bluuebunny/crossref_metadata_2025_split huggingface dataset:
Ordering Eyeglasses Using 3D Head Scan Technology versus Established Online and Storefront Clinic Methods
Authors: Fontaine Nicolas , Hanssens Jean-Marie , Nguyen Marina , Bérubé Odile 
Published: March 2023

SIGNIFICANCE
This study investigates how a new smartphone scanning technology compares with established online and storefront vendors in providing remote measurement and adjustment of prescription eyeglasses.

PURPOSE
This study aimed to evaluate a new technology for ordering prescription eyeglasses online.

METHODS
Thirty participants with 2.00 to 2.75 D of presbyopia (aged 49 to 74 years) were asked to order eyeglasses with progressive addition lenses from four vendors: one online vendor using a new head scan technology (Topology, San Francisco, CA), two established Web site–based vendors (vendors A and B), and one storefront vendor: Université de Montréal's Vision Clinic (UMVC). The resulting measurements were compared with those of opticians. Participant-reported preferences on visual and physical comfort of eyeglasses were collected after 15-minute trials of eyeglasses from each vendor.

RESULTS
Pupillary half-distance measured with Topology matched optician measurements, but online vendors A and B diverged (mean difference, − 0.80 mm [Z = −2.79; P = .005]). Topology and vendor B segment addition heights were similar to optician measurements, but vendor A diverged (mean, −1.40 mm [Z = −2.58; P = .01]). The personalization parameter values obtained with Topology were different from optician measurements for pantoscopic angle (−5.30° [Z = −4.12; P < .001]) and face wrap angle (+1.25° [Z = −2.94; P = .003]). The UMVC eyeglasses scored best for adjustment (8.71/10 [Z = −5.53; P < .001]), with Topology coming second (7.23/10). Topology scores were equivalent to UMVC scores for all eight items of patient-reported preferences (nonparametric Friedman analysis of variance, P < .05).

CONCLUSIONS
Basic lens centration measurements obtained with Topology compare well with those of opticians, but some aspects of the methodology for measuring personalization parameters could be improved. In comparison with two established online vendors, resulting measurements with Topology are more consistent. Initial wearer satisfaction with Topology eyeglasses was also better.
Microbiome and Diet in Colon Cancer Development and Treatment
Authors: Kato Ikuko , Sun Jun 
Published: March 2023

Abstract
Diet plays critical roles in defining our immune responses, microbiome, and progression of human diseases. With recent progress in sequencing and bioinformatic techniques, increasing evidence indicates the importance of diet-microbial interactions in cancer development and therapeutic outcome. Here, we focus on the epidemiological studies on diet-bacterial interactions in the colon cancer. We also review the progress of mechanistic studies using the experimental models. Finally, we discuss the limits and future directions in the research of microbiome and diet in cancer development and therapeutic outcome. Now, it is clear that microbes can influence the efficacy of cancer therapies. These research results open new possibilities for the diagnosis, prevention, and treatment of cancer. However, there are still big gaps to apply these new findings to the clinical practice.
Radiation Therapy and the Microbiome; More Than a Gut Feeling
Authors: Amit Uri , Facciabene Andrea , Ben-Josef Edgar 
Published: March 2023

Abstract
It is increasingly recognized that heterogeneities in tumor response and severity of adverse effects in irradiated patients can be attributed to the tumor microenvironment and host-related factors. Among the latter, a growing body of literature in recent years has demonstrated the role of the patient's microbiome in modulating both tumor and normal tissue response to radiotherapy (RT). Upon contact with the environment after birth, the infant's gastrointestinal tract is rapidly colonized by microbiota, which is low in diversity and predominantly characterized by 2 dominant species, Actinobacteria and Proteobacteria. With time, intestinal microbiota diversity increases, and colonization of Firmicutes and Bacteroidetes becomes dominant. By the time a child reaches 3 years, the gut microbiota composition has been reshaped and is relatively similar to that of an adult. The microbiome colonizing the different body organs comprises various species and abundances, which may impact human health. Although the adult microbiome composition is thought to remain stable in health, microbiome diversity and composition respond to different environmental and pathological conditions, including pharmaceutical interventions and RT. Our review focuses on how the gut microbiota modulates normal tissue toxicity and tumor control. Readers who want to learn more about how RT shapes gut microbiome diversity and composition are referred to several excellent recently published reviews.
Psychometric Properties of the BAASIS: A Meta-analysis of Individual Participant Data
Authors: Denhaerynck Kris , Dobbels Fabienne , Košťálová Barbora , De Geest Sabina ,   on behalf of the BAASIS Consortium
Published: March 2023

Background.
Nonadherence to immunosuppressives, a risk factor for poor posttransplant outcomes, can be assessed by self-report using the Basel Assessment of Adherence to Immunosuppressive Medications Scale (BAASIS). Available in written and interview versions, and previously validated on content, the BAASIS is widely used in research and clinical practice. The aim of this study was to investigate its psychometric properties.

Methods.
Using a literature search and our BAASIS database, this meta-analysis identified completed studies in adult transplant recipients whose data were usable to examine the BAASIS’ reliability and 3 validity aspects: (1) relationships with other variables (electronic monitoring, other self-report scales, tacrolimus blood-level variability, collateral report, depressive symptoms, psycho-behavioral constructs, and interventions); (2) response processes; and (3) internal structure. Testing used random-effects logistic regressions.

Results.
Our sample included 12 109 graft recipients from 26 studies. Of these 26, a total of 20 provided individual participant data. Evidence of the BAASIS’ stability over time supports its reliability. Validity testing of relationships with other variables showed that BAASIS-assessed nonadherence was significantly associated with the selected variables: electronically monitored nonadherence (P < 0.03), other self- and collaterally-reported nonadherence (P < 0.001), higher variability in tacrolimus concentrations (P = 0.02), higher barriers (P < 0.001), lower self-efficacy (P < 0.001), lower intention (P < 0.001), and higher worries (P = 0.02). Nonadherence also decreased after regimen change interventions (P = 0.03). Response process evaluation indicated good readability and slightly higher nonadherence with the written version. Structurally, items on taking and timing shared variability.

Conclusions.
The BAASIS shows good validity and reliability as a self-report instrument to assess medication nonadherence in transplantation.
Estimating the Impact of Pre-Exposure Prophylaxis (PrEP) on Mortality in COVID-19 Patients: A Causal Inference Approach
Authors: Subramanian Ajan , Huang Yong , Pinto Melissa D. , Downs Charles A. , Rahmani Amir M. 
Published: March 2023

AbstractTraditional machine learning (ML) approaches learn to recognize patterns in the data but fail to go beyond observing associations. Such data-driven methods can lack generalizability when the data is outside the independent and identically distributed (i.i.d) setting. Using causal inference can aid data-driven techniques to go beyond learning spurious associations and frame the data-generating process in a causal lens. We can combine domain expertise and traditional ML techniques to answer causal questions on the data. Hypothetical questions on alternate realities can also be answered with such a framework. In this paper, we estimate the causal effect of Pre-Exposure Prophylaxis (PrEP) on mortality in COVID-19 patients from an observational dataset of over 120,000 patients. With the help of medical experts, we hypothesize a causal graph that identifies the causal and non-causal associations, including the list of potential confounding variables. We use estimation techniques such as linear regression, matching, and machine learning (meta-learners) to estimate the causal effect. On average, our estimates show that taking PrEP can result in a 2.1% decrease in the death rate or a total of around 2,540 patients’ lives saved in the studied population.
mRNA challenge predicts brain cancer immunogenicity and response to checkpoint inhibitors
Authors: Castillo Paul , Ogando-Rivas Elizabeth , Geffrard Hilary , Pepe Alfonso , Liu Ruixuan , Nguyen Duy T , Pedro Diego I , Zhang Dingpeng , DeVries Anna , Qdaisat Sadeem , Karachi Aida , Rahman Maryam , Weidert Frances , Milner Rowan , Huang Jianping , Silver Natalie L. , Ligon John , Li Derek , Lee Ji-Hyun , Carrera-Justiz Sheila , Mitchell Duane A , Mendez-Gomez Hector , Sawyer W Gregory , Sayour Elias J 
Published: March 2023

AbstractTo prospectively determine whether brain tumors will respond to immune checkpoint inhibitors (ICIs), we developed a novel mRNA vaccine as a viral mimic to elucidate cytokine release from brain cancer cells in vitro. Our results indicate that cytokine signatures following mRNA challenge differ substantially from ICI responsive versus non-responsive murine tumors. These findings allow for creation of a diagnostic assay to quickly assess brain tumor immunogenicity, allowing for informed treatment with ICI or lack thereof in poorly immunogenic settings.
PGM3 inhibition Shows cooperative Effects With Erastin inducing Pancreatic cancer cell death via activation of the Unfolded Protein Response
Authors: Zerbato Barbara , Gobbi Maximilian , Ludwig Tobias , Brancato Virginia , Pessina Alex , Brambilla Luca , Wegner Andre , Chiaradonna Ferdinando 
Published: March 2023

1AbstractPancreatic ductal adenocarcinoma (PDAC) is a highly aggressive cancer with a poor patient prognosis. Remarkably, PDAC is one of the most aggressive and deadly tumor types and is notorious for its resistance to all types of treatment. PDAC resistance is frequently associated with a wide metabolic rewiring and in particular of the glycolytic branch named Hexosamine Biosynthetic Pathway (HBP). Here we show the effect of the combined treatment between an HBP’s Phosphoglucomutase 3 (PGM3) enzyme inhibitor, named FR054, and erastin (ERA), a recognized ferroptosis inducer, on PDAC cell growth and survival. Noteworthy, the combined treatment applied to PDAC cell lines induces a significant decrease in cell proliferation and a concurrent enhancement of cell death. Furthermore, we show that this combined treatment induces Unfolded Protein Response (UPR), NFE2 Like BZIP Transcription Factor 2 (NRF2) activation, a change in cellular redox state, a greater sensitivity to oxidative stress, a major dependence on glutamine metabolism, and finally ferroptosis cell death. Our study discloses that HBP inhibition enhances, through UPR activation, the ERA effect and therefore might be a novel anticancer mechanism to be exploited as PDAC therapy.
GREMI: an Explainable Multi-omics Integration Framework for Enhanced Disease Prediction and Module Identification
Authors: Liang Hong , Luo Haoran , Sang Zhiling , Jia Miao , Jiang Xiaohan , Wang Zheng , Yao Xiaohui , Cong Shan 
Published: March 2023

AbstractMulti-omics integration has demonstrated promising performance in complex disease prediction. However, existing research typically focuses on maximizing prediction accuracy, while often neglecting the essential task of discovering meaningful biomarkers. This issue is particularly important in biomedicine, as molecules often interact rather than function individually to influence disease outcomes. To this end, we propose a two-phase framework named GREMI to assist multi-omics classification and explanation. In the prediction phase, we propose to improve prediction performance by employing a graph attention architecture on sample-wise co-functional networks to incorporate biomolecular interaction information for enhanced feature representation, followed by the integration of a joint-late mixed strategy and the true-class-probability block to adaptively evaluate classification confidence at both feature and omics levels. In the interpretation phase, we propose a multi-view approach to explain disease outcomes from the interaction module perspective, providing a more intuitive understanding and biomedical rationale. We incorporate Monte Carlo tree search (MCTS) to explore local-view subgraphs and pinpoint modules that highly contribute to disease characterization from the global-view. Extensive experiments demonstrate that the proposed framework outperforms state-of-the-art methods in seven different classification tasks, and our model effectively addresses data mutual interference when the number of omics types increases. We further illustrate the functional- and disease-relevance of the identified modules, as well as validate the classification performance of discovered modules using an independent cohort. Code and data are available athttps://github.com/Yaolab-fantastic/GREMI.
Inferring compound heterozygosity from large-scale exome sequencing data
Authors: Guo Michael H. , Francioli Laurent C. , Stenton Sarah L. , Goodrich Julia K. , Watts Nicholas A. , Singer-Berk Moriel , Groopman Emily , Darnowsky Philip W. , Solomonson Matthew , Baxter Samantha ,   gnomAD Project Consortium, Tiao Grace , Neale Benjamin M. , Hirschhorn Joel N. , Rehm Heidi L. , Daly Mark J. , O’Donnell-Luria Anne , Karczewski Konrad J. , MacArthur Daniel G. , Samocha Kaitlin E. 
Published: March 2023

AbstractRecessive diseases arise when both the maternal and the paternal copies of a gene are impacted by a damaging genetic variant in the affected individual. When a patient carries two different potentially causal variants in a gene for a given disorder, accurate diagnosis requires determining that these two variants occur on different copies of the chromosome (i.e., are intrans) rather than on the same copy (i.e. incis). However, current approaches for determining phase, beyond parental testing, are limited in clinical settings. We developed a strategy for inferring phase for rare variant pairs within genes, leveraging genotypes observed in exome sequencing data from the Genome Aggregation Database (gnomAD v2, n=125,748). When applied to trio data where phase can be determined by transmission, our approach estimates phase with 95.7% accuracy and remains accurate even for very rare variants (allele frequency < 1×10−4). We also correctly phase 95.9% of variant pairs in a set of 293 patients with Mendelian conditions carrying presumed causal compound heterozygous variants. We provide a public resource of phasing estimates from gnomAD, including phasing estimates for coding variants across the genome and counts per gene of rare variants intrans, that can aid interpretation of rare co-occurring variants in the context of recessive disease.
High throughput detection of variation in single-cell whole transcriptome through streamlined scFAST-seq
Authors: Sang Guoqin , Chen Jiaxin , Zhao Meng , Shi Huanhuan , Han Jinhuan , Sun Jiacheng , Guan Ying , Ma Xingyong , Zhang Guangxin , Gong Yuyan , Zhao Yi , Jiao Shaozhuo 
Published: March 2023

AbstractHigh-throughput single-cell full-length RNA sequencing is a powerful tool to explore the entire transcriptome, including non-polyadenylated transcripts. We developed asinglecellFull-length RNA SequenceTranscriptomesequencing method (scFAST-seq), which combines semi-random primers with high reverse transcription efficiency, template switching and convenient rRNA removal methods, allowing the construction of full-length RNA libraries of up to 12,000 cells within 8 hours. Compared to regular 3’ scRNA-seq, scFAST-seq has similar sensitivity to mRNA detection, sequencing cost and experimental workflow. Moreover, scFAST-seq has clear advantages in detecting non-polyadenylated transcripts, covering longer transcript length, and identifying more splice junctions. In addition, scFAST-seq can more accurately predict the direction of cell differentiation by calculating RNA velocity. Furthermore, we demonstrated that scFAST-seq combined with target region enrichment can simultaneously identify somatic mutations and cellular status of individual tumor cells, which is valuable information for precision medicine.
Phased chromosome-scale genome assembly of an asexual, allopolyploid root-knot nematode reveals complex subgenomic structure
Authors: Winter Michael R , Taranto Adam P , Yimer Henok Zemene , Blundell Alison Coomer , Siddique Shahid , Williamson Valerie M , Lunt David H 
Published: March 2023

AbstractWe present the chromosome-scale genome of the allopolyploid root-knot nematodeMeloidogyne javanica. We show that theM. javanicagenome is predominantly allotetraploid, comprising two subgenomes, A and B, that most likely originated from hybridisation of two ancestral parental species. The assembly is annotated using full-length non-chimeric transcripts, comparison to reference databases, andab initioprediction techniques, and the subgenomes are phased using ancestral k-mer spectral analysis. Subgenome B appears to show greater fission of chromosomal contigs, and while there is substantial synteny between subgenomes, we also identify regions lacking synteny that may have diverged in the ancestral genomes prior to or following hybridisation. Indels are common both between alleles within a subgenome, and between the A and B subgenomes, suggesting theM. javanicagenome exists in a dynamic hypo-tetraploidy where copy number can vary along the chromosome. This annotated and phased genome assembly forms a significant resource for understanding the origins and genetics of these globally important plant pathogens.Author SummaryRoot-knot nematodes represent one of the most significant crop parasites globally. Despite their agricultural importance, only rudimentary genomic resources have been published to date, leaving a gap in the understanding of genetic mechanisms driving genome evolution and crop virulence. Here, we have used modern genomic and bioinformatic approaches to create a chromosome-scale reference genome to investigate the origins and genomic constitution of the root-knot nematode speciesMeloidogyne javanica. This species reproduces by ameiotic parthenogenesis and has an allopolyploid genome and is among the most damaging plant parasitic nematodes with a large and evolving plant host range.Utilising modern long-range DNA sequencing and bioinformatics approaches, we successfully phased the genome into its constituent subgenomes, a first for this agriculturally important clade. While we find the genomic landscape is mostly syntenic between subgenomes, we identified regions of minimal similarity, and highlight structural divergence between subgenomes. We demonstrate that this species was originally tetraploid, but insertions and deletions have been the major force in generating diversity, resulting in a hypo-tetraploid genome with local variations in ploidy.
Establishment and validation of a High-throughput Micro-Neutralization assay for Respiratory Syncytial Virus (subtypes A and B)
Authors: Bonifazi Carolina , Trombetta Claudia Maria , Barneschi Irene , Latanza Simona , Leopoldi Sara , Benincasa Linda , Leonardi Margherita , Semplici Claudia , Piu Pietro , Marchi Serena , Montomoli Emanuele , Manenti Alessandro 
Published: March 2023

AbstractThe validation of a bioanalytical method allows us to determine its validity for a designated purpose and to guarantee the reliability of its analytical results. The virus neutralization assay has proved to be suitable for the detection and quantization of specific serum neutralizing antibodies against respiratory syncytial virus subtypes A and B. Respiratory syncytial virus is a negative-sense RNA virus and is responsible for the majority of acute lower respiratory tract infections in infants and older adults worldwide. Owing to its widespread infection, the WHO considers it a target for the development of preventive vaccines. Despite the high impact of its infections, however, no vaccine candidate is currently available.The aim of this paper is to provide a detailed validation process for the micro-neutralization assay and to demonstrate that this method can effectively support the efficacy assessment of candidate vaccines and the definition of correlates of protection.
Simultaneous application of enzyme and thermodynamic constraints to metabolic models using an updated Python implementation of GECKO
Authors: Carrasco Muriel Jorge , Long Christopher , Sonnenschein Nikolaus 
Published: March 2023

AbstractGEnome-scale Metabolic (GEM) models are knowledge bases of the reactions and metabolites of a particular organism. These GEM models allow for the simulation of the metabolism - e.g. calculating growth and production yields - based on the stoichiometry, reaction directionality and uptake rates of the metabolic network. Over the years, several extensions have been added to take into account other actors in metabolism, going beyond pure stoichiometry. One such extension is enzyme-constraint models, which enable the integration of kinetic data and proteomics data into GEM models. Given its relatively recent formulation, there are still challenges in standardization and data reconciliation between the model and the experimental measurements. In this work, we present geckopy 3.0 (Genome-scale model Enzyme Constraints, using Kinetics and Omics in python), an actualization from scratch of the previous python implementation of the same name. This update tackles the aforementioned challenges, in an effort to reach maturity in enzyme-constraint modeling. With the new geckopy, proteins are typed in the SBML document, taking advantage of the SBML Groups extension, in compliance with community standards. Additionally, a suite of relaxation algorithms - in the form of linear and mixed-integer linear programming problems - has been added to facilitate reconciliation of raw proteomics data with the metabolic model. Several functionalities to integrate experimental data were implemented, including an interface layer with pytfa for the usage of thermodynamics and metabolomics constraints. Finally, the relaxation algorithms were benchmarked against public proteomics datasets inEscherichia colifor different conditions, revealing targets for improving the enzyme constrained model and/or the proteomics pipeline.IMPORTANCEThe metabolism of biological cells is an intricate network of reactions that interconvert chemical compounds, gathering energy and using that energy to grow. The static analysis of these metabolic networks can be turned into a computational model which is able to efficiently output the distribution of fluxes in the network. With the inclusion of enzymes in the network, we can also interpret the role and concentrations of the metabolic proteins. However, the models and the experimental data often clash, resulting in a network that cannot grow. Here, we tackle this situation with a suite of relaxations algorithms in a package called geckopy. Additionally, to ensure that enzyme-constraint models follow the community standards, a format for the proteins is postulated. Geckopy also integrates with other software to allow for adding thermodynamic and metabolomic constraints. We hope that the package and algorithms presented here will serve useful for the constraint-based modeling community.