bluuebunny/crossref_metadata_2025_split huggingface dataset:
Machine Learning-Based Identification of High-Risk Patterns in Atrial Fibrillation Ablation Outcomes
Authors: Oloko-Oba Mustapha , Liu Yijun , Wood Kathryn , Lloyd Michael S. , Ho Joyce C. , Hertzberg Vicki Stover 
Published: December 2024

AbstractBackgroundAtrial fibrillation (AF) is one of the most common types of cardiac arrhythmias, often leading to serious health issues such as stroke, heart failure, and higher mortality rates. Its global impact is rising due to aging populations and growing comorbidities, creating an urgent need for more effective treatment methods. AF ablation, a key treatment option, has success rates that vary widely among patients. Conventional predictors of ablation outcomes, which primarily rely on sociodemographic and clinical factors, fall short of capturing the heterogeneity within patient populations, highlighting the potential for data-driven methods to provide deeper insights into procedural success.ObjectivesTo uncover meaningful patient subgroups based on AF ablation outcomes and identify diagnostic codes associated with failure.MethodsMachine learning clustering with must-link and cannot-link constraints was applied to electronic health records to discover meaningful clusters, revealing patient-specific factors influencing procedural success or failure. Statistical analyses, including chi-square tests, were used to identify diagnostic codes significantly associated with ablation failure.ResultsOut of the 145 diagnostic codes examined, thirteen significant codes were identified and categorized into four primary risk groups, ranked by their impact on procedural outcomes: (1) direct contributors affecting cardiovascular health, (2) indirect factors that contribute to systemic stress, (3) complications related to anticoagulation and hemorrhagic risks that can impact bleeding management, and (4) broader health indicators reflecting a general health burden that reduce patients resilience to procedural stress.ConclusionsThis study shows the importance of cardiovascular and non-cardiovascular factors in AF ablation outcomes, emphasizing the need for a more comprehensive pre-procedural evaluation. It also contributes to the application of machine learning in personalized risk assessment for AF and advancing individualized care strategies that may improve ablation success.
Quantifying and Visualizing Emergency Physician Workflow: Results of an Observational Time-Motion Study
Authors: Henreid Andrew J. , Ioannides Kimon L.H. , Pevnick Joshua M. , Cohen Tara N. , Torbati Sam S. , Nuckols Teryl K. , Berdahl Carl T. 
Published: December 2024

ABSTRACTBackgroundEmergency physicians face considerable workflow challenges due to unpredictable work environments, frequent interruptions, and mounting documentation requirements. Excessive time away from direct patient care is increasingly viewed as detrimental to care quality, communication, and patient safety. This study aimed to quantify and visualize the time emergency physicians spend on specific activities during their clinical shifts, particularly computer usage.MethodsThis observational time-motion study was conducted in a high-volume, urban emergency department (ED). An observer used a web-based application to track physician activities including computer use, direct patient care, and all other major tasks carried out on shift. Electronic health record (EHR) event log data was queried to measure computer use after each physician’s scheduled shift. The primary outcome was total minutes of computer time (during and after shift) per scheduled hour of clinical work.ResultsThe observer tracked 20 emergency physicians for one 8-9h clinical shift each, which generated 150.0 hours of real-time observation data quantifying physicians’ ED workflow. In total, emergency physicians spent a median 29.8 minutes (IQR 25.6-38.5) on the computer per scheduled hour of their ED shift. Physicians spent a median 34.1% of their shift time using the computer and 26.9% with patients. Other activities included verbal communication with staff (15.9%), phone use (9.5%), miscellaneous tasks (5.5%), personal time (3.9%), electrocardiogram review (0.7%), and procedures (0.4%). EHR log analysis showed that physicians spent an additional median 1.3h (IQR 0.5-2.6) using the computer after their scheduled shifts.ConclusionEmergency physicians spent more than one-third of their ED shift working on the computer, which was more time than they spent with patients. They also spent 1-2 hours using the computer after their shifts. These findings demonstrate the need for strategies aimed at reducing unnecessary computer use during and after clinical shifts to enhance efficiency and improve patient care.
Automated Evaluation for Pericardial Effusion and Cardiac Tamponade with Echocardiographic Artificial Intelligence
Authors: Chiu I-Min , Vukadinovic Milos , Sahashi Yuki , Cheng Paul P. , Cheng Chi-Yung , Cheng Susan , Ouyang David 
Published: December 2024

AbstractBackgroundTimely and accurate detection of pericardial effusion and assessment cardiac tamponade remain challenging and highly operator dependent.ObjectivesArtificial intelligence has advanced many echocardiographic assessments, and we aimed to develop and validate a deep learning model to automate the assessment of pericardial effusion severity and cardiac tamponade from echocardiogram videos.MethodsWe developed a deep learning model (EchoNet-Pericardium) using temporal-spatial convolutional neural networks to automate pericardial effusion severity grading and tamponade detection from echocardiography videos. The model was trained using a retrospective dataset of 1,427,660 videos from 85,380 echocardiograms at Cedars-Sinai Medical Center (CSMC) to predict PE severity and cardiac tamponade across individual echocardiographic views and an ensemble approach combining predictions from five standard views. External validation was performed on 33,310 videos from 1,806 echocardiograms from Stanford Healthcare (SHC).ResultsIn the held out CSMC test set, EchoNet-Pericardium achieved an AUC of 0.900 (95% CI: 0.884– 0.916) for detecting moderate or larger pericardial effusion, 0.942 (95% CI: 0.917–0.964) for large pericardial effusion, and 0.955 (95% CI: 0.939–0.968) for cardiac tamponade. In the SHC external validation cohort, the model achieved AUCs of 0.869 (95% CI: 0.794–0.933) for moderate or larger pericardial effusion, 0.959 (95% CI: 0.945–0.972) for large pericardial effusion, and 0.966 (95% CI: 0.906–0.995) for cardiac tamponade. Subgroup analysis demonstrated consistent performance across ages, sexes, left ventricular ejection fraction, and atrial fibrillation statuses.ConclusionsOur deep learning-based framework accurately grades pericardial effusion severity and detects cardiac tamponade from echocardiograms, demonstrating consistent performance and generalizability across different cohorts. This automated tool has the potential to enhance clinical decision-making by reducing operator dependence and expediting diagnosis.
AI-HOPE: An AI-Driven conversational agent for enhanced clinical and genomic data integration in precision medicine research
Authors: Yang Ei-Wen , Velazquez-Villarreal Enrique 
Published: December 2024

AbstractIntroduction: The increasing complexity of clinical cancer research necessitates the development of automated tools capable of integrating clinical and genomic data while accelerating discovery efforts. Artificial Intelligence agent for High-Optimization and Precision mEdicine (AI-HOPE) is introduced as an innovative conversational AI platform powered by Large Language Models (LLMs), designed to empower domain experts to perform integrative data analyses through natural language input, eliminating the need for programming expertise. AI-HOPE offers robust analytical capabilities, enabling the generation of actionable insights in clinical and translational research. Methods: AI-HOPE interprets user instructions in natural language and translates them into executable code to analyze locally stored data. It facilitates subset comparisons for clinical prevalence and survival analysis, generating statistical outputs such as odds ratios, Kaplan-Meier survival curves, and hazard ratios. Its capabilities were demonstrated through two case-control studies using The Cancer Genome Atlas (TCGA): (1) analyzing TP53 mutation enrichment in early-stage versus late-stage colorectal cancer (CRC) patients, and (2) comparing progression-free survival among FOLFOX-treated patients with or without RAS mutations. Results: In the first study, AI-HOPE identified a significant enrichment of TP53 mutations in late-stage (III/IV) CRC compared to early-stage (I/II) cases. In the second study, AI-HOPE revealed a significant association between KRAS mutations and poorer progression-free survival in FOLFOX-treated patients. These findings align with established literature, demonstrating AI-HOPE’s capability to independently uncover meaningful insights without prior user assumptions. Conclusions: AI-HOPE represents a transformative advancement in precision medicine research, offering a scalable, user-friendly framework for integrating clinical and genomic data. Its versatility extends beyond cancer research, supporting applications across diverse biomedical fields. Future enhancements, such as real-time data integration and multi-omics capabilities, will further solidify its role as a pivotal resource for advancing translational research and improving patient outcomes. AI-HOPE bridges the gap between data complexity and research needs, accelerating discoveries in precision medicine research.
Financial incentives to motivate treatment for hepatitis C with direct acting antivirals among Australian adults (The Methodical evaluation and Optimisation of Targeted IncentiVes for Accessing Treatment of Early-stage hepatitis C: MOTIVATE-C): Statistical analysis plan
Authors: Jones Mark , Totterdell James , Fathima Parveen , Snelling Thomas L 
Published: December 2024

AbstractThe MOTIVATE-C study explores a critical question in hepatitis C treatment: How do financial incentives influence patients’ decisions to initiate direct-acting antiviral (DAA) therapy? Using an innovative Bayesian adaptive design, the research aims to determine the precise relationship between monetary support and treatment initiation among individuals with untreated hepatitis C virus. The study’s unique approach involves response-adaptive randomization, which dynamically allocates participants to different financial incentive levels. As the trial progresses, doses more likely to encourage treatment will receive increased emphasis, while less effective incentive levels may be systematically eliminated through pre-defined futility stopping rules. Participants will be tracked for DAA therapy initiation within 12 weeks of enrollment, with dedicated study navigators assisting them through the treatment access process. The primary analysis will adhere to the intention-to-treat principle, ensuring a comprehensive and unbiased evaluation of the intervention’s effectiveness. This manuscript details the statistical analysis plan, presenting the precise methodological framework, decision-making criteria, and analytical thresholds that will guide the study’s interpretation of how financial incentives might overcome barriers to hepatitis C treatment.
Clients’ Satisfaction With The Services Of An Outpatient Pharmacy At A University Hospital In Nigeria: A Cross-Sectional Study
Authors: Agu Mercy Chisom , Chima Uzochukwu Emmanuel , Eze Oluebubechukwu Praise , Maduekwe Stanley Ndubuisi , Okafor Victor Udochukwu , Chibuoke Ozioma Maryfrances , Nwankwo Chioma Bertha , Okoye Christabel Ogechukwu , Ngige Amauche Pearl , Chigbo Desirée Chimdimma , Akunne Maureen Ogochukwu 
Published: December 2024

AbstractBackgroundPharmaceutical care involves the pharmacist’s responsibility to optimize medication therapy and improve patient outcomes. Hence, patient/client satisfaction is regarded as an important measure of the effectiveness of pharmaceutical services in achieving treatment objectives. This study assessed the level of client satisfaction with the services of an outpatient pharmacy at a university hospital in Nigeria.MethodAn institution online-based cross-sectional study was conducted among clients who visited the University Medical Centre at the University of Nigeria Nsukka, Nsukka campus within the period of 15thSeptember to 15thOctober 2023. A 2-section, 30-item validated, semi-structured, interviewer-administered questionnaire was used in the study. Descriptive statistics were used to describe all the study variables while independent samplet-test and one-way ANOVA were used to determine the mean difference in satisfaction level among various sociodemographic characteristics. The statistical significance level was set atp<0.05.ResultsOut of the 166 participants in the study, the majority were students (n = 111, 66.9%), and receiving free medications (n = 117, 70.5%). The overall mean satisfaction score for pharmacy services was 3.11 out of a maximum 5.00 score. Notably, mean scores for most of the items exceeded 3.00 with the highest mean score obtained for the items:“The privacy of my conversations with the pharmacist”(3.59), and“The fairness of cost of medications in the pharmacy”(3.46) while the lowest score for the items:“The way my pharmacist works together with my doctor to make sure my medications are the best for me”(2.50) and“The availability of medications that are prescribed to me in the pharmacy”(2.63). First-time visitors reported significantly higher levels of satisfaction than return visitors (3.77 (0.686) vs 3.07 (0.637),p=<0.001)].ConclusionThe study revealed a moderate level of satisfaction among the clients of the outpatient pharmacy represented in this study. The reasons for this level of satisfaction lay a foundation for the improvement of pharmacy services.
Self-medication practices for oral health problems: A community-based cross-sectional study in Sri Lanka
Authors: Gammulle Kavithrini Anunadika , Premarathne Sudeha M 
Published: December 2024

AbstractThe prevalence of self-medication is a common practice among Sri Lankans. However, the practice of self-medication for oral health problems has not been studied in the country. The current study aimed to assess the prevalence and the associated factors of self-medication practices for oral conditions. This community-based cross-sectional study was conducted among 441 adults in Piliyandala, Sri Lanka. The participants who had experienced an oral health-related concern within the past year were selected using a multi-stage cluster sampling method with probability proportionate to size technique. An interviewer-administered questionnaire was used for data collection. The results revealed that 68.9% practiced self-medication for an oral health condition during the past year. Dental pain was the most common reason for self-medicating. Allopathic medications were used by 85.2%. Amoxicillin and Metronidazole were taken by 10.2% and 12.8% respectively. Poor attitude towards oral healthcare services and low family income were significant predictors of self-medication and despite the availability of oral health services, a considerable number of participants resorted to self-medication due to the perceived simplicity of the condition and the high cost of dental treatments.
Early Pandemic Associations of Latitude, Sunshine Duration, and Vitamin D Status with COVID-19 Incidence and Fatalities: A Global Analysis of 187 Countries
Authors: Mogire Reagan M. 
Published: December 2024

AbstractIn the face of the COVID-19 pandemic, understanding the interplay between environmental factors and virus spread is crucial for global preparedness strategies. This study explores how geographic latitude, sunshine duration, and vitamin D status were associated with the incidence and fatality rates of COVID-19 across 187 countries during the crucial early months of the outbreak. Data on the total number of COVID-19 cases by country were obtained from the United Nations database as of June 30, 2020. Univariate and multivariate regression analyses were conducted to determine the associations between COVID-19 cases and latitude, average hours of sunshine from January to June, and mean 25-hydroxyvitamin D (25(OH)D) levels. The average COVID-19 prevalence and mortality per million population were 2,087 and 69, respectively, with a case fatality rate of 3.19%. COVID-19 case fatality rate was positively associated with latitude (β = 0.030; 95% CI: 0.008, 0.052) and negatively associated with hours of sunshine (β = -1.51; 95% CI: -4.44, 1.41) and 25(OH)D levels (β = - 0.054; 95% CI: -0.089, -0.019) in adjusted regression analyses. Findings were similar for COVID-19 prevalence and mortality rate. These findings indicate that higher latitude and lower 25(OH)D levels was associated with increased COVID-19 severity and mortality.While the data highlight potential links between vitamin D status and COVID-19 outcomes, causality cannot be inferred. Further research, including large-scale, well-controlled trials, is essential to determine whether vitamin D plays a definitive role in COVID-19 prevention and management.
Causal relationship between inflammatory cytokines, metabolites and arrhythmia: a mendelian randomization study
Authors: Xie Yu-fei , Tang Ling-hui , Huang Feng , Zeng Zhi-yu 
Published: December 2024

AbstractBackgroundThis study aims to explore the causal relationships between inflammatory cytokines (ICs), metabolites, and the risk of arrhythmia through Mendelian Randomization (MR) analysis.MethodsThe causal associations were analyzed using five different MR analysis methods. Additionally, reverse MR analysis was performed to assess the impact of arrhythmias on these ICs and their metabolites.ResultsThe MR analysis revealed that Oncostatin-M receptor (OSM) was significantly associated with an increased risk of arrhythmia (OR = 1.0812, p < 0.05), along with other ICs such as CXCL11 (OR = 1.0586), SIRT2 (OR = 1.0521), and FGF5 (OR = 1.0520). Five were positively correlated with arrhythmia risk, including X-22776 (OR = 1.071, p = 0.022) and tricosanoylsphingomyelin (OR = 1.066, p = 0.035).Mediation analysis demonstrated that FGF5 influences arrhythmia risk through its metabolite 1-palmitoyl-2-oleoyl-GPE, with a mediated effect accounting for 5.1% of the total effect.ConclusionsOur findings suggest that specific ICs and metabolites contribute to the pathogenesis of arrhythmia. In particular, FGF5 and its metabolite 1-palmitoyl-2-oleoyl-GPE are implicated in increased arrhythmia risk, highlighting potential metabolic targets for therapeutic intervention.
Dynamics of Reactive Oxygen Species and Nitrogen Cycling in Soils as a Mechanism for Volatile Reactive Nitrogen Oxide Production
Authors: Purchase Megan L. , Raff Jonathan D. , Gale Alexander G. , Vaughan Alannah , Bending Gary D. , Mushinski Ryan M. 
Published: December 2024

AbstractHeterotrophic bacteria and fungi are responsible for the decomposition of organic matter in soil. During this process, reactive oxygen species (ROS) can be produced directly and indirectly as extracellular byproducts of respiration. It is well known that nitrogen (N) cycle processes lead to the formation of volatile reactive nitrogen oxides (NOy), a group of climate-active gases that contribute to atmospheric chemistry and negatively impact human health. Primary microbial sources of NOyinclude ammonia- oxidising bacteria and denitrifying bacteria and fungi. Despite soil being a significant source of global NOyemissions, studies to date have primarily focused on N emissions from agricultural soils and there remains a large scope for investigating mechanisms of soil NOyproduction in a wider context in order to better constrain terrestrial and climate process models. Here, we propose a potential microbial mechanism involving ROS that could influence the production of NOyfrom soil. We utilised metagenomics and metatranscriptomics alongside continuous gas flux measurements and analysis of soil properties to evaluate the connection between microbial ROS and soil-sourced NOy. Our findings suggest that more NOy, particularly nitric oxide (NO), is produced in the presence of increased abundance of ammonia-oxidising and denitrifying taxa. NO can be lost to the environment or reacts with superoxide, an ROS produced via the enzymatic activity of soil organic matter (SOM) decomposers. This reaction produces peroxynitrite (ONOO-), which we have demonstrated to enhance nitrogen dioxide (NO2) emissions from soil. We have shown that the extent of NOyproduction through this pathway may be dependent on SOM composition, and the associated variability in carbon and nitrogen content.
A dual role in virion attachment and entry makes the human cytomegalovirus gHgLgO trimer the central player in virion infectivity
Authors: Thiessen Lena , Garuti Roberto , Kubic Lucie , Kösters Miwako , Selvakumar Divya Amarambedu , Krey Thomas , Görzer Irene , Fröhlich Thomas , Adler Barbara 
Published: December 2024

AbstractGlycoproteins in the viral envelope of human cytomegalovirus (HCMV) orchestrate virion tethering, receptor recognition and fusion with cellular membranes. The glycoprotein gB acts as fusion protein. The gHgL complexes gHgLgO and gHgLpUL(128,130,131A) define the HCMV cell tropism. Studies with HCMV lacking gO had indicated that gO, independently of binding to its cellular receptor PDGFRα, plays an important second role in infection. Here, we identified a gO mutation which abolished virus particle infectivity by preventing the interaction of gHgLgO with host cell heparan sulfate proteoglycans (HSPGs). We could not only show that gHgLgO – HSPG interactions are a genuine second role of gHgLgO, but also that gHgLgO is the main player in determining the infectivity of HCMV virus particles. This challenges long-accepted textbook knowledge on the role of gB and gMgN complexes in virion tethering. Additionally, it adds the gHgLgO complex to the antigens of interest for future HCMV vaccines or treatments.
Cryo-EM structures of the MnmE-MnmG complex reveal large conformational changes and provide new insights into the mechanism of tRNA modification
Authors: Maes Laila , Martin Ella , Bickel David , Claeys Siemen , Vranken Wim , Fislage Marcus , Galicia Christian , Versées Wim 
Published: December 2024

AbstractMnmE and MnmG form a conserved protein complex responsible for the addition of a 5-carboxymethylaminomethyl (cmnm5) group onto the wobble uridine of several tRNAs. Within this complex, both proteins collaborate intensively to catalyze a tRNA modification reaction that involves glycine as a substrate in addition to three different cofactors, with FAD and NADH binding to MnmG and methylenetetrahydrofolate (5,10-CH2-THF) to MnmE. Without structures of the MnmEG complex it remained enigmatic how these substrates and co-factors can be brought together in a concerted manner. Prior SAXS data suggested that the MnmE (α2) and MnmG (β2) homodimers can adopt either an α2β2or α4β2complex, depending on the nucleotide state of MnmE. Here, we report the cryo-EM structures of the MnmEG complex in the α2β2and α4β2oligomeric states. These structures reveal that MnmE undergoes large conformational changes upon interaction with MnmG, resulting in an asymmetric MnmE dimer. In particular, the functionally important C-terminal helix of MnmE relocates from the 5,10-CH2-THF binding pocket of MnmE to the FAD binding pocket of MnmG, thus suggesting a mechanism for the transfer of an activated methylene group from one active site to the other. Together, these findings provide crucial new insights into the MnmEG-catalyzed reaction.Graphical abstract