bluuebunny/crossref_metadata_2025_split huggingface dataset:
Promoting sleep and rest of infants using nonpharmacological interventions within the neonatal intensive care unit at Children's of Mississippi
Authors: Robinette Bethany , Palokas Michelle 
Published: September 2022

ABSTRACT

Objective:
This project aimed to promote evidence-based practices regarding the promotion of sleep and rest using nonpharmacological interventions for infants in the neonatal intensive care unit (NICU) at Children's of Mississippi.

Introduction:
Sleep is a basic physiological need that plays a fundamental role in the growth and development of infants. Unfortunately, infants admitted to the NICU after birth may not receive adequate sleep and rest, especially when compared to infants discharged home after delivery.

Methods:
The project used the Joanna Briggs Institute's Practical Application of Clinical Evidence System and Getting Research in Practice audit tool for promoting change in healthcare practice. A baseline audit of NICU staff and patient caregivers was conducted and measured against five best practice recommendations, followed by implementation of targeted strategies and a follow-up audit.

Results:
The project team discovered two barriers to sleep promotion and rest in the NICU at Children's of Mississippi: lack of nurse knowledge of evidence-based practices for promotion of sleep and rest for infants in the NICU and a lack of location in the electronic health record to document sleep quality of infants. Strategies were implemented to address these barriers and compliance improved in four of the five audit criteria.

Conclusions:
Implementation of an educational PowerPoint that focused on sleep promotion of infants in the NICU was developed and distributed to all of the nurses and resulted in average increased compliance from 43% to 53% for all audit criteria.
Generative Editing via Convolutional Obscuring (GECO): A Generative Adversarial Network for MRI de-artifacting
Authors: Bagley Bryce Allen , Petrov Sergei , Cheng Ge , Armanasu Matei , Fischbein Nancy , Jiang Bin , Iv Michael , Tranvinh Eric , Zeineh Michael , Gevaert Olivier 
Published: September 2022

AbstractMagnetic resonance imaging (MRI) is the dominant diagnostic technique to non-invasively image the brain, and deep learning has proven a powerful tool for analyzing these images. However, machine learning models trained on such MRI data have empirically shown an ability to detect complex and invisible artifacts, such as which type of machine a scan was taken from to a high degree of accuracy. Such artifacts are potentially invisible to the human eye, but can be identified by machine learning systems, leading them to focus on irrelevant features rather than scientifically and/or medically useful ones. For example, machine learning systems can often “shortcut” past the actual features researchers would like to detect and utilize separate spurious correlations to make predictions. Several such undesired features have been reported to interfere with cross-institutional medical imaging deep learning research, and more are likely to be identified as time goes on. Here, we develop a method capable of removing these spurious correlations in an unsupervised manner, leveraging generative techniques to produce images which maintain image quality while learning how to remove technical artifacts. Generative Adversarial Networks are a class of deep learning architectures which have shown impressive efficacy in image generation and editing tasks, and our work builds upon this success. Here, we propose Generative Editing via Convolutional Obscuring (GECO), a Generative Adverserial Network for MRI deartifacting. GECO is based on a CycleGAN, a GAN architecture designed for image-to-image translation that is transforming an input image into a new image with one or more desirable properties. By formulating the CycleGAN loss as a two-player game with a regularization term and incentivizing the generator to erase spurious correlations the original image quality can be better preserved. Beginning with classifiers trained on original images to identify images based on artifacts of interest, GECO reduced the classifiers’ ability to detect these spurious correlations from 97% down to a difference which is nearly equal to a classifier making purely random guesses. We also observe over 98% structural similarity between the original and deartifacted images, indicating the preservation of the vast majority of non-spurious information contained in the original images. In addition to solving the known problem of avoiding artifacts from scanner type, this method opens the door to potentially removing many other types of spurious correlations from medical images and other data modalities across many fields.Software availabilityhttps://github.com/gevaertlab/GECO1.
Multimodal data fusion of adult and pediatric brain tumors with deep learning
Authors: Steyaert Sandra , Qiu Yeping Lina , Zheng Yuanning , Mukherjee Pritam , Vogel Hannes , Gevaert Olivier 
Published: September 2022

AbstractThe introduction of deep learning in both imaging and genomics has significantly advanced the analysis of biomedical data. For complex diseases such as cancer different data modalities may reveal different disease characteristics, and the integration of imaging with genomic data has the potential to unravel additional information then when using these data sources in isolation. Here, we propose a DL framework that by combining histopathology images with gene expression profiles can predict prognosis of brain tumors. Using two separate cohorts of 783 adult and 305 pediatric brain tumors, the developed multimodal data models achieved better prediction results compared to the single data models, but also leads to the identification of more relevant biological pathways. Importantly, when testing our adult models on a third independent brain tumor dataset, we show our multimodal framework is able to generalize and performs better on new data from different cohorts. Furthermore, leveraging the concept of transfer learning, we demonstrate how our multimodal models pre-trained on pediatric glioma can be used to predict prognosis for two more rare (less available samples) pediatric brain tumors, i.e. ependymoma and medulloblastoma. To summarize, our study illustrates that a multimodal data fusion approach can be successfully implemented and customized to model clinical outcome of adult and pediatric brain tumors.
Abatacept for Treatment of Adults Hospitalized with Moderate or Severe Covid-19
Authors: Ko Emily R. , Anstrom Kevin J. , Panettieri Reynold A. , Lachiewicz Anne M. , Maillo Martin , O’Halloran Jane A. , Boucher Cynthia , Smith P. Brian , McCarthy Matthew W. , Segura Nunez Patricia , Mendivil Tuchia de Tai Sabina , Khan Akram , Mena Lora Alfredo J. , Salathe Matthias , Kedar Eyal , Capo Gerardo , Rodríguez Gonzalez Daniel , Patterson Thomas F. , Palma Christopher , Ariza Horacio , Patelli Lima Maria , Blamoun John , Nannini Esteban C. , Sprinz Eduardo , Mykietiuk Analia , Wang Jennifer P. , Parra-Rodriguez Luis , Der Tatyana , Willsey Kate , Benjamin Daniel K. , Wen Jun , Zakroysky Pearl , Halabi Susan , Silverstein Adam , McNulty Steven E. , O’Brien Sean M. , Al-Khalidi Hussein R. , Butler Sandra , Atkinson Jane , Adam Stacey J. , Chang Soju , Maldonado Michael A. , Proscham Michael , LaVange Lisa , Bozzette Samuel A. , Powderly William G. ,   the ACTIV-1 IM study group members
Published: September 2022

AbstractBackgroundWe investigated whether abatacept, a selective costimulation modulator, provides additional benefit when added to standard-of-care for patients hospitalized with Covid-19.MethodsWe conducted a master protocol to investigate immunomodulators for potential benefit treating patients hospitalized with Covid-19 and report results for abatacept. Intravenous abatacept (one-time dose 10 mg/kg, maximum dose 1000 mg) plus standard of care (SOC) was compared with shared placebo plus SOC. Primary outcome was time-to-recovery by day 28. Key secondary endpoints included 28-day mortality.ResultsBetween October 16, 2020 and December 31, 2021, a total of 1019 participants received study treatment (509 abatacept; 510 shared placebo), constituting the modified intention-to-treat cohort. Participants had a mean age 54.8 (SD 14.6) years, 60.5% were male, 44.2% Hispanic/Latino and 13.7% Black. No statistically significant difference for the primary endpoint of time-to-recovery was found with a recovery-rate-ratio of 1.14 (95% CI 1.00–1.29; p=0.057) compared with placebo. We observed a substantial improvement in 28-day all-cause mortality with abatacept versus placebo (11.0% vs. 15.1%; odds ratio [OR] 0.62 [95% CI 0.41– 0.94]), leading to 38% lower odds of dying. Improvement in mortality occurred for participants requiring oxygen/noninvasive ventilation at randomization. Subgroup analysis identified the strongest effect in those with baseline C-reactive protein >75mg/L. We found no statistically significant differences in adverse events, with safety composite index slightly favoring abatacept. Rates of secondary infections were similar (16.1% for abatacept; 14.3% for placebo).ConclusionsAddition of single-dose intravenous abatacept to standard-of-care demonstrated no statistically significant change in time-to-recovery, but improved 28-day mortality.Trial registrationClinicalTrials.gov(NCT04593940).
Development and validation of a methodology to measure exhaled carbon dioxide (CO2) and control indoor air renewal
Authors: Baselga Marta , Alba Juan J. , Schuhmacher Alberto J. 
Published: September 2022

AbstractThe measurement of CO2 has positioned itself as a low-cost and straightforward technique to indirectly control indoor air quality, allowing the reduction of the concentration of potentially pathogen-loaded aerosols to which we are exposed. However, on numerous occasions, bad practice limits the technique for CO2 level interpreting and does not apply methodologies that guarantee air renewal. This work proposes a new methodology for measuring and controlling CO2 levels for indoor air in shared spaces. The proposed methodology is based on three stages: diagnosis, correction protocols, and monitoring/control/surveillance (MCS). The procedure is explained using a cultural center as an actual base case study. Additionally, the procedure was validated by implementing 40 voluntary commercial spaces in Zaragoza (Spain). Standardization of methods is suggested so that the measurement of CO2 becomes an effective strategy to control the airborne transmission of pathogens and thus prevent future Covid-19 outbreaks and novel pandemics.
Evaluation of S/F94 as a proxy for COVID-19 severity
Authors: Swets Maaike C , Kerr Steven , Scott-Brown James , Brown Adam B , Gupta Rishi , Millar Jonathan E , Spata Enti , McCurrach Fiona , Bretherick Andrew D , Docherty Annemarie , Harrison David , Rowan Kathy , Young Neil ,   ISARIC4C Investigators, Groeneveld Geert H , Dunning Jake , Nguyen-Van-Tam Jonathan S , Openshaw Peter , Horby Peter W. , Harrison Ewen , Staplin Natalie , Semple Malcolm G , Lone Nazir , Baillie J Kenneth 
Published: September 2022

AbstractOptimising statistical power in early-stage trials and observational studies accelerates discovery and improves the reliability of results. Ideally, intermediate outcomes should be continuously distributed and lie on the causal pathway between an intervention and a definitive outcome such as mortality. In order to optimise power for an intermediate outcome in the RECOVERY trial, we devised and evaluated a modification to a simple, pragmatic measure of oxygenation function - the SaO2/FIO2 (S/F) ratio.We demonstrate that, because of the ceiling effect in oxyhaemoglobin saturation, S/F ceases to reflect pulmonary oxygenation function at high values of SaO2. Using synthetic and real data, we found that the correlation of S/F with a gold standard (PaO2/FIO2, P/F ratio) improved substantially when measurements with SaO2 ≥ 0.94 are excluded (Spearman r, synthetic data: S/F : 0.31; S/F94: 0.85). We refer to this measure as S/F94.In order to test the underlying assumptions and validity of S/F94 as a predictor of a definitive outcome (mortality), we collected an observational dataset including over 39,000 hospitalised patients with COVID-19 in the ISARIC4C study. We first demonstrated that S/F94 is predictive of mortality in COVID-19. We then compared the sample sizes required for trials using different outcome measures (S/F94, the WHO ordinal scale, sustained improvement at day 28 and mortality at day 28) ensuring comparable effect sizes. The smallest sample size was needed when S/F94 on day 5 was used as an outcome measure.To facilitate future study design, we provide an online user interface to quantify real-world power for a range of outcomes and inclusion criteria, using a synthetic dataset retaining the population-level clinical associations in real data accrued in ISARIC4C https://isaric4c.net/endpoints.We demonstrated that S/F94 is superior to S/F as a measure of pulmonary oxygenation function and is an effective intermediate outcome measure in COVID-19. It is a simple and non-invasive measurement, representative of disease severity and provides greater statistical power to detect treatment differences than other intermediate endpoints.
A Visual, Inexpensive, and Wireless Capillary Rheometer
Authors: Du Jianyi , Sinha Soham , Lee Stacey , Skylar-Scott Mark A. 
Published: September 2022

AbstractComplex fluid systems can exhibit highly non-linear and time-dependent flow behaviors that arise from microscale reorganization. Rheological measurements to understand these flow-induced properties in soft condensed matter and biology are critical to optimize manufacturing processes that rely upon the use of complex fluids, such as the creation of viscoelastic bioinks for 3D bioprinting. While typical rheological characterizations are performed on bulky and expensive rheometers, these options do not readily enable in situ microscopic visualization of flow behavior, which can provide microstructural insights into the mechanisms and patterns of yielding. To address these limitations, we present a visual, inexpensive (approximately $200), and wireless capillary rheometer (VIEWR) assembled from chiefly 3D printed components. We validate the accuracy and reliability of the portable rheometer by comparing measurements of multiple rheological parameters, including viscosity and yield stress, with measurements obtained from a commercial oscillatory rheometer. At its core, VIEWR employs an easily interchangeable glass capillary channel to enable real-time microscopic observation under flow conditions. To facilitate various experimental conditions, a communication protocol based on Internet of Things (IoT) is adapted to support wireless transmission of data. This unique rheometer design can be used for characterizing and visualizing numerous living and non-living complex fluids.Specifications table
Increase the flow rate and improve hydrogen deuterium exchange mass spectrometry
Authors: Peterle Daniele , DePice David , Wales Thomas E. , Engen John R. 
Published: September 2022

AbstractReversed-phase peptide separation in hydrogen deuterium exchange (HDX) mass spectrometry (MS) must be done with conditions where the back exchange is the slowest possible, the so-called quench conditions of low pH and low temperature. To retain maximum deuterium, separation must also be done as quickly as possible. The low temperature (0 °C) of quench conditions complicates the separation and leads primarily to a reduction in separation quality and an increase in chromatographic backpressure. To improve the separation in HDX MS, one could use a longer gradient, smaller particles, a different separation mechanism (for example, capillary electrophoresis), or multi-dimensional separations such as combining ion mobility separation with reversed-phase separation. Another way to improve separations under HDX MS quench conditions is to use a higher flow rate where separation efficiency at 0 °C is more ideal. Higher flow rates, however, require chromatographic systems (both pumps and fittings) with higher backpressure limits. We tested what improvements could be realized with a commercial UPLC/UHPLC system capable of ~20,000 psi backpressure. We found that a maximum flow rate of 225 μL/min (using a 1×50mm column packed with 1.8 μm particles) was possible and that higher flow rate clearly led to higher peak capacity. HDX MS analysis of both simple and particularly complex samples improved, permitting both shorter separation time, if desired, and providing more deuterium recovery.
IL-12-expressing highly immunogenic recombinant modified vaccinia virus Ankara reprograms tumor-infiltrating myeloid cells to overcome immune resistance
Authors: Liu Shuaitong , Mazo Gregory , Yang Ning , Zhang Tuo , Wang Yi , Tariq Shanza Baseer , Wang Yueqi , Hirschhorn-Cymerman Daniel , Ji Liangliang , Tan Adrian , Wang Jiahu , Yan Wei , Choi John , Xiang Jenny Zhaoying , Li Ming O. , Merghoub Taha , Wolchok Jedd D. , Deng Liang 
Published: September 2022

AbstractNovel strategies to reprogram tumor-infiltrating myeloid cells for cancer immunotherapy are urgently needed, given that the primary and acquired resistance to immune checkpoint blockade (ICB) therapy has hindered the overall success of immunotherapy. Modified vaccinia virus Ankara (MVA) is a highly attenuated, non-replicative vaccinia virus and an approved vaccine against smallpox and monkeypox. Here we report rational engineering of recombinant MVA, MQ833, by removing three immune suppressive genes, E5R, E3L, and WR199, from the MVA genome and inserting three transgenes encoding Flt3L, OX40L, and IL-12. Intratumoral (IT) delivery of MQ833 generates potent antitumor responses dependent on CD8+T cells, neutrophils, and M1-like macrophages, the nucleic acid-sensing pathways mediated by MDA5/STING, and interferon feedback loop. IT MQ833 promotes the recruitment and activation of neutrophils and inflammatory monocytes into the injected tumors, depletion of M2-like macrophages, and expansion of M1-like macrophages, generating potent antitumor immunity against tumors resistant to ICB.
Effects of Simvastatin on RBL-2H3 cell degranulation
Authors: Yoshii Michiko , Kitazaki Ai , Ozawa Koichiro 
Published: September 2022

SummaryHypercholesterolemia is a major complication of arteriosclerosis. Mast cells in arteriosclerosis plaques induce inflammatory reactions, and promote arterial sclerosis. In this study, we evaluated the pharmacological effects of simvastatin (SV)-3-hydroxy-3-methylglutaryl-CoA (HMG-CoA) reductase inhibitors on the degranulation of rat basophilic leukemia (RBL)-2H3 cells, which are commonly used as mast cell models. SV significantly decreased degranulation induced by three types of stimulation: antigen antibody reaction (Ag-Ab), thapsigargin (Tg) serosal endoplasmic reticulum calcium ATPase (SERCA) inhibitor, and A23187 calcium ionophore. SV had a stronger inhibitory effect on degranulation induced by Ag-Ab stimulation than the other two stimulations. However, SV had no effect on increases of intracellular Ca2+ concentrations. Mevalonate or geranylgeraniol co-treatment with SV completely prevented the inhibitory effect of SV on degranulation induced by these stimulations. Immunoblotting results showed that SV inhibited protein kinase C (PKC) delta translocation that was induced by Ag-Ab but not by Tg or A23187. SV induced a reduction in active Rac1, and actin filament rearrangements.In conclusion, SV inhibits RBL-2H3 cell degranulation by inhibiting downstream signaling pathways, including the sequential degranulation pathway. These inhibitory effects were completely reversed by the addition of geranylgeraniol and might be induced by changes in the translocation of the small GTPase families Rab and Ras and Rho, which are related to vesicular transport and PKC delta activation, respectively. These changes are caused by the inhibition of HMG-CoA reductase by SV following the synthesis of farnesyl and geranylgeranyl pyrophosphates, which play important roles in the activation of small GTPases.
Genetic influences on the shape of brain ventricular and subcortical structures
Authors: Zhao Bingxin , Li Tengfei , Yang Xiaochen , Shu Juan , Wang Xifeng , Luo Tianyou , Yang Yue , Wu Zhenyi , Fan Zirui , Jiang Zhiwen , Chen Jie , Shan Yue , Tang Jiarui , Xiong Di , Zhu Ziliang , Gao Mufeng , Guan Wyliena , Tomlinson Chalmer E. , Dong Qunxi , Li Yun , Stein Jason L. , Wang Yalin , Zhu Hongtu 
Published: September 2022

AbstractBrain ventricular and subcortical structures are heritable both in size and shape. Genetic influences on brain region size have been studied using conventional volumetric measures, but little is known about the genetic basis of ventricular and subcortical shapes. Here we developed pipelines to extract seven complementary shape measures for lateral ventricles, subcortical structures, and hippocampal subfields. Based on over 45,000 subjects in the UK Biobank and ABCD studies, 60 genetic loci were identified to be associated with brain shape features (P < 1.09 × 10-10), 19 of which were not detectable by volumetric measures of these brain structures. Ventricular and subcortical shape features were genetically related to cognitive functions, mental health traits, and multiple brain disorders, such as the attention-deficit/hyperactivity disorder. Vertex-based shape analysis was performed to precisely localize the brain regions with these shared genetic influences. Mendelian randomization suggests brain shape causally contributes to neurological and neuropsychiatric disorders, including Alzheimer’s disease and schizophrenia. Our results uncover the genetic architecture of brain shape for ventricular and subcortical structures and prioritize the genetic factors underlying disease-related shape variations.