bluuebunny/crossref_metadata_2025_split huggingface dataset:
Sodium/Potassium ATPase Alpha 1 Subunit Fine-tunes Platelet GPCR Signaling Function and is Essential for Thrombosis
Authors: Li Oliver Q. , Yue Hong , DeHart Autumn R. , Roytenberg Renat , Aguilar Rodrigo , Olanipekun Olalekan , Bai Fang , Liu Jiang , Fedorova Olga , Kennedy David , Thompson Ellen , Pierre Sandrine V. , Li Wei 
Published: May 2024

AbstractBackgroundThrombosis is a major cause of myocardial infarction and ischemic stroke. The sodium/potassium ATPase (NKA), comprising α and β subunits, is crucial in maintaining intracellular sodium and potassium gradients. However, the role of NKA in platelet function and thrombosis remains unclear.MethodsWe utilized wild-type (WT, α1+/+) and NKA α1 heterozygous (α1+/-) mice, aged 8 to 16 weeks, of both sexes. An intravital microscopy-based, FeCl3-induced carotid artery injury thrombosis model was employed for in vivo thrombosis assessment. Platelet transfusion assays were used to evaluate platelet NKA α1 function on thrombosis. Human platelets isolated from healthy donors and heart failure patients treated with/without digoxin were used for platelet function and signaling assay. Complementary molecular approaches were used for mechanistic studies.ResultsNKA α1 haplodeficiency significantly reduced its expression on platelets without affecting sodium homeostasis. It significantly inhibited 7.5% FeCl3-induced thrombosis in male but not female mice without disturbing hemostasis. Transfusion of α1+/-, but not α1+/+, platelets to thrombocytopenic WT mice substantially prolonged thrombosis. Treating WT mice with low-dose ouabain or marinobufagenin, both binding NKA α1 and inhibiting its ion-transporting function, markedly inhibited thrombosis in vivo. NKA α1 formed complexes with leucine-glycine-leucine (LGL)-containing platelet receptors, including P2Y12, PAR4, and thromboxane A2 receptor. This binding was significantly attenuated by LGL>SFT mutation or LGL peptide. Haplodeficiency of NKA α1 in mice or ouabain treatment of human platelets notably inhibited ADP-induced platelet aggregation. While not affecting 10% FeCl3-induced thrombosis, NKA α1 haplodeficiency significantly prolonged thrombosis time in mice treated with an ineffective dose of clopidogrel.ConclusionNKA α1 plays an essential role in enhancing platelet activation through binding to LGL-containing platelet GPCRs. NKA α1 haplodeficiency or inhibition with low-dose ouabain and marinobufagenin significantly inhibited thrombosis and sensitized clopidogrel’s anti-thrombotic effect. Targeting NKA α1 emerges as a promising antiplatelet and antithrombotic therapeutic strategy.
Gill ionocyte remodeling mediates blood pH regulation in rockfish (Sebastes diploproa) exposed to environmentally relevant hypercapnia
Authors: Kwan Garfield T. , Clifford Alexander M. , Prime Kaelan J. , Harter Till S. , Tresguerres Martin 
Published: May 2024

AbstractMarine fishes excrete excess H+using basolateral Na+/K+-ATPase (NKA) and apical Na+/H+-exchanger 3 (NHE3) in gill ionocytes. However, the mechanisms that regulate H+excretion during exposure to environmentally relevant hypercapnia (ERH) remain poorly understood. Here, we explored transcriptomic, proteomic, and cellular responses in gills of juvenile splitnose rockfish (Sebastes diploproa) exposed to three days of ERH conditions (pH ∼7.5; ∼1,600 μatmpCO2). Blood pH was fully regulated at ∼7.75 despite a lack of significant changes in gill (1) mRNAs coding for proteins involved in blood acid-base regulation, (2) total NKA and NHE3 protein abundance, and (3) ionocyte density. However, ERH-exposed rockfish demonstrated increased NKA and NHE3 abundance on the ionocyte plasma membrane coupled with wider apical membranes and greater extension of apical microvilli. The observed gill ionocyte remodeling is consistent with enhanced H+excretion that maintains blood pH homeostasis during exposure to ERH and does not necessitate changes at the expression or translation levels. These mechanisms of phenotypic plasticity may allow fishes to regulate blood pH during environmentally relevant acid-base challenges, and thus have important implications for both understanding how organisms respond to climate change and for selecting appropriate metrics to evaluate its impact on marine ecosystems.
Plastic microfibers found in high numbers around Kantamanto, the world’s largest secondhand textile market in Accra, Ghana
Authors: Deheyn Dimitri D. , Ayesu Joe ,   The Or Foundation, Skinner Branson 
Published: May 2024

AbstractLocated in Accra (Ghana), Kantamanto is the world’s largest secondhand textile market where tons of garments are exported every week by Global North countries, including the USA, Canada, the UK, the EU27, China and South Korea. We looked at the abundance of microfibers from textiles in this very dynamic environment. Continuous monitoring of air quality using PurpleAir sensors showed large numbers of airborne particles around the market, which were shown to drop with rain events. Rainwater was collected and shown to contain dozens to hundreds of microfibers and microplastics, depending on the intensity of the rain and on whether dry weather preceded the rain. The first hour of rain seemed to bring down the larger numbers of airborne microfibers, especially if it had not rained for 3-4 days. Microfiber and microplastics counts from airborne samples showed numbers about >20x to >100x greater than those reported from other mega-cities, respectively. Comparison of these counts from water of the adjacent Korle Lagoon was similarly striking, with about >100x more microfibers and >200x more microplastics than reported for coastal waters around the world. The impacts of garments were also demonstrated beyond the microscale, with a strong similarity of global brand assemblages in the textile waste found in the market and on the nearby beaches.
Engineered Cardiac Microtissue Biomanufacturing Using Human Induced Pluripotent Stem Cell Derived Epicardial Cells
Authors: Butler Kirk , Ahmed Saif , Jablonski Justin , Hookway Tracy A. 
Published: May 2024

AbstractEpicardial cells are a crucial component in constructingin vitro3D tissue models of the human heart, contributing to the ECM environment and the resident mesenchymal cell population. Studying the human epicardium and its development from the proepicardial organ is difficult, but induced pluripotent stem cells can provide a source of human epicardial cells for developmental modeling and for biomanufacturing heterotypic cardiac tissues. This study shows that a robust population of epicardial cells (approx. 87.7% WT1+) can be obtained by small molecule modulation of the Wnt signaling pathway. The population maintains WT1 expression and characteristic epithelial morphology over successive passaging, but increases in size and decreases in cell number, suggesting a limit to their expandability in vitro. Further, low passage number epicardial cells formed into more robust 3D microtissues compared to their higher passage counterparts, suggesting that the ideal time frame for use of these epicardial cells for tissue engineering and modeling purposes is early on in their differentiated state. Additionally, the differentiated epicardial cells displayed two distinct morphologic sub populations with a subset of larger, more migratory cells which led expansion of the epicardial cells across various extracellular matrix environments. When incorporated into a mixed 3D co-culture with cardiomyocytes, epicardial cells promoted greater remodeling and migration without impairing cardiomyocyte function. This study provides an important characterization of stem cell-derived epicardial cells, identifying key characteristics that influence their ability to fabricate consistent engineered cardiac tissues.
A microscopy reporter for cGAMP reveals rare cGAS activation following DNA damage, and a lack of correlation with micronuclear cGAS enrichment
Authors: Lebrec Vivianne , Afshar Negar , Davies Lauren R. , Kujirai Tomoya , Kanellou Alexandra , Tidu Federico , Zierhut Christian 
Published: May 2024

SummaryCyclic GMP-AMP (cGAMP) synthase (cGAS) is the primary intracellular responder to pathogen DNA. Upon DNA-binding, cGAS generates cGAMP, which binds to STING, ultimately driving inflammatory signalling. Although normally silenced on self-DNA, cGAS can be activated during genotoxic stress. A universal by-product of these conditions are micronuclei, which accumulate cGAS, and which are therefore thought to be major cGAS activators. However, due to the inability to visualise cGAS activation in single cells, this hypothesis remains largely untested. Here we solve this question with an improved intracellular cGAMP reporter, which is compatible with microscopy, flow-cytometry and plate reader setups. Surprisingly, cGAS activation in response to multiple types of genotoxic stress is limited to a subfraction of cells and does not correlate with cGAS enrichment in micronuclei. Overall, our findings suggest a revised model of innate immune signalling in response to genotoxic stress, and introduce a novel and flexible tool with which to examine this model in future.
Differential effects of aging and epilepsy in discriminating and reactivating memories
Authors: Tai Xin You , Sen Arjune , Husain Masud , Manohar Sanjay 
Published: May 2024

AbstractPattern separation and pattern completion are distinct neurocognitive processes involved in encoding and retrieval of memories. However, there is currently no robust behavioural task in humans to measure both processes within the same paradigm. We describe the Memory Pinhole task, a novel paradigm which offers a distinct measure of each process, applied to healthy young, healthy older and people with epilepsy. Both pattern separation and pattern completion are observed in healthy younger individuals. A pattern completion deficit is seen in people with epilepsy, while the healthy older cohort show a deficit in pattern separation. To understand the neural mechanisms, we simulated human performance using an auto-associative neural network. Modelling indicated that disruption in different neuronal populations could explain the distinct memory profiles observed in ageing and epilepsy. These results demonstrate that pattern separation and pattern completion are distinct processes that can be measured from a single behavioural task and are differentially affected by ageing and epilepsy.
Epigenetic control of CD8+T cell tissue homing and tissue resident memory T cell precursors by the histone methyltransferase SUV39H1
Authors: Suarez Guadalupe , Heurtebise-Chrétien Sandrine , Bonté Pierre-Emmanuel , Beuchet Héloïse , Fuentealba Jaime , Goudot Christel , Lantz Olivier , Amigorena Sebastian 
Published: May 2024

AbstractActivation of CD8+ T cells leads to the differentiation of short-lived terminal effectors and memory precursors. Some of these memory precursors remain in lymphoid organs and become long-lived central memory T cells (TCM), while others home to non-lymphoid peripheral tissues early after antigen recognition and differentiate into tissue resident memory T cells (TRM). The early stages of memory precursor tissue homing and TRMdifferentiation remain poorly understood. We show here that at steady state, during space-induced “homeostatic” expansion, and after flu infection, deletion of the histone 3-lysine 9 methyltransferase SUV39H1 in CD8+T cells, increases the homing to non-lymphoid tissues (including liver, lungs, gut and skin). SUV39H1-defective cells in tissues express CD49d and differentiate into CD69+/CD103-TRMafter adoptive transfer or Flu infection. SUV39H1-defective T cells that accumulate in lungs are fully functional in both Flu re-infection and lung tumor models. We conclude that SUV39H1 restrains CD8+T cell tissue homing and TRMdifferentiation in WT mice. These results should encourage the use of SUV39H1-depletion in the context of adoptive T cell therapies to enhance tissue homing, thereby optimizing the efficiency of target cell eradication and long-term protection in the context of infection and cancer.
GenAI Exceeds Clinical Experts in Predicting Acute Kidney Injury following Paediatric Cardiopulmonary Bypass2
Authors: Sharabiani Mansour , Mahani Alireza , Bottle Alex , Srinivasan Yadav , Issitt Richard , Stoica Serban 
Published: May 2024

AbstractThe emergence of large language models (LLMs) offers new opportunities to leverage, often unused, information in clinical text. This study examines the utility of text embeddings generated by LLMs in predicting postoperative acute kidney injury (AKI) in paediatric cardiopulmonary bypass (CPB) patients using electronic health record (EHR) text, and to explore methods for explaining their output. AKI is a significant complication in paediatric CPB and its prediction can significantly improve patient outcomes by enabling timely interventions. We evaluate various text embedding algorithms such as Doc2Vec, top-performing sentence transformers on Hugging Face, and commercial LLMs from Google and OpenAI. We benchmark the out-of-sample predictive performance of these ‘AI models’ against a ‘baseline model’ as well as an established clinically-defined ‘expert model’. The baseline model includes patient gender, age, height, body mass index and length of operation. The majority of AI models surpass, not only the baseline model, but also the expert model. An ensemble of AI and clinical-expert models improves discriminative performance by nearly 23% compared to the baseline model. Consistency of patient clusters formed from AI-generated embeddings with clinical-expert clusters - measured via the adjusted rand index and adjusted mutual information metrics - illustrates their medical validity. We use text-generating LLMs to explain the output of embedding LLMs, e.g., by summarising the differences between AI and expert clusters, and/or by providing descriptive labels for the AI clusters. Such ‘explainability’ can increase medical practitioners’ trust in the AI applications, and help generate new hypotheses, e.g., by correlating cluster memberships with outcomes of interest.HighlightsLLMs outperform clinical experts in predicting risk of AKI after paediatric CPB.LLMs generate clinically plausible explanations and hypotheses using embeddings.Successful application of LLMs in paediatric CPB suggests potential in other specialised fields.Fine-tuning LLMs on domain data and forming ensembles of AI and clinical experts may boost accuracy.
Delving into PubMed Records: Some Terms in Medical Writing Have Drastically Changed after the Arrival of ChatGPT
Authors: Matsui Kentaro 
Published: May 2024

AbstractIt is estimated that ChatGPT is already widely used in academic paper writing. This study aims to investigate whether the usage of specific terminologies has increased, focusing on words and phrases frequently reported as overused by ChatGPT. The list of 118 potentially AI-influenced terms was curated based on posts and comments from anonymous ChatGPT users, and 75 common academic phrases were used as controls. PubMed records from 2000 to 2024 (until April) were analyzed to track the frequency of these terms. Usage trends were normalized using a modified Z-score transformation. A linear mixed-effects model was used to compare the usage of potentially AI-influenced terms to common academic phrases over time. A total of 26,403,493 PubMed records were investigated. Among the potentially AI-influenced terms, 75 displayed a meaningful increase (modified Z-score ≥ 3.5) in usage in 2024. The linear mixed-effects model showed a significant effect of potentially AI-influenced terms on usage frequency compared to common academic phrases (p < 0.001). The usage of potentially AI-influenced terms showed a noticeable increase starting in 2020. This study revealed that certain words and phrases, such as “delve,” “underscore,” “meticulous,” and “commendable,” have been used more frequently in medical and biological fields since the introduction of ChatGPT. The usage rate of these words/phrases has been increasing for several years before the release of ChatGPT, suggesting that ChatGPT might have accelerated the popularity of scientific expressions that were already gaining traction. The identified terms in this study can provide valuable insights for both LLM users, educators, and supervisors in these fields.Author SummaryArtificial intelligence systems have rapidly integrated into academic writing, particularly in the medical and biological fields. This study investigates changes in the frequency of specific terminologies reported as overused by ChatGPT. By analyzing PubMed records from 2000 to 2024, we tracked 118 potentially AI-influenced terms and compared them with 75 common academic phrases. The study’s findings reveal that terms such as ‘delve,’ ‘underscore,’ ‘meticulous,’ and ‘commendable’ saw a marked increase in usage in 2024. However, this trend actually began around 2020. This suggests that while some of these terms were already gaining popularity before the release of ChatGPT, the large language model may have accelerated their adoption in scientific literature. Furthermore, the analysis highlights that the impact of ChatGPT extends beyond new terminologies to altering the frequency and style of commonly used academic phrases. Understanding these trends can help researchers and educators see how AI tools are shaping academic writing.
The Pediatric Sleep Questionnaire as a screening tool for sleep-related breathing disorders: An Umbrella Review
Authors: Okuji David , Newcity Amanda , Yavari Amir , Shahraki Farnaz , Erhenede Oghenerukevwe , Ahmed Qaiser , Patenaude Rachel , Twi-Yeboah Alberta , McGowan Richard 
Published: May 2024

ABSTRACTPurposeThe purpose of this umbrella review was to assess available systematic reviews and meta-analyses reporting on the use of the Pediatric Sleep Questionnaire as a screening tool for identifying sleep related breathing disorders in children, with subsequent referral from dentists to physicians.MethodsA comprehensive literature search was conducted with electronic databases to identify systematic reviews and meta-analyses reporting on the Pediatric Sleep Questionnaire as a screening tool for pediatric sleep related breathing disorders. Eligible reviews were systematically selected and were assessed qualitatively with the AMSTAR 2 tool and quantitatively with meta-analyses methods.ResultsA total of 129 articles for possible inclusion were identified, and five studies were selected for quantitative analysis. Of these five studies, the overall AMSTAR 2 quality assessment showed two with moderate and three with low quality ratings. The five studies showed point-estimate mean values for prevalence, sensitivity, specificity, positive predictive value, positive predictive value, negative predictive value as, respectively, respectively, 50.58, 74.97, 49.68, 58.16, 62.24 percent, and the diagnostic odds ratio as 6.94 percent for the Pediatric Sleep Questionnaire tool.ConclusionsDentists should adhere to the American Academy of Pediatric Dentistry guidelines and know that there is low to moderate evidence to support the utilization of the Pediatric Sleep Questionnaire as a sufficiently accurate screening tool for the diagnosis of sleep related breathing disorders in children, followed by an appropriate referral to a physician for comprehensive assessment and management.
Nuclear magnetic resonance-based metabolomics with machine learning for predicting progression from prediabetes to diabetes
Authors: Li Jiang , Yu Yuefeng , Sun Ying , Fu Yanqi , Shen Wenqi , Cai Lingli , Tan Xiao , Cai Yan , Wang Ningjian , Lu Yingli , Wang Bin 
Published: May 2024

AbstractBackgroundIdentification of individuals with prediabetes who are at high risk of developing diabetes allows for precise interventions. We aimed to determine the role of nuclear magnetic resonance (NMR)-based metabolomic signature in predicting the progression from prediabetes to diabetes.MethodsThis prospective study included 13,489 participants with prediabetes who had metabolomic data from the UK Biobank. Circulating metabolites were quantified via NMR spectroscopy. Cox proportional hazard (CPH) models were performed to estimate the associations between metabolites and diabetes risk. Supporting vector machine, random forest, and extreme gradient boosting were used to select the optimal metabolite panel for prediction. CPH and random survival forest (RSF) models were utilized to validate the predictive ability of the metabolites.ResultsDuring a median follow-up of 13.6 years, 2,525 participants developed diabetes. After adjusting for covariates, 94 of 168 metabolites were associated with risk of progression to diabetes. A panel of nine metabolites, selected by all three machine learning algorithms, was found to significantly improve diabetes risk prediction beyond conventional risk factors in the CPH model (area under the receiver operating characteristic curve [AUROC], 1-year: 0.823 for risk factors + metabolites vs 0.759 for risk factors, 5-year: 0.830 vs 0.798, 10-year: 0.801 vs 0.776, allP<0.05). Similar results were observed from the RSF model. Categorization of participants according to the predicted value thresholds revealed distinct cumulative risk of diabetes.ConclusionsOur study lends support for use of the metabolite markers to help determine individuals with prediabetes who are at high risk of progressing to diabetes and inform targeted and efficient interventions.FundingShanghai Municipal Health Commission (2022XD017). Innovative Research Team of High-level Local Universities in Shanghai (SHSMU-ZDCX20212501). Shanghai Municipal Human Resources and Social Security Bureau (2020074). Clinical Research Plan of Shanghai Hospital Development Center (SHDC2020CR4006). Science and Technology Commission of Shanghai Municipality (22015810500).