bluuebunny/crossref_metadata_2025_split huggingface dataset:
Effects of Disease on Renal Blood Flow Regulation: Assessing Autoregulation Disruption
Authors: Maddineni Bhavishya , Versypt Ashlee N. Ford 
Published: December 2024

AbstractThe functioning of kidneys on blood flow regulation was investigated, particularly under diseased conditions such as chronic kidney disease, which includes conditions of diabetic nephropathy and other glomerular damage. A mathematical model was developed to better understand how variations based on the glomerular filtration rate impact key kidney function outputs, such as afferent arteriolar diameter, smooth muscle activation, and the chloride ion concentration at the macula densa. We have analyzed these factors by considering the dynamics of the mathematical model of ordinary and partial differential equations to study blood flow control in the kidney, which has provided new insights into the maintenance of autoregulation. By simulating the processes of renal blood flow—specifically through the afferent arteriole and glomerulus, and detailing the process of chloride transport within the renal tubule—the model offers a comprehensive view of how the kidney regulates glomerular filtration rate amidst fluctuating systemic blood pressures and disease-specific changes. Central to this model are the myogenic response that adjusts afferent arteriole muscle tone in reaction to pressure changes and the tubuloglomerular feedback, which controls arteriole size based on chloride levels at the macula densa. The model’s simulations reveal the robustness of renal autoregulation across a spectrum of chronic kidney disease stages, showing stability under normal conditions but indicating a breakdown in regulation with advanced chronic kidney disease. This breakdown is attributed to disruptions in the vascular and feedback systems. The findings from this model shed light on the progression of renal dysfunction in chronic kidney disease and underscore the potential for developing targeted treatments to maintain kidney health.
Test-retest reliability of auditory MMN measured with OPM-MEG
Authors: Demko Laszlo , Iglesias Sandra , Mellor Stephanie , Brand Katja , Kalberer Alexandra , Köchli Laura , Marino Stephanie , Zimmermann Noé , Heinzle Jakob , Stephan Klaas Enno 
Published: December 2024

AbstractIn this paper, we report results from an investigation of auditory mismatch responses as measured by magnetoencephalography (MEG) based on optically pumped magnetometers (OPM). Specifically, as part of a quality control study, we examined the reliability and validity of auditory mismatch negativity (MMN) recordings, obtained with a newly installed OPM-MEG system. Based on OPM-MEG data from 30 healthy volunteers, measured twice with an established auditory MMN paradigm with frequency deviants, we examined the following questions:First, we focused on construct validity and examined whether OPM-MEG measurements of MMN responses (in terms of event-related fields, ERFs) were qualitatively comparable to previous MMN findings from studies using EEG or MEG based on superconducting quantum interference devices (SQUIDs). In particular, we examined whether significant MMN responses measured by OPM-MEG occurred in a comparable time window and showed a similar topography as in previous EEG/MEG studies of MMN. Second, we quantified test-retest reliability of MMN amplitude and latency over two separate measurement sessions.The results of our analyses show that MMN responses recorded with OPM-MEG are in good agreement with previously reported MMN results in terms of timing and topography. Furthermore, the comparison of group-level MMN topographies and timeseries shows excellent consistency across the two measurement sessions. Our quantitative test-retest reliability analyses at the sensor level indicate good reliability for MMN amplitude, but poor reliability for MMN latency.Overall, our findings suggest that OPM-MEG measurements of auditory MMN (i) are comparable to results from EEG and SQUID-based MEG and (ii) show good test-retest reliability for amplitude measures at the sensor level. Notably, these results were achieved in an “out of the box” state of the OPM-MEG system, shortly after installation and without further optimisation. The reason for the insufficient reliability for MMN latency we observed is currently under investigation and represents an important target for future improvements.
Axon initial segment plasticity caused by auditory deprivation degrades time difference sensitivity in a model of neural responses to cochlear implants
Authors: Jing Anna , Xi Sylvia , Fransazov Ivan , Goldwyn Joshua H. 
Published: December 2024

AbstractSynaptic and neural properties can change during periods of auditory deprivation. These changes may disrupt the computations that neurons perform. In the brainstem of chickens, auditory deprivation can lead to changes in the size and biophysics of the axon initial segment (AIS) of neurons in the sound source localization circuit. This is the phenomenon of axon initial segment (AIS) plasticity. Individuals who use cochlear implants (CIs) experience periods of hearing loss, and so we ask whether AIS plasticity in neurons of the medial superior olive (MSO), a key stage of sound location processing, would impact time difference sensitivity in the scenario of hearing with cochlear implants. The biophysical changes that we implement in our model of AIS plasticity include enlargement of the AIS and replacement of low-threshold Potassium conductance with the more slowly-activated M-type Potassium conductance. AIS plasticity has been observed to have a homeostatic effect with respect to excitability. In our model, AIS plasticity has the additional effect of converting MSO neurons from phasic firing type to tonic firing type. Phasic firing is known to have greater temporal sensitivity to coincident inputs. Consistent with this, we find AIS plasticity degrades time difference sensitivity in the auditory deprived MSO neuron model across a range of stimulus parameters. Our study illustrates a possible mechanism of cellular plasticity in a non-peripheral stage of neural processing that could impose barriers to sound source localization by bilateral cochlear implant users.
Improved prediction of antimicrobial resistance inKlebsiella pneumoniaeusing machine learning
Authors: Sevilla-Fortuny Jordi , González-Candelas Fernando , García-González Neris 
Published: December 2024

AbstractKlebsiella pneumoniaeis an important cause of healthcare-associated infections, with high levels of antimicrobial resistance (AMR) to critical antibiotics such as carbapenems and third-generation cephalosporins (3GCs). Accurate antimicrobial susceptibility detection is essential for guiding appropriate treatment. In this study, we evaluated the efficacy of machine learning (ML) models for predicting AMR phenotypes inK. pneumoniaeparticularly for antibiotics for which rule-based approaches fail. We analyzed a dataset of 5,907K. pneumoniaegenomes from public databases and a genomic surveillance project in Spanish hospitals. ML models were trained to predict AMR phenotypes using genomic features, and their performance was compared to ResFinder, which implements a conventional rule-based approach. Models were evaluated based on predictive accuracy across antibiotics. Additionally, we conducted a detailed analysis of the genomic features associated with AMR identified by ML to identify new putative AMR determinants. ResFinder exhibited low prediction accuracy for amikacin, fosfomycin, and piperacillin/tazobactam, whereas ML models significantly improved the prediction accuracy for these antibiotics. Moreover, we provide insights into why rule-based methods failed in these cases, specifically related to the genesacc(6)-Ib-cr,fosA, andblaOXA-1, respectively. Finally, we found possible genetic factors related to resistance for each antibiotic. Our findings underscore the value of ML models in AMR prediction based on genome information forK. pneumoniae, especially in challenging cases where traditional methods have low success rates. Continued evaluation and refinement of ML approaches are essential for applying these methods to enhance AMR detection in clinical and public health contexts.ImportanceTo combat antimicrobial resistance (AMR), the rapid and accurate identification of resistance phenotypes is essential for guiding appropriate therapy. In this study, we demonstrate the significant potential of machine learning (ML) to improve AMR prediction inKlebsiella pneumoniaeusing genomic data. Our findings reveal that gold standard rule-based methods for predicting AMR from genomic data underperform for antibiotics such as amikacin, fosfomycin, and piperacillin/tazobactam. In this study, we identified the genomic determinants that mislead resistance predictions in rule-based methods providing insights that can refine existing rule-based approaches. Moreover we used ML models that improved the prediction accuracy for these antibiotics and used these models to uncover potential new AMR-associated genes that contribute to a deeper understanding of resistance mechanisms. While these findings are specific toK. pneumoniae, the ML approach is broadly applicable to other pathogens facing similar challenges, enabling improved AMR prediction without reliance on prior knowledge.
The multi-scale complexity of human genetic variation beyond continental groups
Authors: Palma-Martínez María J. , Posadas-García Yuridia S. , López-Ángeles Brenda E. , Quiroz-López Claudia , Lewis Anna C. F. , Bird Kevin A. , Lasisi Tina , Zaidi Arslan A. , Sohail Mashaal 
Published: December 2024

AbstractTraditional clustering and visualization approaches in human genetics often operate under frameworks that assume inherent, discrete groupings1,2. These methods can inadvertently simplify multifaceted relationships, functioning to entrench the idea of typological groups3. We introduce a network-based pipeline and visualization tool grounded in relational thinking4, which constructs networks from a variety of genetic similarity metrics. We identify communities at multiple resolutions, departing from typological models of analysis and interpretation that categorize individuals into a (predefined) number of sets. We applied our pipeline to a dataset merged from the 1000 Genomes and Human Genome Diversity Project5, revealing the limitations of traditional groupings and capturing the complexities introduced by demographic events and evolutionary processes. This method embraces the context-specificity of genetic similarities that are salient depending on the question, markers of interest, and study individuals. Different numbers of communities are revealed depending on the resolution chosen and metric used, underscoring a fluid spectrum of genetic relationships and challenging the notion of universal categorization. We provide a web application (https://sohail-lab.shinyapps.io/GG-NC/) for interactive visualization and engagement with these intricate genetic landscapes.
Juvenile fluoxetine treatment affects the maturation of the medial prefrontal cortex and behavior of adolescent female rats
Authors: Kryst Joanna , Chocyk Agnieszka , Solarz-Andrzejewska Anna , Majcher-Maślanka Iwona 
Published: December 2024

AbstractBackgroundSerotonin is strongly involved in the regulation of brain development. An early-life imbalance in brain serotonin levels may influence the proper formation of neuronal circuits and synaptic plasticity. One of the factors that can affect serotonin concentrations is exposure to fluoxetine (FLX), a selective serotonin reuptake inhibitor, the first-line pharmacological treatment for depression and anxiety in the pediatric population. Women are more prone to depression and anxiety from a young age. The safety of early-life FLX treatment is still questionable. We hypothesized that juvenile FLX treatment influences the brain maturation and behavior of adolescent females.MethodsOn postnatal days (PNDs) 20–28, juvenile female rats were injected once daily with FLX. Five days later, anxiety- and fear-related behaviors and the response to amphetamine were assessed. On PND 40, the numbers of neurons and glial cells in the medial prefrontal cortex (mPFC) and hippocampus were estimated via stereological methods. Additionally, the mRNA expression of cell survival/apoptosis and synaptic plasticity markers was evaluated via RT‒qPCR.ResultsJuvenile FLX attenuated anxiety-like behaviors, impaired fear memory and blunted the locomotor response to amphetamine in adolescent females. Simultaneously, FLX increased the regional volume and the numbers of neurons and astrocytes in specific subregions of the mPFC but not in the hippocampus. Additionally, FLX-treated females presented increased expression of genes regulating cell survival and reduced mRNA levels of AMPA glutamate receptors in the mPFC.ConclusionsJuvenile FLX affects the maturation of the mPFC; therefore, this psychotropic drug should be used with caution in young people.
From North to South: Transmission Dynamics of H1N1pdm09 Swine Influenza A Viruses in Italy
Authors: Giovanetti Marta , Cella Eleonora , Soliani Laura , Prosperi Alice , Mescoli Ada , Nucci Ambra , della Ventura Carla , Junqueira Dennis Maletich , Trovão Nídia S. , Branda Francesco , Carrera Maya , Lelli Davide , Rosignoli Carlo , Faccini Silvia , Fiorentini Laura , Guarneri Flavia , Zehender Gianguglielmo , Ciccozzi Massimo , Chiapponi Chiara , Moreno Ana 
Published: December 2024

AbstractThe influenza A H1N1pdm09 virus continues to be a significant pathogen, posing potential risks to both animal and human health due to its zoonotic potential. Italy, which has one of the largest swine populations in Europe, plays a crucial role in monitoring the evolution of influenza viruses in livestock. This study aims to address the existing knowledge gaps regarding the genetic diversity and transmission dynamics of H1N1pdm09 circulating in Italian swine populations. Utilizing whole genome sequencing and dynamic modeling, we conducted a comprehensive analysis of virus samples collected from swine farms across Italy. Our results reveal that multiple independent viral introductions have occurred into the country, with most cases resulting in self-limited infections and limited onward transmission. However, six distinct transmission clusters were identified, suggesting instances of sustained viral spread. These clusters were found across multiple regions of Italy, highlighting the broad geographic distribution of virus lineages. Our findings indicate that while many introductions led to localized containment, certain virus lineages were able to spread within specific regions of Italy. Through a detailed examination of selective pressures, we observed that most viral genes are under strong purifying selection in both swine and human hosts, as reflected by dN/dS ratios well below 1. The hemagglutinin (HA) gene exhibited a notably higher dN/dS ratio in swine (∼0.28) compared to humans (∼0.22), indicating slightly relaxed selection in swine. In contrast, other genes, such as neuraminidase (NA) and non-structural protein (NS), showed similarly strong purifying selection across both hosts. These results reflect a general trend of selective pressures affecting multiple viral components, rather than emphasizing specific genes. Our study emphasizes the importance of ongoing genomic surveillance in detecting viral circulation and mitigating risks to both animal and public health. Italy’s efforts contribute significantly to global influenza monitoring and highlight the importance of a One Health approach that integrates human, animal, and environmental health. These findings provide essential data to inform public health policies and enhance preparedness against future zoonotic influenza outbreaks.
Approach-Avoidance Bias in Virtual and Real-World Simulations: Insights from a Systematic Review of Experimental Setups
Authors: Grasso-Cladera Aitana , Madrid-Carvajal John , Walter Sven , König Peter 
Published: December 2024

AbstractBackgroundApproach and avoidance bias (AAB) describes automatic behavioral tendencies to react toward environmental stimuli regarding their emotional valence. Traditional setups have provided evidence but often lack ecological validity. The study of the AAB in naturalistic contexts has recently increased, revealing significant methodological challenges. This systematic review evaluates the use of virtual reality (VR) and real-world setups to study the AAB, summarizing methodological innovations and challenges.MethodsWe systematically reviewed peer-reviewed articles employing VR and real-world setups to investigate the AAB. We analyzed experimental designs, stimuli, response metrics, and technical aspects to assess their alignment with research objectives and identify limitations.ResultsThis review included 21 studies revealing diverse methodologies, stimulus types, and novel behavioral responses, highlighting significant variability in design strategies and methodological coherence. Several studies used traditional reaction time measures yet varied in their application of VR technology and participant interaction paradigms. Some studies showed discrepancies between simulated and natural bodily actions, while others showcased more integrated approaches that preserved their integrity. Only a minority of studies included control conditions or acquired (neuro)physiological data.ConclusionsVR offers a potential ecological setup for studying the AAB, enabling dynamic and immersive interactions. Our results underscore the importance of establishing a coherent framework for investigating the AAB tendencies using VR. Addressing the foundational challenges of developing baseline principles that guide VR-based designs to study the AAB within naturalistic contexts is essential for advancing the AAB research and application. This will ultimately contribute to more reliable and reproducible experimental paradigms and develop effective interventions that help individuals recognize and change their biases, fostering more balanced behaviors.
B-Cells Extracellular Vesicles Shape Melanoma Response to Immune Checkpoint Therapy
Authors: Al Hrout Ala’a , Balayev Agshin , Cervantes-Gracia Karla , Gkelis Konstantinos , Benke Stephan , Matínez Gómez Julia M. , Silina Karina , Levesque Mitchell P. , Chahwan Richard 
Published: December 2024

ABSTRACTThe immune tumor microenvironment (TME) is increasingly recognized as a dynamic ecosystem where B cells play pivotal roles in modulating therapeutic responses, particularly in the context of immune checkpoint blockade (ICB) therapy. While B cells have traditionally been viewed as bystanders in tumor immunity, recent evidence suggests they may actively influence anti-tumor immunity, albeit with conflicting reports regarding their pro-tumor or anti-tumor roles. This study explores the crucial roles played by B cells and their secreted extracellular vesicles (EVs) in shaping melanoma responses to ICB therapy. We show a significant enrichment of B cells in ICB therapy responders compared to non-responders, pre-treatment, through retrospective analyses of melanoma patient tumors. Functional assays demonstrate that B cell depletion impairs T cell-mediated tumor cytotoxicity, underscoring the importance of B cells in anti-tumor responses. To investigate the clinical relevance, EVs were isolated from melanoma patient tumors, and fractioned into tumor and immune subpopulations. MiRNA profiling of CD19+ EVs identifies miR-99a-5p as a top candidate, among several others, upregulated in responders. Functional assays show that miR-99a-5p silencing in B cells diminishes T cell-mediated anti-tumor activity, suggesting its role in promoting B cell-mediated immune responses. Mechanistically, miR-99a-5p influences B cell maturation within the TME by mediating class-switch recombination. Our findings highlight the important role of B cells and their derived EVs in shaping the efficacy of melanoma immunotherapy, paving the way for novel therapeutic strategies targeting B cell-related pathways.Graphical abstract (created with Biorender)
Deficient cell-cell cohesion is linked with lobular localization in simplified models of lobular carcinomain situ(LCIS)
Authors: Christgen Matthias , Caetano Rodrigo A. , Eisenburger Michael , Traulsen Arne , Altrock Philipp M. 
Published: December 2024

AbstractLobular carcinomain situ(LCIS) is a precursor of invasive lobular carcinoma of the breast. LCIS cells lack cell-cell cohesion due to the loss of E-cadherin. LCIS cells grow in mammary lobules rather than in ducts. The etiology of this pattern, especially its dependence on cellular cohesion, is incompletely understood. We simulated passive intra-glandular scattering of carcinomain situ(CIS) cells in an ultra-simplified hollow mold tissue replica (HMTR) and a discrete-time mathematical model featuring particles of variable sizes representing single cells (LCIS-like particles) or groups of cohesive carcinoma cells (DCIS-like particles). The HMTR features structures reminiscent of a mammary duct with associated lobules. The discrete mathematical model characterizes spatial redistribution over time and includes transition probabilities between ductal or lobular localizations. Redistribution of particles converged toward an equilibrium depending on particle size. Strikingly, equilibrium proportions depended on particle properties, which we also confirm in a continuous-time mathematical model that considers controlling lobular properties such as crowding. Particles of increasing size, representing CIS cells with proficient cohesion, showed increasingly higher equilibrium ductal proportions. Our investigations represent two conceptual abstractions implying a link between loss of cell-cell cohesion and lobular localization of LCIS, which provide a much-needed logical foundation for studying the connections between collective cell behavior and cancer development in breast tissues. In light of the findings from our simplified modeling approach, we discuss multiple avenues for near-future research that can address and evaluate the redistribution hypothesis mathematically and empirically.
Acute Myeloid Leukemia Skews Therapeutic WT1-specific CD8 TCR-T Cells Towards an NK-like Phenotype that Compromises Function and Persistence
Authors: Mazziotta Francesco , Martin Lauren E. , Eagan Daniel N. , Bar Merav , Kinsella Sinéad , Paulson Kelly G. , Voillet Valentin , Lahman Miranda C. , Hunter Daniel , Schmitt Thomas M. , Duerkopp Natalie , Yeung Cecilia , Tang Tzu-Hao , Gottardo Raphael , Asano Yuta , Wilcox Elise C. , Lee Bo , Zhang Tianzi , Lopedote Paolo , Penter Livius , Wu Catherine J , Milano Filippo , Greenberg Philip D. , Chapuis Aude G. 
Published: December 2024

AbstractAcute myeloid leukemia (AML) that is relapsed and/or refractory post-allogeneic hematopoietic cell transplantation (HCT) is usually fatal. In a prior study, we demonstrated that AML relapse in high-risk patients was prevented by post-HCT immunotherapy with Epstein-Barr virus (EBV)-specific donor CD8+T cells engineered to express a high-affinity Wilms Tumor Antigen 1 (WT1)-specific T-cell receptor (TTCR- C4). However, in the present study, infusion of EBV- or Cytomegalovirus (CMV)-specific TTCR-C4did not clearly improve outcomes in fifteen patients with active disease post-HCT. TCRC4-transduced EBV-specific T cells persisted longer post-transfer than CMV-specific T cells. Persisting TTCR-C4skewed towards dysfunctional natural killer-like terminal differentiation, distinct from the dominant exhaustion programs reported for T-cell therapies targeting solid tumors. In one patient with active AML post-HCT, a sustained TTCR-C4effector-memory profile correlated with long-term TTCR-C4persistence and disease control. These findings reveal complex mechanisms underlying AML-induced T-cell dysfunction, informing future therapeutic strategies for addressing post-HCT relapse.