bluuebunny/crossref_metadata_2025_split huggingface dataset:
Adaptability of the Pulsta valve to the diverse main pulmonary artery shape of native right ventricular outflow tract disease
Authors: Park Woo Young , Kim Gi Beom , Lee Sang Yun , Kim Ah Young , Choi Jae Young , Chang So Ick , Kim Seong Ho , Cha Seul Gi , Wang Jou-Kou , Lin Ming-Tai , Chen Chun-An 
Published: November 2023

AbstractBackgroundPulsta valve is increasingly used for percutaneous pulmonary valve implantation (PPVI) in patients with large native right ventricular outflow tract (RVOT). The aim of this study is to reveal Pulsta valve implantation outcomes within the native RVOT and to evaluate the adaptability of Pulsta valve to diverse native main pulmonary artery (PA).MethodsA multicenter retrospective study encompassing 183 patients with moderate to severe pulmonary regurgitation (PR) in the native RVOT who underwent PPVI with Pulsta valves® between February 2016 and August 2023 at the five Korean and Taiwanese tertiary referral centers.ResultsSuccessful implantation of the Pulsta valves was achieved in 180 out of 183 patients (98.4 %) with an average age of 26.6 ± 11.0 years. Mean follow-up duration was 29 months. Baseline assessments revealed enlarged right ventricle (RV) volume (mean indexed RV end-diastolic volume: 163.1(Interquartile range, IQR, 152.2-179.9) mL/m2), which significantly decreased to 123.0(IQR: 106.9-137.2) mL/m2after one year. In this study, the main PA types were classified as follows: pyramidal (3.8%), straight (38.3%), reverse pyramidal (13.7%), convex (26.2%), and concave (18.0%) shapes. Pulsta valve placement was adapted, with distal main PA for pyramidal shapes and proximal or mid-PA for reverse pyramidal shapes. The remaining patients underwent Pulsta valve implantation in the proximal or mid part of the main PA, depending on the anatomical features and size of the main PA. Two patients experienced Pulsta valve embolization to RV, necessitating surgical removal, and one patient encountered valve migration to the distal main PA, necessitating surgical fixation.ConclusionsCustomized valve insertion sites are pivotal in self-expandable PPVI considering diverse native RVOT shape. Rather soft and compact structure of Pulsta valve has characteristics to be adaptable to diverse native RVOT geometries.What is Known?The Pulsta valve is a self-expandable knitted nitinol-wire stent mounted with a treated tri-leaflet α-Gal-free porcine pericardial valve for percutaneous pulmonary valve implantation in patients with native right ventricular outflow tract lesions.What the study adds?To this date, this study is the largest study among the previous reports that examined the outcomes of the Pulsta valve. The Pulsta valve consistently demonstrated favorable clinical and hemodynamic outcomes over a 2.4-year period. Pulsta valve showed adaptability in the diverse native right ventricular outflow tract geometries from the compact shape of Pulsta valve.
The Antiviral Mechanism of Action of Molnupiravir in Humans with COVID-19
Authors: Sheahan Timothy P. , Stevens Laura J. , Narowski Tara M. , Krajewski Taylor J. , Lee Chanhwa , Mollan Katie R. , Gribble Jennifer , Moreira Fernando R. , Castillo Izabella N. , Cuadra Edwing , Alabanza Paul , Loftis Amy James , Coombs Robert W. , Goecker Erin A. , Greninger Alexander L. , Chappell James D. , Brown Ariane J. , Won John , Lipansky Felicia , Holman Wayne , Szewczyk Laura J. , Baric Ralph S. , Painter Wendy P. , Eron Joseph J. , Premkumar Lakshmanane , Denison Mark R. , Fischer William A. 
Published: November 2023

AbstractMeaningful metrics of antiviral activity are essential for determining the efficacy of therapeutics in human clinical trials. Molnupiravir (MOV) is a broadly acting antiviral nucleoside analog prodrug that acts as a competitive alternative substrate for the SARS-CoV-2 RNA-dependent RNA polymerase (RdRp). We developed an assay, Culture-PCR, to better understand the impact of MOV therapy on infectious SARS-CoV-2. Culture-PCR revealed MOV eliminated infectious virus within 48 hours in the nasopharyngeal compartment, the upper airway location with the greatest levels of infectious virus. MOV therapy was associated with increases in mutations across the viral genome but select regions were completely unaffected, thus identifying regions where mutation likely abrogates infectivity. MOV therapy did not alter the magnitude or neutralization capacity of the humoral immune response, a documented correlate of protection. Thus, we provide holistic insights into the function of MOV in adults with COVID-19.
Predicting postoperative delirium assessed by the Nursing Screening Delirium Scale in the recovery room for non-cardiac surgeries without craniotomy: A retrospective study using a machine learning approach
Authors: Giesa Niklas , Haufe Stefan , Menk Mario , Weiß Björn , Spies Claudia , Piper Sophie K. , Balzer Felix , Boie Sebastian D. 
Published: November 2023

AbstractBackgroundPostoperative delirium (POD) contributes to severe outcomes such as death or development of dementia. Thus, it is desirable to identify vulnerable patients in advance during the perioperative phase. Previous studies mainly investigated risk factors for delirium during hospitalization and further used a linear logistic regression (LR) approach with time-invariant data. Studies have not investigated patients’ fluctuating conditions to support POD precautions.ObjectiveIn this single-center study, we aimed to predict POD in a recovery room setting with a non-linear machine learning (ML) technique using pre-, intra-, and postoperative data.MethodsThe target variable POD was defined with the Nursing Screening Delirium Scale (Nu-DESC) ≥ 1. Feature selection was conducted based on robust univariate test statistics and L1regularization. Non-linear multi-layer perceptron (MLP) as well as tree-based models were trained and evaluated – with the receiver operating characteristics curve (AUROC), the area under precision recall curve (AUPRC), and additional metrics – against LR and published models on bootstrapped testing data.ResultsThe prevalence of POD was 8.2% in a sample of 73,181 surgeries performed between 2017 and 2020. Significant univariate impact factors were the preoperative ASA status, the intraoperative amount of given remifentanil, and the postoperative Aldrete score. The best model used pre-, intra-, and postoperative data. The tree-based model achieved a mean AUROC of 0.854 and a mean AUPRC of 0.418 outperforming linear LR, well as best applied and retrained baseline models.ConclusionsOverall, non-linear machine learning models using data from multiple perioperative time phases were superior to traditional ones in predicting POD in the recovery room. Class imbalance was seen as a main impediment for model application in clinical practice.Author SummaryCurrently, the pathophysiology of postoperative delirium (POD) is unknown. Hence, there is no dedicated medication for treatment. Patients who experience POD are oftentimes mentally disturbed causing pressure on related family members, clinicians, and the health system. With our study, we want to detect POD before onset trying to give decision support to health professionals. Vulnerable patients could be transferred to delirium wards mitigating the risk of severe outcomes such as permanent cognitive decline. We also provide insides into clinical parameters - recorded before, during, and after the surgery - that could be adapted for reducing POD risk. Our work is openly available, developed for clinical implementation, and could be transferred to other clinical institutions.
Prevalence of Coronary Atherosclerosis in Master Female Endurance Athletes
Authors: Papatheodorou Efstathios , Aengevaeren Vincent L. , Eijsvogels Thijs M.H. , Fakih Khaled Al , Hughes Rebecca Kathryn , Merghani Ahmed , Kissel Christine K. , Fyyaz Saad , Bakalakos Athanasios , Wilson Mathew G. , Dey Damini , Finocchiaro Gherardo , Parry-Williams Gemma , Torlasco Camilla , Papadakis Michael , Moon James C. , Sharma Sanjay 
Published: November 2023

ABSTRACTBACKGROUNDStudies in ostensibly healthy male master athletes have revealed a greater prevalence of coronary artery calcification (CAC) and coronary plaques compared with relatively sedentary counterparts. In contrast, data relating to potentially adverse coronary remodelling in female master athletes is sparse and conflicting. We investigated the prevalence of coronary atherosclerosis in a cohort of predominantly post-menopausal female master athletes with a low atherosclerotic risk profile.METHODS196 female athletes with a mean age of 55±7 years-old and median exercise duration of 33 years (25-39) and 59 relatively sedentary females of similar age underwent cardiovascular investigations including a coronary computed tomogram angiography with assessment of CAC scores, coronary plaques, and pericoronary adipose tissue attenuation. 70% of the athletes and 68% of the control group were post-menopausal.RESULTSAthletes and controls had a similarly low Framingham 10-year risk (1.49% versus 2.1%; P=0.68), but body mass index and blood pressure were lower and HDL-C was higher in the athletes. The prevalence of CAC score >0 Agatston units (AU) was low and did not differ between athletes and controls (21%versus32%;P=0.073). Female athletes had a lower prevalence of a CAC score >50thcentile (19%versus32%;P<0.03) and >75thcentile (14%versus25%;P=0.045) for age compared with controls, but the prevalence of a CAC score >100 AU did not differ between the groups (3.6%versus8.5%;P=0.12). There were also no differences between the groups in the prevalence of individuals with coronary plaques (21%versus32%;P=0.09), total plaque volume (16 mm3[IQR 3-56]versus49 [5-142] mm3;P=0.08), or plaque burden (10.8% [2.8-21]versus15.4% [4.6-28];P=0.46). Coronary plaques were predominantly calcified in both athletes and controls (80%versus63%;P=0.08). Age, blood pressure and HDL-C were independent predictors for a CAC score >0 AU among athletes.CONCLUSIONSIn contrast with previous studies in male master athletes, lifelong exercise in female counterparts does not appear to be associated with increased CAC score, coronary plaque burden or any qualitative differences in coronary plaque compared with relatively sedentary healthy counterparts. Coronary atherosclerosis in master female athletes is mainly driven by traditional risk factors.
Large Language Models and Medical Knowledge Grounding for Diagnosis Prediction
Authors: Gao Yanjun , Li Ruizhe , Croxford Emma , Tesch Samuel , To Daniel , Caskey John , W. Patterson Brian , M. Churpek Matthew , Miller Timothy , Dligach Dmitriy , Afshar Majid 
Published: November 2023

AbstractWhile Large Language Models (LLMs) have showcased their potential in diverse language tasks, their application in the healthcare arena needs to ensure the minimization of diagnostic errors and the prevention of patient harm. A Medical Knowledge Graph (KG) houses a wealth of structured medical concept relations sourced from authoritative references, such as UMLS, making it a valuable resource to ground LLMs’ diagnostic process in knowledge. In this paper, we examine the synergistic potential of LLMs and medical KG in predicting diagnoses given electronic health records (EHR), under the framework of Retrieval-augmented generation (RAG). We proposed a novel graph model: Dr.Knows, that selects the most relevant pathology knowledge paths based on the medical problem descriptions. In order to evaluate Dr.Knows, we developed the first comprehensive human evaluation approach to assess the performance of LLMs for diagnosis prediction and examine the rationale behind their decision-making processes, aimed at improving diagnostic safety. Using real-world hospital datasets, our study serves to enrich the discourse on the role of medical KGs in grounding medical knowledge into LLMs, revealing both challenges and opportunities in harnessing external knowledge for explainable diagnostic pathway and the realization of AI-augmented diagnostic decision support systems.
Effects of Maternal Fortified Balanced Energy-Protein Supplementation on the Mother-Infant Gut Microbiome: A Sub-Study of the MISAME-III Randomized Controlled Trial
Authors: Deng Lishi , Taelman Steff , Olm Matthew R. , Toé Laéticia Céline , Balini Eva , Ouédraogo Lionel , Bastos-Moreira Yuri , Argaw Alemayehu , Tesfamariam Kokeb , Sonnenburg Erica D. , Ouédraogo Moctar , Ganaba Rasmané , van Criekinge Wim , Kolsteren Patrick , Stock Michiel , Lachat Carl , Sonnenburg Justin L. , Dailey-Chwalibóg Trenton 
Published: November 2023

AbstractBiological pathways, including individual gut microbiome are potential barriers for maternal nutritional supplementation to improvement in infant growth. We evaluated the impact of balanced energy-protein (BEP) supplementation during pregnancy and the first six months of lactation on the composition and functionality of gut microbiome in mothers and their infants in rural Burkina Faso. Our findings reveal that BEP supplementation led to a significant increase in microbiome diversity during pregnancy. In the second trimester, there was a notable decrease in the abundance of anOscillospiraceaespecies, while postpartum, the abundance ofBacteroides fragilisincreased. We identified concerted enriched or depleted microbial pathways associated with BEP supplementation, including the phosphotransferase system, a critical mechanism for bacterial carbohydrates uptake, which exhibited enrichment in infants born to BEP-supplemented mothers. Despite these observations, the intricate biological connections with other omics necessitate further analysis to fully elucidate the underlying comprehensive biological pathways.
Deep learning-based patient stratification for prognostic enrichment of clinical dementia trials
Authors: Birkenbihl Colin , de Jong Johann , Yalchyk Ilya , Fröhlich Holger ,   the Alzheimer’s Disease Neuroimaging Initiative
Published: November 2023

AbstractDementia probably due to Alzheimer’s disease (AD) is a progressive condition that manifests in cognitive decline and impairs patients’ daily life. Affected patients show great heterogeneity in their symptomatic progression, which hampers the identification of efficacious treatments in clinical trials. Using artificial intelligence approaches to enable clinical enrichment trials serves a promising avenue to identify treatments.In this work, we used a deep learning method to cluster the multivariate disease trajectories of 283 early dementia patients along cognitive and functional scores. Two distinct subgroups were identified that separated patients into ‘slow’ and ‘fast’ progressing individuals. These subgroups were externally validated and independently replicated in a dementia cohort comprising 2779 patients. We trained a machine learning model to predict the progression subgroup of a patient from cross-sectional data at their time of dementia diagnosis. The classifier achieved a prediction performance of 0.70 ± 0.01 AUC in external validation.By emulating a hypothetical clinical trial conducting patient enrichment using the proposed classifier, we estimate its potential to decrease the required sample size. Furthermore, we balance the achieved enrichment of the trial cohort against the accompanied demand for increased patient screening. Our results show that enrichment trials targeting cognitive outcomes offer improved chances of trial success and are more than 13% cheaper compared to conventional clinical trials. The resources saved could be redirected to accelerate drug development and expand the search for remedies for cognitive impairment.
gQuant: A Robust and Generalizable Algorithm for Identifying Normalizer Genes in qRT-PCR Data: A Case Study on Urinary Exosomal miRNAs
Authors: Pathak Abhay Kumar , Kural Sukhad , Singh Shweta , Kumar Lalit , Yadav Mahima , Gupta Manjari , Das Parimal , Jain Garima 
Published: November 2023

AbstractThe emergent role of nucleic acid-based biomarkers, such as microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and messenger RNAs (mRNAs), is becoming increasingly prominent in the realms of disease diagnostics and risk assessment. Quantitative reverse transcription PCR (qRT-PCR) is the primary analytical method for quantitative measurement of biomarkers. Yet, the relative infancy of non-coding RNAs’ (ncRNAs) recognition as biomarkers poses a challenge due to the absence of a consensus on a universally accepted normalizer gene, which is pivotal for accurate quantification. Current tools for selecting normalizer genes in qRT-PCR are fraught with limitations, including inadequate handling of null values, reliance on elementary statistical tools, use of a biased integrated approach, outlier sensitivity, and suboptimal graphical user interface for data visualization. These deficiencies underscore the necessity for a more nuanced and algorithmically balanced tool tailored to handle qRT-PCR datasets and facilitate the discernment of the most appropriate normalizer gene for specific datasets.Addressing the identified challenges, we have developed ‘gQuant,’ a tool crafted to address the limitations present in existing methods. In ‘gQuant’ we employed voting classifiers as an ensemble technique that combines predictions from multiple statistical methods to make more accurate rankings than any individual statistical measures. The tool’s efficacy was substantiated through rigorous validation against datasets from the Gene Expression Omnibus (GEO) database and corroborated with experimental data derived from urinary exosomal miRNAs. Comparative analysis with existing tools revealed that their integrated methodologies could skew the ranking of normalizer genes, whereas ‘gQuant’ consistently yielded rankings characterized by lower standard deviation, reduced covariance, and enhanced kernel density estimation (KDE) values. Given ‘gQuant’s’ promising performance, normalizer gene identification will be greatly improved, improving the precision of gene expression quantification in a variety of research scenarios.
At matched loads, aging does not alter ankle, muscle, or tendon stiffness
Authors: Jakubowski Kristen L. , Ludvig Daniel , Lee Sabrina S.M. , Perreault Eric J. 
Published: November 2023

ABSTRACTOlder adults have difficulty maintaining balance when faced with postural disturbances, a task that is influenced by the stiffness of the triceps surae and Achilles tendon. Age-related changes in Achilles tendon stiffness have been reported at matched levels of effort, but measures typically have not been made at matched loads, which is important due to age-dependent changes in strength. Moreover, age-dependent changes in muscle stiffness have yet to be tested. Here, we investigate how age alters muscle and tendon stiffness and their influence on ankle stiffness. We hypothesized that age-related changes in muscle and tendon contribute to reduced ankle stiffness in older adults and evaluated this hypothesis when either load or effort were matched. We used B-mode ultrasound with joint-level perturbations to quantify ankle, muscle, and tendon stiffness across a range of loads and efforts in seventeen healthy younger and older adults. At matched loads, there was no significant difference in ankle, muscle, or tendon stiffness between groups (all p>0.13). However, at matched effort, older adults exhibited a significant decrease in ankle (27%; p=0.008), muscle (37%; p=0.02), and tendon stiffness (22%; p=0.03) at 30% of maximum effort. This is consistent with our finding that older adults were 36% weaker than younger adults in plantarflexion (p=0.004). Together these results indicate that, at the loads tested in this study, there are no age-dependent changes in the mechanical properties of muscle or tendon, only differences in strength that result in altered ankle, muscle, and tendon stiffness at matched levels of effort.New andNoteworthyWe provide the first simultaneous estimates of ankle, muscle, and tendon stiffness in younger and older adults. In contrast to earlier conclusions, we found that muscle and tendon mechanical properties are unaffected by age when compared at matched loads. However, due to age-related decreases in strength, mechanical properties do differ at matched efforts. As such, it is important to assess the relevance of the comparisons being made relative to the functional tasks under consideration.
Mitigating inflammatory bone loss in post-menopausal osteoporosis via targeting the IL-9 producing osteoclastogenic Th9 cells
Authors: Sapra Leena , Saini Chaman , Mishra Pradyumna K. , Chattopadhya Naibedya , Srivastava Rupesh K. 
Published: November 2023

AbstractRecent discoveries have established the pivotal role of IL-9-secreting Th9 cells in a wide spectrum of inflammatory and autoimmune diseases. However, little is known about how Th9 cells contribute to the etiology of inflammatory bone loss in post-menopausal osteoporosis (PMO). We observed that IL-9 has a pathological impact on inflammatory bone loss in ovariectomized (Ovx) mice. Our in vivo temporal kinetics analysis further revealed that estrogen deprivation increased the release of IL-9 from Th which in turn enhances the IL-17-producing Th17 cells. Both ex vivo and in vivo studies corroborated these findings in Ovx mice, as estrogen diminishes IL-9’s effect on the differentiation of Th17 cells as well as the potential of Th9 cells to produce IL-9. Mechanistically, Th9 cells in an IL-9-dependent manner enhance osteoclastogenesis and thereby establish themselves as a novel and independent osteoclastogenic Th subset. Blocking IL-9 improves bone health in Ovx mice by inhibiting the differentiation and function of both osteoclasts and Th9/Th17 cells. Our clinical findings further attested to the osteoporotic role of Th9 cells in post-menopausal osteoporotic human subjects. Collectively, our study establishes IL-9-secreting Th cells as the critical regulator of bone loss observed in PMO and highlights the fundamental implications of IL-9/Th9 targeted immunotherapies as an innovative approach for the treatment of inflammatory bone loss observed in osteoporosis.
Qualitative Observations of Transgenically Individualized Serotonergic Fibers in the Mouse Telencephalon
Authors: Haiman Justin H. , Mays Kasie C. , Roostaeyan Helen A. , Elyasi Nasim , Janušonis Skirmantas 
Published: November 2023

ABSTRACTThe matrix of serotonergic axons (fibers) is a constant feature of neural tissue in vertebrate brains. Its fundamental role appears to be associated with the spatiotemporal control of neuroplasticity. The densities of serotonergic fibers vary across brain regions, but their development and maintenance remain poorly understood. A specific fiber concentration is achieved as the result of the dynamics of a large number of individual fibers, each of which can make trajectory decisions independently of other fibers. Bridging these processes, operating on very different spatial scales, remains a challenge in neuroscience. The study provides the first qualitative description of individually-tagged serotonergic axons in four selected telencephalic regions (cortical and subcortical) of the mouse brain. Based on our previous implementation of the Brainbow toolbox in this system, serotonergic fibers were labeled with random intensity combinations of three fluorophores and imaged with high-resolution confocal microscopy. All examined regions contained serotonergic fibers of diverse identities and morphologies, often traveling in close proximity to one another. Some fibers transitioned among several morphologies in the same imaged volume. High fiber densities appeared to be associated with highly tortuous fiber segments produced by some individual fibers. This study supports efforts to predictively model the self-organization of the serotonergic matrix in all vertebrates, including regenerative processes in the adult human brain.