bluuebunny/crossref_metadata_2025_split huggingface dataset:
Sorting the mob: using geometric morphometrics and machine learning to differentiate kangaroo postcrania for zooarchaeological applications
Authors: Mein Erin , Manne Tiina , Veth Peter , Weisbecker Vera 
Published: December 2024

AbstractTaxonomic identification of bone is one of the building blocks of zooarchaeological research into human foraging behaviour. However, it can prove difficult in regions, such as Australia, that have biodiverse taxa that are difficult to differentiate using bone morphology. One such case are the kangaroos and wallabies (macropods), one of the most speciose groups of marsupials whose remains are frequently recovered from Australian archaeological sites. Despite their clear importance to Indigenous economies, little research has been undertaken on how to reliably differentiate the postcranial remains of extant macropods. Here we address this gap by applying three-dimensional geometric morphometrics to describe how the astragalus and calcaneus differs between several large macropod genera. We describe taxonomically diagnostic anatomical attributes between genera and identify the size related (allometric) shape variation that could be mistaken for taxonomic differences. We then compare several machine learning models to demonstrate how these can be applied to geometric morphometric data to statistically classify unknown specimens from palaeozoological contexts with a high degree of accuracy. Our results show that non-linear methods of supervised machine learning outperform classical discriminant function analysis when used on our geometric morphometric data. Statistical classification of palaeozoological specimens has the potential to be a valuable tool, where differentiating skeletal remains of closely related taxa continues to prove challenging.
Transgenic augmentation of erythroferrone in mice ameliorates anemia in adenine-induced chronic kidney disease
Authors: Czaya Brian , Olivera Joseph D. , Zhang Moya , Lundin Amber , Castro Christian D. , Jung Grace , Nemeth Elizabeta , Ganz Tomas 
Published: December 2024

AbstractAnemia is a common and disabling complication of chronic kidney disease (CKD). Current therapies can be burdensome, and full correction of anemia is limited by cardiovascular side effects. New approaches that may offer additional therapeutic options are needed. We explored the anti-anemic effects of erythroferrone, an erythroid hormone that induces iron mobilization by suppressing the master iron-regulatory hormone hepcidin. In a preclinical murine model of adenine-induced CKD, transgenic augmentation of erythroferrone mobilized iron, increased hemoglobin concentrations by approximately 2 g/dl, and modestly improved renal function without affecting systemic or renal inflammation, fibrosis, or markers of mineral metabolism. This study supports the concept that therapeutic augmentation of erythroferrone is a promising approach for alleviating CKD-associated anemia.
Anti-obesity compounds, Semaglutide and LiPR, do not change the proportion of human and mouse POMC+ neurons
Authors: Jörgensen Sara K.M. , Surridge-Smith May , Jones Kimberley , Maletínská Lenka , Allen Nicholas D. , Petrik David 
Published: December 2024

AbstractAnti-obesity medications (AOMs) have become one of the most prescribed drugs in human medicine. While AOMs are known to impact adult neurogenesis in the hypothalamus, their effects on the functional maturation of hypothalamic neurons remain unexplored. Given that AOMs target neurons in the Medial Basal Hypothalamus (MBH), which play a crucial role in regulating energy homeostasis, we hypothesized that AOMs might influence the functional maturation of these neurons, potentially rewiring the MBH. To investigate this, we exposed hypothalamic neurons derived from human induced pluripotent stem cells (hiPSCs) to Semaglutide and lipidized prolactin-releasing peptide (LiPR), two anti-obesity compounds. Contrary to our expectations, treatment with Semaglutide or LiPR during neuronal maturation did not affect the proportion of anorexigenic, Pro-opiomelanocortin-expressing (POMC+) neurons. Additionally, LiPR did not alter the morphology of POMC+ neurons or the expression of selected genes critical for the metabolism or development of anorexigenic neurons. Furthermore, LiPR did not impact the proportion of adult-generated POMC+ neurons in the mouse MBH. Taken together, these results suggest that AOMs do not influence the functional maturation of anorexigenic hypothalamic neurons.
Collablots: Quantification of collagen VI levels and its structural disorganisation in cell cultures from patients with collagen VI-related dystrophies
Authors: Osegui-Barcenilla Nadia , Sendino Maria , Martín-González Sergio , Gómez-Moro Itziar , Benito-Agustino Ainhoa , Torres-Conde Noemi , López-Martínez Andrea , Jiménez-Mallebrera Cecilia , López-Márquez Arístides , Arechavala-Gomeza Virginia 
Published: December 2024

AbstractAimsThis study aims to develop a quantitative method for assessing collagen VI expression in cell cultures, which is crucial for the diagnosis and treatment of collagen VI-related dystrophies.MethodsWe developed a combined in-cell western (ICW) and on-cell western (OCW) assay, that we have called ‘collablot’ to quantify collagen VI and its organisation in the extracellular matrix of cell cultures from patients and healthy controls. To optimise it, we optimised cell density and the protocols to induce collagen expression in cultures, as well as the cell fixation and permeabilisation methods. This was completed with a thorough selection of collagen antibodies and a collagen hybridising peptide (CHP). We then used collablots to compare cultures from patients and controls and evaluate therapeutic interventions in the cultures.ResultsCollablots enabled the quantification of collagen VI expression in both control and patient cells, aligning with immunocytochemistry findings and detecting variations in collagen VI expression following treatment of the cultures. Additionally, CHP analysis revealed a marked increase in collagen network disruption in patients compared to the controls.ConclusionsThe collablot assay represents an optimal method for quantifying collagen VI expression and its organisation in culture and assessing the effect of therapies.Key Points- Evaluating therapies for collagen VI-related dystrophies (COL6-RD) requires the quantification of collagen VI levels.- Collablot assays are a novel method for quantifying collagen VI expression and its structural organisation in cell culture.- Due to the significant role of phenotype heterogeneity in this complex disease, quantifying collagen alone might not be adequate for diagnosing COL6-RD, but the addition of a peptide to quantify collagen disorganisation could help in the characterisation of patient cultures.
Deep predictive coding networks partly capture neural signatures of short-term temporal adaptation in human visual cortex
Authors: Brands Amber Marijn , Ortiz Paulo , Groen Iris Isabelle Anna 
Published: December 2024

AbstractPredictive coding is a leading theory of cortical function which posits that the brain continually makes predictions of incoming sensory stimuli using a hierarchical network of top-down and bottom-up connections. This theory is supported by prior work showing that PredNet, a deep learning network designed according to predictive coding principles, exhibits several characteristics of neural responses commonly observed in primate visual cortex. However, one ubiquitous neural phenomenon that has not yet been investigated is short-term visual adaptation: the adjustment of neural responses over time when exposed to static visual inputs that are either prolonged or directly repeated. Here, we examine whether PredNet exhibits two neural signatures of temporal adaptation previously observed in intracranial recordings of human participants viewing prolonged and repeated stimuli (Brands et al., 2024). We find that, like human visual cortex, PredNet adapts to static images, evidenced by subadditive temporal response summation: a non-linear accumulation of response magnitudes when prolonging stimulus durations, which results from neurally plausible transient-sustained dynamics in the unit activation time courses. However, PredNet activations also show a systematic response to stimulus offsets, which is absent in the human neural data. For repeated stimuli, PredNet shows slight response suppression for any two images presented in quick succession, but no repetition suppression, a comparatively stronger response reduction for identical than for non-identical image pairs that is robustly observed throughout human visual cortex. We show that these results are stable across multiple training datasets and two different types of loss computation. Lastly, in both PredNet and the neural data, we find a relationship between temporal adaptation and visual input properties, showing that temporally sustained activity is enhanced for more complex scenes containing clutter. All together, these results suggest that the emergent temporal dynamics in the PredNet only partly align with neural data and are linked to low-level properties of the visual input rather than high-level predictions arising from top-down processes.
Donor HLA-DQ reactive B cells clonally expand under chronic immunosuppression and include atypical CD21lowCD27−B cells with high-avidity germline B-cell receptors
Authors: Russum Steven , Sayin Ismail , Shwetar Jasmine , Baughan Eleonore , Jeong Jong Cheol , Kim Andrea , Reyentovich Alex , Moazami Nader , Zeevi Adriana , Chong Anita S , Habal Marlena 
Published: December 2024

AbstractLong-term allograft survival is limited by humoral-associated chronic allograft rejection, suggesting inadequate constraint of humoral alloimmunity by contemporary immunosuppression. Heterogeneity in alloreactive B cells and the incomplete definition of which B cells participate in chronic rejection in immunosuppressed transplant recipients limits our ability to develop effective therapies. Using a double-fluorochrome single-HLA tetramer approach combined with single-cellin vitroculture, we investigated the B-cell receptor (BCR) repertoire characteristics, avidity, and phenotype of donor HLA-DQ reactive B cells in a transplant recipient with end-stage donor specific antibody (DSA)-associated cardiac allograft vasculopathy while receiving maintenance immunosuppression (tacrolimus, mycophenolate mofetil, prednisone). Donor DQB1*03:02/DQA1*03:01 (DQ8)-reactive IgG+ B cells were enriched for minimally mutated and germline encoded high avidity BCRs (median KD4.26×10-09) with an atypical, antigen-experienced and proliferative phenotype (CD27-CD21lowCD71+CD11c+/-). These B cells coexisted with a smaller subset of more highly mutated, affinity matured IgG+CD27+ B cells. Circulating donor-reactive B cells and DSA remained detectable after rituximab, contrasting with the marked reduction in DSA after allograft explant and retransplant. Together, these findings define the persistence of germline high-avidity HLA-DQ alloreactive B cells and their co-existence with affinity matured clones that were both driven by the allograft despite conventional immunosuppression.
Scouter: Predicting Transcriptional Responses to Genetic Perturbations with LLM embeddings
Authors: Zhu Ouyang , Li Jun 
Published: December 2024

AbstractThis paper addresses the challenging problem of predicting transcriptional outcomes— the expression levels of all genes—in gene perturbation experiments and introduces a novel method called Scouter. By leveraging the capabilities of large language models and employing a neural network that facilitates easy training, Scouter overcomes key limitations of current approaches and accurately predicts the outcomes of single-gene and two-gene perturbations, reducing the error of state-of-the-art methods by half or more.
QUANTIFYING EXPLAINABLE AI METHODS IN MEDICAL DIAGNOSIS: A STUDY IN SKIN CANCER
Authors: Sangwan Hardik 
Published: December 2024

AbstractDeep learning models have shown substantial promise in assisting medical diagnosis, offering the potential to improve patient outcomes and reduce clinician workloads. However, the widespread adoption of these models in clinical practice has been hindered by concerns surrounding their trustworthiness, transparency, and interpretability. Addressing these challenges requires not only the development of explainable AI (xAI) techniques but also quantitative metrics to evaluate their effectiveness.This study presents a comprehensive framework for training, explaining, and quantitatively assessing deep learning models for skin cancer diagnosis. Leveraging the HAM10000 dataset of seven diagnostic skin lesion categories, multiple convolutional neural network architectures—including custom CNNs, DenseNet, MobileNet, and ResNet—were trained and optimized using augmentation, oversampling, and hyperparameter tuning. Following model training, explainability techniques such as SHAP, LIME, and Integrated Gradients were deployed to generate post hoc explanations. Critically, the primary contribution of this work is the quantitative evaluation of these explanation methods using metrics related to faithfulness, robustness, and complexity. All code, models, and results are publicly available, providing a reproducible pathway toward more trustworthy, explainable diagnostic tools.
Cross-Field Strength and Multi-Vendor Validation of MagDensity for MRI-based Quantitative Breast Density Analysis
Authors: Ying Jia , Cattell Renee , Huang Chuan 
Published: December 2024

AbstractPurposeBreast density (BD) is a significant risk factor for breast cancer, yet current assessment methods lack automation, quantification, and cross-platform consistency. This study aims to evaluate MagDensity, a novel magnetic resonance imaging (MRI)-based quantitative BD measure, for its validity and reliability across different imaging platforms.MethodsTen healthy volunteers participated in this prospective study, undergoing fat-water MRI scans on three scanners: 3T Siemens Prisma, 3T Siemens Biograph mMR, and 1.5T GE Signa. Great effort was made to schedule all scans within a narrow three-hour window on the same day to minimize any potential intraday variations, highlighting the logistical challenges involved. BD was assessed using the MagDensity technique, which included combining magnitude and phase images, applying a fat-water separation technique, employing an automated whole-breast segmentation algorithm, and quantifying the volumetric water fraction. The agreement between measures was analyzed using mean differences, two-tailed t-tests, Pearson’s correlation coefficients, and Bland-Altman plots.ResultsNo statistically significant differences in BD measurements by MagDensity within the same field strength and vendor (3T Siemens), with high correlation (Pearson’s r > 0.99) and negligible mean differences (< 0.2%). Cross-platform comparison between the 3T Siemens and the 1.5T GE scanners showed mean differences of < 5%. After linear calibration, these variations were reduced to insignificant levels, yielding a strong correlation (Pearson’s r > 0.97) and mean differences within ±0.2%.ConclusionMagDensity, an MRI-based BD measure, exhibits robustness and reliability across diverse scanner models, vendors, and field strengths, marking a promising advancement towards standardizing BD measurements across multiple MRI platforms. It provides a valuable tool for monitoring subtle longitudinal changes in BD, which is vital for breast cancer prevention and personalized treatment strategies.
Long-Term Carotid Plaque Progression and the Role of Intraplaque Hemorrhage: A Deep Learning-Based Analysis of Longitudinal Vessel Wall Imaging
Authors: Guo Yin , Akcicek Ebru Yaman , Hippe Daniel S. , HashemizadehKolowri SeyyedKazem , Wang Xin , Akcicek Halit , Canton Gador , Balu Niranjan , Geleri Duygu Baylam , Kim Taewon , Shibata Dean , Zhang Kaiyu , Ma Xiaodong , Ferguson Marina S. , Mossa-Basha Mahmud , Hatsukami Thomas S. , Yuan Chun 
Published: December 2024

AbstractBackgroundCarotid atherosclerosis is a major contributor in the etiology of ischemic stroke. Although intraplaque hemorrhage (IPH) is known to increase stroke risk and plaque burden, its long-term effects on plaque dynamics remain unclear. This study aimed to evaluate the long-term impact of IPH on carotid plaque burden progression using deep learning-based segmentation on multi-contrast magnetic resonance vessel wall imaging (VWI).MethodsTwenty-eight asymptomatic subjects with carotid atherosclerosis underwent an average of 4.7 ± 0.6 VWI scans over 5.8 ± 1.1 years. Deep learning pipelines were developed and validated to segment the carotid vessel walls and IPH. Bilateral plaque progression was analyzed using generalized estimating equations, and linear mixed-effects models evaluated long-term associations between IPH occurrence, IPH volume, and plaque burden (%WV) progression.ResultsIPH was detected in 23/50 of arteries. Of arteries without IPH at baseline, 11/39 developed new IPH that persisted, while 5/11 arteries with baseline IPH exhibited it throughout the study. Bilateral plaque growth was significantly correlated (r = 0.54, p < 0.001), but this symmetry was weakened with IPH presence. The progression rate for arteries without IPH was -0.001 %/year (p = 0.90). However, IPH presence or development at any point was associated with a 2.3% absolute increase in %WV on average (p < 0.001). The volume of IPH was also positively associated with increased %WV (p = 0.005).ConclusionsDeep learning-based segmentation pipelines were utilized to identify IPH, quantify IPH volume, and measure their effects on carotid plaque burden during long-term follow-up. Findings demonstrated that IPH may persist for extended periods. While arteries without IPH demonstrated minimal progression under contemporary treatment, presence of IPH and greater IPH volume significantly accelerated long-term plaque growth.
Optimizing long-term prevention of cardiovascular disease with reinforcement learning
Authors: Zhou Yekai , Luo Ruibang , Blais Joseph Edgar , Tan Kathryn , Lui David , Yiu Kai Hang , Tsun Lai Francisco Tsz , Fai Wan Eric Yuk , Cheung CL , Wong Ian CK , Chui Celine SL 
Published: December 2024

AbstractThe prevention of chronic disease is a long-term combat with continual fine-tuning to adapt to the course of disease. Without comprehensive insights, prescriptions may prioritize short-term gains but deviate from trajectories toward long-term survival. Here we introduce Duramax, a fully evidence-based framework to optimize the dynamic preventive strategy in the long-term. This framework synchronizes reinforcement learning with real-world data modeling, leveraging the diverse treatment trajectories in electronic health records (EHR). In our study, Duramax learned from millions of treatment decisions of lipid-modifying drugs, becoming specialized in cardiovascular disease (CVD) prevention. The extensive volume of implicit knowledge Duramax harnessed far exceeded that of individual clinicians, resulting in superior performance. Specifically, when clinicians’ treatment decisions aligned with those suggested by Duramax, a reduction in CVD risk was observed. Moreover, post hoc analysis confirmed that Duramax’s decisions were transparent and reasonable. Our research showcases how tailored computational analysis on well-curated EHR can achieve high nuance in personalized disease prevention.
Cerebral oximetry monitoring versus usual care for extremely preterm infants: a detailed statistical analysis plan for the 2-year follow-up of the SafeBoosC-III randomised clinical trial
Authors: Rasmussen Marie Isabel S , Hansen Mathias Lühr , Pellicer Adelina , Gluud Christian , Dempsey Eugene , Mintzer Jonathan , Hyttel-Sørensen Simon , Heuchan Anne Marie , Hagmann Cornelia , Ergenekon Ebru , Dimitriou Gabriel , Pichler Gerhard , Naulaers Gunnar , Tkaczyk Jakub , Fuchs Hans , Fumagalli Monica , Nesargi Saudamini , Fredly Siv , Szczapa Tomasz , Plomgaard Anne Mette , Hansen Bo Mølholm , Olsen Markus Harboe , Jakobsen Janus Christian , Greisen Gorm 
Published: December 2024

AbstractBackgroundThe SafeBoosC-III trial investigated treatment guided by cerebral oximetry monitoring for the first 72 hours after birth in extremely preterm infants and showed no effects on mortality or severe brain injury at 36 weeks’ postmenstrual age versus usual care. As severe brain injury in the neonatal period is not a strong predictor of long-term neurodevelopmental outcomes, the SafeBoosC-III follow-up study aims to assess the long-term benefits and harms of the experimental intervention versus usual care at two years of corrected age. This detailed statistical analysis plan outlines our approach for analysing outcomes in the SafeBoosC-III follow-up study.MethodsThe co-primary outcomes are 1) a composite of death or moderate-to-severe neurodevelopmental disability and 2) the mean Bayley-III/IV cognitive score. We will employ a 3-tier data model, incorporating routine clinical follow-up, parental questionnaires, and informal assessments to minimize missing data. All randomised participants with available data will be included in all analyses. Mixed-effect linear and logistic regression will be used to analyse the dichotomous and continuous co-primary outcomes, respectively. Sensitivity analyses will be conducted to address missing data and assess the robustness of our findings.DiscussionThe statistical analysis plan aims to ensure transparency and reduce the risk of outcome reporting bias. By including dichotomous and continuous co-primary outcomes, we aim to provide a comprehensive evaluation of the intervention’s effect on long-term benefits and harms.