19.5% (95% CI 3.9-35.2, I2= 99.60%, p<0.001), and 17.7% (95% CI 7.3-28.0, I2= 99.36%, p<0.001), respectively. For the macrovascular diabetes complications, the pooled prevalence of peripheral arterial disease and diabetic foot disease was 32.2% (95% CI 15.8-48.7, I2= 97.67%, p<0.001) and 5.5% (95% CI 1.7-9.2, I2= 90.22%, p<0.001), respectively.Hypertension comorbidity, physical inactivity, family history of diabetes, body mass index ≤30 kg/m2, and pregnancy were predictors of diabetic nephropathy in three studies. In two studies, a history of a foot ulcer and age >60 years were predictors of diabetic neuropathy while female sex, hypertension comorbidity, and use of glibenclamide were predictors of peripheral arterial disease.DiscussionChronic diabetes complications are very common in adult Ugandans with diabetes, especially diabetic neuropathy and peripheral arterial disease. Regular screening and optimal management of diabetes and its complications should be emphasised in Uganda.
Detection of Acute Myocardial Infarction Using Deep Learning on Lead-I ECG Data
Authors: Davarmanesh Parmida , Lin Qian , Tenison Irene , Jabbour Gabriel , Alam Ridwan 
Published: October 2024

AbstractMyocardial Infarction (MI) is a major global health concern due to its high mortality and morbidity rates. Early detection of MI is crucial for timely medical intervention and improved patient outcomes. In this study, we investigate the feasibility of predicting MI using lead-I of electrocardiogram (ECG) data, with a focus on practical applications for wearable monitoring systems. Utilizing the PTB-XL dataset, which includes a comprehensive collection of 12-lead ECG recordings with both normal and various MI samples, we employ deep learning techniques to develop a binary classification model. For MI detection using lead-I, we achieved an AUC of 0.92 and an AUPR of 0.82 on the test set. In comparison, using 6-lead and 12-lead configurations both resulted in an AUC of 0.99. These findings demonstrate the potential for detecting MI using only lead-I, as measured by wearable devices. This advancement could significantly enhance clinical outcomes for MI patients by enabling timely detection and intervention.
Sickle cell, thalassemia, and heat: risk estimates and equity policy considerations from an exploratory timeseries study in California
Authors: Pearson Dharshani , Winger Beth Apsel , Ebisu Keita 
Published: October 2024

ABSTRACTBackgroundPeople born with hemoglobinopathies, such as sickle cell disease (SCD) and thalassemia, as well as people who are carriers for these disorders [sickle cell trait (SCT), thalassemia trait or thalassemia minor (TM)], may experience increased symptom-triggers and related illnesses from heat because their cells are susceptible to dehydration. Additionally, historical, and structural injustices could also exacerbate risk vulnerabilities among some communities.MethodsIn this work, we (1) present an overview of biological plausibility linking heat and hemoglobinopathy-related hospitalizations; (2) analyze the relationship between daily apparent temperature and such hospitalizations in California using Patient Discharge Data (PDD) and a two-stage timeseries study design with Poisson regression to estimate a state-wide hospitalization risk estimate; (3) discuss how structural barriers working in conjunction with demographic and genetic factors have conferred added risk to some Californians, thereby presenting deep-seeded equity ramifications.ResultsOur final dataset, restricted to the warm season, consisted of 96,908 daily counts of any hemoglobinopathy, listed as primary or secondary hospital diagnoses in PDD data. For an overall result, we observed an elevated risk of 3.0% (95% CI: 0.6, 5.5) per 10-degree Fahrenheit (°F) increase in apparent temperature at the last 30-day cumulative exposure window. For secondary hospital diagnoses of SCT-associated outcomes, when limited to very populous areas, we observed an elevated association of 7.0% at lag 23 (95% CI: 2.2, 12.1).ConclusionsWe observed excess symptom associations at various heat exposure levels, highlighting the need to examine longer exposure periods and potential care delays (stigma, medical racism, healthcare access). Differences and uncertainties could also stem from other comorbidities, age, genetics, housing and socioeconomic characteristics, or lifestyle variations. Education on the possible links between climate and hemoglobinopathy symptoms, changing demographics, and long overdue research funding may help close the gap in this interconnecting, critical environmental justice issue.HighlightsPeople with hemoglobinopathies, or inherited red blood cell abnormalities, can experience health effects from high outdoor temperatures.Using over 20 years of hospitalization records, we examined a potential relationship among those who have an inherited hemoglobinopathy or those who are carriers.We found a connection between heat and hemoglobinopathy-related conditions over varying exposure periods lasting up to a month.Structural barriers to care and medical racism may exacerbate health outcomes during heat episodes for some of those who have inherited hemoglobinopathies, highlighting the need for novel strategies to combat disparities in healthcare access and quality of care.
Increased Insular Functional Connectivity During Repetitive Negative Thinking in Major Depression and Healthy Volunteers
Authors: Edwards Landon S , Ganesan Saampras , Tay Jolene , Elliott Eli S , Misaki Masaya , White Evan J , Paulus Martin P , Guinjoan Salvador M , Tsuchiyagaito Aki 
Published: October 2024

AbstractBackgroundRepetitive negative thinking (RNT) in major depressive disorder (MDD) involves persistent focus on negative self-related experiences. Resting-state fMRI shows that the functional connectivity (FC) between the insula and the superior temporal sulcus is critical to RNT intensity. This study examines how insular FC patterns differ between resting-state and RNT-induction in MDD and healthy participants (HC).MethodsForty-one individuals with MDD and twenty-eight HCs (total n=69) underwent resting-state and RNT-induction fMRI scans. Seed-to-whole brain analysis using insular subregions as seeds was performed.ResultsNo diagnosis-by-run interaction effects were observed across insular subregions. MDD participants showed greater FC between bilateral anterior, middle, and posterior insular regions and the cerebellum (z = 4.31 to 6.15). During RNT-induction, both MDD and HC participants demonstrated increased FC between bilateral anterior and middle insula and key brain regions, including prefrontal cortices, parietal lobes, posterior cingulate cortex, and medial temporal gyrus, encompassing the STS (z = 4.47 to 8.31). Higher trait-RNT was associated with increased FC between the right dorsal anterior and middle insula and regions in the DMN and salience network in MDD participants (z = 4.31 to 6.15). Greater state-RNT scores were linked to increased FC in similar insular regions, the bilateral angular gyrus and right middle temporal gyrus (z = 4.47 to 8.31).ConclusionsHyperconnectivity in insula subregions during active rumination, especially involving the DMN and salience network, supports theories of heightened self-focused and negative emotional processing in depression. These findings emphasize the neural basis of RNT when actively elicited in MDD.
Impact of Participation Bias on Disease Prevalence Estimation in theAll of UsResearch Program: A Case Study of Ischemic Heart Disease and Stroke
Authors: Lee Younga Heather , Patil Ankita , Clark Cheryl R. , Botero Monik C. , Stein David W. , Karlson Elizabeth W. 
Published: October 2024

AbstractImportanceDisease prevalence estimation is highly sensitive to sample characteristics shaped by recruitment and data collection strategies. Using follow-up study modules that require active participant engagement may introduce participation bias, affecting the accuracy of disease prevalence estimation.ObjectiveTo estimate the prevalence of ischemic heart disease (IHD) and stroke using electronic health records (EHR) and the self-reported Personal Medical History (PMH) survey collected in theAll of UsResearch Program.Design and settingsCross-sectional study aimed at estimating the prevalence of IHD and stroke among 266,472 participants with EHR in the latest release of theAll of UsRegistered Tier Curated Data Repository (R2022Q4R9).Main outcomes and measuresPrimary outcomes were IHD and stroke, ascertained using expert-curated diagnostic and procedure codes recorded in EHR. Secondary outcomes were IHD and stroke, ascertained using responses from the PMH survey. To mitigate the impact of participation bias in the PMH survey responses, we applied poststratification weighting based on annual household income and education.ResultsOf the 266,472 participants with EHR, 17,054 (6.4%) were identified as having IHD and 7,461 (2.8%) as having stroke based on the EHR definitions. Among PMH survey respondents, the EHR-based prevalence was lower at 5.6% (95% CI, 5.4-5.7) for IHD and 2.2% (95% CI, 2.1-2.3) for stroke, compared to 7.2% (95% CI, 7.0-7.3) for IHD and 3.3% (95% CI, 3.2-3.4) for stroke among non-respondents. The PMH survey-based prevalence among respondents was 5.9% (95% CI, 5.7-6.0) for IHD and 3.6% (95% CI, 3.5-3.7) for stroke, with higher estimates among non-Hispanic White participants after applying poststratification weights.Conclusion and relevance:Our findings suggest that while the currentAll of Uscohort with EHR reflects the general US population for IHD and stroke prevalence, participants completing the PMH survey are skewed toward higher socioeconomic status and medical literacy. Future research should refine bias mitigation strategies when using voluntary follow-up data to estimate disease prevalence in this cohort.Key PointsQuestionWhat is the prevalence of ischemic heart disease (IHD) and stroke in theAll of UsResearch Program cohort with electronic health records (EHR), and in the subset of these participants who also completed the Personal Medical History (PMH) survey?FindingsThe EHR-based prevalence estimates of IHD and stroke were 6.4% and 2.8%, respectively. They were significantly lower among PMH survey respondents but higher among non-respondents.MeaningParticipants who complete follow-up study modules, such as the PMH survey, may disproportionately represent those with higher socioeconomic status and better health, potentially leading to an underestimation of IHD and stroke prevalence.
Interethnic Validation of an ECG Image Analysis Software for Detecting Left Ventricular Dysfunction in Emergency Department Population
Authors: Lee Haemin , Kwon Woon Yong , Song Kyoung Jun , Jo You Hwan , Kim Joonghee , Cho Youngjin , Hwang Ji Eun , Choi Yeongho 
Published: October 2024

AbstractBackgroundWe previously developed and validated an AI-based ECG analysis tool (ECG Buddy) in a Korean population. This study aims to validate its performance in a U.S. population, specifically assessing its LV Dysfunction Score and LVEF-ECG feature for predicting LVEF <40%, using NT-ProBNP as a comparator.MethodsWe identified emergency department (ED) visits from the MIMIC-IV dataset with information on LVEF <40% or ≥40%, along with matched 12-lead ECG data recorded within 48 hours of the ED visit. The performance of ECG Buddy’s LV Dysfunction Score and LVEF-ECG feature was compared with NT-ProBNP using Receiver Operating Characteristic - Area Under the Curve (ROC-AUC) analysis.ResultsA total of 22,599 ED visits were analyzed. The LV Dysfunction Score had an AUC of 0.905 (95% CI: 0.899 - 0.910), with a sensitivity of 85.4% and specificity of 80.8%. The LVEF-ECG feature had an AUC of 0.908 (95% CI: 0.902 - 0.913), sensitivity 83.5%, and specificity 83.0%. NT-ProBNP had an AUC of 0.740 (95% CI: 0.727 - 0.752), with a sensitivity of 74.8% and specificity of 62.0%. The ECG-based predictors demonstrated superior diagnostic performance compared to NT-ProBNP (all p<0.001).In the Sinus Rhythm subgroup, the LV Dysfunction Score achieved an AUC of 0.913, and LVEF-ECG had an AUC of 0.917, both outperforming NT-ProBNP (0.748, 95% CI: 0.732 - 0.763, all p<0.001).ConclusionECG Buddy demonstrated superior accuracy compared to NT-ProBNP in predicting LV systolic dysfunction, validating its utility in a U.S. ED population.
Integration of Mendelian Randomization and Genome-wide Association Analysis Reveals Potential Benefits of Cheese Intake on Human Intelligence
Authors: Chen Peng , Cao Zhikang , Feng Jing , Li Zhipeng , Nie Shaoping 
Published: October 2024

AbstractThe brain relies on a range of nutrients for optimal functioning. However, the causal nature between dietary components and human intelligence are still unclear. This study integrates Mendelian randomization (MR) and genome-wide associations analyses to investigate the relationships. The results show that cheese exerts the most remarkable genetic correlation and causal association with cognition-related traits. We conduct a large-scale meta-analysis of genome-wide associations studies (GWAS) consisting of over 800,000 participants and discover 71 genetic determinants for cheese consumption. Additional MR using the GWAS results recovers the causal effects of cheese on all the cognition-related traits, and also reveals its effects on education, psychological states and gene expressions in multiple brain tissues. Additionally, knockout mouse models and pathway enrichment analysis indicate that the loci mapped genes are involved in cognitive functions and brain characteristics. In conclusion, our comprehensive analysis supports the potential benefits of cheese consumption on human intelligence.
Association of echocardiographic cardiac valve calcification with major adverse cardiovascular events in patients on acute myocardial infarction
Authors: Chen Chuyun , Wang Na , Yu Yang , Jia Jia , Fan Fangfang , Yang Ying , Zhang Yan 
Published: October 2024

BACKGROUNDThe role of cardiac valve calcification (CVC) in secondary prevention post-acute myocardial infarction (AMI) remains unclear. This study investigated the association between CVC and major adverse cardiovascular events (MACE) in AMI.METHODS AND RESULTSAMI patients hospitalized in Peking University First Hospital were consecutively enrolled. Participants were grouped according to the measurement of transthoracic echocardiography on admission. According to aortic valve calcification (AVC) and mitral annular calcification (MAC), patients were divided into Group 1 (with no valve calcification), Group 2 (with single valve calcification) and Group 3 (with both valve calcification). The primary endpoint was MACE (defined as a composite of nonfatal stroke, nonfatal myocardial infarction and cardiac death). A cohort of 911 AMI patients was recruited in this retrospective study. There were 513(56.31%), 337(36.99%) and 61(6.70%) in Group 1, Group 2 and Group 3, respectively. During follow-up (median, 5.11 years), 277(30.41%) patients developed MACE, 94(10.32%) had nonfatal stroke, 155(17.01%) had recurrent myocardial infarction (MI) and 106(11.64%) were identified as cardiac death. Comparing with Group 1, the adjusted hazard ratios (HR) for primary endpoint and recurrent MI in Group 3 was 1.61 (1.06, 2.43) and 1.70 (1.00, 2.89), respectively. And the adjusted HR of primary endpoint and cardiac death in moderate and severe AVC group was 1.50 (1.01, 2.23) and 1.97 (1.10, 3.51) compared with non-AVC group.CONCLUSIONSAMI patients with CVC were at significantly increased risk of MACE, although existing differences of the position and the severity of calcification. Screening for AVC and MAC is necessary in secondary prevention.RESEARCH PERSPECTIVEWhat Is New?This study is the first to detail the association between cardiac valve calcification (CVC), specifically aortic valve calcification (AVC) and mitral annular calcification (MAC), with major adverse cardiovascular events (MACE) in patients after an acute myocardial infarction (AMI).It also identifies the concurrent presence of MAC and AVC as an independent predictor of MACE.What Are the Clinical Implications?The stud y suggests that screening for AVC and MAC post-AMI could help identify patients at higher risk of MACE, leading to more targeted preventive care and potentially improving outcomes.