bluuebunny/crossref_metadata_2025_split huggingface dataset:
Citation Contamination of Systematic Review Literature in the Life Sciences by Paper Mills
Authors: Tang Gengyan , Cai Hao 
Published: January 2025

AbstractImportancePaper mills are systematic fraud organizations that mass-produce fabricated papers, submit them under researchers’ names, and profit by charging fees or selling authorship. Their products have infiltrated scientific literature databases, yet the academic community remains uncertain about their potential impact on scholarly research.ObjectiveOur study investigates the citation contamination of systematic reviews in the life sciences by paper mill articles, aiming to determine whether these fabricated papers undermine the status of systematic reviews as the “gold standard” of evidence synthesis.Evidence ReviewWe conducted a cross-sectional study of 100,000 systematic reviews published in the life sciences between 2013 and 2023, as indexed in the Web of Science. We extracted their references and matched them against retracted articles in the Retraction Watch Database, specifically those retracted due to paper mills. Descriptive statistical analyses were used to characterize the features of contaminated systematic reviews, including their subject areas, journals of publication, and citation patterns.FindingsA total of 179 systematic reviews were contaminated by paper mill articles, representing a contamination rate of 0.179%. Although the overall extent of contamination was small, an increasing trend was observed. Notably, 61 citations occurred after the articles had been retracted. Oncology was the most severely affected field. Four systematic reviews each cited five or more paper mill articles; all were published by journals under the same academic publisher. Among those citing three or more paper mill articles, 23 (76.67%) were published in journals ranked in the top 50% of their respective fields by impact factor (i.e., those classified as Q1 or Q2).Conclusions and RelevanceSystematic reviews, as comprehensive syntheses of high-quality evidence, must not incorporate systematically fabricated articles. The scientific community should remain vigilant about the growing trend of paper mill contamination in life sciences systematic reviews. Correcting and retracting previously contaminated reviews, and developing new tools to help researchers identify potential paper mill articles in literature databases, will be essential steps to ensure the integrity of evidence synthesis.Key PointsQuestionDo paper mill articles contaminate systematic reviews in the life sciences, and what is their potential impact on the integrity of evidence synthesis?FindingsIn a cross-sectional study of 100,000 systematic reviews published between 2013 and 2023, 0.179% were contaminated by paper mill articles, with an increasing trend observed over time. Oncology was the most affected field, and 61 post-retraction citations were identified.MeaningThese findings underscore the need for heightened vigilance, systematic correction of contaminated reviews, and development of tools to identify paper mill articles to safeguard the integrity of evidence synthesis.
Genomic diversity andBCL9Lmutational status in CTC pools predict overall survival in metastatic colorectal cancer
Authors: Alves Joao M. , Estévez-Gómez Nuria , Piñeiro Roberto , Muinelo-Romay Laura , Mondelo-Macía Patricia , Salgado Mercedes , Iglesias-Gómez Agueda , Codesido-Prada Laura , Diez-Martín Astrid , Cubiella Joaquin , Posada David 
Published: January 2025

AbstractThe genomic diversity of circulating tumor cells (CTCs) and its clinical implications remain poorly understood. In this study, we characterized the mutational landscape of CTC pools stemming from 29 metastatic colorectal cancer (mCRC) patients and examined its relationship with disease progression. Our analysis revealed substantial variation in mutational burden among patients, with all CTC pools harboring non-silent mutations in key CRC driver genes. Importantly, higher genomic diversity in CTC pools was significantly associated with reduced overall survival. Furthermore, the presence of non-silent mutations inBCL9Lemerged as a strong predictor of patient survival. Taken together, these findings underscore the potential of CTC genomic profiling as a promising prognostic tool in mCRC and highlight the need for further research into its clinical applications.
Extended Plasmonic Nanostructures Templated by Tobacco Mosaic Virus Coat Protein
Authors: Abu-Baker Ismael , Al-Feghali Alexander , Zolfaghar Elliot , Velpula Gangamallaiah , Biela Artur P. , De Feyter Steven , Heddle Jonathan G. , Cosa Gonzalo , Blum Amy Szuchmacher 
Published: January 2025

AbstractOptical and magnetic metamaterials possess interesting properties that cannot be achieved with conventional materials. However, there is currently no synthetic method offering both scalability and nanometer spatial precision. Biotemplating is a promising technique that has the potential to organize nanoscale components with high precision while being scalable and low-cost. Here we demonstrate a versatile template using hexahistidine-tagged tobacco mosaic virus coat protein. The protein self-assembles into disks which further assemble into extended nanostructures under mild conditions. Large sheets with either hexagonal or square packing and core-shell nanorods were formed, and gold nanoparticles were attached to the disks within each nanostructure to form assemblies of nanoparticle rings.
Implementation and utilization of real-time prescription benefit tools across three large academic health systems
Authors: Ali Muhammad , Schwartz Jeremy I. , Wang Jiangxia , Liu Jing , Potu Niteesh , Rottier William , Dziedzic Peter , Smith Erika , Mehta Tanvi , Kashyap Nitu , Fliotsos Michael J. , Luo Jing , Epstein Jeremy , Segal Jodi B. , Lehmann Harold , Crotty Bradley H. , Woreta Fasika A. 
Published: January 2025

ABSTRACTBackgroundReal-time prescription benefit (RTPB) tools provide clinicians an out-of-pocket cost (OOPC) estimate at the time of prescribing and may help them select medications with lower OOPC for their patients.ObjectiveTo evaluate how RTPB tools altered medication orders during clinical encounters and assess patient and clinician characteristics associated with the display of RTPB tool and alternative selection.DesignRetrospective cohort studySettingOutpatient encounters at three academic medical centersParticipantsPatients and cliniciansMeasurementsPatient characteristics, clinician characteristics and medication alternatives suggested by RTPB tool were compared across the three sites.ResultsFrom April 2019 to October 2021, 6,562,442 patient encounters occurred between 3,624 clinicians and 1,261,551 unique patients. Medications were prescribed in 2,152,772 (32.8%) encounters, of which RTPB tool retrieved and displayed alternatives in 968,811 (45.0%) encounters. The clinician selected an alternative in 68,731/968,811 (7.1%) of the encounters during which 89,050 medications were prescribed. The unit cost of alternative medications remained the same for most orders (n=41,212; 58.4%), while 18,629 (26.4%) had lower cost and 10,728 (15.2%) alternate orders had higher cost. Clinicians selected a different pharmacy among 39,634 (44.5%) and a different pharmacy type (mail vs retail) among 7,508 (12.7%) of the alternate medication orders, of which most were to mail order 4,680 (62.3%).LimitationsWe could not assess the role of pharmacy benefit manager coverage, cash-based alternative pricing, and impact of prior authorizations which may be assessed during future investigations.ConclusionAlternate prescribing after implementation of RTPB tools was low across the three institutions. Unit cost of the medication did not often change. Most changes reflected pharmacy choice. Further studies are needed to assess the barriers in adoption of RTPB tools.
Subregional Biomarkers in FDG PET for Alzheimer’s Diagnosis and Staging: An Interpretable and Explainable model
Authors: Rasi Ramin , Guvenis Albert ,   the Alzheimer’s Disease Neuroimaging Initiative
Published: January 2025

AbstractObjectiveTo investigate the radiomics features of the hippocampus and the amygdala subregions in FDG-PET images that can best differentiate Mild Cognitive Impairment (MCI), Alzheimer’s Disease (AD), and healthy patients.MethodsBaseline FDG-PET data from 555 participants in the ADNI dataset were analyzed, comprising 189 cognitively normal (CN) individuals, 201 with MCI, and 165 with AD. The hippocampus and amygdala were segmented based on the DKT-Atlas, with additional subdivisions guided by probabilistic atlases from Freesurfer. Then radiomic features (n=120) were extracted from 38 hippocampal subregions and 18 amygdala nuclei using PyRadiomics. Various feature selection techniques, including ANOVA, PCA, Chi-square, and LASSO, were applied alongside nine machine learning classifiers.ResultsThe Multi-Layer Perceptron (MLP) model combined with LASSO demonstrated excellent classification performance: ROC AUC of 0.957 for CN vs. AD, ROC AUC of 0.867 for MCI vs. AD, and ROC AUC of 0.782 for CN vs. MCI. Key regions, including the accessory basal nucleus, presubiculum head, and CA4 head, were identified as critical biomarkers. Features including GLRLM (Long Run Emphasis) and Small Dependence Emphasis (GLDM) showed strong diagnostic potential, reflecting subtle metabolic and microstructural changes often preceding anatomical alterations.ConclusionSpecific hippocampal and amygdala subregions and their four radiomic features were found to have a significant role in the early diagnosis of AD, its staging, and its severity assessment by capturing subtle shifts in metabolic patterns. Furthermore, these features offer potential insights into the disease’s underlying mechanisms and model interpretability.
Novel blood-based proteomic signatures across multiple neurodegenerative diseases
Authors: Durcan Robert , Heslegrave Amanda , Swann Peter , Goddard Julia , Chouliaras Leonidas , Murley Alexander G , Savulich George , Bevan-Jones W Richard , Swann Owen , Ashton Nicholas J , Blennow Kaj , McEwan William , Zetterberg Henrik , Rowe James B , O’Brien John T , Malpetti Maura 
Published: January 2025

AbstractINTRODUCTIONBlood-based biomarkers have the potential to support early and accurate diagnosis of neurodegenerative diseases, which is sensitive to molecular pathology and predictive of outcome. We evaluated a novel multiplex proteomic method in people with diverse neurodegenerative diseases.METHODSSerum from people with Alzheimer’s disease (N=36), Lewy body dementia (N=34), frontotemporal dementia (N=36) and progressive supranuclear palsy (N=36) and age-matched controls (N=30) was analysed with the NUcleic acid Linked Immuno-Sandwich Assay (NULISA) central nervous system panel (∼120 analytes) and inflammation panel (250 analytes). Biomarkers were compared across groups and included as predictors of survival.RESULTSThe NULISA panels demonstrated high sensitivity and reliability for detecting multiple biomarkers across neurodegenerative disorders. There were condition-specific proteomic biomarkers, while NfL, CRH, CD276 and S100A12 were significant transdiagnostic outcome predictors.DISCUSSIONThe sensitive NULISA multiplex approach supports differential diagnosis and target identification, with prognostically informative dementia-related biomarkers.
Untangling structural molecular details of the endocytic adaptor protein CALM upon binding with phosphatidylinositol 4,5-bisphosphate-containing model membranes
Authors: Santamaria Andreas , Pereira Daniel , Pawar Nisha , Kelly Bernard T. , Carrascosa-Tejedor Javier , Sardo Mariana , Mafra Luís , Fragneto Giovanna , Owen David J. , Marín-Montesinos Ildefonso , Guzmán Eduardo , Zaccai Nathan R. , Maestro Armando 
Published: January 2025

AbstractClathrin assembly lymphoid myeloid leukemia protein (CALM) is involved in the formation of clathrin-mediated endocytic coats by virtue of binding many proteins involved in the process, including clathrin itself and AP2 cargo adaptor complex. CALM is able to specifically recognize the inner leaflet of the plasma membrane by binding the membrane’s phosphatidylinositol 4,5-bisphosphate (PtdIns(4,5)P2). Here, a biophysical approach, primarily using neutron and X-ray scattering and solid-state NMR experiments, was exploited to investigate CALM interaction with PtdIns(4,5)P2-presenting model membranes. The presented experimental data reveal how the CALM folded domain is partly accommodated within the lipid membrane, directly interacting with PtdIns(4,5)P2phosphates. Moreover, these data suggest that CALM’s amphiphilic N-terminal helix buries into the membrane, not only stabilising the protein docking to the membrane but also providing a mechanism to induce membrane curvature.
Development of a high-throughput 3D culture microfluidic platform for
multi-parameter phenotypic and omics profiling of patient-derived
organoids
Authors: Botrugno Oronza A. , Bianchi Elena , Bruno Jolanda M. , Felici Claudia , Gallo Giovanni F.M. , Sommella Eduardo , Stefano Paola De , Giansanti Valentina , Rossella Valeria , Lazarevic Dejan , Merciai Fabrizio , Caponigro Vicky , Golino Valentina , Gioia Danila La , Malinconico Antonio , Colombo Emanuele , Giovannoni Giulio , Gatti Guido , Ratti Francesca , Buonanno Silvia , Pedica Federica , Tacchetti Carlo , Falletta Paola , Zambrano Samuel , Mazza Davide , Cittaro Davide , Campiglia Pietro , Aldrighetti Luca , Tonon Giovanni , Dubini Gabriele 
Published: January 2025

Abstract
Patient-derived organoids (PDOs) are poised to become central tools in
clinical practice, to preemptively identify patient optimal treatments, and in
drug discovery, overcoming the limitations of cancer cell lines. However, the
implementation of PDOs in both these settings has been hampered by several
bottlenecks including sample requirements, assay time and handling in the
context of high-throughput-based drug assays. We report here the development of
a microfluidic-based device
(MicrofluidicPlatform
forOrganoids culture, MPO) that
miniaturises and greatly simplifies PDO cultures in a 384-plate format. Both
retrospective and prospective clinical studies demonstrate its predictive value
and the swift and straightforward implementation in the clinical setting.
Obtaining comprehensive functional and molecular information on the response to
drugs is becoming a requirement in drug discovery. MPO allows subcellular
phenotypic and imaging screenings, target engagement for the assessment of the
efficacy of therapies, alongside the ability to comprehensively and
concomitantly define PDOs genomic, transcriptomic, proteomic, lipidomic and
metabolomic landscape. In all, we demonstrate the potential of our platform to
impact clinical practice generating relevant information on drug sensitivity
within a time frame that could inform treatment decisions alongside exploration
of the mechanisms underlying compound response and resistance in drug discovery
efforts.
Inverted topologies in sequential fitness landscapes enable evolutionary control
Authors: Chen Peng , Krishnan Nikhil , Stacy Anna , Weaver Davis T. , Barker-Clarke Rowan , Hinczewski Michael , Maltas Jeff , Scott Jacob G. 
Published: January 2025

ABSTRACTAdaptive populations rarely evolve in a static environment. Therefore, understanding and ultimately controlling the evolution of a population requires consideration of fluctuating selective pressures. The fitness landscape metaphor has long been used as a tool for representing the selective pressures a given environment imposes on a population. Much work has already been done to understand the dynamics of evolution on a single fitness landscape. More recently, evolution on fluctuating or sequentially applied landscapes has come to the fore of evolutionary biology. As more empirical landscapes are described, metrics for describing salient features of paired landscapes will have uses for understanding likely evolutionary dynamics. Currently, Pearson correlation coefficient and collateral sensitivity likelihoods are used to quantify topological relatedness or dissimilarity of a pair of landscapes. Here, we introduce the edge flip fraction, a new metric for comparing landscapes, which quantifies changes in the directionality of evolution between pairs of fitness landscapes. We demonstrate that the edge flip fraction captures topological differences that traditional metrics may overlook. By applying this metric to both empirical and synthetic fitness landscapes, we show that it partially predicts the collateral sensitivity likelihoods and can inform the optimality of drug sequences. We show that optimal drug sequences that keep populations within lower fitness regions require shifts in evolutionary directions, which are quantified by the edge flip fraction. Edge flip fraction complements existing measures and may help researchers understand how populations evolve under changing environmental conditions.
Socioecological benefits of academic greenspace for human health, plant, and pollinator diversity: a mixed-method study protocol
Authors: Kehinde T.O. , Akinsulore A. , Mapayi B.O. , Okorie V.O. , Oziegbe M. , Ndiribe C.C. , Adedoja O.A. , Balogun I. 
Published: January 2025

AbstractIntroductionSignificant risks to the health of humans and ecosystems are posed by environmental pollution, urban warming, and fragmentation, which are primary urban factors that contribute to the deterioration of urban ecosystems. The purpose of this multidisciplinary study is to compare greenspaces in university campuses and the host cities in order to assess how valuable they are for promoting human and ecosystem health in Nigeria.MethodsMixed-methods research that will be conducted in five tertiary institutions and their host cities in southwestern Nigeria. Based on the objectives, the study is divided into four work packages (WP). WP 1 will use suitable sampling traps and scheduled field observations to quantify the diversity of plants and pollinators. Quantitative evaluation of well-being and mental health will be done in WP 2. In WP 3, the relationship between ecosystem health and mental health will be examined and in-depth interviews will be used to explore the socioecological perceptions and interaction of people with specific indicators of ecosystem health in greenspace. In WP 4, a nature-based intervention will be developed and evaluated in a pilot study to determine its feasibility and acceptability.ResultsThe 2023 TETFund National Research Fund Intervention provided funding for this work, which is currently ongoing.ConclusionThis project will provide scientific knowledge to support evidence-based policy for relevant stakeholders and regulatory organisations, with the goal of promoting greenspace infrastructure in Nigerian tertiary institutions.
Utilization Analysis and Fraud Detection in Medicare via Machine Learning
Authors: Tajrobehkar Mahan , Guo Xin , Nguyen Diane , Chigullapally Nikita , Shravah Varun , Yee Shannon , Chozhan Abishek , Hoang Kathy , Chang Robert T. , Gutierrez Carolina , Lee Scott 
Published: January 2025

AbstractHealthcare fraud and overutilization pose significant challenges in the United States, leading to substantial financial losses and compromised patient care. Medicare, a vital federal healthcare program, is particularly susceptible to such abuses. With over 63 million Americans enrolled in Medicare and growing expenses, the need for effective fraud detection is paramount. Traditional methods relying on manual audits have proven insufficient, allowing a significant portion of fraudulent activity to go undetected. Machine learning (ML), however, has gained significant attention in recent years due to its potential for improving the efficiency of fraud detection and prevention. Nonetheless, there are several issues with the existing studies utilizing ML that limit their effectiveness. The most common issue is the heavy reliance on the List of Excluded Individuals and Entities (LEIE) from the Office of the Inspector General for model training and evaluation. Apart from the severe class imbalance issue (with a fraud rate between 0.038% and 0.074%), another notable problem associated with using the LEIE dataset is that many of the providers listed there were prosecuted due to overt and deliberate fraudulent billing. Consequently, using this dataset to train ML models can help detect brazen, outlandish billing patterns, but would be unable to pinpoint instances of more subtle fraud from which a majority of the financial loss and waste occurs. In this paper, we leverage the experience of seasoned physicians and medical billers to create a labeled dataset that overcomes the issues of class imbalance and the exclusive focus on overtly fraudulent providers. We leverage our access to domain knowledge by focusing on the field of ophthalmology. Additionally, using the labeled dataset, we conduct a comparative study of various machine learning models for the task of predicting Medicare overutilization within ophthalmology. The results indicate that our proposed ensemble outperforms individual models such as extreme gradient boosting and multilayer perceptron in detecting overutilization, achieving Area Under the Receiver Operating Characteristic Curve (AUROC score) of 0.907. By deploying the stacking ensemble model, our paper estimates nationwide and jurisdiction-specific overutilization rates, revealing that approximately 8.6% of ophthalmologists engaged in overutilization practices in 2021. We also highlight potential monetary losses of $437.1 million attributed to overutilization activities within ophthalmology for that year alone. Furthermore, feature importance analysis using SHAP (SHapley Additive exPlanations) values provides insights into the key factors influencing the model’s overutilization predictions. Notably, the ratio of total Medicare payments to the total number of patients emerges as a crucial feature in identifying potential overutilizers.
How to deal with darkness: Modelling and visualization of zero-inflated personal light exposure data on a logarithmic scale
Authors: Zauner Johannes , Guidolin Carolina , Spitschan Manuel 
Published: January 2025

AbstractPersonal light exposure, the pattern of ocular light levels across time under free-living conditions measured with wearable devices, has become increasingly important in circadian and myopia research. Very small measurement values in light exposure patterns, especially zero, are regularly recorded in field studies. These zero-lux values are problematic for commonly applied logarithmic transformations, and should neither be dismissed nor be unduly influential in visualizations and statistical models. Common approaches used in zero-inflated data sets fail in at least one of these regards. We compare four ways to visualize such data on a linear, logarithmic, hybrid, or symlog scale and we model the light exposure patterns with a generalized additive model by removing zero-lux values, adding a very small or −1 log10lux value to the dataset, or using the Tweedie error distribution. We show that asymlog-transformed visualization displays relevant features of light exposure across scales, including zero-lux, while at the same time reducing the emphasis on the small values (<1 lux).Symlogis well-suited to visualize differences in light exposure covering heavy-tailed negative values. The open-source software packageLightLogRincludes the symlog transformation for easy access. We further show that small but not negligible value additions to the light exposure data of -1 log10lux for statistical modelling allow for acceptable models on a logarithmic scale, while very small values distort results. We also demonstrate the utility of the Tweedie distribution, which does not require prior transformations, models data on a logarithmic scale, and includes zero-lux values, capturing personal light exposure patterns satisfactorily. Data from field studies of personal light exposure requires appropriate handling of zero-lux values in a logarithmic context.Symlogscales for visualizations and an appropriate addition to input values for modelling, or the Tweedie distribution, provide a solid basis.