bluuebunny/crossref_metadata_2025_split huggingface dataset:
Dexamethasone modulate TWIST mediated EndMT changes in venous EC under acute shear stress. Implications for vein grafts disease
Authors: Ladak Shameem , McQueen Liam W , Tomkova Kristina , Adebayo Adewale , Suleiman Saadeh , George Sarah J , Murphy Gavin J , Zakkar Mustafa 
Published: June 2023

AbstractBackgroundThe use of vein grafts in coronary artery surgery is complicated by a high late restenosis rate resulting from the development of intimal hyperplasia, and accelerated atherosclerosis. TGFβ has been implicated in the process of intimal hyperplasia but the role of TGFβ driven Endothelial to mesenchymal is not fully understood. Here, we have investigated the hypothesis that arterial shear stress (flow) can trigger Endothelial to mesenchymal changes in venous ECs mediated by TGFβ / SMAD pathwayin-vitroandex-vivoand that a brief pretreatment of vein with Dexamethasone can suppress such changes.Methods and ResultsComparative reverse-transcriptase polymerase chain reaction, immunostaining and Western blotting revealed that arterial shear stress induced TGFβ / SMAD dependent in HUVEC which was regulated by TWIST 1&2 as the selective inhibition of TWIST 1 or 2 using specific siRNA suppressed EndMT in response to shear stress. We also noted that brief pretreatment of HUVECs with Dexamethasone can modulate EndMT changes in response to shear stress. Using spatial cell sequencing in human long saphenous vein segments exposed to acute arterial flow identified a cluster of cells that had both EC and SMC phenotypes where TWIST2 was significantly upregulated. We validated the untargeted spatial findings in segments of veins under acute arterial flowex-vivousing comparative reverse-transcriptase polymerase chain reaction, immunostaining and RNAscope and observed that Dexamethasone can suppress EndMT changes in vein segments by suppressing TGFβ / SMAD/ TWIST1 &2.ConclusionDexamethasone brief pretreatment can suppress EndMT changes triggered by acute exposure of long saphenous vein segments to arterial haemodynamics by modulating TGFβ / SMAD / TWIST1 &2 pathway.
Value of information dynamics in Disease X vaccine clinical trials
Authors: Houy Nicolas , Flaig Julien 
Published: June 2023

AbstractBackgroundSolutions have been proposed to accelerate the development and rollout of vaccines against a hypothetical disease with epidemic or pandemic potential called Disease X. This may involve resolving uncertainties regarding the disease and the new vaccine. However the value for public health of collecting this information will depend on the time needed to perform research, but also on the time needed to produce vaccine doses. We explore this interplay, and its effect on the decision on whether or not to perform research.MethodWe simulate numerically the emergence and transmission of a disease in a population using a susceptible-infected-recovered (SIR) compartmental model with vaccination. Uncertainties regarding the disease and the vaccine are represented by parameter prior distributions. We vary the date at which vaccine doses are available, and the date at which information about parameters becomes available. We use the expected value of perfect information (EVPI) and the expected value of partially perfect information (EVPPI) to measure the value of information.ResultsAs expected, information has less or no value if it comes too late, or (equivalently) if it can only be used too late. However we also find non trivial dynamics for shorter durations of vaccine development. In this parameter area, it can be optimal to implement vaccination without waiting for information depending on the respective durations of dose production and of clinical research.ConclusionWe illustrate the value of information dynamics in a Disease X out-break scenario, and present a general approach to properly take into account uncertainties and transmission dynamics when planning clinical research in this scenario. Our method is based on numerical simulation and allows us to highlight non trivial effects that cannot otherwise be investigated.
Feature Selection for an Explainability Analysis in Detection of COVID-19 Active Cases from Facebook User-Based Online Surveys
Authors: Rufino Jesús , Ramírez Juan Marcos , Aguilar Jose , Baquero Carlos , Champati Jaya , Frey Davide , Lillo Rosa Elvira , Fernández-Anta Antonio 
Published: June 2023

ABSTRACTIn this paper, we introduce a machine-learning approach to detecting COVID-19-positive cases from self-reported information. Specifically, the proposed method builds a tree-based binary classification model that includes a recursive feature elimination step. Based on Shapley values, the recursive feature elimination method preserves the most relevant features without compromising the detection performance. In contrast to previous approaches that use a limited set of selected features, the machine learning approach constructs a detection engine that considers the full set of features reported by respondents. Various versions of the proposed approach were implemented using three different binary classifiers: random forest (RF), light gradient boosting (LGB), and extreme gradient boosting (XGB). We consistently evaluate the performance of the implemented versions of the proposed detection approach on data extracted from the University of Maryland Global COVID-19 Trends and Impact Survey (UMD-CTIS) for four different countries: Brazil, Canada, Japan, and South Africa, and two periods: 2020 and 2021. We also compare the performance of the proposed approach to those obtained by state-of-the-art methods under various quality metrics: F1-score, sensitivity, specificity, precision, receiver operating characteristic (ROC), and area under ROC curve (AUC). It should be noted that the proposed machine learning approach outperformed state-of-the-art detection techniques in terms of the F1-score metric. In addition, this work shows the normalized daily case curves obtained by the proposed approach for the four countries. It should note that the estimated curves are compared to those reported in official reports. Finally, we perform an explainability analysis, using Shapley and relevance ranking of the classification models, to identify the most significant variables contributing to detecting COVID-19-positive cases. This analysis allowed us to determine the relevance of each feature and the corresponding contribution to the detection task.
The Safety, Acceptability and Efficacy ofAlena, a Modularized CBT-based Mobile App Intervention for Social Anxiety: a Randomised Controlled Trial
Authors: Garvert Mona M. , Linke Stuart , McCloud Tayla , Meyer Sofie S. , Sobanska Sandra , Long Alexander , Huys Quentin J. M. , Ahmadi Mandana 
Published: June 2023

AbstractSocial anxiety disorder is a debilitating mental health condition characterized by intense fear of social situations that can significantly impair daily life if left untreated. Cognitive behavioral therapy (CBT) is an effective treatment, but many patients experience slow progress, possibly due to the heterogeneity of cognitive dysfunction contributing to the maintenance of the disease that is not adequately reflected in a typical one-size-fits-all CBT approach. In addition, many patients only seek treatment late, because human therapists can themselves constitute phobic stimuli. To address these challenges, we developedAlena,a digital CBT program based on the Clark and Wells model of social anxiety (Clark and Wells, 1995) that was reorganized to target the four key cognitive functions associated with the maintenance of social anxiety disorder in separate therapy modules: negative beliefs, self-directed attention, rumination, and avoidance behaviors. Here, we tested the safety, acceptability and efficacy of this therapy program. In a randomized controlled trial, primary outcomes showed the app to be safe and acceptable. Secondary endpoint analyses showed that SPIN scores were significantly reduced in the treatment compared to the control group, and a larger number of participants who completed the four-week digital CBT program showed a reliable reduction in their social anxiety scores compared to a waitlist control group. Our findings suggest that targeted digital CBT without therapist involvement was safe, acceptable and showed promising signs of rapid efficacy in the treatment of social anxiety disorder.
Towards Longitudinal Glioma Segmentation: Evaluating combined pre- and post-treatment MRI training data for automated tumor segmentation using nnU-Net
Authors: Ranjbar Sara , Singleton Kyle W. , Curtin Lee , Paulson Lisa , Clark-Swanson Kamala , Hawkins-Daarud Andrea , Mitchell J. Ross , Jackson Pamela R. , Swanson Kristin R. 
Published: June 2023

AbstractIdentification of key phenotypic regions such as necrosis, contrast enhancement, and edema on magnetic resonance imaging (MRI) is important for understanding disease evolution and treatment response in patients with glioma. Manual delineation is time intensive and not feasible for a clinical workflow. Automating phenotypic region segmentation overcomes many issues with manual segmentation, however, current glioma segmentation datasets focus on pre-treatment, diagnostic scans, where treatment effects and surgical cavities are not present. Thus, existing automatic segmentation models are not applicable to post-treatment imaging that is used for longitudinal evaluation of care. Here, we present a comparison of three-dimensional convolutional neural networks (nnU-Net architecture) trained on large temporally defined pre-treatment, post-treatment, and mixed cohorts. We used a total of 1563 imaging timepoints from 854 patients curated from 13 different institutions as well as diverse public data sets to understand the capabilities and limitations of automatic segmentation on glioma images with different phenotypic and treatment appearance. We assessed the performance of models using Dice coefficients on test cases from each group comparing predictions with manual segmentations generated by trained technicians. We demonstrate that training a combined model can be as effective as models trained on just one temporal group. The results highlight the importance of a diverse training set, that includes images from the course of disease and with effects from treatment, in the creation of a model that can accurately segment glioma MRIs at multiple treatment time points.
Gamma spectral event power is elevated in Fragile X Syndrome and associated with single trial gamma power during auditory chirp
Authors: Liu Yanchen , Liu Rui , Horn Paul S. , Westerkamp Grace , Blank Elizabeth , Erickson Craig , Pedapati Ernest V. 
Published: June 2023

AbstractBackgroundFragile X syndrome (FXS) is a neurodevelopmental disorder resulting from silencing of the FMR1 gene. One of the most common and debilitating symptoms of FXS is sensory hyperarousal, especially in the auditory domain. Although the neural mechanisms of auditory hyperarousal in FXS are not well understood, electroencephalography (EEG) studies demonstrate increases in background gamma power during auditory paradigms, which are associated with more severe behavior and impairments in auditory synchronization.MethodsHigh-frequency neural responses to the auditory chirp stimulus were studied in 36 individuals with FXS and 39 controls. Gamma Non-continuous high power events (spectral events) were quantified and compared from source localized EEG recordings. Correlation testing of spectral event properties was performed to averaged EEG features and clinical measures.ResultsOur results show that gamma event peak power was increased in the temporal source of male subjects with FXS (p<0.001, adj. p=0.008) as well as correlated with background average gamma power, while event number, event duration, and frequency span did not differ between groups. Further, absolute event power was positively correlated with clinical measures of obsessive behavior (R=0.63, adj. p=0.011) and stereotypic behavior (R=0.57, adj. p=0.031).ConclusionsOur results indicate that gamma event peak absolute power likely underlies the increased background single trial gamma power observed during auditory processing in FXS, and that the temporal dynamics of gamma activity do not differ.
Social genomics, cognition, and well-being during the COVID-19 pandemic
Authors: Bateman James R. , Krishnamurthy Sudarshan , Quillen Ellen E. , Waugh Christian E. , Kershaw Kiarri N. , Lockhart Samuel N. , Hughes Timothy M. , Seeman Teresa E. , Cole Steve W. , Craft Suzanne 
Published: June 2023

AbstractINTRODUCTIONAdverse psychosocial exposure is associated with increased proinflammatory gene expression and reduced type-1 interferon gene expression, a profile known as the conserved transcriptional response to adversity (CTRA). Little is known about CTRA activity in the context of cognitive impairment, although chronic inflammatory activation has been posited as one mechanism contributing to late-life cognitive decline.METHODSWe studied 171 community-dwelling older adults from the Wake Forest Alzheimer’s Disease Research Center who answered questions via a telephone questionnaire battery about their perceived stress, loneliness, well-being, and impact of COVID-19 on their life, and who provided a self-collected dried blood spot sample. Of those, 148 had adequate samples for mRNA analysis, and 143 were included in the final analysis, which including participants adjudicated as having normal cognition (NC,n= 91) or mild cognitive impairment (MCI,n= 52) were included in the analysis. Mixed effect linear models were used to quantify associations between psychosocial variables and CTRA gene expression.RESULTSIn both NC and MCI groups, eudaimonic well-being (typically associated with a sense of purpose) was inversely associated with CTRA gene expression whereas hedonic well-being (typically associated with pleasure seeking) was positively associated. In participants with NC, coping through social support was associated with lower CTRA gene expression, whereas coping by distraction and reframing was associated with higher CTRA gene expression. CTRA gene expression was not related to coping strategies for participants with MCI, or to either loneliness or perceived stress in either group.DISCUSSIONEudaimonic and hedonic well-being remain important correlates of molecular markers of stress, even in people with MCI. However, prodromal cognitive decline appears to moderate the significance of coping strategies as a correlate of CTRA gene expression. These results suggest that MCI can selectively alter biobehavioral interactions in ways that could potentially affect the rate of future cognitive decline and may serve as targets for future intervention efforts.
Evaluating ChatGPT’s Performance in Responding to Questions About Endoscopic Procedures for Patients
Authors: Ali Hassam , Patel Pratik , Obaitan Itegbemie , Mohan Babu P. , Sohail Amir Humza , Smith-Martinez Lucia , Lambert Karrisa , Gangwani Manesh Kumar , Easler Jeffrey J. , Adler Douglas G. 
Published: June 2023

AbstractBackground and aimsWe aimed to assess the accuracy, completeness, and consistency of ChatGPT’s responses to frequently asked questions concerning the management and care of patients receiving endoscopic procedures and to compare its performance to Generative Pre-trained Transformer 4 (GPT-4) in providing emotional support.MethodsFrequently asked questions (N = 117) about esophagogastroduodenoscopy (EGD), colonoscopy, endoscopic ultrasound (EUS), and endoscopic retrograde cholangiopancreatography (ERCP) were collected from professional societies, institutions, and social media. ChatGPT’s responses were generated and graded by board-certified gastroenterologists and advanced endoscopists. Emotional support questions were assessed by a psychiatrist.ResultsChatGPT demonstrated high accuracy in answering questions about EGD (94.8% comprehensive or correct but insufficient), colonoscopy (100% comprehensive or correct but insufficient), ERCP (91% comprehensive or correct but insufficient), and EUS (87% comprehensive or correct but insufficient). No answers were deemed entirely incorrect (0%). Reproducibility was significant across all categories. ChatGPT’s emotional support performance was inferior to the newer GPT-4 model.ConclusionChatGPT provides accurate and consistent responses to patient questions about common endoscopic procedures and demonstrates potential as a supplementary information resource for patients and healthcare providers.
Albumin-binding RNAi Conjugate for Carrier Free Treatment of Arthritis
Authors: Colazo Juan M. , Hoogenboezem Ella N. , Keech Megan C. , Francini Nora , Shah Veeraj , Yu Fang , Lo Justin H. , Sorets Alexander G. , McCune Joshua T. , Cho Hongsik , DeJulius Carlisle R. , Michell Danielle L. , Maerz Tristan , Vickers Kacey C. , Gibson-Corley Katherine N. , Hasty Karen A. , Crofford Leslie , Cook Rebecca S. , Duvall Craig L. 
Published: June 2023

AbstractOsteoarthritis (OA) and rheumatoid arthritis (RA) are joint diseases that are associated with pain and lost quality of life. No disease modifying OA drugs are currently available. RA treatments are better established but are not always effective and can cause immune suppression. Here, an MMP13-selective siRNA conjugate was developed that, when delivered intravenously, docks onto endogenous albumin and promotes preferential accumulation in articular cartilage and synovia of OA and RA joints. MMP13 expression was diminished upon intravenous delivery of MMP13 siRNA conjugates, consequently decreasing multiple histological and molecular markers of disease severity, while also reducing clinical manifestations such as swelling (RA) and joint pressure sensitivity (RA and OA). Importantly, MMP13 silencing provided more comprehensive OA treatment efficacy than standard of care (steroids) or experimental MMP inhibitors. These data demonstrate the utility of albumin ‘hitchhiking’ for drug delivery to arthritic joints, and establish the therapeutic utility of systemically delivered anti-MMP13 siRNA conjugates in OA and RA.Editorial summaryLipophilic siRNA conjugates optimized for albumin binding and “hitchhiking” can be leveraged to achieve preferential delivery to and gene silencing activity within arthritic joints. Chemical stabilization of the lipophilic siRNA enables intravenous siRNA delivery without lipid or polymer encapsulation. Using siRNA sequences targeting MMP13, a key driver of arthritis-related inflammation, albumin hitchhiking siRNA diminished MMP13, inflammation, and manifestations of osteoarthritis and rheumatoid arthritis at molecular, histological, and clinical levels, consistently outperforming clinical standards of care and small molecule MMP antagonists.
Signal Improved ultra-Fast Light-sheet Microscope (SIFT) for large tissue imaging
Authors: Prince Md Nasful Huda , Garcia Benjamin , Henn Cory , Yi Yating , Susaki Etsuo A. , Watakabe Yuki , Nemoto Tomomi , Lidke Keith A , Zhao Hu , Remiro Irene Salinas , Liu Sheng , Chakraborty Tonmoy 
Published: June 2023

AbstractLight-sheet fluorescence microscopy (LSFM) in conjunction with tissue clearing techniques enables morphological investigation of large tissues faster and with excellent optical sectioning. Recently, cleared tissue axially swept light-sheet microscope (ctASLM) demonstrated three-dimensional isotropic resolution in millimeter-scaled tissues. But ASLM based microscopes suffer from low detection signal and slow imaging speed. Here we report a simple and efficient imaging platform that employs precise control of two fixed distant light-sheet foci to carry out ASLM. This allowed us to carry out full field of view (FOV) imaging at 40 frames per second (fps) which is a four-fold improvement compared to the current state-of-the-art. In addition, in a particular frame rate, our method doubles the signal compared to the current ASLM technique. To augment the overall imaging performance, we also developed a deep learning based tissue information classifier that enables faster determination of tissue boundary. We demonstrated the performance of our imaging platform on various cleared tissue samples and demonstrated its robustness over a wide range of clearing protocols.
Graph Laplacian Spectrum, topological properties, and high-order interactions of Structural Brain Networks are Subject-Specific, Repeatable but Highly Dependent on Graph Construction Scheme
Authors: Dimitriadis Stavros I 
Published: June 2023

AbstractIt has been proposed that the estimation of the normalized graph Laplacian over a brain network’s spectral decomposition can reveal the connectome harmonics (eigenvectors) corresponding to certain frequencies (eigenvalues). Here, I used test-retest dMRI data from the Human Connectome Project to explore the repeatability, and the influence of graph construction schemes on a) graph Laplacian spectrum, b) topological properties, c) high-order interactions, and d) their associations on from structural brain network (SBN). Additionally, I investigated the performance of subject’s identification accuracy (brain fingerprinting) of the graph Laplacian spectrum, the topological properties, and the high-order interactions. Normalized Laplacian eigenvalues were found to be subject-specific and repeatable across the five graph construction schemes. The repeatability of connectome harmonics is lower than that of the Laplacian eigenvalues and shows a heavy dependency on the graph construction scheme. A repeatable relationship between specific topological properties of the SBN with the Laplacian spectrum was also revealed. The identification accuracy of normalized Laplacian eigenvalues was absolute (100%) across the graph construction schemes, while a similar performance was observed for a combination of topological properties of SBN (communities,3,4-motifs) only for the 9m-OMST. Collectively, Laplacian spectrum, topological properties, and high-order interactions characterized uniquely SBN.
The phenological response of European vegetation to urbanisation is mediated by macrobioclimatic factors
Authors: Galán Díaz Javier , Gutiérrez-Bustillo Adela Montserrat , Rojo Jesús 
Published: June 2023

AbstractPlant phenology is a crucial component of ecosystem functioning and is affected by multiple elements of global change; we therefore need to quantify the current phenological changes associated to human activities and understand their impacts on ecosystems. Urbanisation and the intensification of anthropogenic activities alter meteorological conditions and cause phenological changes in urban vegetation worldwide. We used remote sensing data to evaluate the phenological response (start of season date SOS, length of season LOS and end of season date EOS) to urbanisation in the 69 most populated pan-European metropolitan areas (i.e., those that include cities with a population over 450,000 inhabitants) for the period 2002-2021, taking into account the main vegetation types (evergreen forests, deciduous forests, mixed forests, sparse woody vegetation and grasslands). We found that macrobioclimatic factors strongly determined the strength and direction of the phenological response to urbanisation intensity. In general, SOS advanced and LOS increased with urbanisation intensity across Europe. However, the greatest advances in SOS were registered in metropolitan areas in the Mediterranean region, where there was also more uncertainty in the relationship between SOS and urbanisation. The EOS advanced with urbanisation in metropolitan areas in the Mediterranean macrobioclimate (except for deciduous forests), whereas the opposite trend was observed in metropolitan areas from the Temperate macrobioclimate. Our results suggest that macrobioclimatic constraints operating at the continental scale mediate the relationship between plant phenology and urbanisation intensity.