bluuebunny/crossref_metadata_2025_split huggingface dataset:
Lung cancer and Risk of Cardiovascular Mortality
Authors: Wang Chengshi , Wang Zhu , Yang Jing , Zhang Songbo , Zhang Purong , Yang Ye 
Published: December 2024

AbstractPurposeTo investigate the cardiovascular mortality risk among lung cancer patients compared to the general population.MethodsUsing data from the National Cancer Institute’s Surveillance, Epidemiology, and End Results program, we conducted a population-based cohort study including 278,418 lung cancer patients over 30 years old between January 1, 1990, and December 31, 2020 as well as the general population. Poisson regression was employed to calculate incidence rate ratios (IRRs) for cardiovascular mortality.ResultsPatients exhibited a significantly higher incidence rate ratio (IRR) of cardiovascular mortality risk compared to the general population [IRR 1.74, 95% confidence interval (CI) 1.71-1.77]. The risk was most pronounced in patients aged 30-79 years (IRR 2.61, 95% CI 2.55-2.66), peaking at ages 30-34 (IRR 48.93, 95% CI 21.98-108.92). Elevated cardiovascular mortality risks were observed across all subgroups, including diseases of the heart (IRR 1.79, 95% CI 1.75-1.82), cerebrovascular diseases (IRR 1.52, 95% CI 1.45-1.59), and other cardiovascular diseases (IRR 1.78, 95% CI 1.67-1.90). The first month post-diagnosis presented the highest risk for ages 30-79 (IRR 12.08, 95% CI 11.49-12.70) and ≥80 years (IRR 4.03, 95% CI 3.70-4.39). Clinical characteristics significantly modified cardiovascular mortality.ConclusionsIntegrating cardiovascular disease monitoring and proactive management into lung cancer treatment protocols is essential to the improvement of overall survival and quality of life for lung cancer patients, particularly those who were young or with advanced tumor stage.
Association of Vascular Risk Factors with the Risk of Parkinson’s Disease among Stroke Survivors—A Nationwide Population-Based Study
Authors: Yoon Seo Yeon , Moon Hyun Im , Jung Jin Hyung , Lee Sang Chul , Han Kyungdo , Kim Yong Wook 
Published: December 2024

AbstractBackgroundStroke survivors usually have many vascular risk factors (VRFs), and recent studies have reported the association of VRFs with cognitive decline among stroke survivors, which are also associated with PD risk. Therefore, we aimed to investigate how VRFs influenced the risk of PD occurrence in individuals with stroke.MethodsThe Korean National Health Insurance Service database was used. We selected individuals who experienced new-onset stroke (ICD-10: I60-I64) between 2008 and 2016 and followed up until 2019. PD was diagnosed according to ICD-10 code G20 and rare and intractable disease registration code V124. Lifestyle factors were collected using self-reported questionnaires, and comorbidities were defined based on ICD-10 codes, medication, and laboratory and anthropometric findings. Cox proportional hazard analyses were conducted to evaluate the association of VRFs with PD incidence among stroke survivors.ResultsAmong 284,799 stroke survivors, 3,274 cases of PD were reported over a median follow-up period of 5.04 years. In individuals with a history of stroke, smoking (former smoking, hazard ratio (HR) = 0.80, 95% confidence interval (CI), 0.72–0.88; current smoking, HR = 0.66, 95% CI, 0.58–0.76) and alcohol consumption (moderate drinking, HR = 0.83, 95% CI, 0.75–0.91; heavy drinking, HR = 0.74, 95% CI, 0.59–0.94) were associated with a decreased risk of PD. Physically active individuals who had a stroke showed a reduced trend for PD occurrence (P= 0.0508). As for the association between comorbidities and the PD risk, the PD risk increased among stroke survivors with diabetes mellitus (P for trend <.001), particularly in those with a duration of diabetes mellitus of ≥5 years (HR = 1.38, 95% CI, 1.25–1.52).ConclusionsOur results suggest that VRFs are associated with PD occurrence among stroke survivors. Understanding and managing VRFs in relation to PD risk is needed for disease management after stroke.
Store-operated Ca2+entry is involved in endothelium-to-mesenchymal transition in lung vascular endothelial cells
Authors: Babicheva Aleksandra , Elmadbouh Ibrahim , Song Shanshan , Thompson Michael , Powers Ryan , Jain Pritesh P. , Izadi Amin , Chen Jiyuan , Yung Lauren , Parmisano Sophia , Paquin Cole , Wang Wei-Ting , Chen Yuqin , Wang Ting , Alotaibi Mona , Shyy John Y.-J. , Thistlethwaite Patricia A. , Wang Jian , Makino Ayako , Prakash Y.S. , Pabelick Christina M. , Yuan Jason X.-J. 
Published: December 2024

AbstractEndothelial-to-mesenchymal transition (EndMT) is a biological process that converts endothelial cells to mesenchymal cells with increased proliferative and migrative abilities. EndMT has been implicated in the development of pulmonary vascular remodeling in pulmonary arterial hypertension (PAH), a fatal and progressive lung vascular disease. Transforming growth factor β1(TGF-β1), an inflammatory cytokine, is known to induce EndMT in many types of endothelial cells including lung vascular endothelial cells (LVEC). An increase in cytosolic free Ca2+concentration ([Ca2+]cyt) is a major stimulus for cellular proliferation and phenotypic transition, but it is unknown whether Ca2+signaling is involved in EndMT. In this study we tested the hypothesis that TGF-β1-induced EndMT in human LVEC is Ca2+-dependent. Treatment of LVEC with TGF-β1for 5-7 days resulted in increase in SNAI1/2 expression, induction of EndMT, upregulation of STIM/Orai1 and enhancement of store-operated Ca2+entry (SOCE). Removal (or chelation) of extracellular or intracellular Ca2+with EGTA or BAPTA-AM respectively abolished EndMT in response to TGF-β1. Moreover, EGTA diminished TGF-β1-induced increase in SNAI in a dose-dependent manner. Knockdown of either STIM1 or Orai1 was sufficient to prevent TGF-β-mediated increase in SNAI1/2 and EndMT, but did not rescue the continuous adherent junctions. Blockade of Orai1 channels by AnCoA4 inhibited TGF-β-mediated EndMT and restored PECAM1-positive continuous adherent junctions. In conclusion, intracellular Ca2+signaling plays a critical role in TGF-β-associated EndMT through enhanced SOCE and STIM1-Orai1 interaction. Thus, targeting Ca2+signaling pathways regulating EndMT may be a novel therapeutic approach to treat PAH and other forms of pre-capillary pulmonary hypertension.New & NoteworthyEndMT has been reported to contribute to the pathogenesis of PH. In this study we aimed to determine the role of Ca2+signaling in the development of EndMT in human lung vascular endothelial cells. Our data suggest that TGF-β1requires store-operated Ca2+entry through STIM1/Orai channels to induce SNAI-mediated EndMT. For the first time we demonstrated that TGF-β1-induced EndMT is Ca2+-dependent event while inhibition of STIM1/Orai interaction attenuated EndMT in response to TGF-β1.
Personality predicts dispersal and settlement in a novel habitat – a mesocosm experiment
Authors: Gismann J. , Groothuis T.G.G , Weissing F.J. , Nicolaus M. 
Published: December 2024

AbstractStudying the relationship between dispersal tendencies and personality, or ‘dispersal syndromes’, under ecologically relevant conditions is challenging, especially in fish. Laboratory studies lack environmental complexity and scale, while field-based approaches are often unfeasible. We here mimicked dispersal events; encompassing all three phases of dispersal (departure, transience, settlement) in a large experimental mesocosm containing a gradient of low- and high-quality breeding sites. Using Passive Integrated Transponder (PIT)-tagged three-spined sticklebacks (Gasterosteus aculeatus), we remotely monitored the movements and behaviours of two groups, each consisting of 40 individuals of known personality, continuously over two weeks. We tested whether personality variation explained differences in departure timing, transience movements, settlement success and spatial distribution along the environmental gradient. We found evidence for a dispersal syndrome. Specifically, more active/aggressive individuals dispersed quicker in the mesocosm, spent more time on territories and collected more eggs. However, we did not detect phenotype-environment correlations. Our findings highlight the importance of personality-dependent dispersal, with specific behavioural types driving dispersal and settlement success. Furthermore, our mesocosm setup provides unique insights into dispersal dynamics in a system where detailed observations in all dispersal stages are rare. Despite limitations, behavioural experiments in mesocosms serve as a valuable bridge between laboratory and natural systems.
Fertilization impacts microbiomes along the grassland trophic chain
Authors: Jetter Karoline , Jani Kunal , Wilhelm Kerstin , Stehle Ulrike , Chamedjeu Rostand , Riedel Christian , Wilfert Lena , Schäfer Patrick , Sommer Simone 
Published: December 2024

AbstractAgricultural grasslands are often managed intensively, influencing soil properties and microbial communities. This can result in significant community changes and challenges to health and function at different levels along the trophic chain. This study investigates how fertilization affects microbial communities in grassland ecosystems in multiple connected trophic compartments. Shifts in microbial composition occurred in response to fertilization with effects being host-dependent and changes being more pronounced in belowground compartments. Strong interactions between trophic levels facilitated transmission of bacterial genera from soil and roots to higher trophic levels. Pig slurry- derived microbes were found in all compartments, but their low prevalence suggests an indirect effect of fertilisation, primarily due to changes in nutrient availability. Our findings emphasize the importance of considering both individual compartments and trophic interactions in ecosystems to fully understand the effects of anthropogenic disturbances on environmental and human health.
Disruption of basal CXCR4 oligomers impairs oncogenic properties in lymphoid neoplasms
Authors: Mobach Simon , Bergkamp Nick D. , Ma Ziliang , Haselager Marco V. , Anbuhl Stephanie M. , Jurriens Daphne , Bor Jelle van den , Wang Ziming , Crudden Caitrin , Roos Jamie L. , Perez Almeria Claudia V. , Boergonje Rick A. , Lohse Martin J. , Bosma Reggie , Eldering Eric , Siderius Marco , Wu Wei , Spaargaren Marcel , Tonino Sanne H. , Kater Arnon P. , Smit Martine J. , Heukers Raimond 
Published: December 2024

AbstractThe chemokine receptor CXCR4 is overexpressed in many cancers and contributes to pathogenesis, disease progression, and resistance to therapies. CXCR4 is known to form oligomers, but the potential functional relevance in malignancies remain elusive. Using a newly established nanobody-based BRET method, we demonstrate that oligomerization of endogenous CXCR4 on lymphoid cancer cell lines correlates with enhanced expression levels. Specific disruption of CXCR4 oligomers reduced basal cell migration and pro-survival signaling via changes in the phosphoproteome, indicating the existence of basal CXCR4-oligomer-mediated signaling. Oligomer disruption also inhibited growth of primary CLL 3D spheroids and sensitized primary malignant cells to clinically used Bcl-2 inhibitor venetoclax. Given its limited efficacy in some patients and the ability to develop resistance, sensitizing malignant B-cells to venetoclax is of clinical relevance. Taken together, we established a new, non-canonical and critical role for CXCR4 oligomers in lymphoid neoplasms and demonstrated that selective targeting thereof has clinical potential.Significance statementClass A GPCRs, including the chemokine receptor CXCR4, can form oligomers, but their functional relevance remains poorly understood. This study provides evidence for the role of basal CXCR4 oligomers in lymphoid neoplasms, where they drive pro-survival signaling, migration, and tumor growth. We use a novel nanobody-based BRET method to demonstrate that endogenous CXCR4 constitutively oligomerizes in lymphoid cancer cells, correlating with receptor expression levels. Pharmacological disruption of these oligomers reduces tumor- associated signaling, impairs spheroid growth, and sensitizes patient-derived malignant cells to the apoptosis-inducing drug Venetoclax. Since CXCR4 is frequently overexpressed and potentially clustered in various malignancies, this work offers broader implications for enhancing treatment efficacy, overcoming drug resistance, and potentially reducing side effects across multiple cancer types.
Isolation and manipulation of meiotic spindles from mouse oocytes reveals migration regulated by pulling force during asymmetric division
Authors: Liu Ning , Kawamura Ryo , Qiang Wenan , Balboula Ahmed , Marko John F , Qiao Huanyu 
Published: December 2024

AbstractSpindles are essential for accurate chromosome segregation in all eukaryotic cells. This study presents a novel approach for isolating fresh mammalian spindles from mouse oocytes, establishing it as a valuablein vitromodel system for a wide range of possible studies. Our method enables the investigation of the physical properties and migration force of meiotic spindles in oocytes. We found that the spindle length decreases upon isolation from the oocyte. Combining this observation with direct measurements of spindle mechanics, we examined the forces governing spindle migration during oocyte asymmetric division. Our findings suggest that the spindle migration is regulated by a pulling force and a net tensile force of approximately 680 pN is applied to the spindlein vivoduring the migration process. This method, unveiling insights into spindle dynamics, holds promise as a robust model for future investigations into spindle formation and chromosome separation. We also found that the same approach could not isolate spindles from somatic cells, indicative of mammalian oocytes having a unique spindle organization amenable to isolation.SummaryNing et al. describe an innovative method to isolate fresh mammalian spindles at various stages from oocytes, enabling studies of spindlein vitro. The findings reveal that the spindle migration is regulated by a pulling force and such migration generates stretching tension in the spindle approximately 680 pN.
Spatial single-cell Organellomics reveals nutrient dependent hepatocyte heterogeneity and predicts pathophysiological statusin vivo
Authors: Hillsley Alexander , Adhikari Raghabendra , Johnson Alana Dowdell , Espinosa-Medina Isabel , Funke Jan , Feliciano Daniel 
Published: December 2024

AbstractCellular heterogeneity within complex tissues and organs is essential to coordinate biological processes across biological scales. The effect of local cues and tissue microenvironments on cell heterogeneity has been mainly studied at the transcriptional level. However, it is within the subcellular scale - the organelles - that lays the machinery to conduct most metabolic reactions and maintain cells alive, ensuring proper tissue function. How changes in subcellular organization under different microenvironments define the functional diversity of cells within organs remains largely unexplored. Here we determine how organelles adapt to different microenvironments using the mouse liver as model system, in combination with computational approaches and machine-learning. To understand organelle adaptation in response to changing nutritional conditions, we analyzed 3D fluorescent microscopy volumes of liver samples labeled to simultaneously visualize mitochondria, peroxisomes, and lipid droplets from mice subjected to different diets: a control diet, a high-fat diet, and a control diet plus fasting. ACellposebased pipeline was implemented for cell and organelle segmentation, which allowed us to measure ~100 different organelle metrics and helped us define subcellular architectures in liver samples at the single cell level. Our results showed that hepatocytes display distinct subcellular architectures within different regions of the liver-close to the central vein, in the middle region, and near the portal vein- and across the various diet groups, thus reflecting their adaptation to specific nutritional inputs. Principal component analysis and clustering of hepatocytes based on organelle signatures revealed 12 different hepatocyte categories within the different experimental groups, highlighting a reduction in hepatocyte heterogeneity under nutritional perturbations. Finally, using single cell organelle signatures exclusively, we generated machine learning models that were able to predict with high accuracy different hepatocyte categories, diet groups, and the stages of MASLD. Our results demonstrate how organelle signatures can be used as hallmarks to define hepatocyte heterogeneity and their adaptation to different nutritional conditions. In the future, our strategy, which combines subcellular resolution imaging of liver volumes and machine learning, could help establish protocols to better define and predict liver disease progression.
SubCell: Vision foundation models for microscopy capture single-cell biology
Authors: Gupta Ankit , Wefers Zoe , Kahnert Konstantin , Hansen Jan N. , Leineweber Will , Cesnik Anthony , Lu Dan , Axelsson Ulrika , Ballllosera Navarro Frederic , Karaletsos Theofanis , Lundberg Emma 
Published: December 2024

AbstractCells are the functional units of life, and the wide range of biological functions they perform are orchestrated by myriad molecular interactions within an intricate subcellular architecture. This cellular organization and functionality can be studied with microscopy at scale, and machine learning has become a powerful tool for interpreting the rich information in these images. Here, we introduce SubCell, a suite of self-supervised deep learning models for fluorescence microscopy that are designed to accurately capture cellular morphology, protein localization, cellular organization, and biological function beyond what humans can readily perceive. These models were trained using the metadata-rich, proteome-wide image collection from the Human Protein Atlas. SubCell outperforms state-of-the-art methods across a variety of tasks relevant to single-cell biology. Remarkably, SubCell generalizes to other fluorescence microscopy datasets without any finetuning, including dataset of drug-perturbed cells, where SubCell accurately predicts drug perturbations of cancer cells and mechanisms of action. Finally, we construct the first proteome-wide hierarchical map of proteome organization that is directly learned from image data. This vision-based multiscale cell map defines cellular subsystems with large protein-complex resolution, reveals proteins with similar functions, and distinguishes dynamic and stable behaviors within cellular compartments. In conclusion, SubCell enables deep image-driven representations of cellular architecture applicable across diverse biological contexts and datasets.
Intraoperative superb microvascular ultrasound imaging in glioma: novel quantitative analysis correlates with tumour grade
Authors: Dixon Luke , Weld Alistair , Bhagawati Dolin , Patel Neekhil , Giannarou Stamatia , Grech-Sollars Matthew , Lim Adrian , Camp Sophie 
Published: December 2024

ABSTRACTAccurate grading of gliomas is critical to guide therapy and predict prognosis. The presence of microvascular proliferation is a hallmark feature of high grade gliomas which traditionally requires targeted surgical biopsy of representative tissue. Superb microvascular imaging (SMI) is a novel high resolution Doppler ultrasound technique which can uniquely define the microvascular architecture of whole tumours. We examined both qualitative and quantitative vascular features of gliomas captured with SMI, analysing flow signal density, vessel number, branching points, curvature, vessel angle deviation, fractal dimension, and entropy. Results indicate that high-grade gliomas exhibit significantly greater vascular complexity and disorganisation, with increased fractal dimension and entropy, correlating with known histopathological markers of aggressive angiogenesis. The integrated ROC model achieved high accuracy (AUC = 0.95), highlighting SMI’s potential as a non-invasive diagnostic and prognostic tool. While further validation with larger datasets is required, this study opens avenues for SMI in glioma management, supporting intraoperative decision-making and informing future prognosis.
Extracellular Vesicles as Biomarkers for Steatosis Stages in MASLD Patients: an Algorithmic Approach Using Explainable Artificial Intelligence
Authors: Trifylli Eleni Myrto , Angelakis Athanasios , Kriebardis Anastasios G. , Papadopoulos Nikolaos , Fortis Sotirios P. , Pantazatou Vasiliki , Koskinas Ioannis , Kranidioti Hariklia , Koustas Evangelos , Sarantis Panagiotis , Manolakopoulos Spilios , Deutsch Melanie 
Published: December 2024

Background & AimsMetabolic dysfunction-associated steatotic liver disease (MASLD), formerly known as NAFLD, is a leading cause of chronic liver disease worldwide. Current diagnostic methods, including liver biopsies, are invasive and have significant limitations, emphasizing the need for non-invasive alternatives. This study aimed to evaluate extracellular vesicles (EV) as biomarkers for diagnosing and staging steatosis in MASLD patients, utilizing machine learning (ML) and explainable artificial intelligence (XAI).MethodsThis prospective, single-center cohort study was conducted at the GI-Liver Unit, Hippocration General Hospital, Athens. It included 76 MASLD patients with ultrasound-confirmed steatosis and at least one cardiometabolic risk factor. Patients underwent transient elastography for steatosis and fibrosis staging and blood sampling for EV analysis using nanoparticle tracking. Twenty machine learning models were developed. Six to distinguish non-steatosis (S0) from steatosis (S1-S3), and fourteen to identify severe steatosis (S3). Models incorporated EV measurements (size and concentration), anthropomorphic and clinical features, with performance evaluated using AUROC and SHAP-based interpretability methods.ResultsThe CB-C1a model achieved, on average on 10 random splits of 5-fold cross validation (5CV) of the train set, an AUROC of 0.71/0.86 (train/test) for distinguishing S0 from S1-S3 steatosis stages, relying on EV alone. The CB-C2h-21 model identified severe steatosis (S3), on average on 10 random splits of 3-fold cross validation (3CV) of the train set, with an AUROC of 0.81/1.00 (train/test), demonstrating superior performance when combining EV with anthropomorphic and clinical features such as diabetes and advanced fibrosis. Key EV features, including mean size and concentration, were identified as important predictors. SHAP analysis highlighted complex non-linear relationships between features and steatosis staging.ConclusionsEV are promising non-invasive biomarkers for diagnosing and staging MASLD. The integration of ML-enhanced EV analysis with clinical features offers a scalable, patient-friendly alternative to invasive liver biopsies, advancing precision in MASLD management. Further research should refine these methods for broader clinical application.Abstract Figure