Publications by authors named "Yifei Sun"

113 Publications

Neuromorphic learning with Mott insulator NiO.

Proc Natl Acad Sci U S A 2021 Sep;118(39)

School of Materials Engineering, Purdue University, West Lafayette, IN 47907;

Habituation and sensitization (nonassociative learning) are among the most fundamental forms of learning and memory behavior present in organisms that enable adaptation and learning in dynamic environments. Emulating such features of intelligence found in nature in the solid state can serve as inspiration for algorithmic simulations in artificial neural networks and potential use in neuromorphic computing. Here, we demonstrate nonassociative learning with a prototypical Mott insulator, nickel oxide (NiO), under a variety of external stimuli at and above room temperature. Similar to biological species such as , habituation and sensitization of NiO possess time-dependent plasticity relying on both strength and time interval between stimuli. A combination of experimental approaches and first-principles calculations reveals that such learning behavior of NiO results from dynamic modulation of its defect and electronic structure. An artificial neural network model inspired by such nonassociative learning is simulated to show advantages for an unsupervised clustering task in accuracy and reducing catastrophic interference, which could help mitigate the stability-plasticity dilemma. Mott insulators can therefore serve as building blocks to examine learning behavior noted in biology and inspire new learning algorithms for artificial intelligence.
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http://dx.doi.org/10.1073/pnas.2017239118DOI Listing
September 2021

Additive rates model for recurrent event data with intermittently observed time-dependent covariates.

Stat Methods Med Res 2021 Aug 26:9622802211027593. Epub 2021 Aug 26.

Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY, USA.

Various regression methods have been proposed for analyzing recurrent event data. Among them, the semiparametric additive rates model is particularly appealing because the regression coefficients quantify the absolute difference in the occurrence rate of the recurrent events between different groups. Estimation of the additive rates model requires the values of time-dependent covariates being observed throughout the entire follow-up period. In practice, however, the time-dependent covariates are usually only measured at intermittent follow-up visits. In this paper, we propose to kernel smooth functions involving time-dependent covariates across subjects in the estimating function, as opposed to imputing individual covariate trajectories. Simulation studies show that the proposed method outperforms simple imputation methods. The proposed method is illustrated with data from an epidemiologic study of the effect of streptococcal infections on recurrent pharyngitis episodes.
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http://dx.doi.org/10.1177/09622802211027593DOI Listing
August 2021

Universal phase dynamics in VO switches revealed by ultrafast operando diffraction.

Science 2021 07;373(6552):352-355

Stanford Institute for Materials and Energy Sciences, SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA.

Understanding the pathways and time scales underlying electrically driven insulator-metal transitions is crucial for uncovering the fundamental limits of device operation. Using stroboscopic electron diffraction, we perform synchronized time-resolved measurements of atomic motions and electronic transport in operating vanadium dioxide (VO) switches. We discover an electrically triggered, isostructural state that forms transiently on microsecond time scales, which is shown by phase-field simulations to be stabilized by local heterogeneities and interfacial interactions between the equilibrium phases. This metastable phase is similar to that formed under photoexcitation within picoseconds, suggesting a universal transformation pathway. Our results establish electrical excitation as a route for uncovering nonequilibrium and metastable phases in correlated materials, opening avenues for engineering dynamical behavior in nanoelectronics.
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http://dx.doi.org/10.1126/science.abc0652DOI Listing
July 2021

Recurrent Events Analysis With Data Collected at Informative Clinical Visits in Electronic Health Records.

J Am Stat Assoc 2021 26;116(534):594-604. Epub 2020 Aug 26.

Department of Biostatistics, Columbia University Mailman School of Public Health, New York, NY 10032.

Although increasingly used as a data resource for assembling cohorts, electronic health records (EHRs) pose many analytic challenges. In particular, a patient's health status influences when and what data are recorded, generating sampling bias in the collected data. In this paper, we consider recurrent event analysis using EHR data. Conventional regression methods for event risk analysis usually require the values of covariates to be observed throughout the follow-up period. In EHR databases, time-dependent covariates are intermittently measured during clinical visits, and the timing of these visits is informative in the sense that it depends on the disease course. Simple methods, such as the last-observation-carried-forward approach, can lead to biased estimation. On the other hand, complex joint models require additional assumptions on the covariate process and cannot be easily extended to handle multiple longitudinal predictors. By incorporating sampling weights derived from estimating the observation time process, we develop a novel estimation procedure based on inverse-rate-weighting and kernel-smoothing for the semiparametric proportional rate model of recurrent events. The proposed methods do not require model specifications for the covariate processes and can easily handle multiple time-dependent covariates. Our methods are applied to a kidney transplant study for illustration.
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http://dx.doi.org/10.1080/01621459.2020.1801447DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8261679PMC
August 2020

Modeling and augmenting of fMRI data using deep recurrent variational auto-encoder.

J Neural Eng 2021 07 23;18(4). Epub 2021 Jul 23.

Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, United States of America.

. Recently, deep learning models have been successfully applied in functional magnetic resonance imaging (fMRI) modeling and associated applications. However, there still exist at least two challenges. Firstly, due to the lack of sufficient data, deep learning models tend to suffer from overfitting in the training process. Secondly, it is still challenging to model the temporal dynamics from fMRI, due to that the brain state is continuously changing over scan time. In addition, existing methods rarely studied and applied fMRI data augmentation.. In this work, we construct a deep recurrent variational auto-encoder (DRVAE) that combined variational auto-encoder and recurrent neural network, aiming to address all of the above mentioned challenges. The encoder of DRVAE can extract more generalized temporal features from assumed Gaussian distribution of input data, and the decoder of DRVAE can generate new data to increase training samples and thus partially relieve the overfitting issue. The recurrent layers in DRVAE are designed to effectively model the temporal dynamics of functional brain activities. LASSO (least absolute shrinkage and selection operator) regression is applied on the temporal features and input fMRI data to estimate the corresponding spatial networks.. Extensive experimental results on seven tasks from HCP dataset showed that the DRVAE and LASSO framework can learn meaningful temporal patterns and spatial networks from both real data and generated data. The results on group-wise data and single subject suggest that the brain activities may follow certain distribution. Moreover, we applied DRVAE on four resting state fMRI datasets from ADHD-200 for data augmentation, and the results showed that the classification performances on augmented datasets have been considerably improved.. The proposed method can not only derive meaningful temporal features and spatial networks from fMRI, but also generate high-quality new data for fMRI data augmentation and associated applications.
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http://dx.doi.org/10.1088/1741-2552/ac1179DOI Listing
July 2021

Synthesizing external aggregated information in the penalized Cox regression under population heterogeneity.

Stat Med 2021 Oct 16;40(23):4915-4930. Epub 2021 Jun 16.

Department of Epidemiology & Biostatistics, University of California at San Francisco, San Francisco, California, USA.

Synthesizing external aggregated information has been proven useful in improving estimation efficiency when conducting statistical analysis using a limited amount of data. In this paper, we develop a unified framework for combining information from high-dimensional individual-level data and potentially low-dimensional external aggregate data under the Cox model. We summarize various forms of external aggregated information by population estimating equations and propose a penalized empirical likelihood approach to borrow information from these estimating equations. The proposed methods possess the flexibility to handle the case where individual-level data and external aggregate data are from heterogeneous populations. Specifically, a penalized empirical likelihood ratio test is developed to check for the potential heterogeneity, and a semiparametric density ratio model is postulated to account for the heterogeneity. Moreover, we study the impact of uncertainty in the auxiliary information on the efficiency gain and propose a modified variance estimator to adjust for the uncertainty. The proposed estimators enjoy the oracle property and are asymptotically more efficient than the penalized partial likelihood estimator that does not exploit the external aggregated information. Simulation studies show improvement in both estimation efficiency and variable selection over the competitors. The proposed approaches are applied to the analysis of a pediatric kidney transplant study for illustration.
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http://dx.doi.org/10.1002/sim.9101DOI Listing
October 2021

Sudden Collapse of Magnetic Order in Oxygen-Deficient Nickelate Films.

Phys Rev Lett 2021 May;126(18):187602

Department of Physics, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA.

Antiferromagnetic order is a common and robust ground state in the parent (undoped) phase of several strongly correlated electron systems. The progressive weakening of antiferromagnetic correlations upon doping paves the way for a variety of emergent many-electron phenomena including unconventional superconductivity, colossal magnetoresistance, and collective charge-spin-orbital ordering. In this study, we explored the use of oxygen stoichiometry as an alternative pathway to modify the coupled magnetic and electronic ground state in the family of rare earth nickelates (RENiO_{3-x}). Using a combination of x-ray spectroscopy and resonant soft x-ray magnetic scattering, we find that, while oxygen vacancies rapidly alter the electronic configuration within the Ni and O orbital manifolds, antiferromagnetic order is remarkably robust to substantial levels of carrier doping, only to suddenly collapse beyond 0.21 e^{-}/Ni without an accompanying structural transition. Our work demonstrates that ordered magnetism in RENiO_{3-x} is mostly insensitive to carrier doping up to significant levels unseen in other transition-metal oxides. The sudden collapse of ordered magnetism upon oxygen removal may provide a new mechanism for solid-state magnetoionic switching and new applications in antiferromagnetic spintronics.
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http://dx.doi.org/10.1103/PhysRevLett.126.187602DOI Listing
May 2021

Antibacterial activity and mechanism of plant flavonoids to gram-positive bacteria predicted from their lipophilicities.

Sci Rep 2021 May 18;11(1):10471. Epub 2021 May 18.

Laboratory of Natural Medicine and Microbiological Drug, College of Bioscience and Bioengineering, Jiangxi Agricultural University, Nanchang, 330045, China.

Antimicrobial resistance seriously threatened human health, and new antimicrobial agents are desperately needed. As one of the largest classes of plant secondary metabolite, flavonoids can be widely found in various parts of the plant, and their antibacterial activities have been increasingly paid attention to. Based on the physicochemical parameters and antibacterial activities of sixty-six flavonoids reported, two regression equations between their ACD/LogP or LogD and their minimum inhibitory concentrations (MICs) to gram-positive bacteria were established with the correlation coefficients above 0.93, and then were verified by another sixty-eight flavonoids reported. From these two equations, the MICs of most flavonoids against gram-positive bacteria could be roughly calculated from their ACD/LogP or LogD, and the minimum MIC was predicted as approximately 10.2 or 4.8 μM, more likely falls into the range from 2.6 to 10.2 μM, or from 1.2 to 4.8 μM. Simultaneously, both tendentiously concave regression curves indicated that the lipophilicity is a key factor for flavonoids against gram-positive bacteria. Combined with the literature analyses, the results also suggested that the cell membrane is the main site of flavonoids acting on gram-positive bacteria, and which likely involves the damage of phospholipid bilayers, the inhibition of the respiratory chain or the ATP synthesis, or some others.
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http://dx.doi.org/10.1038/s41598-021-90035-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8131645PMC
May 2021

Unusual Role of Point Defects in Perovskite Nickelate Electrocatalysts.

ACS Appl Mater Interfaces 2021 Jun 18;13(21):24887-24895. Epub 2021 May 18.

College of Energy, Xiamen University, Xiamen 361005, P. R. China.

Low-cost transition-metal oxide is regarded as a promising electrocatalyst family for an oxygen evolution reaction (OER). The classic design principle for an oxide electrocatalyst believes that point defect engineering, such as oxygen vacancies (V) or heteroatom doping, offers the opportunities to manipulate the electronic structure of material toward optimal OER activity. Oppositely, in this work, we discover a counterintuitive phenomenon that both V and an aliovalent dopant (i.e., proton (H)) in perovskite nickelate (i.e., NdNiO (NNO)) have a considerably detrimental effect on intrinsic OER performance. Detailed characterizations unveil that the introduction of these point defects leads to a decrease in the oxidative state of Ni and weakens Ni-O orbital hybridization, which triggers the local electron-electron correlation and a more insulating state. Evidenced by first-principles calculation using the density functional theory (DFT) method, the OER on nickelate electrocatalysts follows the lattice oxygen mechanism (LOM). The incorporation of point defect increases the energy barrier of transformation from OO*(V) to OH*(V) intermediates, which is regarded as the rate-determining step (RDS). This work offers a new and significant perspective of the role that lattice defects play in the OER process.
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http://dx.doi.org/10.1021/acsami.1c04903DOI Listing
June 2021

Bifunctional Ag-Decorated CeO Nanorods Catalysts for Promoted Photodegradation of Methyl Orange and Photocatalytic Hydrogen Evolution.

Nanomaterials (Basel) 2021 Apr 24;11(5). Epub 2021 Apr 24.

Third Institute of Oceanography, Ministry of Natural Resources, Xiamen 361005, China.

The photodegradation of organic pollutants and photocatalytic hydrogen generation from water by semiconductor catalysts are regarded as the of the most promising strategies to resolve the crisis of global environmental issues. Herein, we successfully designed and prepared a series of silver-decorated CeO(Ag/CeO) photocatalysts with different morphologies by a facile hydrothermal route. The physical properties, charge transfer behavior and photocatalytic performances (degradation and hydrogen evolution) over diverse catalysts with nanocubes, nanoparticles and nanorods shapes were comprehensively studied. It was found that the Ag-decorated CeO nanorods (Ag/R-CeO) demonstrate the best activity for both photocatalytic methyl orange (MO) degradation and photocatalytic H production reaction with attractive stability during cycling tests, suggesting its desirable practical potential. The superior performance of Ag/R-CeO can be ascribed to (1) the facilitated light absorption due to enriched surface oxygen vacancies (OVs) and plasmonic Ag nanoparticles on nanorods, (2) the facilitated photo-excited charge carrier (e-h) separation efficiency on a metal/oxide hybrid structure and (3) the promoted formation of active reaction intermediates on surface-enriched Ag and oxygen vacancies reactive sites on Ag/CeO nanorods. This study provides a valuable discovery of the utilization of abundant solar energy for diverse catalytic processes.
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http://dx.doi.org/10.3390/nano11051104DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8145711PMC
April 2021

Nonparametric estimation in an illness-death model with component-wise censoring.

Biometrics 2021 Apr 29. Epub 2021 Apr 29.

Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota, USA.

In disease settings where study participants are at risk for death and a serious nonfatal event, composite endpoints defined as the time until the earliest of death or the nonfatal event are often used as the primary endpoint in clinical trials. In practice, if the nonfatal event can only be detected at clinic visits and the death time is known exactly, the resulting composite endpoint exhibits "component-wise censoring." The standard method used to estimate event-free survival in this setting fails to account for component-wise censoring. We apply a kernel smoothing method previously proposed for a marker process in a novel way to produce a nonparametric estimator for event-free survival that accounts for component-wise censoring. The key insight that allows us to apply this kernel method is thinking of nonfatal event status as an intermittently observed binary time-dependent variable rather than thinking of time to the nonfatal event as interval-censored. We also propose estimators for the probability in state and restricted mean time in state for reversible or irreversible illness-death models, under component-wise censoring, and derive their large-sample properties. We perform a simulation study to compare our method to existing multistate survival methods and apply the methods on data from a large randomized trial studying a multifactor intervention for reducing morbidity and mortality among men at above average risk of coronary heart disease.
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http://dx.doi.org/10.1111/biom.13482DOI Listing
April 2021

X-ray Nanoimaging of Crystal Defects in Single Grains of Solid-State Electrolyte LiAlLaZrO.

Nano Lett 2021 Jun 29;21(11):4570-4576. Epub 2021 Apr 29.

Department of Materials Science and Engineering, Cornell University, Ithaca, New York 14850, United States.

All-solid-state lithium batteries promise significant improvements in energy density and safety over traditional liquid electrolyte batteries. The Al-doped LiLaZrO (LLZO) solid-state electrolyte shows excellent potential given its high ionic conductivity and good thermal, chemical, and electrochemical stability. Nevertheless, further improvements on electrochemical and mechanical properties of LLZO call for an in-depth understanding of its local microstructure. Here, we employ Bragg coherent diffractive imaging to investigate the atomic displacements inside single grains of LLZO with various Al-doping concentrations, resulting in cubic, tetragonal, and cubic-tetragonal mixed structures. We observe coexisting domains of different crystallographic orientations in the tetragonal structure. We further show that Al doping leads to crystal defects such as dislocations and phase boundaries in the mixed- and cubic-phase grain. This study addresses the effect of Al doping on the nanoscale structure within individual grains of LLZO, which is informative for the future development of solid-state batteries.
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http://dx.doi.org/10.1021/acs.nanolett.1c00315DOI Listing
June 2021

The behavior of surface acidity on photo-Fenton degradation of ciprofloxacin over sludge derived carbon: Performance and mechanism.

J Colloid Interface Sci 2021 Sep 8;597:84-93. Epub 2021 Apr 8.

School of Environmental Sciences and Engineering, Nanjing Tech University, Nanjing 211816, PR China; School of Pharmaceutical Sciences, Nanjing Tech University, Nanjing 211816, PR China. Electronic address:

Sludge derived carbon (SC) has been widely used in advanced oxidation processes as an effective and economic catalyst. In this study, we applied surface modified SC for the first time to catalyze the heterogeneous photo-Fenton process with ciprofloxacin, a highly concerned emerging contaminant, as a model substance. HSO was used to acidify the SCs under varying acid dosages, temperatures, and reaction time lengths. The surface acidity of SCs was quantitatively characterized with NH-TPD. A strong correlation between the surface acidity and the catalytic activity was clearly demonstrated. The highest catalytic activity was obtained with SC whose acidity was 0.149 mmol·g after being modified with 6 mol·L HSO at -20 ℃ for 24 h. In addition, XRD, XRF, BET, XPS, and HRTEM were also used to characterize the obtained SC. ·OH radicals were found to be the main reactive species by EPR. Ten transformation products were identified by GC-MS, based on which three possible reaction pathways were proposed.
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http://dx.doi.org/10.1016/j.jcis.2021.03.156DOI Listing
September 2021

Characterization of the Interface Structure of 1-Ethyl-2,3-alkylimidazolium Bis(trifluoromethylsulfonyl)imide on a Au(111) Surface with Molecular Dynamics Simulations.

J Phys Chem B 2021 Apr 2;125(14):3677-3689. Epub 2021 Apr 2.

State Key Laboratory of Complex Non-ferrous Metal Resource Clean Utilization, Kunming University of Science and Technology, Kunming, Yunnan 650093, China.

As a new type of green electrolyte, ionic liquids have been extensively and successfully used in electrochemical systems. It is extremely important to understand the structure and characteristics of their electric double layers. The microscopic structures of room-temperature ionic liquids 1-ethyl-2,3-dimethylimidazolium bis(trifluoromethylsulfonyl)imide ([Emmim]TFSI) and 1-ethyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide ([Emim]TFSI) were studied on a flat Au(111) surface using molecular dynamics simulations. Since the interactions of [Emmim]TFSI, [Emmim], and TFSI with the Au(111) surface are stronger than those of molecules (or ions) in the [Emim]TFSI system, the linear arrangement of [Emmim]TFSI and the worm-like pattern of the [Emim]TFSI system can be found near the Au(111) surface. Meanwhile, cations are all parallel to the electrode in the [Emmim]TFSI/Au(111) system and tilted toward the surface in the [Emim]TFSI/Au(111) system. TFSI presents trans and cis conformations in [Emim]TFSI and [Emmim]TFSI systems adjacent to Au(111), respectively. A Helmholtz-like layer structure with alternating oscillations of anionic and cationic layers can be found in the [Emim]TFSI system, while the molecular layer with cations and anions existing simultaneously can be found in [Emmim]TFSI. Our results confirm that the substitution of hydrogen on C1 by methyl groups in the imidazole ring increases the interaction between the particles. It has also been proved that the change in the anion conformation and cation orientation in the [Emmim]TFSI system can be attributed to the different interaction energies of various particles. The above reasons ultimately make the images on Au(111) different in the two systems. The results provide a new perspective for studying the structure of double layers. They are helpful in deepening the understanding of the interface behavior of ionic liquids and providing a theoretical basis for the design of functional ionic liquids that are suitable for electrochemical equipment.
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http://dx.doi.org/10.1021/acs.jpcb.0c09994DOI Listing
April 2021

Resolving mass fractions and congener group patterns of C-C chlorinated paraffins in commercial products: Associations with source characterization.

Sci Total Environ 2021 May 19;769:144701. Epub 2021 Jan 19.

Beijing Key Laboratory of Bio-Inspired Energy Materials and Devices, School of Space and Environment, Beihang University, Beijing 100191, China. Electronic address:

Commercial chlorinated paraffins (CPs) are a source of CPs in the environment, and clarification of the different CP groups present in commercial products is important for source characterization. Resolving CP congener groups is hindered by the complex CP compositions of commercial products. We used comprehensive two-dimensional gas chromatography coupled with electron capture negative ionization high-resolution time-of-flight mass spectrometry to profile 57 C - C CP congener groups in 18 CP-42, CP-52, and CP-70 commercial products. Very short-chain CPs (vSCCPs), including CCl and CCl CPs, and other chlorinated aromatic compounds were identified in the commercial products. The mass fractions of total vSCCPs, short-chain CPs (SCCPs) and medium-chain CPs (MCCPs) in the commercial products ranged from 0.02% to 3.61%, 0.75% to 51.4%, and 0.39% to 69.1%, respectively. Two-dimensional hierarchical cluster analysis with a heat map plot highlighted variations in the C - C CP congener group patterns among different commercial CP formulations. The principal component analysis results indicated that commercial CPs products might be important contributors to vSCCPs, SCCPs, and MCCPs in various environmental matrices. This study provides comprehensive and well-resolved compositional data for CPs in commercial products, which will be helpful for CP source characterization.
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http://dx.doi.org/10.1016/j.scitotenv.2020.144701DOI Listing
May 2021

Perovskite Chromite With Assembled Ni-Co Nano-Alloys: A Potential Bifunctional Electrode Catalyst for Solid Oxide Cells.

Front Chem 2020 1;8:595608. Epub 2021 Feb 1.

College of Energy, Xiamen University, Xiamen, China.

Solid oxide fuel cell (SOFC) is an advanced electricity generation device with attractive fuel flexibility and conversion efficiency. As its reversed process, solid oxide electrolysis cell (SOEC) can efficiently electrolyze notorious CO to valuable chemical product such as CO, by utilizing renewable energy. To achieve long-term operation, the development of catalytically active electrode materials in both SOFC/SOEC modes is highly desirable, yet still challenging. In this research, an A-site deficient perovskite oxide (lanthanum chromite) decorated with exsolved Ni-Co nano-alloy has been fabricated and applied as a potential fuel electrode for both SOFC/SOEC. The influences of A-site non-stoichiometry and B-site dopant concentration on structural properties and exsolution process have been elaborately studied from various aspects. Diverse characterizations collectively confirm that the existence of A-site deficiency helps the formation of oxygen vacancies and stimulates the exsolution of B-site cations. In addition, the synergistic effect between the dopants of Co and Ni manipulates the reducibility and promotes carbon deposition resistance of the material. The electrolyte-supported SOFC with self-assembled Ni-Co nano-alloy electrode has shown maximum power densities of 329 mW/cm (in H) and 258 mW/cm (in syngas, H + CO) at 850 °C, which are 50% better than those of the fuel cell with the exsolved Ni nanoparticles only. Also, the nano-alloy decorated electrode catalyst promotes a 30% increase in SOEC performance for CO electrolysis with prominently enhanced resistance against carbon deposition, suggesting the versatile functionality of the materials.
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http://dx.doi.org/10.3389/fchem.2020.595608DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7882693PMC
February 2021

CircNR3C2 promotes HRD1-mediated tumor-suppressive effect via sponging miR-513a-3p in triple-negative breast cancer.

Mol Cancer 2021 02 2;20(1):25. Epub 2021 Feb 2.

State Key Laboratory of Reproductive Medicine, Department of Pathology, Nanjing Medical University, Nanjing, Jiangsu, China.

Background: E3 ubiquitin ligase HRD1 (HMG-CoA reductase degradation protein 1, alias synoviolin with SYVN1 as the official gene symbol) was found downregulated and acting as a tumor suppressor in breast cancer, while the exact expression profile of HRD1 in different breast cancer subtypes remains unknown. Recent studies characterized circular RNAs (circRNAs) playing an regulatory role as miRNA sponge in tumor progression, presenting a new viewpoint for the post-transcriptional regulation of cancer-related genes.

Methods: Examination of the expression of HRD1 protein and mRNA was implemented using public microarray/RNA-sequencing datasets and breast cancer tissues/cell lines. Based on public RNA-sequencing results, online databases and enrichment/clustering analyses were used to predict the specific combinations of circRNA/miRNA that potentially govern HRD1 expression. Gain-of-function and rescue experiments in vitro and in vivo were executed to evaluate the suppressive effects of circNR3C2 on breast cancer progression through HRD1-mediated proteasomal degradation of Vimentin, which was identified using immunoblotting, immunoprecipitation, and in vitro ubiquitination assays.

Results: HRD1 is significantly underexpressed in triple-negative breast cancer (TNBC) against other subtypes and has an inverse correlation with Vimentin, inhibiting the proliferation, migration, invasion and EMT (epithelial-mesenchymal transition) process of breast cancer cells via inducing polyubiquitination-mediated proteasomal degradation of Vimentin. CircNR3C2 (hsa_circ_0071127) is also remarkably downregulated in TNBC, negatively correlated with the distant metastasis and lethality of invasive breast carcinoma. Overexpressing circNR3C2 in vitro and in vivo leads to a crucial enhancement of the tumor-suppressive effects of HRD1 through sponging miR-513a-3p.

Conclusions: Collectively, we elucidated a bona fide circNR3C2/miR-513a-3p/HRD1/Vimentin axis that negatively regulates the metastasis of TNBC, suggesting that circNR3C2 and HRD1 can act as potential prognostic biomarkers. Our study may facilitate the development of therapeutic agents targeting circNR3C2 and HRD1 for patients with aggressive breast cancer.
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http://dx.doi.org/10.1186/s12943-021-01321-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7851937PMC
February 2021

Synthesizing external aggregated information in the presence of population heterogeneity: A penalized empirical likelihood approach.

Biometrics 2021 Feb 2. Epub 2021 Feb 2.

Department of Epidemiology & Biostatistics, University of California at San Francisco, San Francisco, California.

With the increasing availability of data in the public domain, there has been a growing interest in exploiting information from external sources to improve the analysis of smaller scale studies. An emerging challenge in the era of big data is that the subject-level data are high dimensional, but the external information is at an aggregate level and of a lower dimension. Moreover, heterogeneity and uncertainty in the auxiliary information are often not accounted for in information synthesis. In this paper, we propose a unified framework to summarize various forms of aggregated information via estimating equations and develop a penalized empirical likelihood approach to incorporate such information in logistic regression. When the homogeneity assumption is violated, we extend the method to account for population heterogeneity among different sources of information. When the uncertainty in the external information is not negligible, we propose a variance estimator adjusting for the uncertainty. The proposed estimators are asymptotically more efficient than the conventional penalized maximum likelihood estimator and enjoy the oracle property even with a diverging number of predictors. Simulation studies show that the proposed approaches yield higher accuracy in variable selection compared with competitors. We illustrate the proposed methodologies with a pediatric kidney transplant study.
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http://dx.doi.org/10.1111/biom.13429DOI Listing
February 2021

Predicting Future Occurrence of Acute Hypotensive Episodes Using Noninvasive and Invasive Features.

Mil Med 2021 01;186(Suppl 1):445-451

Geisel School of Medicine, Dartmouth College, 1 Rope Ferry Rd, Hanover, NH 03755, USA.

Introduction: Early prediction of the acute hypotensive episode (AHE) in critically ill patients has the potential to improve outcomes. In this study, we apply different machine learning algorithms to the MIMIC III Physionet dataset, containing more than 60,000 real-world intensive care unit records, to test commonly used machine learning technologies and compare their performances.

Materials And Methods: Five classification methods including K-nearest neighbor, logistic regression, support vector machine, random forest, and a deep learning method called long short-term memory are applied to predict an AHE 30 minutes in advance. An analysis comparing model performance when including versus excluding invasive features was conducted. To further study the pattern of the underlying mean arterial pressure (MAP), we apply a regression method to predict the continuous MAP values using linear regression over the next 60 minutes.

Results: Support vector machine yields the best performance in terms of recall (84%). Including the invasive features in the classification improves the performance significantly with both recall and precision increasing by more than 20 percentage points. We were able to predict the MAP with a root mean square error (a frequently used measure of the differences between the predicted values and the observed values) of 10 mmHg 60 minutes in the future. After converting continuous MAP predictions into AHE binary predictions, we achieve a 91% recall and 68% precision. In addition to predicting AHE, the MAP predictions provide clinically useful information regarding the timing and severity of the AHE occurrence.

Conclusion: We were able to predict AHE with precision and recall above 80% 30 minutes in advance with the large real-world dataset. The prediction of regression model can provide a more fine-grained, interpretable signal to practitioners. Model performance is improved by the inclusion of invasive features in predicting AHE, when compared to predicting the AHE based on only the available, restricted set of noninvasive technologies. This demonstrates the importance of exploring more noninvasive technologies for AHE prediction.
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http://dx.doi.org/10.1093/milmed/usaa418DOI Listing
January 2021

Early Detection of Hypotension Using a Multivariate Machine Learning Approach.

Mil Med 2021 01;186(Suppl 1):440-444

Geisel School of Medicine, Emergency Medicine, Dartmouth College, Hanover, NH 037, USA.

Introduction: The ability to accurately detect hypotension in trauma patients at the earliest possible time is important in improving trauma outcomes. The earlier an accurate detection can be made, the more time is available to take corrective action. Currently, there is limited research on combining multiple physiological signals for an early detection of hemorrhagic shock. We studied the viability of early detection of hypotension based on multiple physiologic signals and machine learning methods. We explored proof of concept with a small (5 minutes) prediction window for application of machine learning tools and multiple physiologic signals to detecting hypotension.

Materials And Methods: Multivariate physiological signals from a preexisting dataset generated by an experimental hemorrhage model were employed. These experiments were conducted previously by another research group and the data made available publicly through a web portal. This dataset is among the few publicly available which incorporate measurement of multiple physiological signals from large animals during experimental hemorrhage. The data included two hemorrhage studies involving eight sheep. Supervised machine learning experiments were conducted in order to develop deep learning (viz., long short-term memory or LSTM), ensemble learning (viz., random forest), and classical learning (viz., support vector machine or SVM) models for the identification of physiological signals that can detect whether or not overall blood loss exceeds a predefined threshold 5 minutes ahead of time. To evaluate the performance of the machine learning technologies, 3-fold cross-validation was conducted and precision (also called positive predictive value) and recall (also called sensitivity) values were compared. As a first step in this development process, 5 minutes prediction windows were utilized.

Results: The results showed that SVM and random forest outperform LSTM neural networks, likely because LSTM tends to overfit the data on small sized datasets. Random forest has the highest recall (84%) with 56% precision while SVM has 62% recall with 82% precision. Upon analyzing the feature importance, it was observed that electrocardiogram has the highest significance while arterial blood pressure has the least importance among all other signals.

Conclusion: In this research, we explored the viability of early detection of hypotension based on multiple signals in a preexisting animal hemorrhage dataset. The results show that a multivariate approach might be more effective than univariate approaches for this detection task.
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http://dx.doi.org/10.1093/milmed/usaa323DOI Listing
January 2021

Hospital Readmissions After Implementation of a Discharge Care Program for Patients with COVID-19 Illness.

J Gen Intern Med 2021 03 14;36(3):722-729. Epub 2021 Jan 14.

Center for Behavioral Cardiovascular Health, Columbia University Irving Medical Center, 622 W. 168th Street, New York, NY, PH9-311, USA.

Background: The surge of coronavirus 2019 (COVID-19) hospitalizations in New York City required rapid discharges to maintain hospital capacity.

Objective: To determine whether lenient provisional discharge guidelines with remote monitoring after discharge resulted in safe discharges home for patients hospitalized with COVID-19 illness.

Design: Retrospective case series SETTING: Tertiary care medical center PATIENTS: Consecutive adult patients hospitalized with COVID-19 illness between March 26, 2020, and April 8, 2020, with a subset discharged home INTERVENTIONS: COVID-19 Discharge Care Program consisting of lenient provisional inpatient discharge criteria and option for daily telephone monitoring for up to 14 days after discharge MEASUREMENTS: Fourteen-day emergency department (ED) visits and hospital readmissions RESULTS: Among 812 patients with COVID-19 illness hospitalized during the study time period, 15.5% died prior to discharge, 24.1% remained hospitalized, 10.0% were discharged to another facility, and 50.4% were discharged home. Characteristics of the 409 patients discharged home were mean (SD) age 57.3 (16.6) years; 245 (59.9%) male; 27 (6.6%) with temperature ≥ 100.4 °F; and 154 (37.7%) with oxygen saturation < 95% on day of discharge. Over 14 days of follow-up, 45 patients (11.0%) returned to the ED, of whom 31 patients (7.6%) were readmitted. Compared to patients not referred, patients referred for remote monitoring had fewer ED visits (8.3% vs 14.1%; OR 0.60, 95% CI 0.31-1.15, p = 0.12) and readmissions (6.9% vs 8.3%; OR 1.15, 95% CI 0.52-2.52, p = 0.73).

Limitations: Single-center study; assignment to remote monitoring was not randomized.

Conclusions: During the COVID-19 surge in New York City, lenient discharge criteria in conjunction with remote monitoring after discharge were associated with a rate of early readmissions after COVID-related hospitalizations that was comparable to the rate of readmissions after other reasons for hospitalization before the COVID pandemic.
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http://dx.doi.org/10.1007/s11606-020-06340-wDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7808120PMC
March 2021

Stochastic programming for outpatient scheduling with flexible inpatient exam accommodation.

Health Care Manag Sci 2021 Sep 4;24(3):460-481. Epub 2021 Jan 4.

Lahey Hospital and Medical Center, Burlington, MA, USA.

This study is concerned with the determination of an optimal appointment schedule in an outpatient-inpatient hospital system where the inpatient exams can be cancelled based on certain rules while the outpatient exams cannot be cancelled. Stochastic programming models were formulated and solved to tackle the stochasticity in the procedure durations and patient arrival patterns. The first model, a two-stage stochastic programming model, is formulated to optimize the slot size. The second model further optimizes the inpatient block (IPB) placement and slot size simultaneously. A computational method is developed to solve the second optimization problem. A case study is conducted using the data from Magnetic Resonance Imaging (MRI) centers of Lahey Hospital and Medical Center (LHMC). The current schedule and the schedules obtained from the optimization models are evaluated and compared using simulation based on FlexSim Healthcare. Results indicate that the overall weighted cost can be reduced by 11.6% by optimizing the slot size and can be further reduced by an additional 12.6% by optimizing slot size and IPB placement simultaneously. Three commonly used sequencing rules (IPBEG, OPBEG, and a variant of ALTER rule) were also evaluated. The results showed that when optimization tools are not available, ALTER variant which evenly distributes the IPBs across the day has the best performance. Sensitivity analysis of weights for patient waiting time, machine idle time and exam cancellations further supports the superiority of ALTER variant sequencing rules compared to the other sequencing methods. A Pareto frontier was also developed and presented between patient waiting time and machine idle time to enable medical centers with different priorities to obtain solutions that accurately reflect their respective optimal tradeoffs. An extended optimization model was also developed to incorporate the emergency patient arrivals. The optimal schedules from the extended model show only minor differences compared to those from the original model, thus proving the robustness of the scheduling solutions obtained from our optimal models against the impacts of emergency patient arrivals.
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http://dx.doi.org/10.1007/s10729-020-09527-zDOI Listing
September 2021

Multiple Single-Nucleotide Polymorphism Detection for Antimalarial Pyrimethamine Resistance via Allele-Specific PCR Coupled with Gold Nanoparticle-Based Lateral Flow Biosensor.

Antimicrob Agents Chemother 2021 02 17;65(3). Epub 2021 Feb 17.

Department of Human Parasitology, School of Basic Medical Sciences, Hubei University of Medicine, Shiyan, China

Molecular genotyping holds tremendous potential to detect antimalarial drug resistance (ADR) related to single nucleotide polymorphisms (SNPs). However, it relies on the use of complicated procedures and expensive instruments. Thus, rapid point-of-care testing (POCT) molecular tools are urgently needed for field survey and clinical use. Herein, a POCT platform consisting of multiple-allele-specific PCR (AS-PCR) and a gold nanoparticle (AuNP)-based lateral flow biosensor was designed and developed for SNP detection of the dihydrofolate reductase () gene related to pyrimethamine resistance. The multiple-AS-PCR utilized 3' terminal artificial antepenultimate mismatch and double phosphorothioate-modified allele-specific primers. The duplex PCR amplicons with 5' terminal labeled with biotin and digoxin are recognized by streptavidin (SA)-AuNPs on the conjugate pad and then captured by anti-digoxin antibody through immunoreactions on the test line to produce a golden red line for detection. The system was applied to analyze SNPs in Pfdhfr N51I, C59R, and S108N of 98 clinical isolates from uncomplicated malaria patients. Compared with the results from nested PCR followed by Sanger DNA sequencing, the sensitivity was 97.96% (96/98) for N51I, C59R, and S108N. For specificity, the values were 100% (98/98), 95.92% (94/98), and 100% (98/98) for N51I, C59R, and S108N, respectively. The limit of detection is approximately 200 fg/μl for plasmid DNA as the template and 100 parasites/μl for blood filter paper. The established platform not only offers a powerful tool for molecular surveillance of ADR but also is easily extended to interrelated SNP profiles for infectious diseases and genetic diseases.
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http://dx.doi.org/10.1128/AAC.01063-20DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8092547PMC
February 2021

Spatial distributions, source apportionment and ecological risks of C-C chlorinated paraffins in mangrove sediments from Dongzhai Harbor, Hainan Island.

Environ Pollut 2021 Feb 12;270:116076. Epub 2020 Nov 12.

Beijing Key Laboratory of Bio-inspired Energy Materials and Devices, School of Space and Environment, Beihang University, Beijing, 100191, China. Electronic address:

The spatial distributions, possible sources of C-C chlorinated paraffins (CPs), and the ecological risks posed in mangrove sediment in Dongzhai Harbor (Hainan Island, China) were investigated. Comprehensive two-dimensional gas chromatography combined with electron capture negative ionization mass spectrometry was used to determine 50 C-C CP congener groups. The concentrations of C-CPs, short-chain CPs (SCCPs), and medium-chain CPs (MCCPs) in the mangrove sediment samples were 8.28-79.7, 89.2-931, and 58.8-834 ng g dry weight, respectively. The CPs concentrations in the mangrove sediment samples were moderate compared with those found in other regions worldwide. The spatial distributions and congener patterns of the CPs indicated that the CP concentrations were mainly controlled by local emissions and that wastewater discharged from livestock and shrimp breeding facilities and domestic sewage were the main sources of CPs in mangrove sediment in Dongzhai Harbor. CCl and CCl were the dominant SCCP and MCCP congener groups, respectively. The MCCP concentrations and total organic carbon contents significantly correlated (R = 0.607, P < 0.05). Hierarchical cluster analysis and principal component analysis indicated that the SCCP and MCCP congeners were from different commercial CP formulations and sources. Risk assessments suggested that SCCPs and MCCPs in mangrove sediment in Dongzhai Harbor do not currently pose marked risks to sediment-dwelling organisms.
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http://dx.doi.org/10.1016/j.envpol.2020.116076DOI Listing
February 2021

Characterization and degradation mechanism of bimetallic iron-based/AC activated persulfate for PAHs-contaminated soil remediation.

Chemosphere 2021 Mar 4;267:128875. Epub 2020 Nov 4.

School of Energy and Power Engineering, Beihang University, Beijing, 100191, China. Electronic address:

In this research, a novel iron based bimetallic nanoparticles (Fe-Ni) supported on activated carbon (AC) were synthesized and employed as an activator of persulfate in polycyclic aromatic hydrocarbons (PAHs) polluted sites remediation. AC-supported Fe-Ni activator was prepared according to two-step reduction method: the liquid phase reduction and H- reduction under high temperature (600 °C), which was defined as Fe-Ni/AC. Characterizations using micropore physisorption analyzer, X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), and high-resolution transmission electron microscopy (HR-TEM) showed that the synthetic material had large specific surface area, nano-size and carbon-encapsulated metal particles, moreover, the lattice fringes of metals were clearly defined. The PAH compound types and their concentrations were determined by gas chromatography mass spectrometry (GC-MS) with SIM mode, the method detection limit (MDL) was estimated to about 0.21 μg/kg for PAHs, and the average recovery of PAHs was 96.3%. Mechanisms of PAH oxidation degradation with the reaction system of Fe-Ni/AC activated persulfate were discussed, the results showed that short-life free radicals, such as SO·, OH·, and OOH· were generated simultaneously, which acted as strong oxidizing radicals, resulting in the oxidation and almost complete opening of the PAH rings.
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http://dx.doi.org/10.1016/j.chemosphere.2020.128875DOI Listing
March 2021

Catalytic pyrolysis of poplar sawdust: Excellent hydrocarbon selectivity and activity of hollow zeolites.

Bioresour Technol 2020 Dec 3;317:123954. Epub 2020 Aug 3.

Beijing Key Laboratory of Bio-inspired Energy Materials and Devices, School of Space and Environment, Beihang University, Beijing 102206, China.

Hollow zeolites were investigated for catalytic fast pyrolysis (CFP) of biomass to produce hydrocarbon-rich bio-oil. A series of hollow ZSM-5 catalysts were synthesized via a dissolution-recrystallization strategy. The physicochemical properties of the catalysts were investigated by high-resolution transmission electron microscopy, N sorption, X-ray photoelectron spectroscopy, and ammonia temperature-programmed desorption experiments. The hollow zeolite was effective for increasing the hydrocarbon fraction in bio-oil. In particular, hollow HS-ZSM-5(50) afforded the highest hydrocarbon yield (6.8 wt%), which was ~3 times of that achieved with solid ZSM-5(50). The hollowness, acidity, and the presence of secondary wall mesopores in the hollow zeolite were found to affect bio-oil production. The hollow regions stabilized more active biomass intermediates and inhibited their repolymerization to coke, while the interior acid sites continually converted these intermediates to aromatic hydrocarbons. Secondary wall mesopores compromise the hollow space and hinder consecutive catalysis, resulting in phenols as the main product.
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http://dx.doi.org/10.1016/j.biortech.2020.123954DOI Listing
December 2020

'Virtual experience' as an intervention before a positron emission tomography/CT scan may ease patients' anxiety and improve image quality.

J Med Imaging Radiat Oncol 2020 Oct 30;64(5):641-648. Epub 2020 Jun 30.

Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, Shanghai, China.

Introduction: The aim of our study was to investigate the effect of a 'virtual experience' on reducing people's anxiety levels and improving image quality.

Methods: This study included 200 people who underwent F-FDG PET/CT scan for the first time. Healthy people (n = 100) and patients (n = 100) were randomly divided into a control group and an intervention group. In the intervention group, we used a 'virtual experience' as an intervention before the scan. We used the Spielberger State-Trait Anxiety Inventory (STAI) and satisfaction questionnaires for evaluation. Additionally, the image quality was analysed.

Results: In the control group, more patients presented anxiety than healthy people (26(52%) versus 15(30%)) (P = 0.041). However, when the 'virtual experience' was provided, the number of cases of anxiety in the patient group decreased to 19(38%). Furthermore, patients in the intervention group had lower STAI-related scores than those in the control group (STAI-S: 37.08 ± 9.42 versus 43.34 ± 10.49, P = 0.109; STAI-T: 36.24 ± 9.55 versus 40.72 ± 9.00, P = 0.019). With respect to image quality, people who had higher STAI-related scores were more likely to have unqualified images.

Conclusion: A 'virtual experience' provided by an audio-visual installation can ease patients' anxiety and improve image quality.
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http://dx.doi.org/10.1111/1754-9485.13078DOI Listing
October 2020

Modeling task-based fMRI data via deep belief network with neural architecture search.

Comput Med Imaging Graph 2020 07 6;83:101747. Epub 2020 Jun 6.

Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, Georgia, United States. Electronic address:

It has been shown that deep neural networks are powerful and flexible models that can be applied on fMRI data with superb representation ability over traditional methods. However, a challenge of neural network architecture design has also attracted attention: due to the high dimension of fMRI volume images, the manual process of network model design is very time-consuming and not optimal. To tackle this problem, we proposed an unsupervised neural architecture search (NAS) framework on a deep belief network (DBN) that models volumetric fMRI data, named NAS-DBN. The NAS-DBN framework is based on Particle Swarm Optimization (PSO) where the swarms of neural architectures can evolve and converge to a feasible optimal solution. The experiments showed that the proposed NAS-DBN framework can quickly find a robust architecture of DBN, yielding a hierarchy organization of functional brain networks (FBNs) and temporal responses. Compared with 3 manually designed DBNs, the proposed NAS-DBN has the lowest testing loss of 0.0197, suggesting an overall performance improvement of up to 47.9 %. For each task, the NAS-DBN identified 260 FBNs, including task-specific FBNs and resting state networks (RSN), which have high overlap rates to general linear model (GLM) derived templates and independent component analysis (ICA) derived RSN templates. The average overlap rate of NAS-DBN to GLM on 20 task-specific FBNs is as high as 0.536, indicating a performance improvement of up to 63.9 % in respect of network modeling. Besides, we showed that the NAS-DBN can simultaneously generate temporal responses that resemble the task designs very well, and it was observed that widespread overlaps between FBNs from different layers of NAS-DBN model form a hierarchical organization of FBNs. Our NAS-DBN framework contributes an effective, unsupervised NAS method for modeling volumetric task fMRI data.
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http://dx.doi.org/10.1016/j.compmedimag.2020.101747DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7412935PMC
July 2020

Early administration of tirofiban after urokinase-mediated intravenous thrombolysis reduces early neurological deterioration in patients with branch atheromatous disease.

J Int Med Res 2020 May;48(5):300060520926298

Department of Neurology, Suzhou First People's Hospital, Suzhou, Anhui Province, China.

Objectives: To investigate the effects of early administration of tirofiban after intravenous thrombolysis on early neurological deterioration in patients with branch atheromatous disease.

Methods: We analyzed clinical data from patients with branch atheromatous disease. We enrolled seven cases into the urokinase-only (UO) control group and 10 cases into the urokinase + tirofiban (UT) treatment group. National Institutes of Health Stroke Scale (NIHSS) scores were obtained at admission and on days 3 and 5 after admission. Modified Rankin Scale (mRS) scores were obtained 3 months after admission.

Results: Significant differences between the UO and UT groups were evident on days 3 and 5 after admission. In the UT group, there was a significant difference between NIHSS scores at admission and on day 5, while there were no significant differences in scores in the UO group. The early neurological deterioration rates were not significantly different between the two groups. However, there were significant differences in these rates at 72 and 120 hours. Both the mRS scores and the prognoses at 3 months differed between the two groups.

Conclusion: Early administration of tirofiban after urokinase-mediated intravenous thrombolysis reduces early neurological deterioration and improves the long-term prognosis of patients with branch atheromatous disease.
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http://dx.doi.org/10.1177/0300060520926298DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7273788PMC
May 2020
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