bluuebunny/crossref_metadata_2025_split huggingface dataset:
Cues to care: Chronic disease diagnosis in young adult trauma patients
Authors: Adams Ursula , Portelli Tremont Jaclyn , Yohann Avital , Aldridge Joshua , Riggins Stephanie , Brownstein Michelle , Charles Anthony ,   Prevention in Young Adult Trauma Patients Multicenter Study Group, Udekwu Pascal Osita 
Published: October 2023

BACKGROUND
Prevention of chronic disease necessitates early diagnosis and intervention. In young adults, a trauma admission may be an early contact with the health care system, representing an opportunity for screening and intervention. This study estimates the prevalence of previously diagnosed disease and undiagnosed disease (UD)—diabetes mellitus, hypertension, obesity, and alcohol and substance use—in a young adult trauma population. We determine factors associated with UD and examine outcomes in patients with UD.

METHODS
This is a multicenter, retrospective cohort study of adult trauma patients 18 to 40 years old admitted to participating Level I trauma centers between January 2018 and December 2020. Three Level 1 trauma centers in a single state participated in the study. Trauma registry data and chart review were examined for evidence of previously diagnosed disease or UD. Patient demographics and outcomes were compared between cohorts. Multivariable regression modeling was performed to assess risk factors associated with any UD.

RESULTS
The analysis included 6,307 admitted patients. Of these, 4,843 (76.8%) had evidence of at least 1 UD, most commonly hypertension and obesity. In multivariable models, factors most associated with risk of UD were age (adjusted odds ratio [aOR], 0.98; 95% confidence interval [CI], 0.98–0.99), male sex (aOR, 1.43; 95% CI, 1.25–1.63), and uninsured status (aOR, 1.57; 95% CI, 1.38–1.80). Only 24.5% of patients had evidence of a primary care provider (PCP), which was not associated with decreased odds of UD. Clinical outcomes were significantly associated with the presence of chronic disease. Of those with UD and no PCP, only 11.2% were given a referral at discharge.

CONCLUSION
In the young adult trauma population, the UD burden is high, especially among patients with traditional sociodemographic risk factors and even in patients with a PCP. Because of short hospital stays in this population, the full impact of UD may not be visible during a trauma admission. Early chronic disease diagnosis in this population will require rigorous, standard screening measures initiated within trauma centers.

LEVEL OF EVIDENCE
Prognostic and Epidemiological; Level IV.
Characterizing citrullination by mass spectrometry-based proteomics
Authors: Rebak A. S. , Hendriks I. A. , Nielsen M. L. 
Published: October 2023

Citrullination is an important post-translational modification (PTM) of arginine, known to play a role in autoimmune disorders, innate immunity response and maintenance of stem cell potency. However, citrullination remains poorly characterized and not as comprehensively understood compared to other PTMs, such as phosphorylation and ubiquitylation. High-resolution mass spectrometry (MS)-based proteomics offers a valuable approach for studying citrullination in an unbiased manner, allowing confident identification of citrullination modification sites and distinction from deamidation events on asparagine and glutamine. MS efforts have already provided valuable insights into peptidyl arginine deaminase targeting along with site-specific information of citrullination in for example synovial fluids derived from rheumatoid arthritis patients. Still, there is unrealized potential for the wider citrullination field by applying MS-based mass spectrometry approaches for proteome-wide investigations. Here we will outline contemporary methods and current challenges for studying citrullination by MS, and discuss how the development of neoteric citrullination-specific proteomics approaches still may improve our understanding of citrullination networks.
This article is part of the Theo Murphy meeting issue ‘The virtues and vices of protein citrullination’.
Citrullination of matrisomal proteins in health and diseases
Authors: Saifi Mohammad Aslam , Ho I-Cheng 
Published: October 2023

Proteins once translated are subjected to post-translational modifications (PTMs) that can critically modify their characteristics. Citrullination is a unique type of PTM that is catalysed by peptidylarginine deiminase (PAD) enzymes, which regulate a multitude of physiological functions such as apoptosis, gene expression and immune response by altering the structure and function of cellular proteins. However, emerging data have unravelled compelling evidence to support that PAD-mediated citrullination is not exclusive to cellular proteins; rather citrullination of extracellular matrix (ECM) proteins also plays a major contributing role in various physiological/pathological conditions. Here, we discuss putative mechanisms for citrullination-induced alterations in the function of ECM proteins. Further, we put emphasis on influential roles of ECM citrullination in various pathological scenarios to underscore the clinical potential of its manipulation in human diseases.
This article is part of the Theo Murphy meeting issue ‘The virtues and vices of protein citrullination’.
Citrulline is not a major determinant of autoantibody reactivity to neutrophil extracellular traps
Authors: de Bont Cynthia , Pruijn Ger J. M. 
Published: October 2023

One of the main strategies of neutrophils in responding to microbial infections is the formation of neutrophil extracellular traps (NETs). NETs are web-like structures of decondensed chromatin associated with antimicrobial proteins. Citrullination plays an important role during NET formation and a substantial fraction of NET-associated proteins appeared to be citrullinated. The release of citrullinated intracellular proteins from netting neutrophils led to the hypothesis that the production of anti-citrullinated protein autoantibodies by autoimmune patients, in particular patients with rheumatoid arthritis, might be initiated when citrullinated NET components are not properly cleared and are exposed to the immune system. Here, we discuss the processes that lead to NET formation, including the role of peptidylarginine deiminase activation and our current knowledge on citrullinated NET-associated proteins. Citrulline-dependent epitopes do not appear to play a major role in the recognition of NETs by autoantibodies from rheumatoid arthritis and systemic lupus erythematosus patients, even though anti-NET autoantibodies are frequently observed in sera from these patients. The neutrophil proteases associated with NETs have a major impact on the integrity of NET-associated proteins when NET formation is induced by activating isolated human neutrophils. Cleavage/degradation of these proteins also resulted in a strong reduction of the reactivity with autoantibodies.
This article is part of the Theo Murphy meeting issue ‘The virtues and vices of protein citrullination’.
Cancer associated fibroblasts mitigate the efficacy of the combination of chemotherapy and BCL-xL targeting in triple negative breast cancer cells
Authors: Nocquet Lisa , Roul Julie , Duarte Laurine , Campone Mario , Juin Philippe P. , Souazé Frédérique 
Published: October 2023

ABSTRACTTriple negative breast cancers (TNBC) present a poor prognosis primarily due to their resistance to chemotherapy. This resistance is understood to associate with elevated expression of certain antiapoptotic members within the proteins of the BCL-2 family (namely BCL-xL, MCL-1 and BCL-2). These regulate cell death by inhibiting pro-apoptotic protein activation through binding and sequestration and they can be selectively antagonized by BH3 mimetics. Yet the individual influences of BCL-xL, MCL-1, and BCL-2 on the sensitivity of TNBC cells to chemotherapy, and their regulation by cancer-associated fibroblasts (CAFs), major components of the tumor stroma and key contributors to therapy resistance remain to be delineated. By utilizing engineered TNBC cell line MDA-MB-231 deficient in BCL-2 family proteins and BH3 mimetics, we show that BCL-xL and MCL-1 promote cancer cell survival by compensatory mechanisms. This cell line shows limited sensitivity to chemotherapy, in line with the clinical resistance observed in TNBC patients. We elucidate that BCL-xL plays a pivotal role in therapy response, as its depletion or pharmacological inhibition heightened chemotherapy effectiveness. Moreover, BCL-xL expression is associated to chemotherapy resistance in patient-derived tumoroids where its pharmacological inhibition enhances ex vivo response to chemotherapy. In a co-culture model of cancer cells and CAFs, we observe that even in a context where BCL-xL inhibition renders cancer cells more susceptible to chemotherapy, those in contact with CAFs display reduced sensitivity to chemotherapy. Thus CAFs exert a profound pro-survival effect in breast cancer cells, even in a setting highly favoring cell death through combined chemotherapy and inhibition of the main actor of chemoresistance, BCL-xL.
Digital profiling of cancer transcriptomes from histology images with grouped vision attention
Authors: Zheng Yuanning , Pizurica Marija , Carrillo-Perez Francisco , Noor Humaira , Yao Wei , Wohlfart Christian , Marchal Kathleen , Vladimirova Antoaneta , Gevaert Olivier 
Published: October 2023

AbstractCancer is a heterogeneous disease that demands precise molecular profiling for better understanding and management. Recently, deep learning has demonstrated potentials for cost-efficient prediction of molecular alterations from histology images. While transformer-based deep learning architectures have enabled significant progress in non-medical domains, their application to histology images remains limited due to small dataset sizes coupled with the explosion of trainable parameters. Here, we developSEQUOIA, a transformer model to predict cancer transcriptomes from whole-slide histology images. To enable the full potential of transformers, we first pre-train the model using data from 1,802 normal tissues. Then, we fine-tune and evaluate the model in 4,331 tumor samples across nine cancer types. The prediction performance is assessed at individual gene levels and pathway levels through Pearson correlation analysis and root mean square error. The generalization capacity is validated across two independent cohorts comprising 1,305 tumors. In predicting the expression levels of 25,749 genes, the highest performance is observed in cancers from breast, kidney and lung, whereSEQUOIAaccurately predicts the expression of 11,069, 10,086 and 8,759 genes, respectively. The accurately predicted genes are associated with the regulation of inflammatory response, cell cycles and metabolisms. While the model is trained at the tissue level, we showcase its potential in predicting spatial gene expression patterns using spatial transcriptomics datasets. Leveraging the prediction performance, we develop a digital gene expression signature that predicts the risk of recurrence in breast cancer.SEQUOIAdeciphers clinically relevant gene expression patterns from histology images, opening avenues for improved cancer management and personalized therapies.
Deep Learning-enabled Detection of Aortic Stenosis from Noisy Single Lead Electrocardiograms
Authors: Aminorroaya Arya , Dhingra Lovedeep S , Sangha Veer , Oikonomou Evangelos K , Khunte Akshay , Shankar Sumukh Vasisht , Camargos Aline Pedroso , Haynes Norrisa A , Hofer Ira , Ouyang David , Nadkarni Girish N. , Khera Rohan 
Published: October 2023

ABSTRACTBackgroundDue to the lack of a feasible screening strategy, aortic stenosis (AS) is often diagnosed after the development of clinical symptoms, representing advanced stages of disease. Portable and wearable devices capable of recording electrocardiograms (ECGs) can be used for scalable screening for AS, if the diagnosis can be made with a single-lead ECG, despite potentially noisy acquisition.MethodsUsing electronic health records and imaging data from a large, diverse hospital system (2015-2022), we developed a deep learning-based approach to detect moderate/severe AS using a single-lead ECG. We used ECGs paired with echocardiograms obtained within 30 days of each other to develop the model. We extracted lead I signal data from clinical ECG and augmented it with random Gaussian noise. We trained a convolutional neural network (CNN) to identify TTE-confirmed AS using noisy single-lead ECGs. Finally, we used the CNN model probabilities, along with patient age and sex, as predictive inputs to train an extreme gradient boosting (XGBoost) model to detect moderate/severe AS.ResultsThe model was developed in 75,901 ECGs/35,992 patients (median age 61 [interquartile range (IQR) 47-72] years, 54.3% women, 9.5% Black) and validated in 3,733 patients (median age 61 [IQR 47-72] years, 53.4% women, 9.7% Black). In the held-out validation set, the ensemble XGBoost model achieved an AUROC of 0.829 (95% CI: 0.800-0.855), with a sensitivity of 90.4% and specificity of 58.7% for detecting moderate/severe AS. For detecting severe AS, the model’s AUROC was 0.846 (95% CI, 0.778-0.899), with a sensitivity of 94.3% and specificity of 57.0%. In the test set with a 4.5% prevalence of moderate/severe AS, the model had a PPV of 9.3% and an NPV of 99.2%. In simulated cohorts with 1% and 20% prevalence of moderate/severe AS, the model’s NPVs varied from 99.8% to 96.1%, and PPV from 2.2% to 35.4%, respectively.ConclusionWe developed a novel portable– and wearable-adapted deep learning approach for the detection of moderate/severe AS from noisy single-lead ECGs. Our approach represents a highly sensitive, feasible, and scalable strategy for community-based AS screening.
HyperPRI: A Dataset of Hyperspectral Images for Underground Plant Root Study
Authors: Chang Spencer J. , Chowdhry Ritesh , Song Yangyang , Mejia Tomas , Hampton Anna , Kucharski Shelby , Sazzad TM , Zhang Yuxuan , Koppal Sanjeev J. , Wilson Chris H. , Gerber Stefan , Tillman Barry , Resende Marcio F. R. , Hammond William M. , Zare Alina 
Published: October 2023

AbstractCollecting and analyzing hyperspectral imagery (HSI) of plant roots over time can enhance our understanding of their function, responses to environmental factors, turnover, and relationship with the rhizosphere. Current belowground red-green-blue (RGB) root imaging studies infer such functions from physical properties like root length, volume, and surface area. HSI provides a more complete spectral perspective of plants by capturing a high-resolution spectral signature of plant parts, which have extended studies beyond physical properties to include physiological properties, chemical composition, and phytopathology. Understanding crop plants’ physical, physiological, and chemical properties enables researchers to determine high-yielding, drought-resilient genotypes that can withstand climate changes and sustain future population needs. However, most HSI plant studies use cameras positioned above ground, and thus, similar belowground advances are urgently needed. One reason for the sparsity of belowground HSI studies is that root features often have limited distinguishing reflectance intensities compared to surrounding soil, potentially rendering conventional image analysis methods ineffective. Here we present HyperPRI, a novel dataset containing RGB and HSI data for in situ, non-destructive, underground plant root analysis using ML tools. HyperPRI contains images of plant roots grown in rhizoboxes for two annual crop species – peanut (Arachis hypogaea) and sweet corn (Zea mays). Drought conditions are simulated once, and the boxes are imaged and weighed on select days across two months. Along with the images, we provide hand-labeled semantic masks and imaging environment metadata. Additionally, we present baselines for root segmentation on this dataset and draw comparisons between methods that focus on spatial, spectral, and spatialspectral features to predict the pixel-wise labels. Results demonstrate that combining HyperPRI’s hyperspectral and spatial information improves semantic segmentation of target objects.
Genome analysis of the Jomon dogs reveals the oldest domestic dog lineage in Eastern Eurasia
Authors: Terai Yohey , Xiaokaiti Xiayire , Gojobori Jun , Arakawa Nami , Sato Takao , Kasai Kenji , Machida Kenichi , Yamazaki Kyomi , Yamaji Naomitsu , Hongo Hitomi , Gakuhari Takashi 
Published: October 2023

AbstractDog is the oldest domesticated animal that established close relationships with humans. Due to its ancient origin, when, where, and whether a single or dual domestication event occurred is still under debate. The dogs in the Jomon period (Jomon dogs) in the Japanese archipelago had little change in morphology from 10,000 to 3,000 years ago. Therefore, we expected that the ancient genome of the Jomon dogs would provide a clue to reveal the characteristics of the ancient East Asian dogs. Here, we have sequenced the genomes of three 6000-year-old Jomon dogs, one 3000-4000-year-old Jomon dog, and four late 8th century dogs excavated in Japan. Our analyses suggest that the Jomon dogs are a distinct lineage from the previously known ancient dogs and are one of the oldest among the dogs in East Eurasian lineages. In addition, the genome of the Jomon dogs contained 9.5% of the genome of Japanese wolf ancestry due to a single introgression event. We estimated the proportion of the Jomon dog lineage genome in the genomes of dogs, which indicates that the genomic composition derived from the Jomon dog lineage is one of the major sources of modern dog genomes. Furthermore, we estimated the early admixture events of dogs in East Eurasia by analyzing the ancient genomes of the Jomon dogs. Due to the admixture events, the Jomon dog-derived genome has been one of the genomic sources of a wide range of modern dogs.
Benchmarking enrichment analysis methods with the disease pathway network
Authors: Buzzao Davide , Castresana-Aguirre Miguel , Guala Dimitri , Sonnhammer Erik L.L. 
Published: October 2023

ABSTRACTEnrichment analysis (EA) is a common approach to gain functional insights from genome-scale experiments. As a consequence, a large number of EA methods have been developed, yet it is unclear from previous studies which method is the best for a given dataset. The main issues with previous benchmarks include the complexity of correctly assigning true pathways to a test dataset, and lack of generality of the evaluation metrics, for which the rank of a single target pathway is commonly used.We here provide a generalized EA benchmark and apply it to the most widely used EA methods, representing all four categories of current approaches. The benchmark employs a new set of 82 curated gene expression datasets from DNA microarray and RNA-Seq experiments for 26 diseases, of which only 13 are cancers. In order to address the shortcomings of the single target pathway approach and to enhance the sensitivity evaluation, we present the Disease Pathway Network, in which related KEGG pathways are linked. We introduce a novel approach to evaluate pathway EA by combining sensitivity and specificity to provide a balanced evaluation of EA methods. This approach identifies Network Enrichment Analysis methods as the overall top performers compared to overlap-based methods. By using randomized gene expression datasets, we explore the null hypothesis bias of each method, revealing that most of them produce skewedp-values.
Clostridioides difficileutilizes siderophores as an iron source and FhuDBGC contributes to ferrichrome uptake
Authors: Hastie Jessica L. , Carmichael Hannah L. , Werner Bailey M. , Dunbar Kristin E. , Carlson Paul E. 
Published: October 2023

ABSTRACTClostridioides difficileremains a public health threat commonly observed following antibiotic use. Due to the importance of iron in many cell processes, most bacteria, includingC. difficile, have multiple mechanisms of acquiring iron. Previous studies have examined ferrous iron uptake inC. difficile, here we focus on the role of siderophores. In a growth assay, we show thatC. difficilecan use a variety of siderophores as the sole iron source. InC. difficile,two ABC transporters induced under low iron conditions are predicted siderophore importers: FhuDBGC and YclNOPQ. We hypothesized that these transporters are responsible for the uptake of the siderophores we tested. To investigate the specificity of these transporters, we purified the substrate binding proteins and examined siderophore binding using thermal shift. We demonstrate increased stability between one siderophore binding protein, FhuD, and the siderophore ferrichrome, suggesting a binding interaction. This specificity correlates with the inability of anΔfhuDBGCmutant to grow efficiently under iron limiting conditions in the presence of ferrichrome. WhileC. difficileused additional siderophores in our growth experiments, we did not observe increased thermal stability between the receptor proteins and any of the other siderophores tested, suggesting these siderophores do not bind these receptors and other siderophore import mechanisms remain to be elucidated. Redundancy in iron acquisition is a microbial survival adaptation to cope with the constant battle for iron within a host. Greater knowledge about howC. difficileacquires iron will provide insight about howC. difficilecolonizes and persists in the colon.IMPORTANCEThis study is the first example ofC. difficilegrowing with siderophores as the sole iron source and describes the characterization of the ferric hydroxamate uptake ABC transporter (FhuDBGC). This transporter shows specificity to the siderophore ferrichrome. While not required for pathogenesis, this transporter highlights the redundancy in iron acquisition mechanisms whichC. difficileuses to compete for iron during an infection.
Mycobacterium tuberculosisKu stimulates multi-round DNA unwinding by UvrD1 monomers
Authors: Chadda Ankita , Kozlov Alexander G. , Nguyen Binh , Lohman Timothy M. , Galburt Eric A. 
Published: October 2023

AbstractMycobacterium tuberculosisis the causative agent of Tuberculosis. During the host response to infection, the bacterium is exposed to both reactive oxygen species and nitrogen intermediates that can cause DNA damage. It is becoming clear that the DNA damage response inMtband related actinobacteria function via distinct pathways as compared to well-studied model bacteria. For example, we have previously shown that the DNA repair helicase UvrD1 is activated for processive unwinding via redox-dependent dimerization. In addition, mycobacteria contain a homo-dimeric Ku protein, homologous to the eukaryotic Ku70/Ku80 dimer, that plays roles in double-stranded break repair via non-homologous end-joining. Ku has been shown to stimulate the helicase activity of UvrD1, but the molecular mechanism, as well as which redox form of UvrD1 is activated, is unknown. We show here that Ku specifically stimulates multi-round unwinding by UvrD1 monomers which are able to slowly unwind DNA, but at rates 100-fold slower than the dimer. We also demonstrate that the UvrD1 C-terminal Tudor domain is required for the formation of a Ku-UvrD1 protein complex and activation. We show thatMtbKu dimers bind with high nearest neighbor cooperativity to duplex DNA and that UvrD1 activation is observed when the DNA substrate is bound with two or three Ku dimers. Our observations reveal aspects of the interactions between DNA,MtbKu, and UvrD1 and highlight the potential role of UvrD1 in multiple DNA repair pathways through different mechanisms of activation.Declaration of InterestsNone