bluuebunny/crossref_metadata_2025_split huggingface dataset:
A combined computational and experimental approach to successfully predict the behavior of metastatic tumor-associated macrophages in human breast cancer
Authors: Bekkar Amel , Pabois Angélique , Crespo Isaac , Turrini Riccardo , Xenarios Ioannis , Doucey Marie-Agnès 
Published: February 2025

ABSTRACTWe identified in human breast cancer a scarce population of Tumor-Associated Monocytes (TAMs) endowed with a pro-metastatic activity and associated with reduced distant-metastasis free survival of patients. We developed a novel framework combining computational and experimental methodologies to dampen TAM pro-metastatic activity. Multi-modal experimental data from TAMs exposed in vitro to a series of perturbations were collected to build a Boolean dynamical network of TAMs. This framework successfully identified the biological pathways underlying TAM pro-metastatic activity and predicted potent pharmacological interventions that inhibited the pro-metastatic activity of TAMs isolated from patient tumors. This study showcases the power of integrating computational predictions with experimental validation in identifying new therapeutic avenues that can be extended to other cancer types.
Dynamic Predictive Spatial Encoding of Motor Intentions In Area V6A of the Posterior Parietal Cortex
Authors: Buonfiglio Antonio Roberto , Diomedi Stefano , Filippini Matteo , Fattori Patrizia , Stoianov Ivilin Peev 
Published: February 2025

AbstractTo compute motor plans or intentions, the nervous system must translate tar-get locations into body-centered coordinates. Visual stimuli, however, are sensed in retinotopic coordinates, which shift with eye movements. Furthermore, sen-sorimotor delays necessitate predictive processing. How does the brain compute timely gaze-invariant target locations? The dorsal visual pathway encodes spa-tial intentions, yet the underlying dynamic mechanisms remain elusive. Using multilevel analysis, we characterized intention coding in area V6A of the Pos-terior Parietal Cortex during delayed reaching tasks under diverse gaze-target conditions. We revealed a consistent population-level intention coding in V6A as eye positions changed. Next, we identified differential single-cell encoding of gaze and reaching targets in retinotopic, gaze-posture, and body-centered coordinates and elucidated the dynamical spatial normalization. Finally, we demonstrated context-dependent predictive spatial encoding in V6A, advancing our understand-ing of the temporal evolution of predictive visuomotor transformations during motor planning.
A Sacrificial 3D Printed Vessel-on-Chip Demonstrates a Versatile Approach to Model Connective Tissue Pathology
Authors: Jäger Jonas , Berger Phil , Morrison Andrew I. , Erfurth Hendrik , Thon Maria , Dehne Eva-Maria , Gibbs Susan , Koning Jasper J. 
Published: February 2025

AbstractForin vitroorgan models, perfused vasculature is crucial to overcome nutrient diffusion limits and to generate immunocompetent models by allowing trans-endothelial migration of immune cells in and out of the tissue. However, vasculature is often disregarded due to its complexity to generate and the necessity to integrate flow. The aim here was to overcome these limitations by combining 3D printing and multi-organ-chip technology to generate a vascularized, fibroblast-populated connective tissue matrix on-chip. A 3D printed, sacrificial, water-dissolvable structure was incorporated into a multi-organ-chip during its fabrication and hollow channels within a collagen/fibrin hydrogel were generated. Subsequently, channels were populated with endothelial cells and varying hydrogel concentrations (Fhigh/Chigh) used to mimic healthy and diseased connective tissue. The vessels were perfused and stable metabolic/viability conditions (lactate dehydrogenase, glucose, lactate) acquired after 3 days for at least 7 days. Angiogenic sprouting and secretion of angiogenic cytokines were increased in Fhighhydrogels. Perfusion with monocytes revealed differentiation into macrophages and migration across the endothelium into the tissue. In conclusion, this versatile, easy method to pattern hydrogels in a multi-organ-chip can serve as the basis to build the next generation of vascularized, immunocompetent human organ models, and opens new possibilities to study health and disease.
Effects of Interleukin-6 dysregulation in a mouse model of Alzheimer’s disease: unraveling the complexity beyond amyloidosis
Authors: Canal Carla , Aguilar Kevin , Comes Gemma , Sanchis Paula , Molinero Amalia , Giralt Mercedes , Hidalgo Juan 
Published: February 2025

1.AbstractInterleukin-6 dysregulation has been implicated in the pathological progression of Alzheimer’s disease (AD), a leading cause of dementia in the ageing population. Here we show that chronic neuroinflammation elicited by transgenic expression of IL-6 and systemic IL-6 deficiency influences the phenotype of the Tg2576 mouse model of AD, modulating mortality, body weight, behavioral and cognitive traits, amyloidosis, and neuroinflammation. While the conventional understanding of AD pathophysiology emphasizes the central role of Aβ peptides and amyloid plaques in the development and progression the disease, the absence of amyloidosis in the brain in a specific subset of Tg2576 mice challenges this notion. This suggests that several Tg2576-related traits may manifest independently of Aβ, pointing to a potential contribution of alternative APP-related factors or pathways to the phenotypic alterations observed in Tg2576 mice. Together, our findings underscore the complexity of AD pathogenesis and emphasize the multifaceted nature of IL-6 in both physiological and neurodegenerative processes, particularly in the context of AD.
SAMCell: Generalized Label-Free Biological Cell Segmentation with Segment Anything
Authors: VandeLoo Alexandra D. , Malta Nathan J. , Aponte Emilio , van Zyl Caitlin , Xu Danfei , Forest Craig R. 
Published: February 2025

AbstractBackgroundWhen analyzing cells in culture, assessing cell morphology (shape), confluency (density), and growth patterns are necessary for understanding cell health. These parameters are generally obtained by a skilled biologist inspecting light microscope images, but this can become very laborious for high throughput applications. One way to speed up this process is by automating cell segmentation. Cell segmentation is the task of drawing a separate boundary around each individual cell in a microscope image. This task is made difficult by vague cell boundaries and the transparent nature of cells. Many techniques for automatic cell segmentation exist, but these methods often require annotated datasets, model retraining, and associated technical expertise.ResultsWe present SAMCell, a modified version of Meta’s Segment Anything Model (SAM) trained on an existing large-scale dataset of microscopy images containing varying cell types and confluency. We find that our approach works on a wide range of microscopy images, including cell types not seen in training and on images taken by a different microscope. We also present a user-friendly UI that reduces the technical expertise needed to use this automated microscopy technique.ConclusionsUsing SAMCell, biologists can quickly and automatically obtain cell segmentation results of higher quality than previous methods. Further, these results can be obtained through our custom GUI without expertise in Machine Learning, thus decreasing the human labor required in cell culturing.
Identification of cross-reactive vaccine antigen candidates in Gram-positive ESKAPE pathogens through subtractive proteome analysis using opsonic sera
Authors: Sadones Océane , Kramarska Eliza , Sainz-Mejías Maite , Berisio Rita , Huebner Johannes , McClean Siobhán , Romero-Saavedra Felipe 
Published: February 2025

AbstractThe Gram-positive pathogens of the ESKAPE group,Enterococcus faecium,andStaphylococcus aureus, are well-known to pose a serious risk to human health because of their high virulence and numerous drug resistances, making them a real concern in healthcare settings worldwide. To narrow down the list of previously identified promising protein vaccine candidates, a combination of several antigen discovery approaches was performed, in particular a “false positive analysis” of peptides generated by trypsin shaving with a subtractive proteome analysis. The final list of nine potential antigens included AdcAau, a protein performing the same function as AdcAfm, an already discovered antigen in enterococci. Bioinformatic analyses revealed that AdcAauand AdcAfmshare a sequence identity of 41.2% and that the conserved regions had a high antigenicity. AdcAauwas selected for further investigation and the results reported in this manuscript demonstrate the opsonic properties of AdcAau-specific antibodies against theS. aureusstrain MW2, as well as their cross-binding and cross-opsonic activity against severalS. aureus, E. faecium, andE. faecalisstrains. This study suggests that further investigation of cross-reactive activities is a valuable tool for discovering new antigens that cover more than one clinically relevant pathogen.
Inverse modeling of heterogeneous ECM mechanical properties in nonlinear 3DTFM
Authors: Apolinar-Fernández Alejandro , Barrasa-Fano Jorge , Van Oosterwyck Hans , Sanz-Herrera José A. 
Published: February 2025

AbstractAccurate characterization of cellular tractions is crucial for understanding cell-extracellular matrix (ECM) mechanical interactions and their implications in pathology-related situations, yet their direct measurement in experimental setups remains challenging. Traction Force Microscopy (TFM) has emerged as a key methodology to reconstruct traction fields from displacement data obtained via microscopic imaging techniques. While traditional TFM methods assume homogeneous and static ECM properties, the dynamic nature of the ECM through processes such as enzyme–induced collagen degradation or cellmediated collagen deposition i.e. ECM remodeling, requires approaches that account for spatio-temporal evolution of ECM stiffness heterogeneity and other mechanical properties. In this context, we present a novel inverse methodology for 3DTFM, capable of reconstructing spatially heterogeneous distributions of the ECM’s stiffness. Our approach formulates the problem as a PDE-constrained inverse method which searches for both displacement and the stiffness map featured in the selected constitutive law. The elaborated numerical algorithm is integrated then into an iterative Newton-Raphson/Finite Element Method (NR/FEM) framework, bypassing the need for external iterative solvers. We validate our methodology usingin silico3DTFM cases based on real cell geometries, modeled within a nonlinear hyperelastic framework suitable for collagen hydrogels. The performance of our approach is evaluated across different noise levels, and compared versus the commonly used iterative L-BFGS algorithm. Besides the novelty of our formulation, we demonstrate the efficacy of our approach both in terms of accuracy and CPU time efficiency.
Systems Pharmacology Screening Reveals Mertk and other Targetable Kinases in Supratentorial Ependymomas
Authors: Chan Marina , Zhu Songli , Russell Zach , Arora Sonali , Arakaki Aleena , Kumasaka Deby , Szulzewsky Frank , Michealraj Antony , Holland Eric C , Gujral Taranjit S 
Published: February 2025

ABSTRACTEpendymomas (EPN) are central nervous system tumors arising from ependymal cells, primarily affecting young children and accounting for 10% of intracranial tumors in children and 4% in adults. Current treatments are limited to surgery and radiotherapy, with chemotherapy providing minimal benefit. Over 70% of supratentorial ependymomas (ST-EPNs) are driven by ZFTA-RELA fusions (ZFTA-RELAfus), which are associated with poor prognosis. Recent molecular classifications have identified two distinct ST-EPN clusters, E1 and E2, with the E1 cluster predominantly composed of ZFTA-RELAfustumors. However, effective targeted therapies for these tumors remain lacking. Here, we show that the ZFTA-RELAfusmouse model closely mirrors the human E1 cluster, validating its use for therapeutic discovery. Using this model and a systems polypharmacology approach called Kinome Regularization, we identified mer tyrosine kinase (Mertk) as a key regulator of ZFTA-RELAfuscell viability and proliferation. Genetic depletion or pharmacological inhibition of Mertk reduced cell growthex vivoand treatment with the clinical-grade Mertk inhibitor significantly suppressed tumor proliferationin vivo. Mechanistically, both human E1 tumors and mouse ZFTA-RELAfustumors showed elevated Mertk expression, with Mertk inhibition leading to decreased activity of pro-survival pathways. Further, substantial overlap (>80%) between genes upregulated in ZFTA-RELAfustumors and those downregulated following Mertk inhibition treatment suggest a dependency on Mertk-mediated signaling for tumor growth. These findings highlight Mertk as a promising therapeutic target and support the clinical development of Mertk inhibitors for treating human E1 subtype and ZFTA-RELAfus-driven ependymomas.
siRNA-based Therapeutic Candidate Targeting PRDM2 for Inhibition of Lung Cancer Progression
Authors: Kumar Sanjay , Malik Md Zubbair , Chaturvedi Maya , Mishra Mohit , Donato Marzia Di , Casamassimi Amelia , Abbondanza Ciro , Gazzerro Patrizia , Nguyen Chuong , Nuñez Nicole N , Sen Somdutta , Niles Brad , Chaturvedi Rupesh 
Published: February 2025

AbstractLung cancer is the leading cause of tumor-related fatalities worldwide. The current precision therapies are effective but target only those molecules that are impacted in a minority of lung cancer patients. Positive Regulatory Domain 2 (PRDM2), a member of the PRDM gene family, also known as Retinoblastoma Interacting Zinc finger (RIZ) protein. RIZ is found to be misregulated in more than 60% of lung cancer patients, representing a significantly affected population when compared to other mutation-prone oncogenic factors. RIZ expresses two contrasting variants, a tumor suppressor RIZ1, and an oncogenic RIZ2 protein. In lung cancer, the RIZ1/RIZ2 ratio alters, and the epigenetic silencing of RIZ1 promotes RIZ2 overexpression. In this study, we used siRNA (ARIZ-047) to knockdown and inhibit the effect of the oncogenic protein RIZ2. Inhibiting RIZ2 with ARIZ-047 treatment increased RIZ1 expression and decreased the viability of a lung cancer cell line (A549). The ARIZ-047 treatment also upregulated the negative regulators of the Wnt signaling pathway and restrained the tumor progression in the xenograft mice model. In conclusion, this study identifies ARIZ-047 as a targeted therapy in lung cancer, which specifically inhibits RIZ2 and suggests the role of the Wnt signaling pathway in RIZ2 overexpression.Graphical Abstract
Divergence of Functional Brain Network Configurations in Same and Other Race Face Recognition
Authors: Yaros Jessica L. , Houalla Bethany A. , McMillan Liv , Larson Myra S. , Yu Guanqiao , Hollearn Martina , Miranda Blake A. , Jirsaraie Robert , Salama Diana A. , Rapp Paul E. , Yassa Michael A. 
Published: February 2025

AbstractThe “Other-Race Effect” (ORE) refers to the enhanced memory individuals have for faces within their own racial group compared to other races. This effect is attributed to limited exposure to different races and social-motivational factors affecting face processing. While past research has explored this effect through neuroimaging methods, the precise neural mechanisms that underlie the ORE remain a subject of ongoing investigation. Previous studies have largely adopted a modular perspective, concentrating on the differential activation of face-processing regions when comparing same-race and other-race facial recognition behaviors. However, given the multifaceted nature of the ORE, an exclusive focus on specialized regions may miss the pivotal role played by non-face-preferential brain areas in modulating this phenomenon. In the present study, we take a broader data-driven perspective using graph-theoretical techniques to investigate whole-brain network disparities in same- and other-race facial recognition. Our findings reveal a substantial impact of race on functional connectivity during face memorization. While other-race face recognition benefitted from higher local efficiency during encoding, reduced local efficiency was advantageous for memorization of same-race faces. Notably, successful other-race recognition was disproportionately supported by locally efficient processing in regions among the ventral and dorsal attention networks.
LRTM: Left-Right Transition Matrices for Molecular Interaction Prediction
Authors: Zheng Kai , Duan Guihua , Yang Mengyun , Wu Wei , Li Yao-Hang , Wang Jianxin 
Published: February 2025

AbstractMolecular interactions are central to most biological processes. The discovery and identification of potential associations between molecules can provide insights into biological exploration, diagnostic and therapeutic interventions, and drug development. So far many relevant computational methods have been proposed, but most of them are usually limited to specific domains and rely on complex preprocessing procedures, which restricts the models’ ability to be applied to other tasks. Therefore, it remains a challenge to explore a generalized approach to accurately predicting potential associations. In this study, We propose Left-Right Transition Matrices (LRTM) for molecular interaction prediction. From the perspective on the diffusion model, we construct two transition matrices to model undirected graph information propagation. This allows modeling the transition probabilities of links, which facilitates link prediction in molecular bipartite networks. The extensive experimental results show that the proposed LRTM algorithm performs better than the compared methods. Also, the proposed algorithm has the potential for cross-task prediction. Furthermore, case studies show that LRTM is a powerful tool that can be effectively applied to practical applications.
A general kernel machine comparative analysis framework for randomized block designs
Authors: Koh Hyunwook 
Published: February 2025

AbstractMotivationThere are numerous potential confounders, including genetic, environmental, technical, and demographic factors. These factors may be known or unknown, measured or unmeasured; hence, it is extremely challenging to capture them in downstream data analysis. However, randomized block design is an efficient design technique to control confounding factors and to reduce variability within subjects. This helps prevent spurious discoveries and boost test power. I also note that kernel machine comparative analysis is widely employed in high-dimensional omics studies to boost test power by combining possibly weak effects from multiple underlying variants, and also to explore various linear or nonlinear patterns of disparity.ResultsIn this paper, I introduce a general kernel machine comparative analysis framework for randomized block designs, named as KernRBD, to investigate the effects of treatments (e.g., medical treatment, environmental exposure) on the underlying variants. KernRBD is unique in its range of functionalities, including the computation ofP-value for global testing and adjustedP-values for pairwise comparisons, as well as visual representation through ordination plotting. KernRBD is practical, requiring only a kernel as input, and also robustly valid based on a resampling scheme not requiring the assumption of normality to be satisfied. I also introduce its omnibus test for a unified and powerful significance testing across multiple input kernels. While its applications should be much broader, I illustrate its use through human microbiomeβ-diversity analysisin praxis, and its outperformance in significance testing through simulation experimentsin silico.Availability and ImplementationKernRBD is available athttps://github.com/hk1785/kernrbd.
Mitotic polarity oscillation promotes epithelial tumor progression
Authors: Jeyanathan Gayaanan , Cao Ming Meggie , Pellikka Milena , Robinson Sarah , Talukder Parama , Ghorayeb Vanessa , Tepass Ulrich 
Published: February 2025

AbstractMitosis of epithelial cells requires a transient loss of epithelial polarity [1–5]. However, the nature of this mitotic polarity oscillation and its functional consequences for epithelial development are not fully understood. Here we show that the Crumbs (Crb) complex, a key regulator of epithelial polarity, is lost from the membrane during mitosis, and the Crb mutant phenotype is ameliorated when cell division is inhibited. Remarkably, an essential requirement of Crb for epithelial polarity is fully suspended when cell division is blocked in conjunction with inhibition of either cell ingression or cell intercalation. We conclude that the amount of morphogenetic stress induced by mitosis, ingression, and intercalation determines the requirement for Crb. Increased cell division and loss of cell polarity are two main drivers of epithelial cancer [6–8]. Maintaining epithelial polarity is important for limiting proliferation. Whether the loss of polarity during mitosis impacts tissue growth is less clear. We show that increasing cell division in a morphogenetically quiet epithelium not only increases tissue size but also causes hyperplastic to neoplastic transition. Conversely, reducing cell division restores epithelial polarity in neoplastic tissue of tumor mutants. Taken together, our study revealed that a major function of polarity factors in epithelial maintenance is to counteract morphogenetic stress. Moreover, we propose a feedforward mechanism that links cell division and the loss of polarity as a key driver of epithelial cancer.
Distinguishing new from persistent infections at the strain level using longitudinal genotyping data
Authors: Nickols William A. , Schwabl Philipp , Niangaly Amadou , Murphy Sean C. , Crompton Peter D. , Neafsey Daniel E. 
Published: February 2025

AbstractMotivationLongitudinal pathogen genotyping data from individual hosts can uncover strain-specific infection dynamics and their relationships to disease and intervention, especially in the malaria field. An important use case involves distinguishing newly incident from pre-existing (persistent) strains, but implementation faces statistical challenges relating to individual samples containing multiple strains, strains sharing alleles, and markers dropping out stochastically during the genotyping process. Current approaches to distinguish new versus persistent strains therefore rely primarily on simple rules that consider only the time since alleles were last observed.ResultsWe developed DINEMITES (DistinguishingNewMalariaInfections inTime Series), a set of statistical methods to estimate, from longitudinal genotyping data, the probability each sequenced allele represents a new infection harboring that allele, the total molecular force of infection (molFOI, the cumulative number of newly acquired strains over time) for each individual, and the total number of new infection events for each individual. DINEMITES can handle time points with missing sequencing data, incorporate treatment history and covariates affecting the rate of new or persistent infections, and can scale to studies with thousands of samples sequenced across multiple loci containing hundreds of possible alleles. In synthetic evaluations, the DINEMITES Bayesian model, which generally outperformed an alternative clustering-based model also developed in this work, accurately estimated key clinical parameters such as molFOI (bias 2.5, compared to −12.2 for a typical simple rule). When applied to three real longitudinal genotyping datasets, the model detected 33%, 112%, and 359% more average infections per participant than would have been detected by applying a typical simple rule to the equivalent datasets without sequencing.Availability and implementationDINEMITES is freely available as an R package, along with documentation, tutorials, and example data, athttps://github.com/WillNickols/dinemites.