bluuebunny/crossref_metadata_2025_split huggingface dataset:
Volatile profiling distinguishesStreptococcus pyogenesfrom other respiratory streptococcal species
Authors: Berna Amalia Z. , Merriman Joseph A. , Mellet Leah , Parchment Danealle K. , Caparon Michael G. , John Audrey R. Odom 
Published: April 2023

ABSTRACTSore throat is one of the most common complaints encountered in the ambulatory clinical setting. Rapid, culture-independent diagnostic techniques that do not rely on pharyngeal swabs would be highly valuable as a point-of-care strategy to guide outpatient antibiotic treatment. Despite the promise of this approach, efforts to detect volatiles during oropharyngeal infection have yet been limited. In our research study, we sought to evaluate for specific bacterial volatile organic compounds (VOC) biomarkers in isolated culturesin vitro, in order to establish proof-of-concept prior to initial clinical studies of breath biomarkers. A particular challenge for diagnosis of pharyngitis due toStreptococcus pyogenesis the likelihood that many metabolites may be shared byS. pyogenesand other related oropharyngeal colonizing bacterial species. Therefore, we evaluated whether sufficient metabolic differences are present that distinguish the volatile metabolome of Group A streptococci from other streptococcal species that also colonize the respiratory mucosa, such asS. pneumoniaeandS. intermedius. In this work, we identify candidate biomarkers that distinguishS. pyogenesfrom other species, and establish highly produced VOCs that indicate presence ofS. pyogenes in vitro, supporting future breath-based diagnostic testing for streptococcal pharyngitis.IMPORTANCEAcute pharyngitis accounts for approximately 15 million ambulatory care visits in the USA. The most common and important bacterial cause of pharyngitis isStreptococcus pyogenesis, accounting for 15% to 30% of pediatric pharyngitis. Distinguishing between bacterial and viral pharyngitis is key to management in US practice. Culture of a specimen obtained by throat swab is the standard laboratory procedure for the microbiologic confirmation of pharyngitis, however this method is time consuming which delays appropriate treatment. If left untreated,S. pyogenespharyngitis may lead to local and distant complications. In this study, we characterized the volatile metabolomes ofS. pyogenesand other related oropharyngeal colonizing bacterial species. We identify candidate biomarkers that distinguishS. pyogenesfrom other species and provides evidence to support future breath-based diagnostic testing for streptococcal pharyngitis.
Impact of inflammatory preconditioning on murine microglial proteome response induced by focal ischemic brain injury
Authors: Helbing Dario Lucas , Haas Fabienne , Cirri Emilio , Rahnis Norman , Dau Therese Thuy Dung , Sacramento Erika Kelmer , Oraha Nova , Böhm Leopold , Morrison Helen , Bauer Reinhard 
Published: April 2023

AbstractPreconditioning with LPS induces neuroprotection against subsequent cerebral ischemic injury, mainly involving innate immune pathways. Microglia are CNS-resident immune cells that respond early to danger signals through memory-like differential reprogramming. However, the cell-specific molecular mechanisms underlying preconditioning are not fully understood. To elucidate the distinct molecular mechanisms of preconditioning on microglia, we compared these cell-specific proteomic profiles in response to LPS preconditioning and without preconditioning and subsequent transient focal brain ischemia and reperfusion, – using an established mouse model of transient focal brain ischemia and reperfusion. A proteomic workflow, based on isolated microglia obtained from mouse brains by cell sorting and coupled to mass spectrometry for identification and quantification, was applied. Our data confirm that LPS preconditioning induces marked neuroprotection, as indicated by a significant reduction in brain infarct volume. The established brain cell separation method was suitable for obtaining an enriched microglial cell fraction for valid proteomic analysis. The results show a significant impact of LPS preconditioning on microglial proteome patterns by type I interferons, presumably driven by the interferon cluster regulator proteins Stat1/2.
Linear and Neural Network Models for Predicting N-glycosylation in Chinese Hamster Ovary Cells Based on B4GALT Levels
Authors: Seber Pedro , Braatz Richard D. 
Published: April 2023

AbstractGlycosylation is an essential modification to proteins that has positive effects, such as improving the half-life of antibodies, and negative effects, such as promoting cancers. Despite the importance of glycosylation, predictive models have been lacking. This article constructs linear and neural network models for the prediction of the distribution of glycans on N-glycosylation sites. The models are trained on data containing normalized B4GALT levels in Chinese Hamster Ovary cells. The ANN models achieve a median prediction error of 1.39%, which is 10-fold smaller than for previously published models, and a narrow error distribution. We also discuss issues with other models reported in the literature. We provide all of the software used in this work, allowing other researchers to reproduce the work and reuse or improve the code in future endeavors.
Evidence of a pan-tissue decline in stemness during human aging
Authors: dos Santos Gabriel Arantes , Vega Magdaleno Gustavo Daniel , de Magalhães João Pedro 
Published: April 2023

AbstractDespite their biological importance, the role of stem cells in human aging remains to be elucidated. In this work, we applied a machine learning methodology to GTEx transcriptome data and assigned stemness scores to 17,382 healthy samples from 30 human tissues aged between 20 and 79 years. We found that ∼60% of the studied tissues present a significant negative correlation between the subject’s age and stemness score. The only significant exception to this pattern was the uterus, where we observed an increased stemness with age. Moreover, we observed a global trend of positive correlations between cell proliferation and stemness. When analyzing the tissues individually, we found that ∼50% of human tissues present a positive correlation between stemness and proliferation and 20% a negative correlation. Furthermore, all our analyses show negative correlations between stemness and cellular senescence, with significant results in ∼80% of the tissues analyzed. Finally, we also observed a trend that hematopoietic stem cells derived from old patients might have more stemness. In short, we assigned stemness scores to human samples and show evidence of a pan-tissue loss of stemness during human aging, which adds weight to the idea that stem cell deterioration contributes to human ageing.
The Power of Two: integrating deep diffusion models and variational autoencoders for single-cell transcriptomics analysis
Authors: Sadria Mehrshad , Layton Anita 
Published: April 2023

AbstractDiscovering a lower-dimensional embedding of single-cell data can greatly improve downstream analysis. The embedding should encapsulate both the high-level semantics and low-level variations in order to be meaningful and interpretable. Although current generative models have been used to learn such a low-dimensional representation, they have several limitations. Here, we introduce scVAEDer, a scalable deep-learning model that combines the power of variational autoencoders and deep diffusion models to learn a meaningful representation which can capture both global semantics and local variations in the data. By using the learned embedding, we show that scVAEDer can generate novel scRNA-seq data, predict the effect of the perturbation on various cell types, identify changes in gene expression during dedifferentiation, and detect master regulators in a biological process.
Sequential chromogenic immunohistochemistry: spatial analysis of lymph nodes identifies contact interactions between plasmacytoid dendritic cells and plasmablasts
Authors: Claudio N.M. , Nguyen M.T. , Wanner A.A. , Pucci F. 
Published: April 2023

AbstractRecent clinical observations highlight the importance of the spatial organization of immune cells into lymphoid structures for the success of cancer immunotherapy and patient survival. Sequential chromogenic immunohistochemistry (scIHC) supports the analysis of multiple biomarkers on a single tissue section thus providing unique information about relative location of cell types and assessment of disease states. Unfortunately, widespread implementation of scIHC is limited by lack of a standardized, rigorous guide to the development of customized biomarker panels and by the need for user-friendly analysis pipelines able to streamline the extraction of meaningful data. Here, we examine major steps from classical IHC protocols and highlight the impact they have on the scIHC procedure. We report practical examples and illustrations of the most common complications that can arise during the setup of a new biomarker panel and how to avoid them. We described in detail how to prevent and detect cross- reactivity between secondary reagents and carry over between detection antibodies. We developed a novel analysis pipeline based on non-rigid tissue deformation correction, Cellpose-inspired automated cell segmentation and computational network masking of low-quality data. The resulting biomarker panel and pipeline was used to study regional lymph nodes from head and neck cancer patients. We identified contact interactions between plasmablasts and plasmacytoid dendritic cellsin vivo. Given that TLR receptors, which are highly expressed in plasmacytoid dendritic cells play a key role in vaccine efficacy, the significance of this cell-cell interaction decisively warrants further studies. In conclusion, this work streamlines the development of novel biomarker panels for scIHC, which will ultimately improve our understanding of immune responses in cancer.
Acute myeloid leukemia stratifies as two clinically relevant sphingolipidomic subtypes
Authors: Paudel B. Bishal , Tan Su-Fern , Fox Todd E. , Ung Johnson , Shaw Jeremy , Dunton Wendy , Lee Irene , Sharma Arati , Viny Aaron D. , Barth Brian M. , Tallman Martin S. , Cabot Myles , Garrett-Bakelman Francine E. , Levine Ross L. , Kester Mark , Claxton David , Feith David J. , Janes Kevin A. , Loughran Thomas P. 
Published: April 2023

AbstractAcute myeloid leukemia (AML) is an aggressive disease with complex and heterogeneous biology. Although several genomic classifications have been proposed, there is a growing interest in going beyond genomics to stratify AML. In this study, we profile the sphingolipid family of bioactive molecules in 213 primary AML samples and 30 common human AML cell lines. Using an integrative approach, we identify two distinct sphingolipid subtypes in AML characterized by a reciprocal abundance of hexosylceramide (Hex) and sphingomyelin (SM) species. The two Hex-SM clusters organize diverse samples more robustly than known AML driver mutations and are coupled to latent transcriptional states. Using transcriptomic data, we develop a machine-learning classifier to infer the Hex-SM status of AML cases in TCGA and BeatAML clinical repositories. The analyses show that the sphingolipid subtype with deficient Hex and abundant SM is enriched for leukemic stemness transcriptional programs and comprises an unappreciated high-risk subgroup with poor clinical outcomes. Our sphingolipid-focused examination of AML identifies patients least likely to benefit from standard of care and raises the possibility that sphingolipidomic interventions could switch the subtype of AML patients who otherwise lack targetable alternatives.Key Points1.Sphingolipidomics separates acute myeloid leukemia (AML) patients and cell lines into two subtypes.2.The subtype with low hexosylceramide and high sphingomyelin defines a new high-risk subtype with poor clinical outcomes.
Weakly supervised contrastive learning infers molecular subtypes and recurrence of breast cancer from unannotated pathology images
Authors: Liu Hui , Zhang Yang , Zhu Aichun , Sun Zhiqiang , Luo Judong 
Published: April 2023

AbstractThe deep learning-powered computational pathology has led to sig-nificant improvements in the speed and precise of tumor diagnosis,, while also exhibiting substantial potential to infer genetic mutations and gene expression levels. However,current studies remain limited in predicting molecular subtypes and recurrence risk in breast cancer. In this paper, we proposed a weakly supervised contrastive learning framework to address this challenge. Our framework first performed contrastive learning pretraining on large-scale unlabeled patches tiled from whole slide images (WSIs) to extract patch-level features. The gated attention mechanism was leveraged to aggregate patch-level features to produce slide feature that was then applied to various downstream tasks. To confirm the effectiveness of the proposed method, we have conducted extensive experiments on four independent cohorts of breast cancer. For gene expression prediction task, rather than one model per gene, we adopted multitask learning to infer the expression levels of 21 recurrence-related genes, and achieved remarkable performance and generalizability that were validated on an external cohort. Particularly, the predictive power to infer molecular subtypes and recurrence events was strongly validated by cross-cohort experiments. In addition, the learned patch-level attention scores enabled us to generate heatmaps that were highly consistent with pathologist annotations and spatial transcriptomic data. These findings demonstrated that our model effectively established the high-order genotype-phenotype associations, thereby enhances the potential of digital pathology in clinical applications.
Biotic Assessment of Crowdsourced Data Defines Four Ecoregions in Thar: A Novel Approach for Citizen Engagement in Conservation
Authors: Mukherjee Manasi , Paul Angshuman , Mukerji Mitali 
Published: April 2023

AbstractDistinct ecosystems have representative biota that can allow delineation of ecoregions. Despite being one of the most populated desert ecosystems in the world with extreme climatic conditions, Thar has been classified as a single ecoregion by WWF. The present study aimed to test this on the basis of biotic assessment using a citizen science-based crowdsourced data from ebird (492 species of birds through 50,000 checklists of 4000 birders). Unsupervised clustering (k=4) and mathematical validation using cosine similarity revealed the presence of four distinct ecoregionsEastern Thar (ET), Western Thar (WT), Transitional Zone (TZ) and Cultivated Zone (CZ) in Thar. Most strikingly, these ecoregions when overlaid on geographical regions of Thar show that the CZ ecoregion was split between three distant and distinct geographic regions. Further spatial diversity estimates show CZ had the leastαdiversity (273) and highestβdiversity (1.8) indicating the least similarity between the districts that comprised this ecoregion. The results suggest CZ as an evolving ecoregion or a consequence of habitat fragmentation due to anthropogenic effects. The presence of least number of exclusive species in CZ (0.2%) and TZ (0.8%) including the near-threatened species likeLaticilla burnesii(Rufous-vented grass babbler) andPelecanus philippensis(Spot-billed pelican) and an endangered speciesAthene blewitti(Forest Owlet) highlights the need for restoration of these threatened biota and ecoregions. This study for the first time proposes crowd-sourced bird data as an important biota-based tool to understand (a) established ecoregions in an ecosystem, (b) anthropogenic effects of agri-farmlands and (c) relation between geographic regions and biota.
Surveillance of the Impact of Antimicrobial Resistant infections in Immunosuppressed Children’s therapy: A systematic review
Authors: Domo Danielle , Woogeng Ivo Ngundu 
Published: April 2023

AbstractBackgroundAntimicrobial resistance (AMR) is a global problem. Resistant bacteria, for many reasons, do not cease to emerge and re-emerge. The impact of AMR on patient therapy is not scares in literature to date, but there is still much to do, mainly in pediatric settings. It is vital to assess the necessity of observing the impact of such infections on the clinical care trends to which some kids may already be subject, strengthening, improving, and, where necessary, implementing new age policies and regulations that may help contain the spread of AMR.MethodWe performed a yearlong review of literature on antimicrobial resistance in paediatrics immunosuppressed patients until September 2022. We draw-up a protocol of the review, to which we adhered rigorously, following the prescribed including and excluding criteria.ResultsFrom the 110 articles finally selected following the PRISMA workflow diagram, from which 29% of them were in majority randomized controlled trials studies, the remaining selection ranged from case controls to cohort studies, systematic reviews, controlled before and after reports trials, matched case-control and placebo reports trials and few not clearly reported research article types (15%).ConclusionThe process yielded to the confirmation that there are literally good evidences of the serious lethal impact of resistant microbiological infections among immunocompromised children in and out of hospitals’ settings. What lacks the most are practical evidences of such damages both to the patient and to the public health sector, which can be conquer though, through well-programmed cohort–based studies.
Ultra-fast genotyping of SNPs and short indels using GPU acceleration
Authors: Henriksen Jørgen Wictor , Rand Knut Dagestad , Sandve Geir Kjetil , Grytten Ivar 
Published: April 2023

AbstractAs decreasing DNA sequencing costs leads to a steadily increasing rate of generated data, the development of efficient algorithms for processing of the sequence data is increasingly important to reduce costs and energy consumption. Recent work have shown that genotyping can be done efficiently and accurately using alignment-free methods that are based on analyzing kmers from sequenced reads. In particular, we have recently presented the KAGE genotyper, which uses an efficient pangenome representation of known individuals in a population to further increase accuracy and efficiency. While existing genotypers like KAGE use the Central Processing Unit (CPU) to count and analyze kmers, the Graphical Processing Unit (GPU) has shown promising for reducing runtime for similar problems.We here present GKAGE, a new and improved version of KAGE that utilizes the GPU to further increase the computational efficiency. This is done by counting and analyzing large amounts of kmers in the many parallel cores of a GPU. We show that GKAGE is, on hardware of comparable cost, able to genotype an individual up to an order of magnitude faster than KAGE while producing the same output, which makes it by far the fastest genotyper available today. GKAGE can run on consumer-grade GPUs, and enables genotyping of a human sample in only a matter of minutes without the need for expensive high-performance computers. GKAGE is open source and available athttps://github.com/kage-genotyper/kage.
Integrative identification of non-coding regulatory regions driving metastatic prostate cancer
Authors: Woo Brian J , Moussavi-Baygi Ruhollah , Karner Heather , Karimzadeh Mehran , Garcia Kristle , Joshi Tanvi , Yin Keyi , Navickas Albertas , Gilbert Luke A. , Wang Bo , Asgharian Hosseinali , Feng Felix Y. , Goodarzi Hani 
Published: April 2023

AbstractLarge-scale sequencing efforts of thousands of tumor samples have been undertaken to understand the mutational landscape of the coding genome. However, the vast majority of germline and somatic variants occur within non-coding portions of the genome. These genomic regions do not directly encode for specific proteins, but can play key roles in cancer progression, for example by driving aberrant gene expression control. Here, we designed an integrative computational and experimental framework to identify recurrently mutated non-coding regulatory regions that drive tumor progression. Application of this approach to whole-genome sequencing (WGS) data from a large cohort of metastatic castration-resistant prostate cancer (mCRPC) revealed a large set of recurrently mutated regions. We used (i)in silicoprioritization of functional non-coding mutations, (ii) massively parallel reporter assays, and (iii)in vivoCRISPR-interference (CRISPRi) screens in xenografted mice to systematically identify and validate driver regulatory regions that drive mCRPC. We discovered that one of these enhancer regions, GH22I030351, acts on a bidirectional promoter to simultaneously modulate expression of U2-associated splicing factor SF3A1 and chromosomal protein CCDC157. We found that both SF3A1 and CCDC157 are promoters of tumor growth in xenograft models of prostate cancer. We nominated a number of transcription factors, including SOX6, to be responsible for higher expression of SF3A1 and CCDC157. Collectively, we have established and confirmed an integrative computational and experimental approach that enables the systematic detection of non-coding regulatory regions that drive the progression of human cancers.
Electrostatic interactions control the adsorption of extracellular vesicles onto supported lipid bilayers
Authors: Ridolfi Andrea , Cardellini Jacopo , Gashi Fatlinda , van Herwijnen Martijn J.C. , Trulsson Martin , Campos-Terán José , Wauben Marca H. M. , Berti Debora , Nylander Tommy , Stenhammar Joakim 
Published: April 2023

AbstractCommunication between cells located in different parts of an organism is often mediated by membrane-enveloped nanoparticles, such as extracellular vesicles (EVs). EV binding and cell uptake mechanisms depend on the heterogeneous composition of the EV membrane. From a colloidal perspective, the EV membrane interacts with other biological interfaces via both specific and non-specific interactions, where the latter include long-ranged electrostatic and van der Waals forces, and short-ranged repulsive “steric-hydration” forces. While electrostatic forces are generally exploited in most EV immobilization protocols, the roles played by various colloidal forces in controlling EV adsorption on surfaces have not yet been thoroughly addressed. In the present work, we study the interaction and adsorption of EVs with supported lipid bilayers (SLBs) carrying different surface charge densities. By probing the EV-SLB interaction using quartz crystal microbalance with dissipation monitoring (QCM-D) and confocal laser scanning microscopy (CLSM), we demonstrate that EV adsorption onto lipid membranes can be controlled by varying the strength of electrostatic forces. We then model the observed phenomena within the framework of nonlinear Poisson-Boltzmann theory. Modelling results confirm the experimental observations and highlight the crucial role played by attractive electrostatics in EV adsorption onto lipid membranes. Our results provide new fundamental insights into EV-membrane interactions and could be useful for developing novel EV separation and immobilization strategies.