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Article ; Online: Relationship between cortisol and diabetic microvascular complications

Shengnan Sun / Yangang Wang

European Journal of Medical Research, Vol 28, Iss 1, Pp 1-

a retrospective study

2023 Volume 9

Abstract: Abstract Objective We aimed to investigate whether serum cortisol associate with diabetic microvascular compliments in patients with type 2 diabetes mellitus (T2DM). Materials and methods The subjects were recruited from hospitalized patients with T2DM ... ...

Abstract	Abstract Objective We aimed to investigate whether serum cortisol associate with diabetic microvascular compliments in patients with type 2 diabetes mellitus (T2DM). Materials and methods The subjects were recruited from hospitalized patients with T2DM from 2019 to 2021. The odds ratios (OR) and corresponding 95% confidence intervals (CI) in relation to cortisol quartiles were obtained by multiple logistic regression analysis. Results (1) Cortisol level was positively correlated with the severity of microalbuminuria. The OR (95% CI) of microalbuminuria and macroalbuminuria in the last quartile were 3.396 (2.030, 5.682) and 8.407 (3.726, 18.971) compared with the first quartile (p < 0.001). (2) Cortisol level was positively correlated with the severity of diabetic retinopathy (DR). The OR (95% CI) of non-proliferative diabetic retinopathy group (NPDR) and proliferative diabetic retinopathy group (PDR) in the last quartile were 2.007 (1.401, 2.875) and 7.122 (2.525, 20.090) compared with the first quartile. (3) Elevated cortisol level was associated with diabetic peripheral neuropathy. The OR (95% CI) of diabetic peripheral neuropathy (DPN) in the last quartile was 1.956 (1.371, 2.792) and that in the third quartile was 1.854 (1.319, 2.608). Conclusions High serum cortisol levels were significantly associated with diabetic microvascular compliments in inpatients. Its causality remains to be further studied. Clinical trial registration number: ChiCTR2100051749.
Keywords	Type 2 diabetes ; Diabetic microvascular complication ; Cortisol ; Logistic regression analysis ; Medicine ; R
Subject code	616
Language	English
Publishing date	2023-09-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: High-Performance Computing and Artificial Intelligence for Geosciences

Yuzhu Wang / Jinrong Jiang / Yangang Wang

Applied Sciences, Vol 13, Iss 7952, p

2023 Volume 7952

Abstract: Geoscience, as an interdisciplinary field, is dedicated to revealing the operational mechanisms and evolutionary patterns of the Earth system [.] ...

Abstract	Geoscience, as an interdisciplinary field, is dedicated to revealing the operational mechanisms and evolutionary patterns of the Earth system [.]
Keywords	n/a ; Technology ; T ; Engineering (General). Civil engineering (General) ; TA1-2040 ; Biology (General) ; QH301-705.5 ; Physics ; QC1-999 ; Chemistry ; QD1-999
Language	English
Publishing date	2023-07-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: HMDO

Wei Xie / Zhipeng Yu / Zimeng Zhao / Binghui Zuo / Yangang Wang

Graphical Models, Vol 127, Iss , Pp 101178- (2023)

Markerless multi-view hand manipulation capture with deformable objects

2023

Abstract: We construct the first markerless deformable interaction dataset recording interactive motions of the hands and deformable objects, called HMDO (Hand Manipulation with Deformable Objects). With our built multi-view capture system, it captures the ... ...

Abstract	We construct the first markerless deformable interaction dataset recording interactive motions of the hands and deformable objects, called HMDO (Hand Manipulation with Deformable Objects). With our built multi-view capture system, it captures the deformable interactions with multiple perspectives, various object shapes, and diverse interactive forms. Our motivation is the current lack of hand and deformable object interaction datasets, as 3D hand and deformable object reconstruction is challenging. Mainly due to mutual occlusion, the interaction area is difficult to observe, the visual features between the hand and the object are entangled, and the reconstruction of the interaction area deformation is difficult. To tackle this challenge, we propose a method to annotate our captured data. Our key idea is to collaborate with estimated hand features to guide the object global pose estimation, and then optimize the deformation process of the object by analyzing the relationship between the hand and the object. Through comprehensive evaluation, the proposed method can reconstruct interactive motions of hands and deformable objects with high quality. HMDO currently consists of 21600 frames over 12 sequences. In the future, this dataset could boost the research of learning-based reconstruction of deformable interaction scenes.
Keywords	Deformable interaction ; Markerless capture ; Multi-view dataset ; Collaborative reconstruction ; Science ; Q ; Technology (General) ; T1-995
Subject code	004
Language	English
Publishing date	2023-05-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Dual-Encoder Transformer for Short-Term Photovoltaic Power Prediction Using Satellite Remote-Sensing Data

Haizhou Cao / Jing Yang / Xuemeng Zhao / Tiechui Yao / Jue Wang / Hui He / Yangang Wang

Applied Sciences, Vol 13, Iss 1908, p

2023 Volume 1908

Abstract: The penetration of photovoltaic (PV) energy has gained a significant increase in recent years because of its sustainable and clean characteristics. However, the uncertainty of PV power affected by variable weather poses challenges to an accurate short- ... ...

Abstract	The penetration of photovoltaic (PV) energy has gained a significant increase in recent years because of its sustainable and clean characteristics. However, the uncertainty of PV power affected by variable weather poses challenges to an accurate short-term prediction, which is crucial for reliable power system operation. Existing methods focus on coupling satellite images with ground measurements to extract features using deep neural networks. However, a flexible predictive framework capable of handling these two data structures is still not well developed. The spatial and temporal features are merely concatenated and passed to the following layer of a neural network, which is incapable of utilizing the correlation between them. Therefore, we propose a novel dual-encoder transformer (DualET) for short-term PV power prediction. The dual encoders contain wavelet transform and series decomposition blocks to extract informative features from image and sequence data, respectively. Moreover, we propose a cross-domain attention module to learn the correlation between the temporal features and cloud information and modify the attention modules with the spare form and Fourier transform to improve their performance. The experiments on real-world datasets, including PV station data and satellite images, show that our model achieves better results than other models for short-term PV power prediction.
Keywords	transformer ; photovoltaic power forecasting ; satellite images ; deep learning ; Technology ; T ; Engineering (General). Civil engineering (General) ; TA1-2040 ; Biology (General) ; QH301-705.5 ; Physics ; QC1-999 ; Chemistry ; QD1-999
Subject code	006
Language	English
Publishing date	2023-02-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Study on the Characteristics of Traditional Chinese Medicine Syndromes in Patients with Erosive Gastritis Based on Metabolomics.

Shixiong, Zhang / Shaowei, Liu / Zeqi, Yang / Miaochan, Xu / Pingping, Zhou / Haiyan, Bai / Jingjing, Lv / Yangang, Wang

International journal of analytical chemistry

2024 Volume 2024, Page(s) 6684677

Abstract: According to traditional Chinese medicine theory, tongue coatings reflect changes in the body. The goal of this study was to identify a metabolite or a set of metabolites capable of classifying characteristics of traditional Chinese medicine syndromes in ...

Abstract	According to traditional Chinese medicine theory, tongue coatings reflect changes in the body. The goal of this study was to identify a metabolite or a set of metabolites capable of classifying characteristics of traditional Chinese medicine syndromes in erosive gastritis. In this study, we collected tongue coatings of patients with erosive gastritis with damp-heat syndrome (DHS), liver depression and qi stagnation syndrome (LDQSS), and healthy volunteers. Then, we analyzed the differences in metabolic characteristics between the two groups based on metabolomics. We identified 14 potential biomarkers related to the DHS group, and six metabolic pathways were enriched. The differential pathways included pyrimidine metabolism, pantothenate and CoA biosynthesis, citrate cycle (TCA cycle), pyruvate metabolism, glycolysis/gluconeogenesis, and purine metabolism. Similarly, in the LDQSS group, we identified 25 potential biomarkers and 18 metabolic pathways were enriched. The top five pathways were the TCA cycle, sphingolipid metabolism, fatty acid biosynthesis, pantothenate and CoA biosynthesis, and the pentose phosphate pathway. In conclusion, the DHS group and the LDQSS group have different characteristics.
Language	English
Publishing date	2024-01-02
Publishing country	Egypt
Document type	Journal Article
ZDB-ID	2494714-3
ISSN	1687-8779 ; 1687-8760
ISSN (online)	1687-8779
ISSN	1687-8760
DOI	10.1155/2024/6684677
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Human cytomegalovirus infection and its association with gestational diabetes mellitus during pregnancy

Yunyang Wang / Xianjuan Zhang / Xu Zheng / Guanghui Song / Lina Fang / Yangang Wang / Bin Wang

PeerJ, Vol 10, p e

2022 Volume 12934

Abstract: Background Infection is an important risk factor for gestational diabetes mellitus (GDM), while infection of human cytomegalovirus (HCMV) with GDM remains unclear and rarely reported. This study aimed to investigate the association of HCMV infection and ... ...

Abstract	Background Infection is an important risk factor for gestational diabetes mellitus (GDM), while infection of human cytomegalovirus (HCMV) with GDM remains unclear and rarely reported. This study aimed to investigate the association of HCMV infection and serum inflammatory factor levels in pregnancy with GDM. Methods This prospective study included pregnant women who attended at Affiliated Hospital of Qingdao Hospital and Zibo Maternal and Child Health Hospital between December 2018 and August 2020. HCMV specific IgM and serum levels of inflammatory factors, including TNF-α, IL-6, and IL-1β, were analyzed. Results A total of 5,316 pregnant women were included (415 with GDM (107 with HCMV+GDM+ and 308 with HCMV-GDM+) and 4901 GDM-free (759 with HCMV+GDM- and 4142 with HCMV-GDM-)). The prevalence of GDM was 7.81%. The rate of activation of HCMV was 16.29%. Specifically, 107 and 759 women in the GDM and control group exhibited HCMV infection, with positive rates of25.78% and 15.48%, respectively (P < 0.01). TNF-α, IL-6, and IL-1β at 24–28 weeks of gestation were significantly higher in women with GDM and HCMV infection than inthe other groups (all P < 0.01). Multivariable analysis showed that HCMV positive (OR = 1.851; 95% CI [1.425–2.403]; P < 0.001), IL-6 (OR = 1.010; 95% CI [1.002–1.018]; P = 0.013), and IL-1β (OR = 1.410; 95% CI [1.348–1.474]; P < 0.001) were all significantly correlated with GDM. Conclusion This study suggests HCMV infection during pregnancy is an independent risk factor of GDM and could significantly increase its incidence. Further studies are needed to elucidate possible mechanisms underlying associations between HCMV infection and GDM.
Keywords	Gestational diabetes mellitus ; Human cytomegalovirus infection during pregnancy ; Inflammatory factor ; HCMV antibody IgM ; Medicine ; R ; Biology (General) ; QH301-705.5
Subject code	610
Language	English
Publishing date	2022-02-01T00:00:00Z
Publisher	PeerJ Inc.
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: A causal convolutional neural network for multi-subject motion modeling and generation

Shuaiying Hou / Congyi Wang / Wenlin Zhuang / Yu Chen / Yangang Wang / Hujun Bao / Jinxiang Chai / Weiwei Xu

Computational Visual Media, Vol 10, Iss 1, Pp 45-

2023 Volume 59

Abstract: Abstract Inspired by the success of WaveNet in multi-subject speech synthesis, we propose a novel neural network based on causal convolutions for multi-subject motion modeling and generation. The network can capture the intrinsic characteristics of the ... ...

Abstract	Abstract Inspired by the success of WaveNet in multi-subject speech synthesis, we propose a novel neural network based on causal convolutions for multi-subject motion modeling and generation. The network can capture the intrinsic characteristics of the motion of different subjects, such as the influence of skeleton scale variation on motion style. Moreover, after fine-tuning the network using a small motion dataset for a novel skeleton that is not included in the training dataset, it is able to synthesize high-quality motions with a personalized style for the novel skeleton. The experimental results demonstrate that our network can model the intrinsic characteristics of motions well and can be applied to various motion modeling and synthesis tasks.
Keywords	deep learning ; optimization ; motion generation ; motion denoising ; motion control ; Electronic computers. Computer science ; QA75.5-76.95
Language	English
Publishing date	2023-11-01T00:00:00Z
Publisher	SpringerOpen
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Generation of a gene-corrected isogenic iPSC line (AHQUi001-A-1) from a patient with familial hypertriglyceridemia (FHTG) carrying a heterozygous p.C310R (c.928 T > C) mutation in LPL gene using CRISPR/Cas9

Xiaofang Sun / Xiang Zhou / Bingzi Dong / Chen Wang / Xinhua Xiao / Yangang Wang

Stem Cell Research, Vol 52, Iss , Pp 102230- (2021)

2021

Abstract: Mutations in the LPL gene lead to familial hypertriglyceridemia (FHTG) . We have previously generated an iPSC line (AHQUi001-A) from a FHTG patient with a heterozygous p.C310R (c.928 T > C) mutation in the LPL gene. Here we genetically corrected the ... ...

Abstract	Mutations in the LPL gene lead to familial hypertriglyceridemia (FHTG) . We have previously generated an iPSC line (AHQUi001-A) from a FHTG patient with a heterozygous p.C310R (c.928 T > C) mutation in the LPL gene. Here we genetically corrected the C310R mutation in the LPL gene using CRISPR/Cas9 technology to generate AHQUi001-A-1, which demonstrates normal karyotype, morphology, pluripotency, and potential to differentiate towards three germ layers.
Keywords	Biology (General) ; QH301-705.5
Language	English
Publishing date	2021-04-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Generation of the induced pluripotent stem cell(iPSC) line (AHQUi001-A) from a patient with familial hypertriglyceridemia (FHTG) carrying a heterozygous p.C310R (c.928 T > C) mutation in LPL gene

Xiaofang Sun / Xiang Zhou / Xinhua Xiao / Jingwei Chi / Bingzi Dong / Yangang Wang

Stem Cell Research, Vol 45, Iss , Pp - (2020)

2020

Abstract: Familial hypertriglyceridemia (FHTG) is an autosomal dominant disorder of lipoprotein metabolism, partly caused by mutations in the LPL gene, which encodes for the lipoprotein lipase. LPL deficiency can impair triglyceride hydrolysis which causes ... ...

Abstract	Familial hypertriglyceridemia (FHTG) is an autosomal dominant disorder of lipoprotein metabolism, partly caused by mutations in the LPL gene, which encodes for the lipoprotein lipase. LPL deficiency can impair triglyceride hydrolysis which causes elevated plasma triglyceride levels. An induced pluripotent stem cell (iPSC) line was generated from peripheral blood mononuclear cells (PBMCs) of a 53 years-old male patient with FHTG who had a heterozygous p.C310R (c.928 T > C) mutation in the LPL gene based on the sendai virus delivery system. The cellular model will offer a powerful tool to investigate pathogenic mechanisms in FHTG and to develop a treatment for FHTG.
Keywords	Biology (General) ; QH301-705.5
Language	English
Publishing date	2020-05-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Comorbidities and the risk of severe or fatal outcomes associated with coronavirus disease 2019

Yue Zhou / Qing Yang / Jingwei Chi / Bingzi Dong / Wenshan Lv / Liyan Shen / Yangang Wang

International Journal of Infectious Diseases, Vol 99, Iss , Pp 47-

A systematic review and meta-analysis

2020 Volume 56

Abstract: Objectives: Existing findings regarding the relationship between comorbidities and the severity of coronavirus disease 2019 (COVID-19) are inconsistent and insufficient. The aim of this study was to evaluate the association between different ... ...

Abstract	Objectives: Existing findings regarding the relationship between comorbidities and the severity of coronavirus disease 2019 (COVID-19) are inconsistent and insufficient. The aim of this study was to evaluate the association between different comorbidities and the severity of COVID-19. Methods: The PubMed, Embase, and Cochrane Library databases were searched to identify studies reporting the rates of comorbidities in COVID-19 patients with severe/fatal outcomes. Subgroup analyses were conducted according to disease severity and the country of residence. Odds ratios (OR) with 95% confidence intervals (CI) were pooled using random-effects models. Results: A total of 34 eligible studies were identified. In patients with severe/fatal COVID-19, the most prevalent chronic comorbidities were obesity (42%, 95% CI 34–49%) and hypertension (40%, 95% CI 35–45%), followed by diabetes (17%, 95% CI 15–20%), cardiovascular disease (13%, 95% CI 11–15%), respiratory disease (8%, 95% CI 6–10%), cerebrovascular disease (6%, 95% CI 4–8%), malignancy (4%, 95% CI 3–6%), kidney disease (3%, 95% CI 2–4%), and liver disease (2%, 95% CI 1–3%). In order of the prediction, the pooled ORs of the comorbidities in patients with severe or fatal COVID-19 when compared to patients with non-severe/fatal COVID-19 were as follows: chronic respiratory disease, OR 3.56 (95% CI 2.87–4.41); hypertension, OR 3.17 (95% CI 2.46–4.08); cardiovascular disease, OR 3.13 (95% CI 2.65–3.70); kidney disease, OR 3.02 (95% CI 2.23–4.08); cerebrovascular disease, OR 2.74 (95% CI 1.59–4.74); malignancy, OR 2.73 (95% CI 1.73–4.21); diabetes, OR 2.63 (95% CI 2.08–3.33); and obesity, OR 1.72 (95% CI 1.04–2.85). No correlation was observed between liver disease and COVID-19 aggravation (OR 1.54, 95% CI 0.95–2.49). Conclusions: Chronic comorbidities, including obesity, hypertension, diabetes, cardiovascular disease, cerebrovascular disease, respiratory disease, kidney disease, and malignancy are clinical risk factors for a severe or fatal outcome associated with COVID-19, with obesity being the most prevalent and respiratory disease being the most strongly predictive. Knowledge of these risk factors could help clinicians better identify and manage the high-risk populations.
Keywords	Comorbidity ; Severe ; ICU admission ; Fatality ; COVID-19 ; Infectious and parasitic diseases ; RC109-216 ; covid19
Subject code	610
Language	English
Publishing date	2020-10-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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