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Article ; Online: The molecular mechanism of macrophage-adipocyte crosstalk in maintaining energy homeostasis.

Zhang, Yudie / Zhang, Bin / Sun, Xiaobo

2024 Volume 15, Page(s) 1378202

Abstract: Interactions between macrophages and adipocytes in adipose tissue are critical for the regulation of energy metabolism and obesity. Macrophage polarization induced by cold or other stimulations can drive metabolic reprogramming of adipocytes, browning, ... ...

Abstract	Interactions between macrophages and adipocytes in adipose tissue are critical for the regulation of energy metabolism and obesity. Macrophage polarization induced by cold or other stimulations can drive metabolic reprogramming of adipocytes, browning, and thermogenesis. Accordingly, investigating the roles of macrophages and adipocytes in the maintenance of energy homeostasis is critical for the development of novel therapeutic approaches specifically targeting macrophages in metabolic disorders such as obesity. Current review outlines macrophage polarization not only regulates the release of central nervous system and inflammatory factors, but controls mitochondrial function, and other factor that induce metabolic reprogramming of adipocytes and maintain energy homeostasis. We also emphasized on how the adipocytes conversely motivate the polarization of macrophage. Exploring the interactions between adipocytes and macrophages may provide new therapeutic strategies for the management of obesity-related metabolic diseases.
Language	English
Publishing date	2024-04-08
Publishing country	Switzerland
Document type	Journal Article ; Review
ZDB-ID	2606827-8
ISSN	1664-3224 ; 1664-3224
ISSN (online)	1664-3224
ISSN	1664-3224
DOI	10.3389/fimmu.2024.1378202
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: The image recognition of urban greening tree species based on deep learning and CAMP-MKNet model

Sun, Xiaobo / Shi, Yongjun

Urban Forestry & Urban Greening. 2023 July, v. 85 p.127970-

2023

Abstract: The information of urban tree species resources is of vital significance to the planning and design of urban green spaces. Tree organs, such as the bark are used as the primary features of identifying tree species. However, traditional tree ... ...

Abstract	The information of urban tree species resources is of vital significance to the planning and design of urban green spaces. Tree organs, such as the bark are used as the primary features of identifying tree species. However, traditional tree identification methods need to consume a lot of manpower and time costs. In addition, the application of machine image recognition technology to tree species recognition still has problems such as heavy data preprocessing workload, small number of tree species images, uneven distribution of categories, and low recognition accuracy. In order to promote the intelligent management of urban forestry and solve the above problems, it is necessary to establish an automatic image recognition model for urban greening tree species. We captured bark images of 21 urban afforestation tree species in their natural environment and constructed a dataset that was divided into a train set, validation set, and test set in the ratio of 7:1:2. Combining Channel Attention Module (CAM) with algorithms such as Spatial Pyramid Pooling (SPP) and Mixed Depthwise Dilated Convolutional Kernels. The core algorithm is Mixed Convolution Kernel (MK), and a CAMP-MKNet Convolutional Neural Network (CNN) is constructed as a bark image classification model for urban greening tree species. The overall accuracy of the generic models ranged from 41.06% to 82.03%, whereas the overall accuracy of the experimental CAMP-MKNet model was 84.25%, with lower prediction cost. Our study shows that the CAMP-MKNet CNN model with better prediction performance and computational cost and can provide crucial insights and technical support for developing automated urban tree species image recognition systems.
Keywords	afforestation ; algorithms ; automation ; bark ; data collection ; image analysis ; neural networks ; prediction ; trees ; urban forestry ; Tree species recognition ; Convolutional neural network (CNN) ; Bark images ; Spatial Pyramid Pooling (SPP) ; Residual networks
Language	English
Dates of publication	2023-07
Publishing place	Elsevier GmbH
Document type	Article ; Online
ISSN	1618-8667
DOI	10.1016/j.ufug.2023.127970
Database	NAL-Catalogue (AGRICOLA)

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Article: Mitochondrial dysfunction in heart diseases: Potential therapeutic effects of

Cao, Xinxin / Yao, Fan / Zhang, Bin / Sun, Xiaobo

Frontiers in pharmacology

2023 Volume 14, Page(s) 1218803

Abstract: Heart diseases have a high incidence and mortality rate, and seriously affect people's quality of life. Mitochondria provide energy for the heart to function properly. The process of various heart diseases is closely related to mitochondrial dysfunction. ...

Abstract	Heart diseases have a high incidence and mortality rate, and seriously affect people's quality of life. Mitochondria provide energy for the heart to function properly. The process of various heart diseases is closely related to mitochondrial dysfunction.
Language	English
Publishing date	2023-07-20
Publishing country	Switzerland
Document type	Journal Article ; Review
ZDB-ID	2587355-6
ISSN	1663-9812
ISSN	1663-9812
DOI	10.3389/fphar.2023.1218803
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Article ; Online: Comprehensive evaluation of urban waterlogging prevention resilience based on the fuzzy VIKOR method: a case study of the Beijing-Tianjin-Hebei urban agglomeration.

Yang, Haiyan / Luo, Qingda / Sun, Xiaobo / Wang, Zhe

Environmental science and pollution research international

2023 Volume 30, Issue 52, Page(s) 112773–112787

Abstract: The Beijing-Tianjin-Hebei urban agglomeration was used to construct a comprehensive evaluation index system for urban waterlogging prevention and control resilience from five aspects: social resilience, economic resilience, ecological resilience, ... ...

Abstract	The Beijing-Tianjin-Hebei urban agglomeration was used to construct a comprehensive evaluation index system for urban waterlogging prevention and control resilience from five aspects: social resilience, economic resilience, ecological resilience, infrastructure resilience, and institutional resilience. The fuzzy VIKOR method was used to evaluate urban waterlogging prevention and control resilience. The results were analyzed at temporal and spatial scales to reveal regional differences and constraints in urban waterlogging prevention toughness and efficiently locate vulnerable urban areas. The resilience level in most of the Beijing-Tianjin-Hebei region increased during 2015-2019, while that of Beijing, Tianjin, Qinhuangdao, and Handan slightly decreased, indicating that the capacity of these cities to manage waterlogging disasters needs strengthening. The spatial difference in urban waterlogging prevention toughness was significant: Beijing, Tianjin, Handan, Tangshan, Langfang, and Shijiazhuang showed medium and high levels of urban waterlogging prevention toughness; other cities showed low levels. How the expansion speed of the urban scale matches the construction speed of urban waterlogging prevention and control directly affects resilience at all levels. These results support urban waterlogging control and regional integration construction for Beijing-Tianjin-Hebei.
MeSH term(s)	Beijing ; Cities ; China ; Environmental Monitoring
Language	English
Publishing date	2023-10-16
Publishing country	Germany
Document type	Journal Article
ZDB-ID	1178791-0
ISSN	1614-7499 ; 0944-1344
ISSN (online)	1614-7499
ISSN	0944-1344
DOI	10.1007/s11356-023-30326-w
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Leaves and Twigs Image Recognition Based on Deep Learning and Combined Classifier Algorithms

Sun, Xiaobo / Xu, Lin / Zhou, Yufeng / Shi, Yongjun

Forests. 2023 May 24, v. 14, no. 6

2023

Abstract: In recent years, the automatic recognition of tree species based on images taken by digital cameras has been widely applied. However, many problems still exist, such as insufficient tree species image acquisition, uneven distribution of image categories, ...

Abstract	In recent years, the automatic recognition of tree species based on images taken by digital cameras has been widely applied. However, many problems still exist, such as insufficient tree species image acquisition, uneven distribution of image categories, and low recognition accuracy. Tree leaves can be used to differentiate and classify tree species due to their cognitive signatures in color, vein texture, shape contour, and edge serration. Moreover, the way the leaves are arranged on the twigs has strong characteristics. In this study, we first built an image dataset of 21 tree species based on the features of the twigs and leaves. The tree species feature dataset was divided into the training set and test set, with a ratio of 8:2. Feature extraction was performed after training the convolutional neural network (CNN) using the k-fold cross-validation (K-Fold–CV) method, and tree species classification was performed with classifiers. To improve the accuracy of tree species identification, we combined three improved CNN models with three classifiers. Evaluation indicators show that the overall accuracy of the designed composite model was 1.76% to 9.57% higher than other CNN models. Furthermore, in the MixNet XL CNN model, combined with the K-nearest neighbors (KNN) classifier, the highest overall accuracy rate was obtained at 99.86%. In the experiment, the Grad-CAM heatmap was used to analyze the distribution of feature regions that play a key role in classification decisions. Observation of the Grad-CAM heatmap illustrated that the main observation area of SE-ResNet50 was the most accurately positioned, and was mainly concentrated in the interior of small twigs and leaflets. Our research showed that modifying the training method and classification module of the CNN model and combining it with traditional classifiers to form a composite model can effectively improve the accuracy of tree species recognition.
Keywords	automatic detection ; cognition ; color ; data collection ; neural networks ; species identification ; texture ; trees
Language	English
Dates of publication	2023-0524
Publishing place	Multidisciplinary Digital Publishing Institute
Document type	Article ; Online
ZDB-ID	2527081-3
ISSN	1999-4907
ISSN	1999-4907
DOI	10.3390/f14061083
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Effect of electric fields on tungsten distribution in Na

Guo, Yuliang / Sun, Xiaobo / Jiao, Handong / Zhang, Liwen / Qin, Wenxuan / Xi, Xiaoli / Nie, Zuoren

Physical chemistry chemical physics : PCCP

2024 Volume 26, Issue 8, Page(s) 6590–6599

Abstract: Tungsten coatings have unique properties such as high melting points and hardness and are widely used in the nuclear fusion and aviation fields. In experiments, compared to pure ... ...

Abstract	Tungsten coatings have unique properties such as high melting points and hardness and are widely used in the nuclear fusion and aviation fields. In experiments, compared to pure Na
Language	English
Publishing date	2024-02-22
Publishing country	England
Document type	Journal Article
ZDB-ID	1476244-4
ISSN	1463-9084 ; 1463-9076
ISSN (online)	1463-9084
ISSN	1463-9076
DOI	10.1039/d3cp06202c
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Identification and validation of platelet-related diagnostic markers and potential drug screening in ischemic stroke by integrating comprehensive bioinformatics analysis and machine learning.

Geng, Yifei / Liu, Yuchen / Wang, Min / Dong, Xi / Sun, Xiao / Luo, Yun / Sun, Xiaobo

Frontiers in immunology

2024 Volume 14, Page(s) 1320475

Abstract: Background: Ischemic stroke (IS), caused by blood and oxygen deprivation due to cerebral thrombosis, has links to activated and aggregated platelets. Discovering platelet-related biomarkers, developing diagnostic models, and screening antiplatelet drugs ...

Abstract	Background: Ischemic stroke (IS), caused by blood and oxygen deprivation due to cerebral thrombosis, has links to activated and aggregated platelets. Discovering platelet-related biomarkers, developing diagnostic models, and screening antiplatelet drugs are crucial for IS diagnosis and treatment. Methods and results: Combining and normalizing GSE16561 and GSE22255 datasets identified 1,753 upregulated and 1,187 downregulated genes. Fifty-one genes in the platelet-related module were isolated using weighted gene co-expression network analysis (WGCNA) and other analyses, including 50 upregulated and one downregulated gene. Subsequent enrichment and network analyses resulted in 25 platelet-associated genes and six diagnostic markers for a risk assessment model. This model's area under the ROC curve outperformed single genes, and in the peripheral blood of the high-risk group, immune infiltration indicated a higher proportion of CD4, resting CD4 memory, and activated CD4 memory T cells, along with a lower proportion of CD8 T cells in comparison to the low-risk group. Utilizing the gene expression matrix and the CMap database, we identified two potential drugs for IS. Finally, a rat MACO/R model was used to validate the diagnostic markers' expression and the drugs' predicted anticoagulant effects. Conclusion: We identified six IS platelet-related biomarkers (APP, THBS1, F13A1, SRC, PPBP, and VCL) for a robust diagnostic model. The drugs alpha-linolenic acid and ciprofibrate have potential antiplatelet effects in IS. This study advances early IS diagnosis and treatment.
MeSH term(s)	Animals ; Rats ; Drug Evaluation, Preclinical ; Ischemic Stroke/diagnosis ; Ischemic Stroke/genetics ; Machine Learning ; Computational Biology ; Biomarkers
Chemical Substances	Biomarkers
Language	English
Publishing date	2024-01-10
Publishing country	Switzerland
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2606827-8
ISSN	1664-3224 ; 1664-3224
ISSN (online)	1664-3224
ISSN	1664-3224
DOI	10.3389/fimmu.2023.1320475
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Diagnostic value of procalcitonin in patients with periprosthetic joint infection: a diagnostic meta-analysis.

Sun, Xiaobo / Li, Yijin / Lv, Yan / Liu, Yuting / Lai, Zhiwei / Zeng, Yirong / Zhang, Haitao

Frontiers in surgery

2024 Volume 11, Page(s) 1211325

Abstract: Background: The success rate of periprosthetic joint infection (PJI) treatment is still low. Early diagnosis is the key to successful treatment. Therefore, it is necessary to find a biomarker with high sensitivity and specificity. The diagnostic value ... ...

Abstract	Background: The success rate of periprosthetic joint infection (PJI) treatment is still low. Early diagnosis is the key to successful treatment. Therefore, it is necessary to find a biomarker with high sensitivity and specificity. The diagnostic value of serum procalcitonin (PCT) for PJI was systematically evaluated to provide the theoretical basis for clinical diagnosis and treatment in this study. Methods: We searched the Web of Science, Embase, Cochrane Library, and PubMed for studies that evaluated the diagnostic value of serum PCT for PJI (from the inception of each database until September 2020). Two authors independently screened the literature according to the inclusion and exclusion criteria. The quality of each selected literature was evaluated by using the Quality Assessment of Diagnostic Accuracy Studies tool (QUADAS-2) tool. RevMan 5.3 software was used for the quality evaluation. The sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR) were merged by using Meta-DiSc 1.4 software. The area under the curve (AUC) and Q index were calculated after the summary receiver operating characteristic (SROC) was generated. We also performed subgroup analysis. Results: A total of 621 patients were enrolled in the nine studies. The pooled sensitivity of serum PCT for PJI diagnosis was 0.441 [95% confidence interval (CI), 0.384-0.500], the pooled specificity was 0.852 (95% CI, 0.811-0.888), the pooled PLR was 2.271 (95% CI, 1.808-2.853), the pooled NLR was 0.713 (95% CI, 0.646-0.786), and the pooled DOR was 5.756 (95% CI, 3.673-9.026). The area under SROC (the pooled AUC) was 0.76 (0.72-0.79). Q index was 0.6948. Conclusion: This study showed that PCT detection of PJI had poor diagnostic accuracy. Hence, the serum PCT is not suitable as a serum marker for PJI diagnosis.
Language	English
Publishing date	2024-04-10
Publishing country	Switzerland
Document type	Journal Article ; Review
ZDB-ID	2773823-1
ISSN	2296-875X
ISSN	2296-875X
DOI	10.3389/fsurg.2024.1211325
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Article ; Online: Lightweight Deep Learning Models for High-Precision Rice Seedling Segmentation from UAV-Based Multispectral Images.

Zhang, Panli / Sun, Xiaobo / Zhang, Donghui / Yang, Yuechao / Wang, Zhenhua

Plant phenomics (Washington, D.C.)

2023 Volume 5, Page(s) 123

Abstract: Accurate segmentation and detection of rice seedlings is essential for precision agriculture and high-yield cultivation. However, current methods suffer from high computational complexity and poor robustness to different rice varieties and densities. ... ...

Abstract	Accurate segmentation and detection of rice seedlings is essential for precision agriculture and high-yield cultivation. However, current methods suffer from high computational complexity and poor robustness to different rice varieties and densities. This article proposes 2 lightweight neural network architectures, LW-Segnet and LW-Unet, for high-precision rice seedling segmentation. The networks adopt an encoder-decoder structure with hybrid lightweight convolutions and spatial pyramid dilated convolutions, achieving accurate segmentation while reducing model parameters. Multispectral imagery acquired by unmanned aerial vehicle (UAV) was used to train and test the models covering 3 rice varieties and different planting densities. Experimental results demonstrate that the proposed LW-Segnet and LW-Unet models achieve higher F1-scores and intersection over union values for seedling detection and row segmentation across varieties, indicating improved segmentation accuracy. Furthermore, the models exhibit stable performance when handling different varieties and densities, showing strong robustness. In terms of efficiency, the networks have lower graphics processing unit memory usage, complexity, and parameters but faster inference speeds, reflecting higher computational efficiency. In particular, the fast speed of LW-Unet indicates potential for real-time applications. The study presents lightweight yet effective neural network architectures for agricultural tasks. By handling multiple rice varieties and densities with high accuracy, efficiency, and robustness, the models show promise for use in edge devices and UAVs to assist precision farming and crop management. The findings provide valuable insights into designing lightweight deep learning models to tackle complex agricultural problems.
Language	English
Publishing date	2023-11-30
Publishing country	United States
Document type	Journal Article
ISSN	2643-6515
ISSN (online)	2643-6515
DOI	10.34133/plantphenomics.0123
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Article ; Online: New perspective on the immunomodulatory activity of ginsenosides: Focus on effective therapies for post-COVID-19.

Wang, Yixin / Han, Qin / Zhang, Shuxia / Xing, Xiaoyan / Sun, Xiaobo

Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie

2023 Volume 165, Page(s) 115154

Abstract: More than 700 million confirmed cases of Coronavirus Disease-2019 (COVID-19) have been reported globally, and 10-60% of patients are expected to exhibit "post-COVID-19 symptoms," which will continue to affect human life and health. In the absence of ... ...

Abstract	More than 700 million confirmed cases of Coronavirus Disease-2019 (COVID-19) have been reported globally, and 10-60% of patients are expected to exhibit "post-COVID-19 symptoms," which will continue to affect human life and health. In the absence of safer, more specific drugs, current multiple immunotherapies have failed to achieve satisfactory efficacy. Ginseng, a traditional Chinese medicine, is often used as an immunomodulator and has been used in COVID-19 treatment as a tonic to increase blood oxygen saturation. Ginsenosides are the main active components of ginseng. In this review, we summarize the multiple ways in which ginsenosides affect post-COVID-19 symptoms, including inhibition of lipopolysaccharide, tumor necrosis factor signaling, modulation of chemokine receptors and inflammasome activation, induction of macrophage polarization, effects on Toll-like receptors, nuclear factor kappa-B, the mitogen-activated protein kinase pathway, lymphocytes, intestinal flora, and epigenetic regulation. Ginsenosides affect virus-mediated tissue damage, local or systemic inflammation, immune modulation, and other links, thus alleviating respiratory and pulmonary symptoms, reducing the cardiac burden, protecting the nervous system, and providing new ideas for the rehabilitation of patients with post-COVID-19 symptoms. Furthermore, we analyzed its role in strengthening body resistance to eliminate pathogenic factors from the perspective of ginseng-epidemic disease and highlighted the challenges in clinical applications. However, the benefit of ginsenosides in modulating organismal imbalance post-COVID-19 needs to be further evaluated to better validate the pharmacological mechanisms associated with their traditional efficacy and to determine their role in individualized therapy.
MeSH term(s)	Humans ; Ginsenosides/pharmacology ; Ginsenosides/therapeutic use ; COVID-19 Drug Treatment ; Epigenesis, Genetic ; COVID-19 ; Immunologic Factors/pharmacology ; Immunologic Factors/therapeutic use ; Panax
Chemical Substances	Ginsenosides ; Immunologic Factors
Language	English
Publishing date	2023-07-14
Publishing country	France
Document type	Journal Article ; Review
ZDB-ID	392415-4
ISSN	1950-6007 ; 0753-3322 ; 0300-0893
ISSN (online)	1950-6007
ISSN	0753-3322 ; 0300-0893
DOI	10.1016/j.biopha.2023.115154
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