bluuebunny/crossref_metadata_2025_split huggingface dataset:
Creating a General-Purpose Generative Model for Healthcare Data based on Multiple Clinical Studies
Authors: Maruyama Hiroshi , Bito Kotatsu , Saito Yuki , Hibi Masanobu , Katada Shun , Kawakami Aya , Oono Kenta , Charoenphakdee Nontawat , Gao Zhengyan , Igata Hideyoshi , Yoshikawa Masashi , Ota Yoshiaki , Okui Hiroki , Akita Kei , Yamaguchi Shoichiro , Sugawara Yohei , Maeda Shin-ichi 
Published: January 2025

AbstractData for healthcare applications are typically customized for specific purposes but are often difficult to access due to high costs and privacy concerns. Rather than prepare separate datasets for individual applications, we propose a novel approach: building a general-purpose generative model applicable to virtually any type of healthcare application. This generative model encompasses a broad range of human attributes, including age, sex, anthropometric measurements, blood components, physical performance metrics, and numerous healthcare-related questionnaire responses. To achieve this goal, we integrated the results of multiple clinical studies into a unified training dataset and developed a generative model to replicate its characteristics. The model can estimate missing attribute values from known attribute values and generate synthetic datasets for various applications. Our analysis confirmed that the model captures key statistical properties of the training dataset, including univariate distributions and bivariate relationships. We demonstrate the model’s practical utility through multiple real-world applications, illustrating its potential impact on predictive, preventive, and personalized medicine.
Patterns of reporting of influenza and pertussis vaccination during pregnancy to the Australian Immunisation Register, 2022
Authors: Sonneveld Nicole , Wilson Elizabeth , Ennis Sonya , McRae Jocelynne , Macartney Kristine , Liu Bette 
Published: January 2025

AbstractObjectivesTimely, accurate reporting of pregnancy vaccination coverage is key to evaluating pregnancy immunisation programs. We compared influenza and pertussis vaccination reporting on the New South Wales (NSW) Perinatal Data Collection (PDC) and the Australian Immunisation Register (AIR), to understand coverage, reporting, timing of vaccination and provider type.DesignRetrospective population-based cohort study.SettingPeople giving birth at ≥20-weeks gestation (2017–2022) in the NSW PDC, linked to AIR-reported influenza and pertussis vaccinations.Main outcome measuresInfluenza and pertussis coverage according to 1) the PDC and 2) linked AIR records, and agreement between data sources by year (influenza: 2021–2022, pertussis: 2017–2022) and selected characteristics (2022). Description of provider type and timing for vaccinations reported to the AIR (2022).ResultsPDC-reported influenza and pertussis coverage peaked in 2020 (influenza: 58.8% (52,940/90,091), pertussis: 79.0% (71,169/90,091)), decreasing thereafter (influenza: 49.1% (43,744/89,112), pertussis: 77.6% (69,129/89,112) in 2022). Influenza and pertussis vaccination coverage as reported to the AIR increased from 34.7% (33,611/96,789) in 2021 to 44.8% (39,935/89,112) in 2022 and 24.4% (22,857/93,582) in 2017 to 51.6% (46,009/89,112) in 2022, respectively. Agreement between AIR and PDC improved over time, but remained suboptimal (influenza: 75.5%, pertussis: 56.6% in 2022). AIR and PDC agreement differed most by antenatal care model, particularly for pertussis vaccinations. In 2022, of pregnancy vaccinations reported to the AIR, >75% were provided in general practices. Co- administration occurred infrequently (8.8%).ConclusionsDespite mandated reporting to the AIR, there was significant underreporting of pregnancy vaccinations to the AIR as compared with the NSW PDC. National collection of accurate and timely data on pregnancy vaccination coverage is needed to monitor vaccine uptake, and identify and monitor strategies to improve uptake, particularly with the new pregnancy RSV vaccine program in 2025. We identified co-administration as a potential strategy to improve influenza vaccination coverage during pregnancy.
The association of umbilical cord blood neurofilament light with non-reassuring fetal status: a prospective observational study
Authors: Zalcberg David , Kramer Kaitlin , Payne Emma , Payne Thomas , Marathe Shreeya , Mahajan Neha , Liu Ashly , Barry Jessica , Duckworth Andrew , Brookes Mitchell , de Vries Bradley , Gonzalez-Ortiz Fernando , Blennow Kaj , Zetterberg Henrik , Gordon Adrienne , Moran Benjamin , Manning Helen , Sanders Robert D. 
Published: January 2025

AbstractObjectiveEarly detection of hypoxic–ischaemic encephalopathy (HIE) in neonates is critical. We conducted a pilot cohort study to determine the feasibility of collecting umbilical cord blood samples for neurofilament light (NfL) and to assess the association of NfL with non-reassuring fetal status and other cord biomarkers.DesignProspective cohort study.SettingA single, large tertiary and quaternary referral hospital.Patients108 maternal participants consenting to donate cord blood.InterventionUmbilical cord venous blood plasma NfL levels.Main outcome measures(1) Feasibility of cord NfL sample collection and analysis; (2) Association of NfL with non-reassuring fetal status (CTG changes and/or documented non-reassuring fetal status), NICU admission and length of stay; (3) Correlation of NfL with other cord biomarkers.ResultsCord NfL was higher in preterm neonates, and was correlated with cord lactate, pH, and base excess. After controlling for mode of delivery and gestational age, NfL (OR = 2.29, 95%CI: 1.15 to 5.57), but not pH (OR = 0.78, 95%CI: 0.42 to 1.41), base excess (OR = 0.83, 95%CI: 0.37 to 1.86), or lactate (OR = 1.06, 95%CI: 0.51 to 2.12) was associated with non-reassuring fetal status. NfL levels were higher in neonates admitted to NICU (median (IQR): 11.3 (7) versus 8.5 (5.1)).ConclusionsCord blood NfL analysis was feasible and provided correlates of adverse outcomes. Higher venous cord blood NfL levels were associated with non-reassuring fetal status. Further research is needed to validate these findings and establish the role of NfL, if any, in clinical practice.
Development and Validation of the Children’s Medication Self-Efficacy Scale
Authors: Yu Ziying , Yu Jin , Gao Yongqiang , Wang Ting , Wang Xinyu , Liu Pan , Meng Qinghui , Wu Yibo 
Published: January 2025

AimsThis study aims to develop a tool to assess medication self-efficacy in children and validate the reliability and validity of the Children’s Medication Self-Efficacy Scale (CMSES).MethodsBased on Bandura’s theory of self-efficacy and expert recommendations, four items were developed for the scale. Data were collected from 2,258 Chinese children between May and November 2023. Exploratory factor analysis was performed to determine the scale’s structure, followed by confirmatory factor analysis to validate it. Both convergent and discriminant validity were assessed. The internal consistency reliability of the scale was evaluated using Cronbach’s alpha coefficient. Additionally, latent profile analysis (LPA) and receiver operating characteristic (ROC) curve analysis were employed to determine the optimal cut-off point for the scale.ResultsThe Cronbach’s alpha coefficient for the CMSES was 0.92. Exploratory factor analysis revealed that the four items could be decomposed into two factors, namely magnitude and strength, which explained 76% of the total variance. Additionally, the scale demonstrated good convergent and discriminant validity. LPA identified three categories of medication self-efficacy among participating children: low (6.9%), medium (40.5%), and high (52.6%). Setting the cut-off point at 16, ROC analysis successfully distinguished children with low medication self-efficacy from others, yielding an area under the curve (AUC) of 88.5% (95% CI: 86.8%-90.2%).ConclusionThe CMSES is an effective and reliable tool for assessing medication self-efficacy in children.
Molecular profiling of neuronal extracellular vesicles reveals brain tissue specific signals
Authors: Kalia Vrinda , Jackson Gabriela , Dominguez Regina J. , Pinto-Pacheco Brismar , Bloomquist Tessa , Furnari Julia , Banu Matei , Volpert Olga , Manz Katherine E. , Walker Douglas I. , Pennell Kurt D. , Canoll Peter D. , Bruce Jeffrey N. , Eitan Erez , Wu Haotian , Baccarelli Andrea A. 
Published: January 2025

AbstractExtracellular vesicles (EVs) released by neurons (nEVs) provide an opportunity to measure biomarkers from the brain circulating in the periphery. No study yet has directly compared molecular cargo in brain tissue to nEVs found in circulation in humans. We compared the levels microRNAs and environmental chemicals because microRNAs are one of the most studied nEV cargoes and offer great potential as biomarkers and environmental chemical load in nEVs is understudied and could reveal levels of chemicals in the brain. To do so, we leveraged matched sets of brain tissue and serum, and isolated serum total EVs and serum nEVs. We also generated and compared metabolomic profiles in a different set of matched serum, serum total EVs, and serum nEVs since metabolite cargo in nEVs is also understudied but could offer potential biomarkers. Highly expressed brain tissue miRNAs showed stronger correlations with nEVs than serum or total EVs. We detected several environmental chemical pollutant classes in nEVs. The chemical pollutant concentrations in nEVs were more strongly correlated with brain tissue levels than those observed between brain tissue and serum or total EVs. We also detected several endogenous metabolite classes in nEVs. Compared to serum and total EVs, there was enrichment of metabolites with known signaling roles, such as bile acids, oleic acid, phosphatidylserine, and isoprenoids. We provide evidence that nEV cargo is closely correlated to brain tissue content, further supporting their utility as a brain liquid biopsy.
Is it time for the neurologist to use Large Language Models in everyday practice?
Authors: Maiorana N. V. , Marceglia S. , Treddenti M. , Tosi M. , Guidetti M. , Creta M.F. , Bocci T. , Oliveri S. , Martinelli-Boneschi F. , Priori A. 
Published: January 2025

AbstractLarge Language Models (LLMs) such as ChatGPT and Gemini are gaining momentum in healthcare for their diagnostic potential. However, their real-world applicability in specialized medical fields like neurology remains inadequately explored. The possibility to use these tools in everyday diagnostic practice relies on the evaluation of their ability to serve as support for the clinician in assessing the patient, understanding the possible diagnosis and design the diagnostic pathway. To this end, in this study we (1) examined the available literature on the evaluation of LLMs in neurology diagnosis in order to understand whether the methodologies applied were adequate to translate the use of LLMs in everyday practice, and (2) designed and performed an experiment to evaluate the diagnostic accuracy and clinical recommendations of ChatGPT-3.5 and Gemini compared to neurologists using real-world clinical cases presented following the everyday diagnostic practice. In the vast literature of LLMs application in neurology, only 24 studies reported experiences using LLMs in clinical neurology. The experiments reported showed a heterogeneous scenario of prompt engineering and input formats. At present, while responses using structured prompts were well documented, there is a lack of studies using real-world clinical scenarios, and everyday workflows and practice. We therefore conducted a real-world experiment using a cohort of 28 anonymized patient records from the neurology department of the ASST Santi Paolo e Carlo Hospital (Milan, Italy). Cases were presented to ChatGPT-3.5 and Gemini replicating the typical clinical workflows. Diagnostic accuracy and appropriateness of recommended diagnostic tests were assessed against discharge diagnoses and neurologists’ performance.Neurologists achieved a diagnostic accuracy of 75%, outperforming ChatGPT-3.5 (54%) and Gemini (46%). Both LLMs exhibited difficulties in nuanced clinical reasoning and over-prescribed diagnostic tests in 17–25% of cases. Despite their ability to generate structured recommendations, they struggled with complex or ambiguous presentations, requiring additional prompts in some cases. We can therefore conclude that LLMs have potential as supportive tools in neurology but they currently lack the depth required for nuanced clinical decision-making. The findings emphasize the need for further refinement of LLMs and the development of evaluation methodologies that reflect the complexities of real-world neurology practice.
Leveraging Hand-Crafted Radiomics on Multicenter FLAIR MRI for Predicting Disability Progression in People with Multiple Sclerosis
Authors: Khan Hamza , Woodruff Henry C , Giraldo Diana L. , Werthen-Brabants Lorin , Mali Shruti Atul , Amirrajab Sina , Brouwer Edward De , Popescu Veronica , Wijmeersch Bart Van , Gerlach Oliver , Sijbers Jan , Peeters Liesbet M. , Lambin Philippe 
Published: January 2025

AbstractMultiple sclerosis (MS) is a chronic autoimmune disease of the central nervous system that results in varying degrees of functional impairment. Conventional tools, such as the Expanded Disability Status Scale (EDSS), lack sensitivity to subtle changes in disease progression. Radiomics offers a quantitative imaging approach to address this limitation. This study used machine learning (ML) and radiomics features derived from T2-weighted Fluid-Attenuated Inversion Recovery (FLAIR) magnetic resonance images (MRI) to predict disability progression in people with MS (PwMS).A retrospective analysis was performed on real-world data from 247 PwMS across two centers. Disability progression was defined using EDSS changes over two years. FLAIR MRIs were preprocessed using bias-field correction, intensity normalisation, and super-resolution reconstruction for low-resolution images. White matter lesions (WML) were segmented using the Lesion Segmentation Toolbox (LST), and MRI tissue segmentation was performed using sequence Adaptive Multimodal SEGmentation. Radiomics features from WML and normal-appearing white matter (NAWM) were extracted using PyRadiomics, harmonised with Longitudinal ComBat, and reduced via Spearman correlation and recursive feature elimination. Elastic Net, Balanced Random Forest (BRFC), and Light Gradient-Boosting Machine (LGBM) models were evaluated on validation data and subsequently tested on unseen data.The LGBM model with harmonised radiomics and clinical features outperformed the clinical only model by achieving a test performance of PR AUC of 0.20 and a ROC AUC of 0.64. Key predictive features, among others, included GLCM maximum probability (WML), GLDM dependence non uniformity (NAWM). Short-term changes (longitudinal imaging approach) showed limited predictive power by achieving a PR AUC of 0.11 and a ROC AUC of 0.69.These findings support the use of ML models trained on radiomics features integrated with clinical data for predicting disability progression in PwMS. Future studies should validate these findings in larger, balanced datasets and explore advanced approaches, such as deep learning and foundation models, to enhance predictive performance.
Effect of antihypertensive drugs on glaucoma: a Mendelian randomization and triangulation analysis
Authors: Huang Yu , Liu Yiliang , Guo Jingze , Niu Yongyi , Lin Hongliang , Liu Jing , Chen Shuilian , Zhu Zhuoting , Qin Yongjie , Shang Xianwen , Plotnikov Denis , Zhang Hongyang , Wang Huan 
Published: January 2025

AbstractImportanceGlaucoma is one of the world’s leading causes of irreversible blindness and effective interventions with less side effect and less cost were still in need.ObjectiveTo investigate the potential of repurposing antihypertensive drugs to glaucoma treatment and intra ocular pressure (IOP) lowering in cost effective manners.Design, Setting and ParticipantsTwo sample Mendelian Randomization (MR) was used to investigate the genetic effect of antihypertensive drugs on glaucoma protection or IOP lowering. Two strategies (biomarker or eQTL approach) were applied to define the instrumental variable (IV). Genome-wide association study (GWAS) results on systolic blood pressure (SBP) of 526,001 European participants were used as exposure and GWAS on primary open angle glaucoma (POAG, 15,229 cases and 177,473 controls) and on IOP compromising 70832 participants in the UK Biobank were used as the outcome. All analyses were performed between April 2022 and December 2022.ExposuresThe SBP lowering effect of the antihypertensive drug targets.Main Outcomes and MeasuresPOAG or IOP measurements.ResultsUsing IVs defined by the biomarker approach, genetically-proxied lower SBP through diuretics targets was causally associated with a reduced risk of glaucoma (OR per mmHg reduction in SBP 0.87, 95% CI: 0.97-0.78, p = 0.009 for potassium-sparing diuretics [PSD] and OR=0.86, 95% CI: 0.92-0.81,p =2.67e-05 for loop diuretics [LD]). When using eQTL as IVs, the IVW-MR yielded a causal effect estimate on POAG of 0.88 (95% CI 0.79 -0.97, P = 0.011) per 1 mmHg decrease in blood pressure via PSD targets and 0.92 (95% CI 0.85 – 1, P=0.052) via LD. No consistence effects were observed among different MR methods for other antihypertensive drugs on glaucoma or IOP. After triangulation genetic evidence from MR, epidemiological evidence from observational studies and experimental evidence, the protective effect of PSD/LD on POAG was suggested.Conclusions and RelevanceWe found PSD and LD drugs hold promise as potential therapeutic agents to reduce the risk of POAGKey PointsQuestionCan antihypertensive drugs be repurposed to the treatment of ocular hypertension?FindingsDifferent Mendelian Randomization (MR) analysis strategies identified that diuretics (potassium-sparing diuretics [PSD) and loop diuretics [LD] might have a protective effect on primary open angle glaucoma (POAG). Furthermore, the triangulation analysis compared the genetic evidence from MR, epidemiological evidence from observational studies and experimental evidence and found a consistent protective effect of PSD/LD on POAG.MeaningPSD/LD may be repurposed to the treatment of POAG and they may also benefit patients with hypertension as well as POAG.
Activin A-activated ALK4 induces pathogenic Th17 involvement in endothelial–mesenchymal transition in systemic lupus erythematosus-associated pulmonary arterial hypertension
Authors: Jing Shuliang , Qian Junyan , Yao Mingxin , Mao Pei , Zhang Jingyuan , Wu Zhihong , Ying Hongjie , Wang Lie , Li Mengtao , Yang Jun 
Published: January 2025

AbstractObjectiveAutoimmune diseases, such as systemic lupus erythematosus (SLE), are associated with pulmonary arterial hypertension (PAH), a condition that can lead to heart failure. However, whether T cells also contribute to the occurrence of PAH in SLE, has not been clarified. The objective of this study was to elucidate the role of Activin A signaling in the effector cells mainly involved in SLE-PAH.MethodsCyTOF analysis was performed to identify the major affected immune cell population after the treatment in SLE-PAH patients. ELISA showed the serum Activin A and IL-17 levels were significantly higher in patients with SLE-PAH compared to SLE alone and healthy donors. We also conducted Th17 cells coculturing with pulmonary microvascular endothelial cells(PMECs) and constructed a SLE-PH mouse model and CD4+T cells depletion mice together with two rat models to identify the converged target.ResultsThe reduced CD4+T cell number was detected in SLE-PAH patients after treatment. Activin A signals via ALK4 in both Th17 cells and PMECs. When ALK4 was overexpressed in Th17 cells, IL-6 and CTGF gene expression was significantly increased in cocultured PMECs. We found severe SLE-PH in mice by overexpression ALK4, and alleviated hemodynamic changes in CD4+T cells depletion mice. ALK4 inhibitor TEW is effective to treat PAH by repressing CTGF transcription, which was facilitated by synergistic increases in pSmad2 and pSTAT3 levels downstream of ALK4 activation.ConclusionOur findings suggest that Activin A activates ALK4 in Th17 cells to induce IL-17 secretion, meanwhile activated ALK4 via Smad2 phosphorylation to induce EndoMT in hPMECs, indicating that ALK4 is a promising therapeutic target for SLE-PAH.
Anaemia among mother-child dyads in India: trends, drivers, and future projections
Authors: Pedgaonker Sarang , Meher Trupti , Gupta Monali , Chakrabarti Suman , Nguyen Phuong Hong , Singh Shri Kant , Avula Rasmi , Dwivedi Laxmi Kant , Aditi  , Scott Samuel 
Published: January 2025

AbstractAnaemia among mothers and their children is a widespread public health challenge with profound consequences for individuals and societies. While anaemia has been studied separately in women and children, there remains a literature gap examining anaemia in mother-child dyads, limiting insights on interventions that may simultaneously address anaemia in both groups. Our study examines trends and drivers of anaemia among mother-child dyads (N=408,342) in India using nationally-representative data from 2006 to 2021 and estimates the potential future reduction in anaemia based on changes in selected drivers. We employed descriptive statistics, multivariable logistic regression and population attributable fraction (PAF) analysis. The co-occurrence of anaemia among mothers-child dyads decreased from 35% in 2006 to 33% in 2016, but increased to 37% in 2021. Subnational analyses revealed varying trends by states, with Delhi showing the highest increase (17% to 32%) and Sikkim the largest decrease (29% to 16%) between 2006 and 2021. Maternal education, regular consumption of non-vegetarian food and green leafy vegetables, consumption of iron folic acid supplements, utilization of government health services, and improved sanitation at both household and community levels were associated with lower likelihood of anaemia among mother-child dyads. The cumulative PAF suggested that addressing these factors collectively could reduce anaemia prevalence among mother-child dyads by 18% to 28% (under different scenarios) by 2030. The study underscores the need for comprehensive, multi-sectoral interventions targeting both maternal and child health to effectively combat anaemia in mother-child dyads.Key messagesThe co-occurrence of anaemia among mother-child dyads in India increased from 33% in 2016 to 37% in 2021. More effective strategies and interventions to combat anaemia are needed.Realistic improvements in maternal education, dietary practices, toilet facilities and reduction in community open defecation could reduce anaemia prevalence among mother-child dyads by 18% by 2030.More optimistic scenarios could reduce anaemia burden by 28% by 2030, with improvements in education and sanitation being critical.