bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Right Tool for the Task: Body-Weight Supported Treadmill or Total Body Recumbent Stepper for Mobility-Adapted Cardiopulmonary Exercise Testing in Multiple Sclerosis Patients with Disability
Authors: Hadjizadeh Anvar Saman , Kelly Liam P , Newell Caitlin , Alcock Lynsey , Ploughman Michelle 
Published: December 2024

ABSTRACTObjectiveCardiopulmonary Exercise Testing (CPET) is challenging among persons with mobility disability. We sought the optimal adapted device to achieve a maximal CPET.DesignRandomized crossover trial, within-subjects, repeated measures designSettingPrimary Care and Referral CenterParticipantsClinic-referred persons with multiple sclerosis (PwMS) (n=10) with three-month stability, no exercise obstruction, MoCa>24, ability to walk with or without assistance, and sex- and age-matched (±3 years) Controls (n=7) recruited by convenience samplingInterventionsCPET on body weight-supported treadmill (BWST) and total body recumbent stepper (TBRS)Main Outcome MeasuresStandard aerobic metrics (V̇O2max, % normative values for V̇O2max[%V̇O2max], heart rate maximum [HRmax], age-predicted HRmax, and Respiratory Exchange Ratio)ResultsPwMS achieved similar V̇O2max(mL·min-1·kg-1) on the TBRS and BWST (26.53±8.7 vs. 24.24±7.8) while Controls obtained higher values on BWST than TBRS (40.27±7.6 vs. 34.32±7.1,p<0.001). PwMS more consistently achieved criteria for maximum CPET using TBRS. During the preliminary investigation of the MS subgroup with a higher mobility disability, CPET using BWST exaggerated already low CPET metrics.ConclusionsAlthough Controls achieved higher CPET values on BWST, V̇O2maxbetween devices were similar among PwMS. Only when using BWST, PwMS V̇O2maxand %V̇O2maxwere lower than Controls, likely because of leg fatigue and weakness. Using TBRS permits persons with mobility disability to achieve more criteria for a maximum CPET. Our results suggest that CPET using BWST, being reliant on the lower body, likely disadvantages PwMS, especially those with mobility disability.
Determinants of Public Participation in Primary Health Care Worldwide: A Scoping Review Protocol
Authors: Jazi Kimia , Mahmoodi Amirhosein , Azizkhani Mohammadreza , joulaei hassan 
Published: December 2024

AbstractPublic participation is a key component to achieve effective primary health care (PHC) and considering human rights to health. Multiple factors affect community involvement regarding varied health systems worldwide. Obstacles should be addressed in each country, regarding health system regulations and community willingness. This scoping review will systematically map literature on varied models, potential barriers, as well as challenges and future opportunities to public participation in PHC around the world.This study will follow and will be conducted through Joanna Briggs Institute (JBI) guidelines. The PRISMA Extension for Scoping Reviews (PRISMA-ScR) will also guide the reporting. The search will be conducted across PubMed, Scopus, Web of Science Core Collection, Cochrane Central Register of Controlled Trials Embase, SID, and Magiran databases focusing on studies published from 2010 to January 2025 in English and Farsi. Two independent reviewers will screen titles and abstracts, followed by full-text screening. Data will be extracted in a standardized form by two authors and all disparities will be resolved through consulting a third author. Diagrams, tables, and figures will be applied to illustrate extracted data.
Protocol paper: A.I. Based STroke Risk fActor Classification and Treatment (ABSTRACT) study
Authors: Heseltine-Carp William , Kasabe Aishwarya , Courtman Megan , Allen Michael , Streeter Adam , Thurston Mark , Wang Hongrui , Mcgavin Lucy , Ifeachor Emmanuel , Mullin Stephen 
Published: December 2024

AbstractBackgroundStroke is a leading cause of death and disability in the UK. Much of stroke management revolves around addressing risk factors with medications and lifestyle modification. However, 30% of those who suffer stroke have no known risk factors. Hence, there is need to better identify individuals who are at high risk of stroke, and particularly those where the benefit of treatment outweighs the risk.ABSTRACT is a three phase study that looks to address this issue by (1) using artificial intelligence (AI) to predict stroke risk from routine hospital data, (2) to validate this model on external datasets, and (3) validate the ability to improve outcome by guiding clinical decision making. In this paper we focus on phase I of the project.AimsPhase I of this study has 4 main objectivesTo create four separate machine learning (ML) models to predict stroke risk from routine hospital data. One for CT/MRI/PMH brain data, one for ECG/echo/PMH data and one for laboratory test/PMH data.To perform explainability analysis on these models to identify important and novel risk factors for strokeTo calibrate these models and align them with real world probabilitiesTo combine these models to ensemble stroke prediction modelMethodsIn this retrospective observational cohort study we will analyze data from 9155 stroke patients and 109,875 controls in southwest England. Stroke cases will be sourced from the SSNAP database and historical brain imaging (CT/MRI), ECG, echocardiography, laboratory tests, ultrasound and medical history will be obtained from hospital and GP records. These data will then be linked to form a single de-identified dataset of cases and controls. ML techniques will then be trained on these data to predict stroke risk and identify novel risk factors for stroke.DiscussionThis protocol paper outlines phase one of ABSTRACTs approach in creating a novel stroke risk prediction model by integrating multimodal data types such as routine brain imaging, ECG, echocardiography, and laboratory results. In particular we outline a bespoke data handling protocol in order to comply with UK ethical governance when processing large volumes of confidential data. We also discuss our strategy in cleaning and preparing data prior to ML algorithms to predict stroke
LPSGM: A Unified Flexible Large PSG Model for Sleep Staging and Mental Disorder Diagnosis
Authors: Deng Guifeng , Niu Mengfan , Luo Yuxi , Rao Shuying , Sun Jing , Xie Junyi , Yu Zhenghe , Liu Wenjuan , Zhao Sha , Pan Gang , Li Xiaojing , Deng Wei , Guo Wanjun , Li Tao , Jiang Haiteng 
Published: December 2024

AbstractWe present the Large PSG Model (LPSGM), a unified and flexible framework for sleep staging and disease diagnosis using polysomnography (PSG) data. LPSGM is designed to address the challenges of cross-center generalization in sleep staging and to enable fine-tuning for downstream disease diagnosis tasks. LPSGM introduces a unified training framework for heterogeneous datasets and allows flexible channel input adjustments during inference. The model is first trained on 220,500 hours whole-night PSG from 16 public datasets, achieving robust sleep staging performance. It is then fine-tuned on target center data for various disease classification tasks, including narcolepsy diagnosis, anxiety and depression detection, and the classification of healthy versus depressed individuals. LPSGM outperforms baseline models on both sleep staging and disease diagnosis tasks. Our results demonstrate that LPSGM not only enhances sleep staging accuracy but also improves the diagnosis of sleep-related and psychiatric disorders, showing promise for clinical applications in sleep medicine and psychiatry.
Remifentanil as an alternative in modified rapid sequence induction: A Proof-of-concept study in a selected paediatric population
Authors: Pico Julien , Sola Chrystelle , Hertz Laurent , Riera Julien , Scott Christopher , Chapron Kévin , Pirat Philippe , Bringuier Sophie , Dadure Christophe 
Published: December 2024

ABSTRACTIntroductionRapid sequence induction (RSI) is widely used in paediatric anaesthesia. Concerns over complications associated with classical RSI have prompted the exploration of alternatives. This study was conducted to determine the feasibility and safety of using bolus remifentanil in combination with a hypnotic agent for paediatric RSI.MethodsThis proof-of-concept study analysed data from paediatric patients, with at least one risk factor for pulmonary aspiration, undergoing RSI with remifentanil at the Montpellier University Hospital between December 2021 and August 2023. Exclusion criteria included the planned use of a neuromuscular blocking agent for RSI, preoperative hypoxemia, hemodynamic shock or difficult intubation risk factors. Remifentanil was administered by direct intravenous injection with optional prophylactic administration of atropine. Data on intubation success rates, major and minor complications and rescue treatment were collected and analysed.ResultsThe study included 267 children with a mean age of 7.8 +/- 4.4 years. The success rate for the first intubation attempt was 92.9% (95% CI, 88.9-95.6). No major complication or pulmonary aspiration was reported. Minor complications, primarily hypotension, occurred in 15.7% of patients. The prophylactic use of atropine was correlated with a reduction in minor complications.DiscussionThis study supports the use of remifentanil for RSI in a selected paediatric population. Remifentanil offers good intubation conditions with a high success rate at the first attempt and a high safety profile with no major complication reported. Prophylactic atropine administration seems beneficial in reducing minor complications. These findings advocate for integrating remifentanil as an alternative in protocols for modified RSI. Further randomised studies are required to validate such outcomes and refine clinical approaches.
Integrating multi-modal omics to identify therapeutic atherosclerosis pathways for coronary heart disease
Authors: de Ruiter Sophie C. , van Vugt Marion , Finan Chris , Grobbee Diederick E. , de Kleijn Dominique P.V. , Pasterkamp Gerard , den Ruijter Hester M. , Diez Benavente Ernest , Peters Sanne A.E. , Schmidt A. Floriaan 
Published: December 2024

AbstractIntroductionUrinary metabolism breakdown products reflect metabolic changes in atherosclerosis-relevant tissues and may contain relevant therapeutic leads. We integrated data on urinary metabolism breakdown products, plasma proteins, atherosclerotic plaque tissue, and single-cell expression to identify druggable metabolic pathways for coronary heart disease (CHD).MethodsMendelian randomisation was employed to interrogate findings from independent genome-wide association studies on 954 urinary metabolism breakdown products, 1,562 unique proteins, and 181,522 CHD cases, establishing directionally concordant associations. Using the Athero-Express Biobank, concordant plasma proteins were linked to plaque vulnerability using protein and mRNA expression in plaque. Single-cell RNA sequencing data obtained from carotid plaque samples were used to test for differential expression of concordant proteins across plaque cell types.ResultsIn total, 29 urinary metabolism breakdown products associated with CHD, predominantly originating from amino acid metabolism (n=12) or unclassified origin (n=9). We identified 113 plasma proteins with directionally concordant associations with these urinary metabolism breakdown products and CHD. Of the 110 proteins available in plaque, 16 were associated with plaque vulnerability. This included positive control proteins targeted by drugs indicated for CHD, such as CAH1 (targeted by aspirin), IL6R (targeted by tocilizumab), and AT1B2 (targeted by digoxin), as well as two potential repurposing opportunities C1S (targeted by C1-esterase inhibitor and sutimlimab) and CATH (targeted by bortezomib).ConclusionWe have identified amino acid metabolism as an important contributing pathway to CHD risk and prioritised 16 proteins relevant for CHD with involvement in atherosclerotic plaques, providing important insights for drug development.
Epidemiological indices with multiple circulating pathogen strains
Authors: Trevisin Cristiano , Mari Lorenzo , Gatto Marino , Colizza Vittoria , Rinaldo Andrea 
Published: December 2024

AbstractEpidemiological indicators, such as reproduction numbers and epidemicity indices, describe an ongoing epidemic’s long- and short-term behaviour. Their evolving values provide context for designing control measures, as maintaining both indices below one warrants a waning epidemic. However, current models for the computation of epidemiological metrics do not consider the pathogen stratification into variants endowed with different infectivity and epidemiological severity, as was the case with SARS-CoV-2. Failing to account for such stratification prevents the resulting epidemiological indices from spotting the possible onset of uncontrolled growth of one variant within the total of new infections, thus limiting the prognostic value of the indicators. Here, we expand an existing framework for the computation of spatially explicit reproduction numbers and epidemicity indices to account for arising variants. By contrasting the data of the COVID-19 pandemic in Italy, we show that embedding additional layers of complexity in the mathematical model reveals epidemiological metrics at the emergence of new variants significantly exceeding their threshold. Such values foresee a recrudescence in new infections that only became evident after emerging variants effectively replaced the previous strains. These higher values flag the presence of a specific variant that grows more rapidly than the growth of the total number of infections generated by all variants combined. Variant-stratified epidemiological indicators do therefore allow control measures to be better tailored to the shifting severity of an ongoing epidemic.
Development and comparison of natural language processing models for abdominal aortic aneurysm repair identification and classification using unstructured electronic health records
Authors: Thompson Daniel , Mofidi Reza 
Published: December 2024

AbstractBackgroundPatient identification for national registries often relies upon clinician recognition of cases or retrospective searches using potentially inaccurate clinical codes, potentially leading to incomplete data capture and inefficiencies. Natural Language Processing (NLP) offers a promising solution by automating analysis of electronic health records (EHRs). This study aimed to develop NLP models for identifying and classifying abdominal aortic aneurysm (AAA) repairs from unstructured EHRs, demonstrating proof-of-concept for automated patient identification in registries like the National Vascular Registry.MethodUsing the MIMIC-IV-Note dataset, a multi-tiered approach was developed to identify vascular patients (Task 1), AAA repairs (Task 2), and classify repairs as primary or revision (Task 3). Four NLP models were trained and evaluated using 4,870 annotated records: scispaCy, BERT-base, Bio-clinicalBERT, and a scispaCy/Bio-clinicalBERT ensemble. Models were compared using accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve.ResultsThe scispaCy model demonstrated the fastest training (2 mins/epoch) and inference times (2.87 samples/sec). For Task 1, scispaCy and ensemble models achieved the highest accuracy (0.97). In Task 2, all models performed exceptionally well, with ensemble, scispaCy, and Bio-clinicalBERT models achieving 0.99 accuracy and 1.00 AUC. For Task 3, Bio-clinicalBERT and the ensemble model achieved an AUC of 1.00, with Bio-clinicalBERT displaying the best overall accuracy (0.98).ConclusionThis study demonstrates that NLP models can accurately identify and classify AAA repair cases from unstructured EHRs, suggesting significant potential for automating patient identification in vascular surgery and other medical registries, reducing administrative burden and improving data capture for audit and research.
Improving HIV Outcomes in Miami’s Black populations with clinic-based community health workers protocol: The integrated navigation and support for treatment adherence, counseling, and research (INSTACARE) randomized controlled trial
Authors: Kenya Sonjia , Young BreAnne , Escarfuller Sebastian , Jones-Weiss Deborah , Carrasquillo Olveen , Riccio Ana , Yue Pan 
Published: December 2024

ABSTRACTMiami-Dade is an HIV epicenter where Black populations experience excess AIDS-related deaths due to poor medication adherence, which prevents achieving an undetectable HIV viral load (VL). A promising approach to improving HIV outcomes in Black populations has been the use of community health workers (CHWs). Evidence shows CHWs trained in motivational interviewing (MI) may further improve outcomes, however little data exists about Black CHWs trained in MI who support Black patients with HIV. While CHWs traditionally help address social determinants of health in nonclinical locations, there is less information on CHWs who provide support in clinical settings, which may result in even greater improvements in HIV outcomes. To examine effects of CHWs trained in MI who provide HIV care in both clinic and community settings, a randomized controlled trial (RCT) is being conducted in Miami-Dade’s largest public hospital. The Integrated Navigation and Support for Treatment, Adherence, Counseling, and Research intervention is a RCT of 300 Black adults with an unmanaged HIV VL (> 200 copies/mL). CHWs trained in MI are embedded into HIV clinical care teams and participate in hospital rounds with clinicians treating inpatients. Participants randomized into the CHW intervention arm receive 12 months of CHW-led health education and assistance with health care navigation and social services. The primary outcome is change in HIV viral load suppression at 12 months. Secondary outcomes include changes in medication adherence and social determinants of health. Study enrollment began in 2023 and will be completed by 2027. The first results are expected to be submitted for publication in 2025. INSTACARE is one of the first RCTs to examine effects of clinic-based support provided by CHWs trained in MI on Black populations with an unmanaged VL and will provide evidence on the impact of such strategies on medication adherence and social determinants of health.
From Traits to Puffs: The Interplay of Personality, Pandemic Stress, and Smoking Behaviors
Authors: Tian Ying , Xiang Weiyi , Dzhugaryan Silvia , Bae Dayoung , Barrington-Trimis Jessica , Church Terry 
Published: December 2024

AbstractSmoking, a leading cause of chronic diseases, is often used to cope with stress, which has been heightened by the pandemic due to health and economic concerns. Studies have shown that the Big Five personality traits are linked to smoking behavior, suggesting that different personality traits influence nicotine use in varying ways. However, there remains a significant gap in understanding how individuals with different personalities respond to nicotine use under stress. This study aims to investigate how nicotine dependence changes for different Big Five personalities under the pandemic stress and whether other stress-related factors influence nicotine dependence during COVID-19. This cross-sectional study collect data from randomly selected adults aged 18-30 in the US. The Big Five Personality Model assessed personality traits, and nicotine dependence was measured with the Hooked-on Nicotine Checklist. Stress was evaluated using the Perceived Stress Scale, while demographics and other pandemic-related stressors were gathered through structured questions. Correlation and multiple logistic regression models were used for data analysis. The main findings showed that both before (r = −.25, p < .001) and during (r = −.19, p < .001) the pandemic, agreeableness was significantly negatively associated with nicotine dependence, indicating that higher agreeableness was linked to lower nicotine dependence. Similarly, conscientiousness was negatively correlated with nicotine dependence both before (r = −.123, p < .001) and during COVID-19 (r = −.19, p < .001). Although no direct association was found between perceived stress, personality traits, and smoking behavior, the analysis identified that external stressors played a moderating role. These findings emphasize the importance of understanding how different personality traits influence young people’s dependence on nicotine under stress. The outcome can guide the design of targeted nicotine withdrawal interventions and inform effective public health strategies.
“It’s a moving target”: Experiences of pacing to reduce symptom exacerbation among adults living with Long COVID – Results from an international community-engaged qualitative research study
Authors: McDuff Kiera , Brown Darren A. , St. Clair-Sullivan Natalie , Chan Carusone Soo , Erlandson Kristine M. , Avery Lisa , Bannan Ciaran , Bergin Colm , Cheung Angela M. , Harding Richard , Kelly Mary , Martin Jessica M. , McCorkell Lisa , O’Connell Sarah , O’Donovan Imelda , O’Hara Margaret , Roche Niamh , Stokes Ruth , Thomson Catherine , Townsend Liam , Vera Jaimie H. , Wei Hannah , Solomon Patricia , O’Brien Kelly K. 
Published: December 2024

ABSTRACTIntroductionLong COVID is a multisystem condition that negatively impacts daily function. Pacing is a self-management strategy to mitigate symptoms. Our aim was to describe experiences of pacing from the perspectives of adults living with Long COVID.MethodsWe conducted a community-engaged qualitative descriptive study involving one-on-one online interviews with adults living with Long COVID from Canada, Ireland, United Kingdom, and United States to explore experiences of disability. We asked participants about strategies they used to deal with health challenges living with Long COVID. Interviews were audio recorded and transcribed verbatim. We analyzed data using group-based content analytical techniques.ResultsAmong the 40 participants living with Long COVID, the majority were women (n=25; 63%), white (n=29;73%) and heterosexual (n=30;75%). The median age of participants was 39 years (25th, 75th percentile: 32, 49). Most participants (n=37;93%) used pacing to mitigate or prevent symptoms. Participant described experiences of pacing across five main areas: 1) using pacing as a living strategy (pacing to mitigate multidimensional health challenges; applying pacing to many types of activities; process of pacing experienced as a moving target; pacing experienced as a helpful strategy, but not a cure for Long COVID); 2) learning how to pace (acquiring knowledge about pacing; developing strategies and skills to support pacing); 3) encountering challenges with pacing (learning how to pace; experiencing inequitable access to pacing; experiencing stigma and judgement; undergoing psychological and emotional adjustment from beliefs of ‘fighting’ or ‘pushing through’ to balancing rest with activity; making sacrifices; and encountering unexpected obstacles); 4) experiencing consequences of not pacing; and 5) conceptualising and describing pacing using analogies or metaphors.DiscussionPacing is a challenging and complex strategy used to mitigate symptoms of Long COVID. Healthcare providers should work collaboratively with patients to further refine and implement this strategy, when appropriate.