bluuebunny/crossref_metadata_2025_split huggingface dataset:
Plasma proteins are integral to gene-regulatory networks acting within and across blood cells, the arterial wall and major metabolic organs
Authors: Bankier Sean , Talukdar Husain , Khan Mariyam , Mocci Giuseppe , Sukhavasi Katyayani , Hao Ke , Ma Angela , Ruusalepp Arno , Schadt Eric E , Kovacic Jason C. , Michoel Tom , Björkegren Johan LM 
Published: January 2025

SUMMARYThe plasma proteome is the future for diagnostic markers for common diseases, like cardiometabolic disorders (CMDs) and coronary artery disease (CAD). The reliability of these markers requires identifying their source-organ(s). We profiled 974 plasma proteins in 532 CAD-patients of the STARNET study with arterial wall, major metabolic organ, and blood transcriptomic data. 144 plasma cis-pQTLs colocalized with tissue eQTLs including eight CMD/CAD GWAS genes. 262 plasma proteins correlated with their corresponding tissue “seed” genes whereof 101 in the liver. 851 plasma proteins were strongly associated with the activity of gene-regulatory networks (GRNs), particularly cross-tissue GRNs. The Adipose-Liver Plasma LeptIN-regulating GRN78 with the top key driverUCHL1in fat stood out. Beyond genetics, explaining up to 20% of plasma protein variation, and a limited number of mostly hepatic seed genes, plasma proteins are integral to GRNs acting within and across blood cells, the arterial wall and major metabolic organs.
Uncertainty and Inconsistency of COVID-19 Non-Pharmaceutical Intervention Effects with Multiple Competitive Statistical Models
Authors: Müller Bernhard , Padberg Inken , Lorke Michael , Brinks Ralph , Cripps Sally , Gomes M. Gabriela M. , Haake Daniel , Ioannidis John P. A. 
Published: January 2025

AbstractQuantifying the effect of non-pharmaceutical interventions (NPIs) is essential for formulating lessons from the COVID-19 pandemic. To enable a more reliable and rigorous evaluation of NPIs based on time series data, we reanalyse the data for the original official evaluation of NPIs in Germany using an ensemble of 9 competitive statistical methods for estimating the effects of NPIs and other determinants of disease spread on the effective reproduction number ℛ(t) and the associated error bars. A proper error analysis for time series data leads to significantly wider confidence intervals than the official evaluation. In addition to vaccination and seasonality, only few NPIs – such as restrictions in public spaces – can be confidently associated with variations in ℛ(t), but even then effect sizes have large uncertainties. Furthermore, due to multicollinearity in NPI activation patterns, it is difficult to distinguish potential effects of NPIs in public spaces from other interventions that came into force early, such as physical distancing. In future, NPIs should be more carefully designed and accompanied by plans for data collections to allow for a timely evaluation of benefits and harms as a basis for an effective and proportionate response.
Machine learning prediction algorithms for 2- , 5- and 10-year risk of Alzheimer’s, Parkinson’s and dementia at age 65: a study using medical records from France and the UK General Practitioners
Authors: Nedelec Thomas , Zaidi Karim , Montaud Charlotte , Guinebretiere Octave , Sipilä Pyry , Wei Dang , Yang Fen , Freydenzon Anna , Belloir Antoine , Fournier Nemo , Hamieh Nadine , Lekens Beranger , Slaouti Yanis , McRae Allan , Couvy-Duchesne Baptiste , Hswen Yulin , Fang Fang , Kivimäki Mika , Ansart Manon , Durrleman Stanley 
Published: January 2025

Structured abstractBackgroundLeveraging machine learning on electronic health records offers a promising method for early identification of individuals at risk for dementia and neurodegenerative diseases. Current risk algorithms heavily rely on age, highlighting the need for alternative models with strong predictive power, especially at age 65, a crucial time for early screening and prevention.MethodsThis prospective study analyzed electronic health records (EHR) from 76,427 adults (age 65, 52.1% women) using the THIN database. A general risk algorithm for Alzheimer’s disease, Parkinson’s disease, and dementia was developed using machine learning to select predictors from diagnoses, and medications.ResultsMedications (e.g., laxatives, urological drugs, antidepressants), along with sex, BMI, and comorbidities, were key predictors. The algorithm achieved a 38.4% detection rate at a 5% false-positive rate for 2-year dementia prediction.ConclusionThe validated prediction algorithms, easy to implement in primary care, identify high-risk 65-year-olds using medication records. Further refinement and broader validation are needed.
Quantification of HER2-low and ultra-low expression in breast cancer specimens by quantitative IHC and artificial intelligence
Authors: Aidt Frederik , Arbel Elad , Remer Itay , Ben-David Oded , Ben-Dor Amir , Rabkin Daniela , Hoff Kirsten , Salomon Karin , Aviel-Ronen Sarit , Nielsen Gitte , Mollerup Jens , Jacobsen Lars , Tsalenko Anya 
Published: January 2025

AbstractRecent advancements in antibody drug conjugate therapies have significantly broadened treatment options for a substantial subgroup of breast cancer patients. However, sensitive, accurate and quantitative evaluation of HER2 expression based on current immunohistochemistry (IHC) assays remain challenging, especially in low and ultra-low HER2 expression ranges.We developed a novel methodology for quantifying HER2 protein expression, targeting breast cancer cases in the HER2 IHC 0 and 1+ categories. We measured HER2 expression using quantitative IHC (qIHC) that enables precise and tunable HER2 detection across different expression levels as demonstrated in formalin-fixed paraffin-embedded cell lines. Additionally, we developed an AI-based interpretation of HercepTest™ mAb pharmDx (Dako Omnis) (HercepTest™ mAb) using qIHC measurements as the ground truth. Both methodologies allowed spatial resolution and visualization of low and ultra-low levels of HER2 expression across entire tissue sections to demonstrate and enable quantification of heterogeneity of HER2 expression.Serial sections of 82 formalin-fixed paraffin-embedded tissue blocks of invasive breast carcinoma with HER2 IHC scores 0 or 1+ were stained with H&E, HercepTest™ (mAb), qIHC and p63, then scanned and digitally aligned. Tumor areas were manually selected and reviewed by expert pathologists. HER2 expression was quantitatively evaluated based on the qIHC assay in each 128×128µm2area within tumor regions. We observed statistically significant differences in HER2 expression between IHC 0, 0<IHC<1+, and IHC 1+ groups, and high level of spatial heterogeneity of the HER2 expression levels within the same tissue, up to five-fold in some cases. We demonstrated high slide-level tumor region agreement of estimates of HER2 expression between the AI-based interpretation of HercepTest™ mAb and the qIHC ground truth with a Pearson correlation of 0.94, and R2of 0.87.The developed methodologies can be used to stratify HER2 low-expression patient groups, potentially improving the interpretation of IHC assays and maximizing therapeutic benefits. This method can be implemented in histology labs without requiring a specialized workflow.
Spontaneous spatial sorting by cell shape in growing colonies of rod-like bacteria
Authors: Ratman Mateusz , Gonzalez Nuñez Jimmy , Beller Daniel A. 
Published: January 2025

AbstractMechanical interactions among cells in a growing microbial colony can significantly influence the colony’s spatial genetic structure and, thus, evolutionary outcomes such as the fates of rare mutations. Here, we computationally investigate how this spatial genetic structure changes as a result of heritable phenotypic variations in cell shape. By modeling rod-like bacterial cells as lengthening and dividing circo-rectangles in a 2D Brownian dynamics framework, we simulate the growth of a colony containing two populations with different aspect ratios. Compared to monodisperse colonies, such bidisperse colonies exhibit diminished intermixing between sub-populations when the less elongated cells are too short to nematically order, instead forming large clusters. We find that the cells with longer aspect ratio gradually segregate to the colony periphery. We present evidence that this demixing is related to nematic order in the bulk and to active nematic mixing dynamics near the periphery. These findings are qualitatively robust across different growth rate protocols and initial conditions. Because the periphery is often an advantageous position when nutrients are limited, our results suggest a possible evolutionary selective pressure of mechanical origin that favors large cell aspect ratio.
Attenuated adenosine mediated immune-dampening increases natural killer cell activity in early age-related macular degeneration
Authors: Nair Archana Padmanabhan , Ghosh Sayan , Babu Vishnu Suresh , Praveen Machiraju , Xin Ying , Sahu Ganesh Ram , Vaidya Tanuja Arun , Debnath Jayasree , Raja Karthik , Krishna Gadde Santhosh Gopi , Thirumalesh M B , Shetty Naren , Saxena Aishwarya , Shetty Rohit , Hose Stacey , Deshpande Vrushali , Chakrabarty Koushik , Handa James T. , Jiang Qian J. , Sethu Swaminathan , Sinha Debasish , Ghosh Arkasubhra 
Published: January 2025

ABSTRACTNon-exudative age-related macular degeneration (AMD) involves retinal pigment epithelium (RPE) dysfunction and has been linked to altered intraocular immunity. Our investigation focuses on immune cell subsets and inflammation-associated factors in the eyes with early and intermediate AMD. We observed elevated levels of activated natural killer (NK) cells and interferon-γ, concurrent with reduced myeloid-derived suppressor cells (MDSCs) and adenosine in AMD eyes. Aqueous humor from AMD patients had diminished ability to dampen NK cell activation, an effect rescued by adenosine supplementation. TheCryba1cKO mouse model recapitulated these immune alterations, and single-cell RNA-sequencing identified NK cell-related genes and NK cell-RPE interactions. Co-culture of activated NK cells with RPE cells induced barrier dysfunction and Gasdermin-E driven pyroptosis providing a functional link relevant to AMD. These findings suggest a double-hit model where elevated immune activation and loss of immune dampening mechanisms drive AMD progression. Resetting the intraocular immune balance may be a promising therapeutic strategy for managing early and intermediate AMD.Graphical abstractIllustra tion of pro posed mechanism underlying NK cell-RPE interaction in early AMD pathogenesis. Dysregulated NK cell communicate s with stressed RPE in early AMD immunopathology. Aberrant AMD aqueous humor and retina shows increased NK cells and NK effector molecules like IFNγ with reduced MDSC and adenosine in human subjects, don or eye and animal model. Activated NK cells interaction with RPE causes dysfunction and pyroptotic cell death via Gasdermin-E pathway in AMD.. Created withBioRender.com
Dynamic Reprogramming of Stromal Pdgfra-expressing cells during WNT-Mediated Transformation of the Intestinal Epithelium
Authors: Pellon-Cardenas O , Rout P , Hassan S , Fokas E , Ping He , Patel I , Patel J , Plotsker O , Wu A , Kumar R , Akther M , Logerfo A , Wu S , Wagner DE , Boffelli D , Walton KD , Manieri E , Tong K , Spence JR , Bessman NJ , Shivdasani RA , Verzi MP 
Published: January 2025

ABSTRACTStromal fibroblasts regulate critical signaling gradients along the intestinal crypt-villus axis1and provide a niche that supports adjacent epithelial stem cells. Here we report thatPdgfra-expressing fibroblasts secrete ligands that promote a regenerative-like state in the intestinal mucosa during early WNT-mediated tumorigenesis. Using a mouse model of WNT-driven oncogenesis and single-cell RNA sequencing (RNA-seq) of mesenchyme cell populations, we revealed a dynamic reprogramming of Pdgfra+fibroblasts that facilitates WNT-mediated tissue transformation. Functional assays of potential mediators of cell-to-cell communication between these fibroblasts and the oncogenic epithelium revealed that TGFB signaling is notably induced in Pdgfra+fibroblasts in the presence of oncogenic epithelium, and TGFB was essential to sustain regenerative-like growth of organoidsex vivo. Genetic reduction ofCdx2in the β-catenin mutant epithelium elevated the fetal-like/regenerative transcriptome and accelerated WNT-dependent onset of oncogenic transformation of the tissuein vivo. These results demonstrate that Pdgfra+fibroblasts are activated during WNT-driven oncogenesis to promote a regenerative state in the epithelium that precedes and facilitates formation of tumors.
Warming disrupts plant-fungal endophyte symbiosis more strongly in leaves than roots
Authors: Edwards Joseph D. , Kazenel Melanie R. , Luo Yiqi , Lynn Joshua S. , McCulley Rebecca L. , Souza Lara , Young Carolyn , Rudgers Jennifer A. , Kivlin Stephanie N. 
Published: January 2025

AbstractDisruptions to species interactions from global change will negatively impact plant primary production, with broader consequences for species’ abundances, distribution, and community composition. Fungal endophytes that live inside plant leaves and roots could potentially mitigate plant heat stress from global warming. Conversely, disruptions of these symbioses could exacerbate the negative impacts of warming. To better understand the consistency and strength of warming-induced changes to fungal endophytes, we examined fungal leaf and root endophytes in three grassland warming experiments in the US ranging from 2 to 25 years and spanning 2000 km, 12 °C of mean annual temperature, and 600 mm of precipitation. We found that experimental warming disrupted symbiosis between plants and fungal endophytes. Colonization of plant tissues by septate fungi decreased in response to warming by 90% in plant leaves and 35% in roots. Warming also reduced fungal diversity and changed community composition in plant leaves but not roots. The strength, but not direction, of warming effects on fungal endophytes varied by up to 75% among warming experiments. Finally, warming decoupled fungal endophytes from host metabolism. Overall, warming-driven disruption of fungal endophyte community structure and function suggests that this symbiosis may not be a reliable mechanism to promote plant resilience and ameliorate stress responses under global change.
TRAIL-mediated PMN-MDSC depletion prevents CD4+T cell loss in HIV-infected humanized mice
Authors: Abeynaike Shawn , Frank Kayla , Gandarilla Angel , Paust Silke 
Published: January 2025

AbstractMyeloid-derived suppressor cells (MDSCs) are critical regulators of HIV immunity, but the mechanisms behind their expansion and value as a therapeutic target remain unclear. We investigated MDSC function in HIV using a humanized mouse model. Acute HIV-1 infection triggered the systemic expansion of polymorphonuclear MDSCs (PMN-MDSCs), with their frequency correlating positively with plasma viral load. Notably, PMN-MDSC expansion increased further when natural killer (NK) cells and cytotoxic T lymphocytes (CTLs) were depleted, suggesting these immune cells restrain MDSC proliferation. We identified Tumor necrosis factor related apoptosis-inducing ligand (TRAIL)-induced apoptosis as a regulatory mechanism, as MDSCs expressed high levels of TRAIL receptor 2 (R2), while NK cells and CTLs upregulated TRAIL expression during HIV infection. Treating HIV-1-infected mice with a TRAIL-R2 agonist significantly reduced MDSC levels to those of uninfected mice and significantly improved NK cell and CTL immune function. Crucially, in viremic humanized mice, the treatment prevented CD4+ T cell loss, a hallmark of HIV-1 pathogenesis and acquired immune deficiency syndrome (AIDS) progression. These findings reveal the therapeutic potential of targeting MDSC apoptosis to mitigate CD4+ T cell depletion and enhance HIV-specific immunity, offering a new clinical target to prevent AIDS and insights into MDSC– NK/CTL cross-regulation during HIV-1 infection.
Deep learning-driven morphology analysis enables label-free classification of therapeutic agentnaive versus resistant cancer cells
Authors: Ramathal Cyril , Saini Kiran , Lian Zhouyang , Corona Christian , Pham Tiffine , Carelli Ryan , Boutet Stephane C. , Ray Manisha , Sethuraman Sunantha , Prindle Vivian , Lattmann Evelyn , Molvetti Maria-Grazia , Dzung Andreas , Levesque Mitchell P. , Barnes Matt , Jovic Andreja 
Published: January 2025

AbstractTherapeutic drug treatments of solid tumors are often undermined by various resistance mechanisms. Identification of drug-resistance phenotypes at the single cell level is challenging because conventional molecular methods are cell-destructive, labor-intensive, and cost-prohibitive. To overcome these challenges, we developed an orthogonal approach to drug-resistance phenotyping, through the use of deep-learning-driven morphology analysis of single, high resolution cell images.Specifically, we trained deep learning-based drug resistance classifiers using cell images from 5 different cell lines that were rendered resistant to 5 different therapeutic agents, using a foundation model framework. With high accuracy, the classifier correctly predicted naive or resistance phenotypes across all cancer types and across all the therapeutic agent types (chemotherapeutic, targeted) tested. These results showed that morphology can capture complex phenotype information in the context of drug treatment. To demonstrate the potential clinical utility of the drug resistance classifier, it was applied to a dissociated tumor biopsy and the resulting phenotype predictions were in close concordance with scRNASeq analysis of the biopsy.Our study highlights the potential of deep-learning-driven morphology analysis to provide complex phenotype information, and ultimately shape oncology drug treatment strategies at the patient-level in a clinical context.
Capillary-induced bundling of biopolymer networks via protein condensates
Authors: Feigeles Carolyn A. , Brasovs Artis , Puchalski Adam , Laukat Olivia , Kornev Konstantin G. , Weirich Kimberly L. 
Published: January 2025

AbstractWithin the cell, biopolymers form self-organized assemblies that regulate cellular processes. These assemblies can be constructed through either protein interactions or phase separation. It is known that actin filaments, which form the mechanical structure of cells, are assembled into networks and bundles by protein cross-linkers. Different network and bundle microstructures support different physiological functions. Recently, there is evidence that protein condensates interact with biopolymers to create bundles. Here, we show that protein condensates colocalize with actin filaments and form networks of bundles. The condensates absorb on actin bundles and relax into a barrel shaped droplet on bundles, evocative of drops of simple liquids on fibers. We investigate the condensate spreading and measure contact angle that condensates make with bundles. Condensates at the intersection of bundles cause capillary bridges which induce network remodeling. Our results suggest that network formation, bundling, and remodeling in biopolymer assemblies could be induced by capillary interactions due to condensates. Understanding this bundling mechanism could expand our toolkit for making self-assembled fiber-based soft materials.
On the duality of pain and pleasure processing: Why two dimensions of valence may be better than one
Authors: Dulberg Zack , Cohen Jonathan D. 
Published: January 2025

AbstractReinforcement learning treats reward maximization as a single objective, such that pain avoidance is implicit in pleasure seeking. However, humans appear to have distinct neural systems for processing pain and pleasure. This paper investigates the computational advantages of this separation through grid-world experiments. We demonstrate that modular architectures employing distinctmaxandminoperators for value propagation outperform monolithic models in non-stationary environments. This separation allows agents to simultaneously grow and shrink learned values without interference, enabling both efficient reward collection and punishment avoidance. Additionally, these separate systems can be dynamically arbitrated using a mood-like mechanism for rapid adaptation. Our results suggest that separate pain and pleasure systems may have evolved to enable safe and efficient learning in changing environments.Nature has placed mankind under the governance of two sovereign masters, pain and pleasureJeremy Bentham
Text Mining miRNA-gene Interactions for the mx-plore Platform
Authors: Joppich Markus , Klein Samuel , Zimmer Ralf 
Published: January 2025

AbstractmiRNAs are post-transcriptional regulators which bind to the specific mRNA of expressed genes and induce increased mRNA degradation, thereby reducing the gene expression of their target genes. miRNAs perform their regulatory functions in a context-specific manner. Several databases contain collections of miRNA-target interactions, which are either experimentally validated, derived from high-throughput experiments, or computationally predicted or captured from existing literature. For most databases, the specific context of the miRNA-target interaction is unknown or missing in these databases. mx-plore is not only a database of text-dervied miRNA-gene interactions, but it enhances the found interactions including detailed context information from scientific publications. To derive the entries in the mx-plore database, a newly developed text mining strategy combining dependency graph analysis and rule-based systems has been used. The mx-plore platform makes miRNA-gene interactions accessible and searchable on various levels, such as by cell type, disease or involved processes. The platform is available athttps://rest.bio.ifi.lmu.de/mxploreand corresponding source code is deposited on GitHub.Author summaryThe mx-plore platform is a comprehensive database of miRNA:target interactions as described in the scientific literature.mx-plore is a new text-mining approach to extract context-dependent miRNA interactions from Pubmed abstracts and PMC full texts.mx-plore allows to query the comprehensive database in a context-specific manner, both on- and off-line.