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Article ; Online: Diagnostic value of dual-source, dual-energy computed tomography combined with the neutrophil-lymphocyte ratio for discriminating gastric signet ring cell from mixed signet ring cell and non-signet ring cell carcinomas.

Song, Qinxia / Wang, Xiangfa / Zhu, Juan / Shi, Hengfeng

2024

Abstract: Purpose: To explore the diagnostic value of dual-source computed tomography (DSCT) and neutrophil to lymphocyte ratio (NLR) for differentiating gastric signet ring cell carcinoma (SRC) from mixed SRC (mSRC) and non-SRC (nSRC).: Methods: This ... ...

Abstract	Purpose: To explore the diagnostic value of dual-source computed tomography (DSCT) and neutrophil to lymphocyte ratio (NLR) for differentiating gastric signet ring cell carcinoma (SRC) from mixed SRC (mSRC) and non-SRC (nSRC). Methods: This retrospective study included patients with gastric adenocarcinoma who underwent DSCT between August 2019 and June 2021 at our Hospital. The iodine concentration in the venous phase (IC Results: A total of 155 patients (SRC [n = 45, aged 61.22 ± 11.4 years], mSRC [n = 60, aged 61.09 ± 12.7 years], and nSRC [n = 50, aged 67.66 ± 8.76 years]) were included. There were significant differences in NLR, IC Conclusion: DSCT combined with NLR showed high diagnostic efficacy in differentiating SRC from mSRC and nSRC.
Language	English
Publishing date	2024-03-25
Publishing country	United States
Document type	Journal Article
ZDB-ID	2839786-1
ISSN	2366-0058 ; 2366-004X
ISSN (online)	2366-0058
ISSN	2366-004X
DOI	10.1007/s00261-024-04286-9
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Article ; Online: Multilayer Exponential Family Factor models for integrative analysis and learning disease progression.

Wang, Qinxia / Wang, Yuanjia

Biostatistics (Oxford, England)

2022 Volume 25, Issue 1, Page(s) 203–219

Abstract: Current diagnosis of neurological disorders often relies on late-stage clinical symptoms, which poses barriers to developing effective interventions at the premanifest stage. Recent research suggests that biomarkers and subtle changes in clinical markers ...

Abstract	Current diagnosis of neurological disorders often relies on late-stage clinical symptoms, which poses barriers to developing effective interventions at the premanifest stage. Recent research suggests that biomarkers and subtle changes in clinical markers may occur in a time-ordered fashion and can be used as indicators of early disease. In this article, we tackle the challenges to leverage multidomain markers to learn early disease progression of neurological disorders. We propose to integrate heterogeneous types of measures from multiple domains (e.g., discrete clinical symptoms, ordinal cognitive markers, continuous neuroimaging, and blood biomarkers) using a hierarchical Multilayer Exponential Family Factor (MEFF) model, where the observations follow exponential family distributions with lower-dimensional latent factors. The latent factors are decomposed into shared factors across multiple domains and domain-specific factors, where the shared factors provide robust information to perform extensive phenotyping and partition patients into clinically meaningful and biologically homogeneous subgroups. Domain-specific factors capture remaining unique variations for each domain. The MEFF model also captures nonlinear trajectory of disease progression and orders critical events of neurodegeneration measured by each marker. To overcome computational challenges, we fit our model by approximate inference techniques for large-scale data. We apply the developed method to Parkinson's Progression Markers Initiative data to integrate biological, clinical, and cognitive markers arising from heterogeneous distributions. The model learns lower-dimensional representations of Parkinson's disease (PD) and the temporal ordering of the neurodegeneration of PD.
MeSH term(s)	Humans ; Disease Progression ; Parkinson Disease/diagnosis ; Biomarkers ; Neuroimaging/methods
Chemical Substances	Biomarkers
Language	English
Publishing date	2022-06-21
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	2031500-4
ISSN	1468-4357 ; 1465-4644
ISSN (online)	1468-4357
ISSN	1465-4644
DOI	10.1093/biostatistics/kxac042
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Article ; Online: Assessment of the Sustainability of the Resource-Based Province Shanxi, China Using Emergy Analysis

Feiyu Hou / Dunhu Chang / Qinxia Wang

Sustainability, Vol 14, Iss 15706, p

2022 Volume 15706

Abstract: According to the BP Statistical Yearbook of World Energy, China’s coal production and consumption have ranked first in the world in recent years. Shanxi, a central China province, plays an important role in China’s energy supply because of its large coal ...

Abstract	According to the BP Statistical Yearbook of World Energy, China’s coal production and consumption have ranked first in the world in recent years. Shanxi, a central China province, plays an important role in China’s energy supply because of its large coal reserves, long mining history, and high output. The aim of this study was to evaluate the sustainability of the eco-economic system in Shanxi Province, a typical resource-based region. Through emergy analysis, this study quantified the sustainable development of the eco-economic system in Shanxi Province from 2013 to 2020 from five dimensions: basic emergy quantity, social subsystem, economic subsystem, environmental subsystem, and capacity for sustainable development. The results show that Shanxi Province has made great progress in recent years in terms of the emergy value of renewable resources, per capita emergy consumption, and electricity emergy ratio, but the proportion of nonrenewable emergy is still large, the intensity of emergy is high, and the exchange rate of emergy is low. Lastly, the sustainable development indicators ESI and EISD reflect that Shanxi Province is gradually improving the utilization efficiency of resources, and Shanxi Province has achieved certain results after experiencing transition pains. This study, combined with the actual situation of Shanxi Province and the problems found, puts forward corresponding countermeasures. The analysis method used in this study provides a theoretical basis for the scientific evaluation of the sustainable development of a resource-based region, and the research results have profound practical significance for improving the quality of Shanxi’s economic development and helping Shanxi’s economic transformation.
Keywords	energy analysis ; eco-economic system ; sustainable development ; Shanxi Province ; assessment ; Environmental effects of industries and plants ; TD194-195 ; Renewable energy sources ; TJ807-830 ; Environmental sciences ; GE1-350
Subject code	690
Language	English
Publishing date	2022-11-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: An efficient fusion algorithm combining feature extraction and variational optimization for CT and MR images.

Wang, Qinxia / Yang, Xiaoping

Journal of applied clinical medical physics

2020 Volume 21, Issue 6, Page(s) 139–150

Abstract: In medical image processing, image fusion is the process of combining complementary information from different or multimodality images to obtain an informative fused image in order to improve clinical diagnostic accuracy. In this paper, we propose a two- ... ...

Abstract	In medical image processing, image fusion is the process of combining complementary information from different or multimodality images to obtain an informative fused image in order to improve clinical diagnostic accuracy. In this paper, we propose a two-stage fusion framework for computed tomography (CT) and magnetic resonance (MR) images. First, the intensity and geometric structure features in both CT and MR images are extracted by the saliency detection method and structure tensor, respectively, and an initial fused image is obtained. Then, the initial fused image is optimized by a variational model which contains a fidelity term and a regularization term. The fidelity term is to retain the intensity of the initial fused image, and the regularization term is to constrain the gradient information of the fused image to approximate the MR image. The primal-dual algorithm is proposed to solve the variational problem. The proposed method is applied on five pairs of clinical medical CT and MR-T1\MR-T2 images, and the comparison metrics SF, MI,
MeSH term(s)	Algorithms ; Image Processing, Computer-Assisted ; Magnetic Resonance Imaging ; Multimodal Imaging ; Tomography, X-Ray Computed
Language	English
Publishing date	2020-04-19
Publishing country	United States
Document type	Journal Article
ZDB-ID	2010347-5
ISSN	1526-9914 ; 1526-9914
ISSN (online)	1526-9914
ISSN	1526-9914
DOI	10.1002/acm2.12882
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Article ; Online: Comparison of four methods that remove calcium hydroxide from root canals

YANG Nan / WANG Yueyue / SHAN Xiaoyang / DU Qinxia / LI Ningyi / SUN Huibin

口腔疾病防治, Vol 31, Iss 7, Pp 494-

2023 Volume 500

Abstract: Objective To compare the efficiency of four methods that remove calcium hydroxide in root canals and to guide clinical practice. Methods Sixty-five isolated mandibular single root canal premolars were collected. After crown cutting and root canal ... ...

Abstract	Objective To compare the efficiency of four methods that remove calcium hydroxide in root canals and to guide clinical practice. Methods Sixty-five isolated mandibular single root canal premolars were collected. After crown cutting and root canal preparation, a tooth was randomly selected as the blank control group, and the remaining 64 teeth were equally divided into Groups A and B (n = 32). Group A was injected with water-soluble calcium hydroxide, and Group B was injected with oil-soluble calcium hydroxide. After 2 weeks of drug sealing, Groups A and B were randomly divided into 4 groups (n = 8), including the lateral opening syringe group, sonic vibration group, ultrasonic group, and Er: YAG laser group. Before and after calcium hydroxide removal, the samples were scanned by cone-beam CT, and the data were imported into Mimics for 3D reconstruction. The root canal was divided into the following segments: superior root segment, middle and apical, and the calcium hydroxide volume of each segment of the root canal was calculated. The volumes of calcium hydroxide before and after removal were V1 and V2, respectively, with a clearance rate = (V1-V2)/V1×100%. Three-factor ANOVA was used for statistical analysis. After Groups A and B were reconstructed, the apical region with residual calcium hydroxide was selected, and the blank control was observed by scanning electron microscopy (SEM). Results Two types of calcium hydroxide could not be completely removed by the four flushing methods. The clearance rate of water-soluble calcium hydroxide was higher than that of oil-soluble calcium hydroxide (P<0.001). Among the three segments of the root canal, the clearance rate of the apical segment was lower (P<0.05). The Er: YAG laser treatment group showed the highest removal efficiency of two kinds of calcium hydroxide, which was higher than that of the other groups, especially in apical of the root. Compared with the sonic wave washing group and the syringe washing group, the ultrasonic wave washing group exhibited ...
Keywords	calciumhydroxide ; root canal irrigation ; er: yag laser cleaning ; ultrasonic cleaning ; sonic vibration ; mimics software ; Medicine ; R
Subject code	630
Language	Chinese
Publishing date	2023-07-01T00:00:00Z
Publisher	Editorial Department of Journal of Prevention and Treatment for Stomatological Diseases
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: A latent state space model for estimating brain dynamics from electroencephalogram (EEG) data

Wang, Qinxia / Loh, Ji Meng / He, Xiaofu / Wang, Yuanjia

Biometrics. 2023 Sept., v. 79, no. 3 p.2444-2457

2023

Abstract: Modern neuroimaging technologies have substantially advanced the measurement of brain activity. Electroencephalogram (EEG) as a noninvasive neuroimaging technique measures changes in electrical voltage on the scalp induced by brain cortical activity. ... ...

Abstract	Modern neuroimaging technologies have substantially advanced the measurement of brain activity. Electroencephalogram (EEG) as a noninvasive neuroimaging technique measures changes in electrical voltage on the scalp induced by brain cortical activity. With its high temporal resolution, EEG has emerged as an increasingly useful tool to study brain connectivity. Challenges with modeling EEG signals of complex brain activity include interactions among unknown sources, low signal‐to‐noise ratio, and substantial between‐subject heterogeneity. In this work, we propose a state space model that jointly analyzes multichannel EEG signals and learns dynamics of different sources corresponding to brain cortical activity. Our model borrows strength from spatially correlated measurements and uses low‐dimensional latent states to explain all observed channels. The model can account for patient heterogeneity and quantify the effect of a subject's covariates on the latent space. The EM algorithm, Kalman filtering, and bootstrap resampling are used to fit the state space model and provide comparisons between patient diagnostic groups. We apply the developed approach to a case‐control study of alcoholism and reveal significant attenuation of brain activity in response to visual stimuli in alcoholic subjects compared to healthy controls.
Keywords	alcohol abuse ; algorithms ; brain ; case-control studies ; electric potential difference ; electroencephalography ; models ; patients ; signal-to-noise ratio
Language	English
Dates of publication	2023-09
Size	p. 2444-2457.
Publishing place	John Wiley & Sons, Ltd
Document type	Article ; Online
Note	JOURNAL ARTICLE
ZDB-ID	213543-7
ISSN	0099-4987 ; 0006-341X
ISSN	0099-4987 ; 0006-341X
DOI	10.1111/biom.13742
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Evaluating effectiveness of public health intervention strategies for mitigating COVID-19 pandemic.

Xie, Shanghong / Wang, Wenbo / Wang, Qinxia / Wang, Yuanjia / Zeng, Donglin

Statistics in medicine

2022 Volume 41, Issue 19, Page(s) 3820–3836

Abstract: Coronavirus disease 2019 (COVID-19) pandemic is an unprecedented global public health challenge. In the United States (US), state governments have implemented various non-pharmaceutical interventions (NPIs), such as physical distance closure (lockdown), ... ...

Abstract	Coronavirus disease 2019 (COVID-19) pandemic is an unprecedented global public health challenge. In the United States (US), state governments have implemented various non-pharmaceutical interventions (NPIs), such as physical distance closure (lockdown), stay-at-home order, mandatory facial mask in public in response to the rapid spread of COVID-19. To evaluate the effectiveness of these NPIs, we propose a nested case-control design with propensity score weighting under the quasi-experiment framework to estimate the average intervention effect on disease transmission across states. We further develop a method to test for factors that moderate intervention effect to assist precision public health intervention. Our method takes account of the underlying dynamics of disease transmission and balance state-level pre-intervention characteristics. We prove that our estimator provides causal intervention effect under assumptions. We apply this method to analyze US COVID-19 incidence cases to estimate the effects of six interventions. We show that lockdown has the largest effect on reducing transmission and reopening bars significantly increase transmission. States with a higher percentage of non-White population are at greater risk of increased
MeSH term(s)	COVID-19/epidemiology ; COVID-19/prevention & control ; Communicable Disease Control ; Humans ; Pandemics/prevention & control ; Public Health ; SARS-CoV-2 ; United States/epidemiology
Language	English
Publishing date	2022-06-05
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, N.I.H., Extramural
ZDB-ID	843037-8
ISSN	1097-0258 ; 0277-6715
ISSN (online)	1097-0258
ISSN	0277-6715
DOI	10.1002/sim.9482
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Article: Dynamic COVID risk assessment accounting for community virus exposure from a spatial-temporal transmission model.

Chen, Yuan / Fei, Wenbo / Wang, Qinxia / Zeng, Donglin / Wang, Yuanjia

Advances in neural information processing systems

2022 Volume 34, Page(s) 27747–27760

Abstract: COVID-19 pandemic has caused unprecedented negative impacts on our society, including further exposing inequity and disparity in public health. To study the impact of socioeconomic factors on COVID transmission, we first propose a spatial-temporal model ... ...

Abstract	COVID-19 pandemic has caused unprecedented negative impacts on our society, including further exposing inequity and disparity in public health. To study the impact of socioeconomic factors on COVID transmission, we first propose a spatial-temporal model to examine the socioeconomic heterogeneity and spatial correlation of COVID-19 transmission at the community level. Second, to assess the individual risk of severe COVID-19 outcomes after a positive diagnosis, we propose a dynamic, varying-coefficient model that integrates individual-level risk factors from electronic health records (EHRs) with community-level risk factors. The underlying neighborhood prevalence of infections (both symptomatic and pre-symptomatic) predicted from the previous spatial-temporal model is included in the individual risk assessment so as to better capture the background risk of virus exposure for each individual. We design a weighting scheme to mitigate multiple selection biases inherited in EHRs of COVID patients. We analyze COVID transmission data in New York City (NYC, the epicenter of the first surge in the United States) and EHRs from NYC hospitals, where time-varying effects of community risk factors and significant interactions between individual- and community-level risk factors are detected. By examining the socioeconomic disparity of infection risks and interaction among the risk factors, our methods can assist public health decision-making and facilitate better clinical management of COVID patients.
Language	English
Publishing date	2022-08-20
Publishing country	United States
Document type	Journal Article
ZDB-ID	1012320-9
ISSN	1049-5258
ISSN	1049-5258
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: A latent state space model for estimating brain dynamics from electroencephalogram (EEG) data.

Wang, Qinxia / Loh, Ji Meng / He, Xiaofu / Wang, Yuanjia

Biometrics

2022 Volume 79, Issue 3, Page(s) 2444–2457

Abstract	Modern neuroimaging technologies have substantially advanced the measurement of brain activity. Electroencephalogram (EEG) as a noninvasive neuroimaging technique measures changes in electrical voltage on the scalp induced by brain cortical activity. With its high temporal resolution, EEG has emerged as an increasingly useful tool to study brain connectivity. Challenges with modeling EEG signals of complex brain activity include interactions among unknown sources, low signal-to-noise ratio, and substantial between-subject heterogeneity. In this work, we propose a state space model that jointly analyzes multichannel EEG signals and learns dynamics of different sources corresponding to brain cortical activity. Our model borrows strength from spatially correlated measurements and uses low-dimensional latent states to explain all observed channels. The model can account for patient heterogeneity and quantify the effect of a subject's covariates on the latent space. The EM algorithm, Kalman filtering, and bootstrap resampling are used to fit the state space model and provide comparisons between patient diagnostic groups. We apply the developed approach to a case-control study of alcoholism and reveal significant attenuation of brain activity in response to visual stimuli in alcoholic subjects compared to healthy controls.
MeSH term(s)	Humans ; Case-Control Studies ; Computer Simulation ; Electroencephalography ; Brain/diagnostic imaging ; Brain/physiology ; Brain Mapping/methods ; Algorithms
Language	English
Publishing date	2022-09-19
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	213543-7
ISSN	1541-0420 ; 0099-4987 ; 0006-341X
ISSN (online)	1541-0420
ISSN	0099-4987 ; 0006-341X
DOI	10.1111/biom.13742
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Article ; Online: Cavity-enhanced optical bistability of Rydberg atoms.

Wang, Qinxia / Wang, Zhihui / Liu, Yanxin / Guan, Shijun / He, Jun / Zou, Chang-Ling / Zhang, Pengfei / Li, Gang / Zhang, Tiancai

Optics letters

2023 Volume 48, Issue 11, Page(s) 2865–2868

Abstract: Optical bistability (OB) of Rydberg atoms provides a new, to the best of our knowledge, platform for studying nonequilibrium physics and a potential resource for precision metrology. To date, the observation of Rydberg OB has been limited in free space. ... ...

Abstract	Optical bistability (OB) of Rydberg atoms provides a new, to the best of our knowledge, platform for studying nonequilibrium physics and a potential resource for precision metrology. To date, the observation of Rydberg OB has been limited in free space. Here, we explore cavity-enhanced Rydberg OB with a thermal cesium vapor cell. The signal of Rydberg OB in a cavity is enhanced by more than one order of magnitude compared with that in free space. The slope of the phase transition signal at the critical point is enhanced more than 10 times that without the cavity, implying an enhancement of two orders of magnitude in the sensitivity for Rydberg-based sensing and metrology.
Language	English
Publishing date	2023-06-01
Publishing country	United States
Document type	Journal Article
ISSN	1539-4794
ISSN (online)	1539-4794
DOI	10.1364/OL.486914
Database	MEDical Literature Analysis and Retrieval System OnLINE

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