Distinct Sets Research Articles

Factors Impacting Undergraduate’s Satisfaction and Continuance Intention to Use Online Courses in Chengdu, China

Background and Aim: During the COVID-19 pandemic in China, the adoption of online education for college students uncovered various benefits and challenges, reflecting a distinct set of circumstances shaped by the global health crisis. This study aimed to evaluate the impact of pivotal factors—including learning involvement, validation, informational quality, system excellence, service standard, and user contentment on the overall assessment, and affect college students' commitment to ongoing participation in online courses. Materials and Methods: This study adopted a quantitative approach, collecting data through questionnaires distributed among the targeted demographic. To validate the hypotheses and analyze the data, A variety of methods were utilized, including the Index of Item–item-objective congruence (IOC) assessment, a preliminary study, Confirmatory Factor Analysis (CFA), and Structural Equation Modeling (SEM). Results: The results indicated that every component—engagement, confirmation, information quality, system quality, service quality, and satisfaction—had a positive impact on students' intention to continue with online courses, either directly or through mediating variables. Remarkably, satisfaction emerged as the most influential factor driving the desire to continue using online educational platforms. Conclusion: To enhance college students' ongoing engagement with online courses, efforts should concentrate on elevating aspects such as learning engagement, confirmation, information quality, system quality, and service quality.

Resolving spatially distinct phytohormone response zones in Arabidopsis thaliana roots colonized by Fusarium oxysporum.

Jasmonic acid (JA), ethylene (ET) and salicylic acid (SA) are the three major phytohormones coordinating plant defense responses, and all three are implicated in the defense against the fungal pathogen Fusarium oxysporum. However, their distinct modes of action and possible interactions remain unknown, in part because all spatial information on their activity is lacking. Here, we set out to probe this spatial aspect of plant immunity by using live-microscopy with newly developed fluorescence-based transcriptional reporter lines. We have created a GreenGate vector collection of Plant Immune system Promoters (GG-PIPs) that allow us to image local activation of immune pathways with single-cell resolution. Using this system, we demonstrate that SA and JA act spatially separate from each other in distinct sets of root cells neighboring the fungal colonization site, while ET contributes to both sets. SA & ET induce the hypersensitive response as a first line of defense, while JA & ET govern active defense against the pathogen in a separate, second line of defense. Such an approach to resolve the plant's immune responses on an individual cell level has been lacking, and this work demonstrates that this microscopy-based approach can contribute to understanding plant immune responses in detail.

Novel Breeding Techniques and Strategies for Enhancing Greenhouse Vegetable Product Quality

With its controlled environment, protected cultivation is advantageous and effective for breeding programs. This distinct setting also guarantees that fresh vegetables meet high quality standards. The controlled environment allows for precise monitoring and tuning of breeding efforts, a critical factor in continuously improving the quality of fresh vegetable production. Classical breeding strategies include hybridization, pedigree selection, backcrossing, recombination, and marker-assisted breeding. However, advanced techniques like phenomics and genome editing are revolutionizing the field. These methods accelerate phenotyping and aid in identifying traits and genetic variants linked to quality characteristics. Modern biotechnological tools, specifically genetic engineering and gene editing methods like CRISPR/Cas, have enhanced a wide array of traits in numerous vegetable species. These technological advancements have the potential to effectively address challenges associated with stress resistance, product quality, and shelf-life, thereby presenting promising prospects for the advancement of agriculture. The protracted process of developing new vegetable cultivars with reduced physiological issues through contemporary techniques is an enduring endeavor.

Nasal microbiota predictors for methicillin resistant Staphylococcus colonization in critically ill children.

Surveillance cultures to identify patients colonized with methicillin-resistant Staphylococcus aureus (MRSA) is recommended at pediatric intensive care unit (PICU) admission but doesn't capture other methicillin-resistant Staphylococcus and is resource intensive. We determined the prevalence and identified nasal microbiome predictors for methicillin-resistant Staphylococcus colonization at the time of PICU admission. A prospective cohort study was performed in a 20-bed pediatric intensive care unit (PICU) between 2020-2021. Anterior nares nasal swabs processed for MRSA culture, nasal microbiome and mecA+ qPCR were obtained within first five days after PICU admission. Predictive values of methicillin-resistant Staphylococcus carriage on symptoms of infection and for nasal microbiome attributes were calculated. A total of 5 (8.0%) of 62 patients had a nares culture positive for MRSA and 22 (35.5%) of 63 patients had methicillin-resistant Staphylococcus (MRSA or methicillin-resistant coagulase-negative Staphylococci). In univariate analysis, carriage with MRSA or MRCoNS was associated with having a fever during PICU stay. Colonization with a distinct set of microbes (including Haemophilus, Streptococcus, Prevotella and Corynebacterium sp.) was predictive of having methicillin-resistant Staphylococcus colonization. Carriage with methicillin-resistant Staphylococcus may lead to transmission in critically ill pediatric patients. Carriage of particular nasal microbes appears to facilitate colonization with methicillin-resistant Staphylococcus.

Design thinking in action: a quantitative study of design thinking practices in innovation projects

PurposeDesign thinking is widely recognized as an effective problem-solving approach in the professional and academic world, albeit with varying interpretations. It has been studied in multiple forms – as a tool, a practice, a skill and a mindset – leading to ongoing debates about its fundamental nature. This study aims to explore the use of design thinking in practice and determine how its application varies depending on the characteristics of the innovation projects, namely, the types of goals pursued and the level of uncertainty involved.Design/methodology/approachUsing a survey methodology and a knowledge-intensive empirical setting, this study analyzes a data set of 221 innovation consulting projects based on design thinking conducted by European consulting firms and design agencies.FindingsBy analyzing the survey data, the authors identify six distinct sets of design thinking practices: discovering user needs, understanding the problem addressed, challenging existing assumptions, navigating the problem-solution pair, ideating through visualizations and learning through prototypes. The authors also identify configurations of these design thinking practices that are used to address different innovation project goals and levels of uncertainty.Practical implicationsThe study draws attention to the need for design thinking practitioners to be aware of how different innovation project goals and levels of uncertainty can be pursued/addressed through the use of alternative configurations of design thinking practices.Originality/valueTo the best of the authors knowledge, this study is one of the first large-scale quantitative analyses of the nature of design thinking in action, providing a solid foundation for future research on design thinking.

The Data Heterogeneity Issue Regarding COVID-19 Lung Imaging in Federated Learning: An Experimental Study

Federated learning (FL) has emerged as a transformative framework for collaborative learning, offering robust model training across institutions while ensuring data privacy. In the context of making a COVID-19 diagnosis using lung imaging, FL enables institutions to collaboratively train a global model without sharing sensitive patient data. A central manager aggregates local model updates to compute global updates, ensuring secure and effective integration. The global model’s generalization capability is evaluated using centralized testing data before dissemination to participating nodes, where local assessments facilitate personalized adaptations tailored to diverse datasets. Addressing data heterogeneity, a critical challenge in medical imaging, is essential for improving both global performance and local personalization in FL systems. This study emphasizes the importance of recognizing real-world data variability before proposing solutions to tackle non-independent and non-identically distributed (non-IID) data. We investigate the impact of data heterogeneity on FL performance in COVID-19 lung imaging across seven distinct heterogeneity settings. By comprehensively evaluating models using generalization and personalization metrics, we highlight challenges and opportunities for optimizing FL frameworks. The findings provide valuable insights that can guide future research toward achieving a balance between global generalization and local adaptation, ultimately enhancing diagnostic accuracy and patient outcomes in COVID-19 lung imaging.

Decoding the Torsional Dynamics of Main-Chain Atoms Within CαNN Motif Facilitating Specific Anion Recognition.

The structural plasticity of proteins at the molecular level is largely dictated by backbone torsion angles, which play a critical role in ligand recognition and binding. To establish the anion-induced cooperative arrangement of the main-chain (mc) torsion, herein, we analyzed a set of naturally occurring CαNN motifs as "static models" for their anion-binding competence through docking and molecular dynamics simulations and decoded its torsion angle influenced mc-driven anion recognition potential. By comparing a pool of 20 distinct sets of CαNN motif with identical sequences in their "anion bound/present, aP" and "anion free/absent, aA" versions, we could discern that there exists a positive correlation between the "difference of anion residence time (ΔRT)" and "difference among the main-chain torsion angle" of the aP and aA population. Notably, the anion interaction with CαNNs is locally energetically favorable even in a context-free non-proteinaceous environment and if the difference of the mc-torsion angles involving the Cα-1, N0, N1 residues for a population is higher between the aP and aA state, the difference among the ligand RT is also greater. At the atomistic level, the accommodation of anion is highly synergistic and cooperatively sways the interacting mc-atom torsions. By comparing the clustering of H-bonding patterns, the free energy of binding, and RT in both states, we provide evidence that to establish favorable thermodynamics and kinetics of ligand accommodation in these short structural motifs, proper reorientation of local-mc governed by torsions is a prerequisite. Our findings position the CαNN motif as a promising scaffold for peptidomimetic design and emphasize the critical role of loop region dynamics in protein structure-function relationships.

Cultural Perceptions of Parenting and Parental Empowerment: A Sociological Study of Shifting Norms in Urban and Rural Pakistan

This study explores the cultural perceptions and practices of parenting in urban and rural Pakistan, focusing on how traditional values and modern influences shape parenting styles and parental empowerment in these distinct settings. The research also compares shifting parenting norms, assessing the socio-economic, cultural, and gender dynamics that influence parenting decisions. A mixed-methods, cross-sectional research design was employed, combining qualitative (semi-structured interviews and focus group discussions) and quantitative (surveys) data collection methods. A purposive sample of 230 parents (115 from urban areas like Lahore and 115 from rural areas such as Bahawalpur and Multan) was selected based on gender, socio-economic status, and geographic location. The study's findings reveal significant differences in parenting practices and empowerment across urban and rural settings. Urban parents exhibited higher levels of parental empowerment, with greater access to resources and decision-making authority, while rural parents faced challenges in these areas. Urban parenting was characterized by a greater emphasis on independent thinking, emotional expression, and gender equality, with a preference for modern parenting practices influenced by education and media. In contrast, rural parenting remained more traditional, emphasizing respect, obedience, and the involvement of extended family, with a stronger adherence to gender roles. The results highlight the evolving nature of parenting in urban areas, driven by socio-economic factors and modern influences, while rural areas maintain more rigid, culturally embedded norms.

Beyond the Request: Harnessing HTTP Response Headers for Cross-Browser Web Tracker Detection in an Imbalanced Setting

The World Wide Web's connectivity is greatly attributed to the HTTP protocol, with HTTP messages offering informative header fields that appeal to disciplines like web security and privacy, especially concerning web tracking. Despite existing research employing HTTP request messages to identify web trackers, HTTP response headers are often overlooked. This study endeavors to design effective machine learning classifiers for web tracker detection using binarized HTTP response headers. Data from the Chrome, Firefox, and Brave browsers, obtained through the traffic monitoring browser extension T.EX, serves as our dataset. Ten supervised models were trained on Chrome data and tested across all browsers, including a Chrome dataset from a year later. The results demonstrated high accuracy, F1-score, precision, recall, and minimal log-loss error for Chrome and Firefox, but subpar performance on Brave, potentially due to its distinct data distribution and feature set. The research suggests that these classifiers are viable for web tracker detection. However, real-world application testing remains pending, and the distinction between tracker types and broader label sources could be explored in future studies.

SurfR: Riding the wave of RNA-seq data with a comprehensive bioconductor package to identify surface protein-coding genes.

Proteins at the cell surface connect signaling networks and largely determine a cell's capacity to communicate and interact with its environment. In particular, variations in transcriptomic profiles are often observed between healthy and diseased cells, leading to distinct sets of cell-surface proteins. For these reasons, cell-surface proteins may act as biomarkers for the detection of cells of interest in tissues or body fluids, are often the target of pharmaceutical agents, and hold significant promise in the clinical practice for diagnosis, prognosis, treatment development, and evaluation of therapy response. Therefore, implementing robust methods to identify condition-specific cell-surface proteins is of pivotal importance to advance biomedical research. We developed SurfR, an R/Bioconductor package providing a streamlined end-to-end workflow for computationally identifying surface protein-coding genes from expression data. Our user-friendly, comprehensive workflow performs systematic expression data retrieval from public databases, differential gene expression across conditions, integration of datasets, enrichment analysis, identification of targetable proteins on a condition of interest, and data visualization. SurfR is released under GNU-GPL-v3.0 License. Source code, documentation, examples, and tutorials are available through Bioconductor (http://www.bioconductor.org/packages/SurfR). RMD notebooks with the use cases code described in the manuscript can be found on GitHub (https://github.com/auroramaurizio/SurfR_UseCases).

Dissecting SNARE-Mediated Exocytosis in RBL-2H3 Mast Cells.

SNARE-dependent mast cell (MC) exocytosis causes the release of a wide variety of mediators with important physiological/pathological consequences. Unlike synaptic transmission in the brain, which relies primarily on one set of exocytic SNAREs (i.e., Syntaxin1, SNAP-25, and VAMP2), MCs produce a multitude of exocytic SNAREs that can form a minimum of 8 distinct sets of fusogenic trans-SNARE complexes. Here we describe the genetic approaches we have developed to dissect the specific roles of these SNAREs in RBL-2H3 cells, a widely utilized model for studying MC signaling and exocytosis.

Dynamic non-coding RNA biomarker reveals lung injury and repair induced by polystyrene nanoplastics.

Polystyrene nanoplastics (PS-NPs) are omnipresent in the air and can be inhaled by humans. However, their long-term adverse implications and toxicological mechanisms for human respiratory health are unclear. Therefore, this study aims to provide new insights into the pulmonary toxicity of PS-NPs using mice and organoid models. After subacute and subchronic inhalation of PS-NPs, mice showed pronounced lung injury characterized by respiratory rate changes, altered hematology, and histological evidence of tissue damage and oxidative stress. Similarly, repeated PS-NPs exposure also restricted organoid growth and cause oxidative damage. Notably, through BisqueRNA analysis for a single-cell dataset and canonical markers verification, it was found that PS-NPs induced the emergence and accumulation of transitional cells, suggesting impaired alveolar epithelial repair processes. Sequencing analyses revealed dynamic alterations in non-coding RNA (ncRNA) profiles, including circRNAs and lncRNAs, in response to PS-NPs exposure. Moreover, temporal profiling highlighted distinct sets of ncRNAs as early and progression-associated biomarkers of PS-NP-induced lung injury. These biomarkers correlated with aberrant transitional cells, implicating their roles in disrupted cellular differen tiation and repair mechanisms. Overall, this study observed the multifaceted toxicological responses of PS-NPs to the respiratory system, emphasizing the critical involvement of ncRNAs in mediating PS-NP-induce transitional cells, which was crucial for elucidating the pathophysiology of nanoplastic-induced lung injury and developing targeted therapeutic strategies.

Rooted in Landscape: Exploring Place and Identity in Tim Winton’s Novels

Tim Winton’s novels although his characters may be seen to change shape according to the particular environment that surrounds them. How landscape and place can be seen as backdrop for the construction of identity is the main theme of this article Imbued with a detailed analysis of key works such as Cloudstreet, Dirt Music and Breath, this study examines how Winton’s distinctive Australian settings act as powerful catalysts for internal change and ecological enlightenment. By situating his narrative at once within eco-literature and the Australian literary tradition, the paper invites readers to ponder the intertwining relationship between humankind and nature as well as what sort of ethical duty we owe to it. To summarize, Winton’s work presents an even deeper sense of how place has shaped new form both individual and collective for there is a more responsible approach to environmental issues ahead.

Comparison of Canadian urban forest perceptions indicates variations in beliefs and trust across geographic settings

ABSTRACT Urban forests are characterized by relationships between people and trees, where urban trees provide benefits to people and people make decisions impacting trees. People’s perceptions of urban forests are related to the cognitive processes that underpin benefits received from trees, while also influencing support for or against trees and their management. A growing literature has considered urban forest perceptions, but most studies are limited to a single geographic area and focus on socio-economic influences, with less consideration of location and cultural influences. This study explores the relationship between where people live, the language they speak, and multiple perception responses associated with urban forests (i.e. values, beliefs, trust, satisfaction) to better understand commonalities and differences across distinct geographic settings and populations. We conducted an online survey about urban forest perceptions in three Canadian urban regions, allowing us to explore perceptions between regions, locations on an urban gradient and language spoken. We found geographic and language differences primary for beliefs held about urban trees and trust in municipal government’s decision-making about those trees, while values and satisfaction with trees and their management were more stable across geographic settings and language spoken. Our findings suggest that some perceptions vary between populations. Additionally, our findings reinforce the need for urban forest managers to understand the specific perceptions held by different populations, rather than assume universality of perception, to ensure specific and differential urban forest management objectives are in place to supports local people and ecological elements.

Evaluating Ensemble Learning Techniques for Class Imbalance in Machine Learning: A Comparative Analysis of Balanced Random Forest, SMOTE-RF, SMOTEBoost, and RUSBoost

Purpose: This research aims to identify the optimal ensemble learning method for mitigating class imbalance in datasets utilizing various advanced techniques which include balanced random forest (BRF), SMOTE-random forest (SMOTE-RF), RUSBoost, and SMOTEBoost. The methods were systematically evaluated against conventional algorithms, including random forest and AdaBoost, across heterogeneous datasets with varying class imbalance ratios. Methods: This study utilized 13 secondary datasets from diverse sources, each with binary class outputs. The datasets exhibited varying degrees of class imbalance, offering scenarios to assess the effectiveness of ensemble learning techniques and traditional machine learning approaches in managing class imbalance issues. Study data were split into training (80%) and testing (20%), with stratified sampling applied to maintain consistent class proportions across both sets. Each method underwent hyperparameter optimization with distinct settings with repetition over 10 iterations. The optimal method was evaluated based on balanced accuracy, recall, and computation time. Result: Based on the evaluation, the BRF method exhibited the highest performance in balanced accuracy and recall when compared to SMOTE-RF, RUSBoost, SMOTEBoost, random forest, and AdaBoost. Conversely, the classical random forest method outperformed other techniques in terms of computational efficiency. Novelty: This study presents an innovative analysis of advanced ensemble learning techniques, including BRF, SMOTE-random forest, SMOTEBoost, and RUSBoost, which demonstrate significant effectiveness in addressing class imbalance across various datasets. By systematically optimizing hyperparameters and applying stratified sampling, this research produces findings that redefine the benchmarks of balanced accuracy, recall and computational efficiency in machine learning.

Integrating Protein Language Model and Molecular Dynamics Simulations to Discover Antibiofouling Peptides.

Antibiofouling peptide materials prevent the nonspecific adsorption of proteins on devices, enabling them to perform their designed functions as desired in complex biological environments. Due to their importance, research on antibiofouling peptide materials has been one of the central subjects of interfacial engineering. However, only a few antibiofouling peptide sequences have been developed. This narrow scope of antibiofouling peptide materials limits their capacity to adapt to the broad spectrum of application scenarios. To address this issue, we searched for antibiofouling peptides in the vast sequence pool of the microbiome library using a combination of deep learning-based high-throughput search and molecular dynamics (MD) simulations. A random forest-based model with an ensemble of ten independent classifiers was developed. Each classifier was trained by prompt-tuning the foundational protein language model Evolution Scaling Modeling version 2 (ESM2) on a distinct training data set. We constructed the databases containing the same amount of antibiofouling and biofouling peptide sequences to attenuate the bias of the existing databases. MD simulations were conducted to investigate the interfacial properties of six selected peptide candidates and their interactions with a lysozyme protein. Two known antibiofouling peptides, (glutamic acid (E)-lysine (K))15 and (EK-proline (P))10, and one known fouling peptide, (glycine)30, were used as the reference. The MD simulation results indicate that five of the six peptides present the potential to resist biofouling. Our research implies that deep learning and molecular simulations can be integrated to discover functional peptide materials for interfacial applications.

A field resource for the glioma cerebrospinal fluid proteome: impacts of resection and location on biomarker discovery.

While serial sampling of glioma tissue is rarely performed prior to recurrence, cerebrospinal fluid (CSF) is an underutilized longitudinal source of candidate glioma biomarkers for understanding therapeutic impacts. However, the impact of key variables to consider in longitudinal CSF samples for monitoring biomarker discovery, including anatomical location and post-surgical changes, remains unknown. Aptamer-based proteomics was performed on 147 CSF samples from 74 patients, 71 of whom had grade 2-4 astrocytomas or grade 2-3 oligodendrogliomas. This included pre- versus post-resection intracranial CSF samples obtained at early (1-16 days; n=20 patients) or delayed (86-153 days; n=11 patients) timepoints for patients with glioma. Paired lumbar-versus-intracranial glioma CSF samples were also obtained (n=14 patients). Significant differences were identified in the CSF proteome between lumbar, subarachnoid, and ventricular CSF from patients with gliomas. Importantly, we found that resection had a significant, evolving longitudinal impact on the CSF proteome, with distinct sets of proteins present at different timepoints since resection. Our analysis of serial intracranial CSF samples suggests the early potential for disease monitoring and evaluation of pharmacodynamic impact of targeted therapies, such as bevacizumab and immunotherapies. The intracranial glioma CSF proteome serves as a rich and dynamic reservoir of potential biomarkers that can be used to evaluate the effects of resection and other therapies over time. All data within this study, including detailed individual clinical annotations, are shared as a resource for the neuro-oncology community to collectively address these unanswered questions and further understand glioma biology through CSF proteomics.

A Bibliometric Analysis of Research Examining How Space Radiation Affects Human and Rodent Cognition, 1990–2023

The pursuit of exploring the outer space environment for biological research has been a topic of interest for nearly 60 years. The success of the next phase of space exploration depends on the ability to increase crew safety by identifying ways to mitigate these threats. Using a universal scientific citation indexing tool, we extracted data on literature production in terms of the most prolific key terms, authors, countries, institutions, and journals for two distinct topic sets related to space radiation research published from 1 January 1990 to 31 December 2023. The focus of space radiation research in relation to its effects on human health has fluctuated over time, as reflected in the term maps that were generated for each decade. Our bibliometric analysis provides insight into the trends in the top producers in the space radiation research field over the years, as well as into how the focus of such studies has evolved throughout the decades.

Evaluating sex-specific responses to western diet across the lifespan: impact on cardiac function and transcriptomic signatures in C57BL/6J mice at 530 and 640/750 days of age

BackgroundLong-term consumption of Western Diet (WD) is a well-established risk factor for the development of cardiovascular disease (CVD); however, there is a paucity of studies on the long-term effects of WD on the pathophysiology of CVD and sex-specific responses.MethodsOur study aimed to investigate the sex-specific pathophysiological changes in left ventricular (LV) function using transthoracic echocardiography (ECHO) and LV tissue transcriptomics in WD-fed C57BL/6 J mice for 125 days, starting at the age of 300 through 425 days.ResultsIn female mice, consumption of the WD diet showed long-term effects on LV structure and possible development of HFpEF-like phenotype with compensatory cardiac structural changes later in life. In male mice, ECHO revealed the development of an HFrEF-like phenotype later in life without detectable structural alterations. The transcriptomic profile revealed a sex-associated dichotomy in LV structure and function. Specifically, at 530-day, WD-fed male mice exhibited differentially expressed genes (DEGs), which were overrepresented in pathways associated with endocrine function, signal transduction, and cardiomyopathies. At 750 days, WD-fed male mice exhibited dysregulation of several genes involved in various lipid, glucagon, and glutathione metabolic pathways. At 530 days, WD-fed female mice exhibited the most distinctive set of DEGs with an abundance of genes related to circadian rhythms. At 640 days, altered DEGs in WD-fed female mice were associated with cardiac energy metabolism and remodeling.ConclusionsOur study demonstrated distinct sex-specific and age-associated differences in cardiac structure, function, and transcriptome signature between WD-fed male and female mice.Graphical

A machine learning tool for early identification of celiac disease autoimmunity

Identifying which patients should undergo serologic screening for celiac disease (CD) may help diagnose patients who otherwise often experience diagnostic delays or remain undiagnosed. Using anonymized outpatient data from the electronic medical records of Maccabi Healthcare Services, we developed and evaluated five machine learning models to classify patients as at-risk for CD autoimmunity prior to first documented diagnosis or positive serum tissue transglutaminase (tTG-IgA). A train set of highly seropositive (tTG-IgA > 10X ULN) cases (n = 677) with likely CD and controls (n = 176,293) with no evidence of CD autoimmunity was used for model development. Input features included demographic information and commonly available laboratory results. The models were then evaluated for discriminative ability as measured by AUC on a distinct set of highly seropositive cases (n = 153) and controls (n = 41,087). The highest performing model was XGBoost (AUC = 0.86), followed by logistic regression (AUC = 0.85), random forest (AUC = 0.83), multilayer perceptron (AUC = 0.80) and decision tree (AUC = 0.77). Contributing features for the XGBoost model for classifying a patient as at-risk for undiagnosed CD autoimmunity included signs of anemia, transaminitis and decreased high-density lipoprotein. This model’s ability to distinguish cases of incident CD autoimmunity from controls shows promise as a potential clinical tool to identify patients with increased risk of having undiagnosed celiac disease in the community, for serologic screening.