bluuebunny/crossref_metadata_2025_split huggingface dataset:
Fatty acid-mediated induction of CYP2E1 activity in HepaRG cells is not systematically associated with exacerbated acetaminophen cytotoxicity
Authors: Penhoat Clémence , Massart Julie , Pinon Gregory , Catheline Daniel , Rioux Vincent , Fromenty Bernard , Begriche Karima 
Published: November 2024

ABSTRACTHepatic cytochrome P450 2E1 (CYP2E1) is thought to contribute to the pathophysiology of metabolic dysfunction-associated steatotic liver disease (MASLD) formerly known as non-alcoholic fatty liver disease (NAFLD). Indeed, increased activity of CYP2E1 in obese subjects with fatty liver may contribute to higher oxidative stress, mitochondrial dysfunction and the progression to steatohepatitis. Besides, higher CYP2E1 activity in obesity and MASLD is deemed to increase the risk of acetaminophen (APAP)-induced hepatotoxicity because this pain reliever is metabolized by CYP2E1 to N-acetyl-p-benzoquinone imine (NAPQI), a highly toxic metabolite. Although the mechanism of MASLD-associated CYP2E1 induction is still unclear, evidence suggests that hepatic CYP2E1 activity is regulated by fatty acids (FAs). In this study, we investigated the effect of 9 FAs differing by their carbon chain length and their degree of unsaturation on CYP2E1 activity in differentiated HepaRG cells. One-week incubation with palmitic acid (PA), stearic acid (SA) and linoleic acid (LA) induced CYP2E1 activity but only LA exposure induced triglyceride accumulation. APAP hepatotoxicity was then assessed in HepaRG cells cultured with or without PA, SA or LA. Acute APAP cytotoxicity was exacerbated in presence of PA or SA and this was accompanied by more severe mitochondrial dysfunction. These effects were not observed when cells were incubated with LA. Hence, FA-mediated increased CYP2E1 activity in HepaRG cells does not necessarily require steatosis. In addition, FA-mediated CYP2E1 induction does not systematically lead to higher APAP-induced cytotoxicity. By favoring triglyceride accumulation, LA might curb APAP-induced mitochondrial dysfunction and cell death.
A Quantitative Approach for Assessing Multidrug Resistance in Cancer
Authors: Ray Ashley E. , Douglass Eugene F. 
Published: November 2024

AbstractMultidrug resistance (MDR) is a primary barrier to successful cancer treatment with small molecule drugs. One mechanism of resistance is through transporters which pump drug out of cancer cells before they exert any therapeutic effect. The most studied transporter is P-glycoprotein (Pgp), a member of the ATP Binding Cassette (ABC) superfamily of drug pumps. Prior research has focused on determining whether drugs are substrates of Pgp. Pgp specificity for FDA approved drugs is currently unclear due to technical variability in assays that quantify enzyme kinetics using non-cellular experimental models. Unfortunately, there is extreme variability in enzyme parameters for drugs that are characterized and gaps in literature for drugs that have not been characterized. Thus, our overall goal is to develop live cell methods to obtain quantitative scores for substrates in cells and fill gaps in the literature. Studying Pgp in the context of MDR, we improve Pgp specificity scores by leveraging new Pgp expression (cell lines, tissues) and function (drug screening) datasets. We experimentally and computationally integrate functional dataset information to better understand Pgp specificity using an approach based on underlying Michaelis-Menten enzyme kinetics. We obtain consensus scores for Pgp specificity across ∼150 FDA approved oncology drugs and validate them experimentally in a subset of 76 substrates selected to represent the spectrum of drugs for Pgp specificity. These scores can be used to calibrate clinical diagnostics (Pgp expression), and our experimental platform can be used to quantify Pgp function in clinical samples. Overall, we develop a parallel computational and experimental procedure to estimate Pgp selectivity in live cells. This procedure can be expanded to other drug transporters which contribute to MDR to further characterize this phenotype quantitatively.Significance StatementMDR is facilitated through the action of drug transporters which are upregulated in cancer. Even though selective inhibitors have been designed to decrease drug efflux, they failed clinically. So, current clinical practice is to select non-substrates. However, non-substrates are difficult to define. Here, we have developed a quantitative platform for characterizing MDR in cancer that factors in gene expression and enzyme kinetics. Our computational and experimental platform successfully identified the spectrum of substrates for Pgp from the FDA approved oncology drug library. This quantitative approach reflects the multigene phenotype of cancer drug resistance and can be applied to other MDR transporter genes to optimize drug selection clinically.
Staff and Service User Experiences of Specialist Open Prison Personality Disorder Services: How do Pathways Enhanced Resettlement Services Support Progression?
Authors: Mathlin Georgina , Jones Hannah , Lewis Carine , Cooper Claudia , Freestone Mark 
Published: November 2024

AbstractBackgroundThe Offender Personality Disorder (OPD) Pathway, a joint health and criminal justice initiative across England and Wales, aims to support rehabilitation of individuals with a likely diagnosis of personality disorder. Pathways Enhanced Resettlement Services (PERS) is an OPD service currently operating in five open prisons in England, which aims to support people at high risk of being returned to closed conditions or reoffending in the community after release. We aimed to understand service user and staff experiences of PERS.MethodsWe conducted semi-structured interviews with ten staff and nine service users. We then conducted a reflexive thematic analysis generating three themes.ResultsThe three themes identified were: (1) “A shock to the system”, describing the challenges for service users posed by the liminal space of open prison (between higher security conditions and the community); and how in this context PERS might be viewed with suspicion but was for interviewees ultimately a space where they felt valued. (2) “We’ve got some understanding of their journey”; staff and service users described PERS staff developing more trusting relationships with service users than non-PERS staff, where service users felt understood and supported, practically and emotionally; and (3) “internal states can be real barriers to progression”; PERS staff supported service users to understand and overcome barriers, through enabling self-reflection, and tailoring support to times of greater stress, including key milestones such as parole boards or periods of trial leave.ConclusionsStaff and service users feel PERS provides support to progress through open prison, through development of positive trusting relationships and individualised support in a challenging context where such support was not otherwise available.
Neural markers of abstinence in alcohol dependence: Insights from Reward Learning
Authors: Komarnyckyj Mica , Retzler Chris , Murphy Anna , Delis Ioannis , Fouragnan Elsa 
Published: November 2024

ABSTRACTMaladaptive reward learning and decision-making circuity are key factors in the onset and progression of alcohol use disorder and have therefore emerged as key targets for neuropsychological and pharmacological interventions. Probabilistic reversal learning studies have consistently reported impaired learning in recently detoxified alcohol dependent (AD) participants. However, the neural and behavioural changes associated with reward learning which occur throughout abstinence remain unexplored. Here, we show that AD participants, with mean abstinence of 20 months, exhibit intact behavioural performance within an electroencephalography (EEG) probabilistic reversal learning task. Reinforcement learning modelling reveals reward and punishment related learning rates and exploration rates are comparable between AD and healthy control (HC) participants, suggesting recovery of even the nuanced aspects of learning in longer term abstinence. However, EEG analysis indicates that AD, compared to HC participants, show globally elevated event-related potential (ERP) feedback related negativity (FRN) following reward valuation. Furthermore, Feedback-P3 valence prediction error signal is negatively associated with abstinence duration indicating a potential state marker of AD recovery. We then employ unsupervised machine learning (canonical polyadic tensor decomposition) to identify spatiotemporal EEG patterns of reward valuation in a purely data-driven manner. Classification analysis shows these tensor components can predict group membership with 80.4% accuracy. By probing group differences in tensor components, we discover early hyperfunctioning in centro-frontal regions linked to alcohol dependence and associated with early abstinence. The clinically meaningful EEG biomarkers presented here could guide the development of more targeted treatments and support big data approaches to objective patient monitoring.
Genetic regulation ofTERTsplicing contributes to reduced or elevated cancer risk by altering cellular longevity and replicative potential
Authors: Florez-Vargas Oscar , Ho Michelle , Hogshead Maxwell , Lee Chia-Han , Papenberg Brenen W , Forsythe Kaitlin , Jones Kristine , Luo Wen , Teshome Kedest , Blauwendraat Cornelis , Billingsley Kimberly J , Kolmogorov Mikhail , Meredith Melissa , Paten Benedict , Chari Raj , Zhang Chi , Schneekloth John S. , Machiela Mitchell J , Chanock Stephen J , Gadalla Shahinaz , Savage Sharon A , Mbulaiteye Sam M , Prokunina-Olsson Ludmila 
Published: November 2024

ABSTRACTThe chromosome 5p15.33 region, which encodes telomerase reverse transcriptase (TERT), harbors multiple germline variants identified by genome-wide association studies (GWAS) as risk for some cancers but protective for others. We characterized a variable number tandem repeat withinTERTintron 6 (VNTR6-1, 38-bp repeat unit) and observed a strong association between VNTR6-1 alleles (Short: 24-27 repeats, Long: 40.5-66.5 repeats) and GWAS signals withinTERTintron 4. Specifically, VNTR6-1 fully explained the GWAS signals for rs2242652 and partially for rs10069690. VNTR6-1, rs10069690 and their haplotypes were associated with multi-cancer risk and age-related telomere shortening. Both variants reduceTERTexpression through alternative splicing and nonsense-mediated decay: rs10069690-T increases intron 4 retention and VNTR6-1-Long expands a polymorphic G quadruplex (G4, 35-113 copies) within intron 6. Treatment with G4-stabilizing ligands decreased the fraction of the functional telomerase-encodingTERTfull-length isoform, whereas CRISPR/Cas9 deletion of VNTR6-1 increased this fraction and apoptosis while reducing cell proliferation. Thus, VNTR6-1 and rs10069690 regulate the expression and splicing ofTERTtranscripts encoding both functional and nonfunctional telomerase. Altered TERT isoform ratios might modulate cellular longevity and replicative potential at homeostasis and in response to environmental factors, thus selectively contributing to the reduced or elevated cancer risk conferred by this locus.
Annotating CryoET Volumes: A Machine Learning Challenge
Authors: Peck Ariana , Yu Yue , Schwartz Jonathan , Cheng Anchi , Ermel Utz Heinrich , Kandel Saugat , Kimanius Dari , Montabana Elizabeth , Serwas Daniel , Siems Hannah , Wang Feng , Zhao Zhuowen , Zheng Shawn , Haury Matthias , Agard David , Potter Clinton , Carragher Bridget , Harrington Kyle , Paraan Mohammadreza 
Published: November 2024

AbstractCryo-electron tomography (cryoET) has emerged as a powerful structural biology tool for understanding protein complexes in their native cellular environments. Presently, 3D volumes of cellular environments can be acquired in the thousands in a few days where each volume provides a rich and complex cellular landscape. Despite numerous innovations, localizing and identifying the vast majority of protein species in these volumes remains prohibitively difficult. Machine learning based methods provide an opportunity to automate the process of labeling and annotating cryoET volumes. Due to current bottlenecks in the annotation process, and a lack of large standardized datasets, training datasets for machine learning algorithms have been scarce. Here, we present a defined “phantom” sample, along with “ground truth” annotations, that will be the basis of a machine learning challenge to bring cryoET and ML experts together and spur creativity to address this annotation problem. We have also set up a cryoET data portal that provides additional diverse sets of annotated 3D volumes from cryoET experts across the world for the machine learning challenge.
Neutralizing antibody evasion of SARS-CoV-2 JN.1 derivatives KP.3, KP.3.1.1, LB.1, and XEC
Authors: Sano Kaori , Miyakawa Kei , Kato Hideaki , Kimura Yayoi , Goto Atsushi , Ryo Akihide , Watanabe Shinji , Hasegawa Hideki 
Published: November 2024

AbstractThe emergence of SARS-CoV-2 variants poses ongoing challenges to vaccine efficacy. We evaluated neutralizing antibody responses against JN.1 and its derivatives (KP.3, KP.3.1.1, LB.1, and XEC) in healthcare workers who received seven doses of BNT162b2, including XBB.1.5 monovalent vaccine. In COVID-19-naïve individuals, KP.3.1.1 and LB.1 showed substantial immune escape, while previously infected individuals maintained neutralization activity against all variants. We also demonstrated that JN.1-based immunization induces robust cross-neutralizing activity against emerging variants. A single amino acid deletion at position 31 in the spike protein significantly impacted immune evasion. These findings support the potential effectiveness of JN.1-based vaccines while highlighting the need for continued surveillance and vaccine optimization.
MPSE provides fast, flexible, and efficient means to identify newborns who will benefit from whole genome sequencing within the first 48 hours of NICU admission
Authors: Peterson Bennet , Juarez Edwin F. , Moore Barry , Hernandez Edgar Javier , Frise Erwin , Li Jianrong , Lussier Yves , Tristani-Firouzi Martin , Reese Martin G. , Jenkins Sabrina Malone , Kingsmore Stephen F. , Bainbridge Matthew N. , Yandell Mark 
Published: November 2024

AbstractBackgroundIdentifying patients who would benefit from whole genome sequencing (WGS) is difficult and time-consuming due to complex eligibility criteria, lack of neonatologist familiarity with WGS ordering, and evolving clinical features. In previous work, we showed that MPSE, the Mendelian Phenotype Search Engine, can provide automated prioritization of probands for WGS while maintaining current diagnostic rates. MPSE is now in use in multiple hospital networks, but questions still surround how to best prioritize patients for WGS.MethodsHere we use the clinical histories of 2,885 neonatal intensive care unit (NICU) admits from two institutions to explore further questions regarding how to best prioritize NICU admits for WGS. First, we ask if changes to the machine learning (ML) classifier and the clinical natural language processing (CNLP) tools used for generating patient phenotype descriptions might improve MPSE’s performance. Second, we explore the utility of using alternative data types as inputs to MPSE. Lastly, we conduct a longitudinal analysis of MPSE’s ability to identify probands for WGS.ResultsEight different ML classifiers, five CNLP tools, and four previously untested alternative data types were used to train and validate MPSE models. MPSE achieved high predictive performance across multiple classifiers (max AUC=0.93), CNLP tools (max AUC=0.91), and input data types (max AUC=0.91). Longitudinal analysis of MPSE scores revealed a significant separation between cases/controls and diagnostic/non-diagnostic cases within 48 hours of NICU admission.ConclusionsMPSE provides a highly flexible and portable framework for automated prioritization of critically ill newborns for WGS. We find that MPSE’s performance is largely agnostic with respect to CNLP tools. Moreover, structured data such as ICD codes can serve as an effective alternative input to MPSE when access to clinical notes or CNLP pipelines is problematic. Finally, MPSE can identify children most likely to benefit from WGS within 48 hours of admission to the NICU, a critical window for maximally impactful care.
Ebola Disease: Knowledge, attitude, and practices among medical students at a tertiary institution in Uganda
Authors: Gakwerere Daniel , Biryabarema Emmanuel , Namubiru Melanie , Namuyomba Marjorine , Atuhaire Bridget , Musubika Carol , Mukunya David 
Published: November 2024

AbstractBackgroundEbolavirus Disease (EVD) has been a public health threat since its discovery in 1976 with occasional outbreaks on the African continent. This study sought to assess the knowledge, attitudes and practices (KAP) among medical students at a tertiary university in Uganda.MethodsWe conducted a descriptive cross-sectional study using WhatsApp Messenger among students in their clinical years. A pre-validated questionnaire was adapted and modified to assess KAP towards EVD. All analyses were performed using Statistical Package for Social Sciences (SPSS) version 29.0. Bloom’s cutoff of 80% was used to assess sufficient knowledge (≥80%), good attitude (≥6.4) and good practices (≥4.8).ResultsA total of 339 participated, majority were males (n=210, 61.9%) with mean age of 24.2 years (Standard Deviation: 4.0). 77.9% (n=264) were pursuing Bachelor of Medicine and Bachelor of Surgery. Overall, 13.6% (n=46) had sufficient knowledge, 10.9% (n=37) had good attitude and 32.7% (n=111) had good practices. 54.9% (n=186) always avoided patients with signs and symptoms suggestive of EVD while 11.8% (n=40) would accept an approved ebolavirus vaccine.ConclusionThese results revealed suboptimal EVD-related KAP among medical students. We recommend training of students on clinical presentation, transmission, treatment and prevention of EVD to effectively control future outbreaks.
Anatomical insights beyond the center edge angle in Borderline Hip Dysplasia: A Computerized tomography study
Authors: Lara Joaquín , Neira Alejandro , del Río Javier , Tomic Alexander , Garín Alan , García Nicolás , Roby Matías , De la Fuente Carlos 
Published: November 2024

AbstractBackgroundBorderline hip dysplasia (BhD) might be associated with insufficient acetabular coverage. Thus, we investigated potential differences in acetabular anatomical measurements derived from computerized tomography (CT) that characterize BhD compared with healthy controls.MethodsBhD patients (LCEA between 18º and 25º) and healthy controls (LCEA between 25º and 40º) underwent anteroposterior pelvic X-ray and CT to study Wiberg and Tönnis angle, extrusion and Fear index, notch width and depth, anterior and posterior wall height, anterior and posterior articular surfaces, articular circumference, the ratio between the anterior articular surface and the articular circumference, the ratio between the posterior articular surface and the articular circumference, and the ratio between the notch width and the articular circumference. An independent two-tailed t-test, U of Mann-Whitney test, and odds ratios were obtained (α = 5%).ResultsTwenty-three BhD patients aged 31.5 ± 8.3 years and LCEA 21.6° ± 4.0° and thirty-one healthy controls aged 34.1 ± 8.0 years and LCEA 33.7° ± 5.5° were included. The sensitive CT features for BhD were anterior (p< 0.001) acetabular surface, anterior (p= 0.009), anteroposterior (p= 0.008) and posterior (p< 0.008) acetabular surface ratios, and acetabular notch width (p= 0.002).ConclusionsBhD CT characterization provides tridimensional anatomical insights beyond the LCEA. BhD insufficient acetabular coverage can be found along the superior (lower Wiberg angle, and increased Tönnis angle and extrusion index), anterior (lower anterior acetabular surface and anteroposterior acetabular surface ratio, and increased posterior acetabular ratio), and inferior (increased acetabular notch width) axis.
Machine Learning-Driven Identification of Molecular Subgroups in Medulloblastoma via Gene Expression Profiling
Authors: Hourfar Hamdam , Taklifi Pegah , Razavi Mahsa , Khorsand Babak 
Published: November 2024

AbstractBackgroundMedulloblastoma (MB) is the most prevalent malignant brain tumor in children, characterized by substantial molecular heterogeneity across its subgroups. Accurate classification is pivotal for personalized treatment strategies and prognostic assessments.ProcedureThis study utilized machine learning (ML) techniques to analyze RNA sequencing data from 70 pediatric medulloblastoma samples. Five classifiers—K-nearest Neighbors (KNN), Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF), and Naive Bayes (NB)—were employed to predict molecular subgroups based on gene expression profiles. Feature selection identified gene subsets of varying sizes (750, 75, and 25 genes) to optimize classification accuracy.ResultsInitial analyses with the complete gene set lacked discriminative power. However, reduced feature sets significantly enhanced clustering and classification performance, particularly for Group 3 and Group 4 subgroups. The RF, KNN, and SVM classifiers consistently outperformed the DT and NB classifiers, achieving classification accuracies exceeding 90% in many scenarios, especially in Group 3 and Group 4.ConclusionsThis study highlights the efficacy of ML algorithms in classifying medulloblastoma subgroups using gene expression data. The integration of feature selection techniques substantially improves model performance, paving the way for enhanced personalized approaches in medulloblastoma management.
Efficient laminar-distributed interactions and orientation selectivity in the mouse V1 cortical column
Authors: Zou Licheng , Moreni Giulia , Pennartz Cyriel M. A. , Mejias Jorge F. 
Published: November 2024

AbstractThe emergence of orientation selectivity in the visual cortex is a well-known phenomenon in neuroscience, but the details of such emergence and the role of different cortical layers and cell types, particularly in rodents which lack a topographical organization of orientation-selectivity (OS) properties, are less clear. To tackle this question, we use an existing biologically detailed model of the mouse V1 cortical column, which is constrained by existing connectivity data across cortical layers and between pyramidal, PV, SST and VIP cell types. Using this model as a basis, we implemented activity-dependent structural plasticity induced by stimulation with orientated drifting gratings, leading to a good match of tuning properties of pyramidal cells with experimentally observed OS laminar distribution, their evoked firing rate and tuning width. We then employed a mean-field model to uncover the role of co-tuned subnetworks in laminar signal propagation and explain the effects of intra- and inter-laminar coupling distributions. Our plasticity-induced modified model and mean-field model were able to explain both the excitatory enhancement through co-tuned subnetworks and inter-laminar disynaptic inhibition. Overall, our work highlights the importance of the clustering of neural selectivity features for effective excitatory transmission in cortical circuits.