bluuebunny/crossref_metadata_2025_split huggingface dataset:
Sturge-Weber Syndrome in a Multinational Paediatric Cohort: A Systematic Analysis of Different Types
Authors: Disse Sigrid , Ramantani Georgia , Küpper Hanna , Bock Annette , Korenke Georg-Christoph , Weidner Birgit , Preisel Martin , Trollmann Regina , Wiemer-Kruel Adelheid , Wellmann Sven , Brockmann Knut , Schröder Simone , Meyer Sascha 
Published: February 2025

Abstract

Background
Sturge-Weber Syndrome (SWS) is a rare neurocutaneous disease, characterized by cerebral capillary-venous malformation, glaucoma, and facial vascular birthmark. Different types are reflected in the Roach classification. Most previous studies have focussed on classic SWS Type I, but Type III cases, lacking facial birthmark, were mostly described in case reports. We systematically compare cases with and without facial birthmark, with a focus on epilepsy variables, cerebral involvement, use of aspirin, and overall outcome.
Methods
Using a cross-sectional observational study conducted through a well-established child neurologists’ network, we recruited paediatric patients with clinically diagnosed SWS from Germany, Switzerland, and Austria. The patients’ guardians and attending child neurologists filled in detailed questionnaires. All patients were classified according to the Roach classification by both attending child neurologists and the study team.
Results
Out of 111 paediatric SWS patients identified, 47 participated (43.2%). 35 cases (74.5%) fulfilled the criteria for classic SWS (Roach Type I); six cases (12.8%) showed no skin involvement (Roach Type III), the remaining six cases had overlap/atypical phacomatoses with capillary-venous malformation. Cases without facial birthmark were older at diagnosis (p = 0.005), and none showed ophthalmologic involvement. Age at first seizure did not differ significantly after adjustment for multiple comparisons. No significant differences were observed in seizure frequency, seizures types, number of used antiseizure medication (ASM), epilepsy surgery, cerebral involvement including atrophy and calcifications, SWS neuroscores or use of supportive therapies. Multivariable logistic regression showed seizure frequency was independent of SWS type and epilepsy surgery, but positively associated with the number of ASM required for seizure control (p = 0.0056). Half of operated patients were seizure-free at inclusion.
Conclusions
In our multinational cohort, Type I and Type III cases showed comparable epilepsy features, SWS neuroscores, number of required ASM and supportive therapy requirements. Type III patients were older at diagnosis and showed no ophthalmologic involvement, indicating a milder phenotype. Irrespective of SWS type, patients with uncontrolled epilepsy were 3.8-times more likely to require additional ASM. Despite uncontrolled epilepsy, only few patients underwent surgical evaluation or intervention. Larger cohorts are needed to evaluate surgical outcomes across SWS subtypes.
High Living Costs, Urban Agglomeration, and Inequality: Lessons from the Spatial Geo-Economic Politics of Urban Out-Migration in Sub-Saharan African Cities.
Authors: Nzabirinda Etienne 
Published: February 2025

Abstract

This paper examines how high living costs, urban agglomeration, and inequality interact in Sub-Saharan African cities, focusing on the urban out-migration aspect of the spatial geo-economic politics involved. Using data from the World Bank and the GMM technique, the study identifies income inequality, rapid urbanization, and rising living costs as the main determinants of migration. The analysis reveals a net migration rate of 1.092 in large cities like Lagos, Kinshasa, and Johannesburg, driven by urban growth and economic opportunities that attract people. However, due to increasing living costs, high housing costs, and congestion, individuals are forced to move to more affordable and less crowded areas, resulting in out-migration. Income inequality, with a Gini coefficient of 0.81, where a few urban areas accumulate wealth while leaving a large portion of the population in poverty, is also linked to migration dynamics. Rapid urbanization, with an average growth rate of 4.403%, exacerbates spatial disparities and encourages migration towards peri-urban and secondary cities. The study also explores the influence of economic indicators on migration trends, using GDP per capita and GNI growth to show that economic growth and inequality play significant roles. Policy recommendations such as affordable housing, economic decentralization, slum upgrading, and improved access to services are suggested to address migration challenges and promote sustainable urban development.
JEL Classification R23, O18, D31
Integrated UPLC-Q-TOF-MS, network pharmacology and experimental approach to evaluate the effects of blood stasis constitution ointment on lung cancer
Authors: Wang Qiaozhi , Wang Juhe , Dai Chuanhao , Lu Tianming , Xiong Xingjiang , Shen Shuo , Guo Qiuyan , You Hu , Du Maobo 
Published: February 2025

Abstract

Background
Blood stasis constitution ointment (BSCO) is based on the classic formulas Xuefu Zhuyu and Buyang Huanwu decoctions described in Yilin Gaicuo by Wang Qingren of the Qing Dynasty. These formulas have been used to effectively treat blood stasis, which is identified as a pathological factor of lung cancer in traditional Chinese medicine theory.
Aim of the study
: To analyze the chemical components of BSCO, predict target pathways, and evaluate its effects on lung cancer through in vivo and in vitro experiments.
Methods
Ultra-high performance liquid chromatography with quadrupole time-of-flight mass spectrometry was used to identify components of BSCO, and network pharmacology was used to predict their targets and signaling pathways associated with lung cancer. A Lewis lung cancer model was established in mice to evaluate the effects of BSCO by observing tissue morphology, whole animal imaging, and determination of serum biochemical indicators. The effects of BSCO on Lewis cancer cells in vitro were assessed using a CCK-8 cell proliferation assay.
Results
Twenty major chemical components of BSCO were identified, with 341 potential targets identified by network pharmacology. BSCO effectively inhibited tumor growth in the Lewis lung cancer mouse model and normalized serum markers of cancer to varying degrees. The IC50 of BSCO on Lewis cell proliferation was 173 mg/mL. Low- and high-dose BSCO-containing drug serum inhibited proliferation of Lewis cells after 24 and 48 h incubation.
Conclusion
The results suggest that BSCO may exert anticancer effects through targets including GAPDH, AKT1, TP53, TNF, IL6, and the PI3K-Akt signaling pathway, providing a reference for its clinical application in the prevention and treatment of lung cancer.
Learning neuroimaging models from health system-scale data
Authors: Hollon Todd , Lyu Yiwei , Chowdury Asadur , Banerjee Soumyanil , Gologorsky Rachel , Liu Shixuan , Meissner Anna-Katharina , Rao Akshay , Kondepudi Akhil , Jiang Cheng , Hou Xinhai , Joshi Rushekish , Neuschmelting Volker , Sriniva Ashok , Kleindorfer Dawn , Gulani Vikas , Pandey Aditya , Lee Honglak , Athey Brian 
Published: February 2025

Abstract

Neuroimaging is a ubiquitous tool for evaluating patients with neurological diseases. The global demand for magnetic resonance imaging (MRI) studies has risen steadily, placing significant strain on health systems, prolonging turnaround times, and intensifying physician burnout. These challenges disproportionately impact patients in low-resource and rural settings. Here, we utilized a large academic health system as a data engine to develop Prima, the first vision-language model (VLM) serving as an AI foundation for neuroimaging that supports real-world, clinical MRI studies as input. Trained on over 220,000 MRI studies, Prima uses a hierarchical vision architecture that provides general and transferable MRI features. Prima was tested in a 1-year, prospective, health system-wide study that included 30K MRI studies. Across 52 radiologic diagnoses from the major neurologic disorders, including neoplastic, inflammatory, infectious, and developmental lesions, Prima achieved a mean diagnostic area under the ROC curve of 90.1 +/- 5.0%, outperforming other state-of-the-art general and medical VLMs by a large margin. Prima offers explainable differential diagnoses, worklist priority for radiologists, and clinical referral recommendations across diverse patient demographics and MRI systems. Prima demonstrates algorithmic fairness across sensitive groups and can help mitigate health system biases, such as prolonged turnaround times for at-risk populations. These findings highlight the transformative potential of health system-scale VLMs and Prima's role in advancing AI-driven healthcare.
Chemically Programmable DNA Nanostructures for Multiplexed High-Throughput Screening of Cardiovascular Therapeutics
Authors: Wu Ke-Jia 
Published: February 2025

Abstract

The development of small-molecule therapeutics for cardiac hypertrophy is limited by the lack of high-throughput screening platforms capable of multiplexed molecular detection. Here, we present the Multifunctional Nucleic-acid-label-free hESC-CMs Molecular Evolution (MNCME) platform, which integrates tetrahedral DNA nanostructures with human embryonic stem cell-derived cardiomyocytes (hESC-CMs) for chemically programmable, label-free detection of DNA, RNA, proteins, and small molecules. By dynamically substituting three label-free primers, MNCME enables flexible molecular profiling and rapid target adaptation. Machine learning-assisted analysis of known anti-hypertrophic compounds identified key molecular signatures, optimizing high-throughput screening workflows. Structurally organized into five functional zones, MNCME screened a 600-compound library, identifying nine bioactive molecules, including caffeic acid (CA), previously unrecognized for its cardioprotective potential. Mechanistic studies revealed that CA modulates ATP synthesis, Ca²⁺ homeostasis, and Mucin-1 signaling, with validation in cellular and in vivo models. By integrating DNA nanotechnology, chemical biology, and high-throughput phenotypic screening, MNCME provides a scalable and versatile platform for accelerating the discovery of small-molecule modulators targeting cardiac hypertrophy. This approach has the potential to transform therapeutic development for cardiovascular diseases and other complex pathologies, offering a powerful tool for both fundamental research and clinical translation.
Unrealized “build back better” potential in the Era of “Polycrisis”: City climate and energy action learnings during COVID19
Authors: Constabile Kerry , Taylor Nicholas , Phillips Charlotte 
Published: February 2025

Abstract

Mitigating climate change in cities is one of the most important global climate policy aims due to the majority of emissions stemming from city production and consumption (IPCC 2022). Before COVID-19, thousands of city governments set ambitious climate action plans, including net zero and renewable energy commitments. This study investigates how COVID-19 and recovery packages affected city-level plans and actions. This paper draws on net zero, resilience and COVID-19 recovery literature, an original survey of 30 cities from countries in the global north and south: Argentina, Indonesia, Kenya, Mexico, Mozambique, the Philippines, South Africa, Sweden, Turkey the United Kingdom and the United States, and initial interviews with city leaders in select megacities: Jakarta and London. Results show that cities’ climate ambitions vary widely by region, as do the impacts of COVID-19 and recovery progress towards those goals. Many cities experienced delays as they pivoted to crisis response. Others benefited from new national green recovery policy frameworks and financing streams. Almost two years since the declaration of the end of the pandemic, COVID-19 has not fundamentally transformed cities in either the negative or promising ways that were predicted. While these lessons for city transformations are clear, lessons for cities in tackling climate crises in the ongoing era of the “polycrisis” seem useful to underscore. This paper identifies recommendations that would enable cities to leverage their COVID-19 experiences for tackling climate mitigation in the context of a projected increase in frequency and severity of shocks.
Practice abstract
Mitigating climate change in cities is one of the most important global climate policy aims due to the majority of emissions stemming from city production and consumption (IPCC 2022). Before COVID-19, thousands of cities set ambitious climate action plans, including net zero and renewable energy commitments. This study investigates how COVID-19 and recovery packages affected plans and actions. Results show that cities’ climate ambitions vary widely by region, as do the impacts of COVID-19 and recovery progress towards those goals. Many cities experienced delays as they pivoted to crisis response. Others benefited from new national green recovery policy frameworks and financing streams. This paper identifies recommendations that suggest cities might leverage COVID-19 learnings as well as potentially realise more effective responses to ongoing crises.
Improving Elderly Healthcare by Health Monitoring and Access to Social Welfare Services by IoTs integrationin Thailand
Authors: Chokphukhiao Chaturapron , tun Wonn Shweyi Thet , Duankhan Poomin , Masa Sakaowrat , Hongthong Patcharee , Pongskul Cholatip , Chaiayuth Somporn , Loeiyood Jugsun , Kuhirunyaratn Piyathida , Jindawong Bangonsri , Seeooppalat Nipitphon , Chiewchanwattana Sirapat , Patramanon Rina , Sunat Khamron 
Published: February 2025

Abstract

The rapid aging population in Thailand necessitates innovative solutions to improve healthcare access and monitoring for the elderly. In this study, technology access, social welfare rights, and health monitoring systems among elderly populations in Thailand were investigated by employing a mixed-methods approach. A quantitative survey was conducted with 2,005 participants aged 60 and above across four provinces (Khon Kaen, Lampang, Phra Nakhon Si Ayutthaya, and Songkhla), achieving a 96.95% response rate. The study developed a three-tier IoT-based health monitoring system, focusing on smartwatches and blood pressure monitors. High satisfaction rates were found with medical devices. Technology access was significant, with 78.8% owning internet-enabled smartphones and 92% receiving monthly living allowances. The study analyzed quantitative data using multiple regression statistics, revealing that health problems and concerns about technology are the top two factors restricting access to welfare services for the elderly. In addition, the challenges in digital access were noted, including concerns about online fraud (29.9%) and limited technological infrastructure. Access to efficient welfare services was the most strongly associated factor with increased satisfaction among the elderly in using digital health technology. This research encourages developing digital skills and providing appropriate support to enhance elderly individuals’ ability to access services, emphasizing the importance of addressing these factors within the aging population. It contributes to understanding the intersection of IoT technologies, social welfare, and elderly care, offering insights for future advancements in healthcare services for older adults. Clinical Trial Number: not applicable.
Narratives of Divide: The Polarizing Power of Large Language Models in a Turbulent World
Authors: Saqr Khalid 
Published: February 2025

Abstract

Large language models (LLMs) are reshaping information consumption and influencing public discourse, raising concerns about their potential to empower narrative control and amplify polarisation. This study examines the embedded worldviews of four LLMs across key themes using Wittgenstein’s theory of language games to interpret meaning and narrative structures. A two-tiered methodology—Surface (-S) and Deep (-D) analyses—is applied using Natural Language Processing (NLP) to investigate four different LLMs. The -S analysis, evaluating general differences in thematic focus, semantic similarity, and sentiment pattern, found no significant variability across the four LLMs. However, the -D analysis, employing zero-shot classification across geopolitical, ideological, and philosophical dimensions, revealed alarming differences. Liberalism (H = 12.51, p = 0.006), conservatism (H = 8.76, p = 0.033), and utilitarianism (H = 8.56, p = 0.036) emerged as key points of divergence between LLMs. For example, the narratives constructed by one LLM exhibited strong pro-globalization and liberal leanings, while another generated pro-sovereignty narratives, introducing meaning through national security and state autonomy frames. Differences in philosophical perspectives further highlighted contrasting preferences for utilitarian versus deontological reasoning across justice and security themes. These findings demonstrate that LLMs, when deployed at a sufficient scale and connectivity, could be employed as stealth weapons in narrative warfare.
Cognitive impairment and brain atrophy in patients with newly diagnosed aggressive lymphoma undergoing standard chemotherapy: a normative analysis
Authors: Ditchfield Charlotte , Gates Priscilla , Duque Juan Dominguez , Dhillon Haryana M , Dore Vincent , Wilson Carlene , Caeyenberghs Karen 
Published: February 2025

Abstract

Objective
Cancer-related cognitive impairment (CRCI) can impact daily-life functioning of people with aggressive lymphoma. While many studies have examined the neural substrates implicated in CRCI, most have used group-based analyses, which may mask individual differences. In the present study, we used normative analysis to examine longitudinal changes in cognitive functioning and brain morphology at the level of the individual patient.
Methods
Nine participants with newly diagnosed aggressive lymphoma underwent neuropsychological assessment and anatomical MR before and 6–8 weeks after chemotherapy. Cognitive test scores were converted to T-scores and classified as impaired if ≤ 30. Deviations in cortical thickness and surface area in the superior frontal gyrus (SFG) and anterior cingulate cortex (ACC) were computed at the level of the individual using the novel CentileBrain tool, with z-scores below \(\:-\)1.96 and above 1.96 classified as infranormal and supranormal, respectively.
Results
Analyses revealed substantial between-subject variability over time and across outcome measures. Cognitive impairments in executive function and verbal memory were identified in three participants before and/or after chemotherapy. CentileBrain results showed seven participants had infranormal cortical thickness and/or surface area in the SFG at one or both time points, and one patient had infranormal values in the ACC. No participants exhibited supranormal values in either region at any time point.
Conclusions
Our findings provide a valuable foundation for developing more personalised interventions tailored to the specific cognitive and neural profiles of lymphoma survivors, paving the way for improved clinical care to alleviate and mitigate the impact of CRCI on long-term quality of life.
CO₂ Emissions Projections for 2100: A Comparative Machine Learning Study of U.S. and Multimodal Approach of Global Trends
Authors: Murgod Sanjana , Garg Kartik , Magadum Triveni , Yadav Vivek , Mittal Harshit , Kushwaha Omkar Singh 
Published: February 2025

Abstract

The continuous rise in CO₂ emissions is a major contributor to climate change, affecting ecosystems, economies, and public health. Predicting future emissions accurately is crucial for designing effective policies and mitigation strategies. This study explores multiple machine learning models for CO₂ emissions forecasting, comparing traditional methods like Support Vector Machines (SVM), Linear Regression, and Decision Trees with advanced deep learning techniques such as Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), and Multi-Layer Perceptron (MLP). Using a time-series approach, we forecast emissions up to 2100 and assess model performance through key metrics like Mean Squared Error (MSE), Mean Absolute Error (MAE), and R² score. Our results indicate that deep learning models, especially LSTM and GRU, outperform traditional methods in capturing complex patterns and trends in emissions data. Additionally, we generate geospatial visualizations to highlight regions facing the highest risks. These insights provide valuable guidance for policymakers and environmental researchers, enabling data-driven decisions for emission reduction, resource allocation, and long-term sustainability planning in the fight against climate change.
Streamflow prediction using Long Short-Term Memory networks: a case study at the Kratie Hydrological Station, Mekong River Basin
Authors: Nguyen Nhu Y , Kha Dang Dinh , Ninh Luu Van , Anh Vu Tuan , Anh Tran Ngoc 
Published: February 2025

ABSTRACT

Accurate streamflow prediction is vital for hydropower operations, agricultural planning, and water resource management. This study assesses the effectiveness of Long Short-Term Memory (LSTM) networks in daily streamflow prediction at the Kratie station, investigate different network structures and hyperparameters to optimize predictive accuracy while considering computational efficiency. Our findings underscore the significance of LSTM models in addressing streamflow prediction challenges. Training LSTM on historical streamflow data reveals the significance of the training dataset size; spanning 2013–2022 yields optimal results. Incorporating a hidden layer with a nonlinear activation function, and adding a fully connected layer improve prediction ability. However, increasing the number of neurons and layers introduces complexity and computational overhead. Careful parameter tuning, including epochs, dropout, and the number of LSTM units, is crucial for optimal performance without sacrificing efficiency. The stacked LSTM with sigmoid activation demonstrates exceptional performance, boasting a high Nash–Sutcliffe Efficiency of 0.95 and a low relative root mean square error (rRMSE) of approximately 0.002%. Moreover, the model excels in forecasting streamflow for 5–15 antecedent days, with 5 days exhibiting particularly high accuracy. These findings offer valuable insights into LSTM networks for streamflow prediction for water management in the Vietnam Mekong Delta.