bluuebunny/crossref_metadata_2025_split huggingface dataset:
Development and Validation of OncoOrigin: An Integrative AI Tool for Primary Cancer Site Prediction
Authors: Brlek Petar , Bulić Luka , Shah Nidhi , Shah Parth , Primorac Dragan 
Published: December 2024

ABSTRACTImportanceCancers of unknown primary origin (CUPs) represent a significant diagnostic and therapeutic challenge in the field of oncology. With the limitations of current diagnostic tools in these cases, novel approaches must be brought forward to improve treatment outcomes for these patients.ObjectiveThe objective of this study was to develop a machine-learning-based software for primary cancer site identification (OncoOrigin), based on genetic data acquired from tumor DNA sequencing.DesignBy design, this was anin silicodiagnostic study.SettingThis study was conducted using data from the cBioPortal database (accessed on 21 September 2024) and several data processing and machine-learning Python libraries.ParticipantsThis study involved over 20,000 tumor samples with information on patient age, sex, and the presence of genetic variants in over 600 genes.Main Outcomes and MeasuresThe main outcome of interest in this study was machine-learning-based discrimination between cancer type classes, based on the provided data. Model quality was assessed by train set cross-validation and evaluation on a segregated test set. Finally, the optimal model was incorporated with a graphical user interface into the OncoOrigin software. Feature importances for class discrimination were also determined on the optimal model.ResultsOut of the four tested machine-learning estimators, the XGBoostClassifier-based model proved superior on test set evaluation, with a top-2 accuracy of 0.91 and ROC-AUC of 0.97. Class sensitivity values for prostate cancer, breast cancer, melanoma, and colorectal cancer were over 0.85, while all class specificity values were equal to or higher than 0.95. The top 3 significant features were patient sex, and genetic alterations in theAPCandKRASgenes.Conclusions and RelevanceIn this study, we have successfully developed a machine-learning-based software for primary cancer site identification with high-quality evaluation metrics. Through simple clinical implementation, such a tool has the potential to significantly improve the diagnostics and treatment outcomes for patients suffering from CUP.KEY POINTSQuestionCan a machine-learning-based tool (OncoOrigin) accurately predict the primary cancer site from tumor DNA sequencing data in patients with cancers of unknown primary origin (CUP)?FindingsIn thisin silicostudy, the XGBoostClassifier model demonstrated high predictive performance, achieving a top-2 accuracy of 0.91 and ROC-AUC of 0.97 for the classification of ten cancer types using genetic data from over 20,000 tumor samples.MeaningThe OncoOrigin software shows potential for improving the diagnosis and treatment of CUP by providing accurate primary cancer site identification based on genetic data.
Methods for analytical validation of novel digital clinical measures: A simulation study
Authors: Turner Simon , Chen Chen , Acosta Rolando , Daza Eric J , Floden Lysbeth , Langford Joss , Simmatis Leif , Terluin Berend , Fromy Piper 
Published: December 2024

AbstractAnalytical validation (AV) is a crucial step in the evaluation of algorithms that process data from sensor-based digital health technologies (sDHTs). AV of novel digital measures can be complicated when reference measures with direct correspondence are not available. To address this, we conducted a simulation study. Data was simulated assuming a latent physical ability trait, indirectly accessed through an sDHT-derived target measure collecting step count data, and the items of a clinical outcome assessment measuring self-reported physical activity. We quantified the ability of two methods to assess the latent relationship between reference and target measures: the Pearson Correlation Coefficient (PCC) (a classical, simple method frequently used for AV), and factor correlations from a two-factor confirmatory factor analysis (CFA) model (a more novel approach to AV, able to capture more complex relationships). Additionally, three multiple linear regression models were used to evaluate if multiple clinical outcome assessment reference measures can more completely represent a target measure of interest.Our findings show that PCC was more stable, easier to compute, and relatively robust with respect to violations of parametric assumptions than CFA, particularly with small sample sizes. However, CFA was less biased than PCC in all scenarios investigated. We demonstrate that using both PCC and CFA allows a researcher to be more confident that their AV results reflect the true relationship between the sDHT-derived target measure and a reference measure. Finally, regression results suggest that incorporating multiple reference measures with more frequent collection time points can provide a more complete presentation of the sDHT’s analytical validity.Novel digital measures are being developed at an accelerating pace and promise to revolutionize public health, patient care, and medical product development. Our findings provide investigators with crucial information for choosing appropriate methods to perform rigorous AV of these novel measures.
CRISPRa-mediated activation of genes associated with inherited retinal dystrophies in acutely isolated human cells for diagnostic purposes
Authors: Weber Valentin J. , Reschigna Alice , Gerhardt Maximilian-J. , Hinrichsmeyer Klara S. , Otify Dina Y. , Heigl Thomas , Blaser Frank , Meneau Isabelle , Biel Martin , Michalakis Stylianos , Becirovic Elvir 
Published: December 2024

AbstractMany patients suffering from inherited diseases do not receive a genetic diagnosis and are therefore excluded as candidates for treatments, such as gene therapies. Analyzing disease-related gene transcripts from patient cells would improve detection of mutations that have been missed or misinterpreted in terms of pathogenicity during routine genome sequencing. However, the analysis of transcripts is complicated by the fact that a biopsy of the affected tissue is often not appropriate, and many disease-associated genes are not expressed in tissues or cells that can be easily obtained from patients. Here, using CRISPR/Cas-mediated transcriptional activation (CRISPRa) we developed a robust and efficient approach to activate genes in skin-derived fibroblasts and in freshly isolated peripheral blood mononuclear cells (PBMCs) from healthy individuals. This approach was successfully applied to blood samples from patients with inherited retinal dystrophies (IRD). We were able to efficiently activate several IRD-linked genes and detect the corresponding transcripts using different diagnostically relevant methods such as RT-qPCR, RT-PCR and long- and short-read RNA sequencing. The detection and analysis of known and unknown mRNA isoforms demonstrates the potential of CRISPRa-mediated transcriptional activation in PBMCs. These results will contribute to ceasing the critical gap in the genetic diagnosis of patients with IRD or other inherited diseases.Graphical abstract
Spatial position relative to group members affects weight gain in meerkats (Suricata suricatta)
Authors: Mosia Rasekuwane , Demartsev Vlad , le Roux Aliza , Manser Marta B , Strandburg-Peshkin Ariana , Johnson-Ulrich Lily 
Published: December 2024

ABSTRACTSocial animals often face a trade-off between the costs of foraging competition among group members and the benefits of protection from predators offered by group living. The spatial position of an individual in relation to the other group members during foraging can mediate the effects of this trade-off as individuals at the front or edge may have better access to food resources, but also higher predation risk than individuals near the centre of the group. Using meerkats (Suricata suricatta) as a model species, we investigated the effect of individual spatial position within a group on foraging success. We determined the spatial position of individuals in a meerkat group by fitting the animals with high-resolution GPS loggers. As a proxy of foraging success, we used meerkats’ individual body weight differences between the start and the end of daily data collection over foraging periods (3 hours). We found significant individual differences in meerkats’ spatial positions within the group. In addition, age-dependent differences in spatial position became obvious, with younger individuals spending more time in the centre of the group and less time in the front. However, younger individuals who spent more time on the side of the group relative to older individuals had higher daily weight gain, indicating more successful foraging. In older individuals, we found that the dominant females tended to spend more time towards the front of the group, but gained less weight in this location, contrary to the predicted association between front edge of the group and better access to food resources. Our results suggest that the relationship between weight gain and spatial position is highly nuanced and likely dependent on more than just trade-offs between foraging success and predation risk.HighlightsSpatial position relative to other group members during foraging was a highly repeatable trait for individual meerkats, though time spent near the front of the group was more strongly influenced by individual traits (age, sex, and social rank)Younger meerkats spent less time in the front and more time in the centre of the group relative to older meerkats, but had the highest foraging success towards the sides of the groupDominant female meerkats spent more time near the front of the group, but had reduced foraging success in this positionMeerkats may trade-off more than just foraging success and predation risk when making decisions about where to position themselves relative to other group members
Hypoxia inhibits newt skeletal muscle dedifferentiation
Authors: Vdovin Georgii , Walters Hannah E. , Troyanovskiy Konstantin E. , Tsuchiya Yugo , Yun Maximina H 
Published: November 2024

ABSTRACTUnlike mammals, newts are able to regenerate lost muscle tissue by dedifferentiating postmitotic muscle cells, which re-enter the cell cycle to form myogenic progenitors. This ability is considered central to the process of limb regeneration, yet the mechanisms underlying it remain poorly understood. Here, we leverage high-resolution time course bulk transcriptomics profiling to characterise molecular changes during differentiation and dedifferentiation of newt myotubes. We uncover that myogenesis is accompanied by a metabolic shift from glycolysis to OXPHOS, which is partially reverted upon dedifferentiation. By analysing early events during dedifferentiation, we identify TGFβ and members of the oxygen-responsive HIF family as putative plasticity inducers. We detect a stark, transient upregulation of HIF3A early during dedifferentiation, followed by a gradual downregulation of HIF2A. Finally, we show that hypoxia inhibits myotube cell cycle re-entry. These data provide a valuable resource to identify regulators of cellular plasticity and offer insights into the impact of oxygen signalling in regenerative processes.
Gray matter abnormalities in sight deprivation and sight restoration
Authors: Pedersini Caterina A. , Fracasso Alessio , Dogar Amna , Rokers Bas , Sinha Pawan 
Published: November 2024

AbstractBlindness provides a unique model for investigating brain plasticity in response to sensory deprivation. While structural changes in both gray and white matter have been widely documented, particularly in cases of early or congenital visual deprivation, gray matter studies have traditionally focused on cortical thickness, often finding cortical thickening in posterior regions. However, other aspects of gray matter integrity, such as cortical myelin content, remain underexplored. In this study, we examined the effects of visual deprivation on cortical structure in a cohort of congenitally blind individuals who received eye surgery during adolescence, expanding beyond conventional measures to include cortical thickness, curvature, and T1-weighted signal intensity. This multi-faceted approach offers a more comprehensive view of cortical adaptations to congenital sensory deprivation.While blindness offers valuable insights into sensory-driven brain plasticity, an intriguing and unresolved question is whether structural plasticity reverses after sight restoration, enabling typical visual processing circuits to develop despite the initial period of deprivation. To address this, we assessed the effect of sight-recovering eye surgery on gray matter changes. Critically, individuals in this cohort received surgery after the closure of the sensitive period for visual development. We did not find evidence of gray matter changes after surgery. However, in a previous study conducted on the same cohort, we reported that notable plasticity in white matter emerged in this same population. These results suggest that white matter alterations, rather than gray matter changes, may potentially serve as a biomarker of structural plasticity following sight restoration, even beyond the sensitive developmental window.
extgfa: a low-memory on-disk representation of genome graphs
Authors: Dabbaghie Fawaz 
Published: December 2024

The representation of genomes and genomic sequences through graph structures has undergone a period of rapid development in recent years, particularly to accommodate the growing size of genome sequences that are being produced. Genome graphs have been employed extensively for a variety of purposes, including assembly, variance detection, visualization, alignment, and pangenomics. Many tools have been developed to work with and manipulate such graphs. However, the majority of these tools tend to load the complete graph into memory, which results in a significant burden even for relatively straightforward operations such as extracting subgraphs, or executing basic algorithms like breadth-first or depth-first search. In procedurally generated open-world games likeMinecraft, it is not feasible to load the complete world into memory. Instead, a mechanism that keeps most of the world on disk and only loads parts when needed is necessary. Accordingly, the world is partitioned into chunks which are loaded or unloaded based on their distance from the player. Furthermore, to conserve memory, the system unloads chunks that are no longer in use based on the player’s movement direction, sending them back to the disk. In this paper, we investigate the potential of employing a similar mechanism on genome graphs. To this end, we have developed a proof-of-concept implementation, which we called “extgfa” (for external GFA). Our implementation applies a similar chunking mechanism to genome graphs, whereby only the necessary parts of the graphs are loaded and the rest stays on disk. We demonstrate that this proof-of-concept implementation improves the memory profile when running an algorithm such as BFS on a large graph, and is able to reduce the memory profile by more than one order of magnitude for certain BFS parameters.AvailabilityOur implementation is written inPythonand available on Github under the MIT licensehttps://github.com/fawaz-dabbaghieh/extgfa
An mTurquoise2-based glucose biosensor
Authors: Botman Dennis , Tielman Annemoon , Goedhart Joachim , Teusink Bas 
Published: November 2024

AbstractGlucose is an important substrate for organisms to acquire energy needed for cellular growth. Despite the importance of this metabolite, single-cell information at a fast time-scale about the dynamics of intracellular glucose levels is difficult to obtain as the current available sensors have drawbacks in terms of pH sensitivity or glucose affinity. To address this, we developed a convenient method to make and screen biosensor libraries using yeast as workhorse. This resulted in TINGL (Turquoise INdicator for GLucose), a robust and specific biosensor for intracellular glucose detection. We calibrated the sensor in vivo through equilibration of internal and external glucose in a yeast mutant unable to phosphorylate glucose. Using this method, we measured dynamic glucose levels in budding yeast during transitions to glucose. We found that glucose concentrations reached levels up to 1 mM as previously determined biochemically. Furthermore, the sensor showed that intracellular glucose dynamics differ based on whether cells are glucose-repressed or not. We believe that this sensor can aid researchers interested in cellular carbohydrate metabolism.
A novel multiplex RNAi therapy simultaneously targetsHif1aandHif2ato defy retinal degeneration in two models of AMD
Authors: Ebner Lynn J. A. , Imsand Cornelia , Karademir Duygu , Peters Florian , Kiessling Eva , Fottner Antonia , Matter Claudia , Fajardo Diego S. , Merolla Luca , Wögenstein Gabriele M. , Tsioti Ioanna , Govers Larissa P. , Blaser Frank , Meneau Isabelle , Boye Sanford L. , Boye Shannon E. , Grimm Christian , Samardzija Marijana 
Published: November 2024

AbstractAge-related tissue changes lead to reduced oxygen delivery to photoreceptors and the retinal pigment epithelium (RPE), and contribute to the pathology of age-related macular degeneration (AMD). The implication of hypoxia-inducible factors (HIFs) in this process makes them good candidates as therapeutic targets for AMD. We developed a multiplex dual-acting therapy utilizing the shRNAmir system, delivered by a single AAV, that reduces mRNA levels ofHif1ain photoreceptors andHif2ain the RPE. This RNA interference-based strategy demonstrated a strong therapeutic effect, potently preserving photoreceptors and the RPE in two models of pseudo- and true hypoxia up to 61 weeks post-injection. The efficacy of our dual-acting virus proved superior to single-acting viruses targeting onlyHif1ain photoreceptors orHif2ain the RPE. By targeting a common, conserved disease pathway, this gene-agnostic RNAi therapy shows significant potential to protect tissues from chronic hypoxic insults in complex diseases such as AMD.
Fate (or state) of CA2 neurons in a mineralocorticoid receptor knockout
Authors: Harris Erin P. , Jones Stephanie M. , Alexander Georgia M. , Kandemir Başak , Ward James M. , Wang TianYuan , Proaño Stephanie , Xu Xin , Dudek Serena M. 
Published: November 2024

AbstractHippocampal area CA2 has emerged as a functionally and molecularly distinct part of the hippocampus and is necessary for several types of social behavior, including social aggression. As part of the unique molecular profile of both mouse and human CA2, the mineralocorticoid receptor (MR;Nr3c2) appears to play a critical role in controlling CA2 neuron cellular and synaptic properties. To better understand the fate (or state) of the neurons resulting from MR conditional knockout, we used a spatial transcriptomics approach. We found that without MRs, ‘CA2’ neurons acquire a CA1-like molecular phenotype. Additionally, we found that neurons in this area appear to have a cell size and density more like that in CA1. These finding support the idea that MRs control at least CA2’s ‘state’ during development, resulting in a CA1-like ‘fate’.
Active Remote Focus Stabilization in Oblique Plane Microscopy
Authors: Nguyen Trung Duc , Rahmani Amir , Ponjavic Aleks , Millett-Sikking Alfred , Fiolka Reto 
Published: December 2024

AbstractLight-sheet fluorescence microscopy (LSFM) has demonstrated great potential in the life sciences owing to its efficient volumetric imaging capabilities. For long term imaging, the light-sheet typically needs to be stabilized to the detection focal plane for the best imaging results. Current light-sheet stabilization methods rely on fluorescence emission from the sample, which may interrupt the scientific imaging and add to sample photobleaching. Here we show that for oblique plane microscopes (OPM), a subset of LSFM where a single primary objective is used for illumination and detection, light-sheet stabilization can be achieved without expending sample fluorescence. Our method achieves ~43nm axial precision and maintains the light-sheet well within the depth of focus of the detection system for hour-long acquisition runs in a lab environment that would otherwise detune the system. We demonstrate subcellular imaging of the actin skeleton in melanoma cancer cells with a stabilized OPM.
Extracellular Vesicles as Biomarkers for Steatosis Stages in MASLD Patients: an Algorithmic Approach Using Explainable Artificial Intelligence
Authors: Trifylli Eleni Myrto , Angelakis Athanasios , Kriebardis Anastasios G. , Papadopoulos Nikolaos , Fortis Sotirios P. , Pantazatou Vasiliki , Koskinas Ioannis , Kranidioti Hariklia , Koustas Evangelos , Sarantis Panagiotis , Manolakopoulos Spilios , Deutsch Melanie 
Published: November 2024

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