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Chen J, Sun Q, Wang Y, Yin W. Revealing the key role of cuproptosis in osteoporosis via the bioinformatic analysis and experimental validation of cuproptosis-related genes. Mamm Genome 2024:10.1007/s00335-024-10049-0. [PMID: 38904833 DOI: 10.1007/s00335-024-10049-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2024] [Accepted: 06/14/2024] [Indexed: 06/22/2024]
Abstract
The incidence of osteoporosis has rapidly increased owing to the ageing population. Cuproptosis, a novel mechanism that regulates cell death, may be a new therapeutic approach. However, the relevance of cuproptosis in the immune microenvironment and osteoporosis immunotherapy is still unknown. We intersected the differentially expressed genes from osteoporotic samples with 75 cuproptosis-related genes to identify 16 significantly expressed cuproptosis genes. We further explored the connection between the cuproptosis pattern, immune microenvironment, and immunotherapy. The weighted gene co-expression network analysis algorithm was used to identify cuproptosis phenotype-associated genes, and we used quantitative real-time PCR and immunohistochemistry in mouse femur tissues to verify hub gene (MAP2K2, FDX1, COX19, VEGFA, CDKN2A, and NFE2L2) expression. Six hub genes and 59 cuproptosis phenotype-associated genes involved in immunisation were identified among the osteoporosis and control groups, and the majority of these 59 genes were enriched in the inflammatory response, as well as in signal transducers, Janus kinase, and transcription pathway activators. In addition, two different clusters of cuproptosis were found, and immune infiltration analysis showed that gene Cluster 1 had a greater immune score and immune infiltration level. Further analysis revealed that three key genes (COX19, MAP2K2, and FDX1) were highly correlated with immune cell infiltration, and external experiments validated the association of these three genes with the prognosis of osteoporosis. We used the three key mRNAs COX19, MAP2K2, and FDX1 as a classification model that may systematically elucidate the complex connection between cuproptosis and the immune microenvironment of osteoporosis. New insights into osteoporosis pathogenesis and immunotherapy prospects may be gained from this study.
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Affiliation(s)
- Jianxing Chen
- Department of Joint Surgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, 150000, China
| | - Qifeng Sun
- Department of Joint Surgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, 150000, China
| | - Yi Wang
- Department of Joint Surgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, 150000, China
| | - Wenzhe Yin
- Department of Joint Surgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, 150000, China.
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Liu FS, Huang HL, Deng LX, Zhang QS, Wang XB, Li J, Liu FB. Identification and bioinformatics analysis of genes associated with pyroptosis in spinal cord injury of rat and mouse. Sci Rep 2024; 14:14023. [PMID: 38890348 PMCID: PMC11189416 DOI: 10.1038/s41598-024-64843-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Accepted: 06/13/2024] [Indexed: 06/20/2024] Open
Abstract
The mechanism of spinal cord injury (SCI) is highly complex, and an increasing number of studies have indicated the involvement of pyroptosis in the physiological and pathological processes of secondary SCI. However, there is limited bioinformatics research on pyroptosis-related genes (PRGs) in SCI. This study aims to identify and validate differentially expressed PRGs in the GEO database, perform bioinformatics analysis, and construct regulatory networks to explore potential regulatory mechanisms and therapeutic targets for SCI. We obtained high-throughput sequencing datasets of SCI in rats and mice from the GEO database. Differential analysis was conducted using the "limma" package in R to identify differentially expressed genes (DEGs). These genes were then intersected with previously reported PRGs, resulting in a set of PRGs in SCI. GO and KEGG enrichment analyses, as well as correlation analysis, were performed on the PRGs in both rat and mouse models of SCI. Additionally, a protein-protein interaction (PPI) network was constructed using the STRING website to examine the relationships between proteins. Hub genes were identified using Cytoscape software, and the intersection of the top 5 hub genes in rats and mice were selected for subsequent experimentally validated. Furthermore, a competing endogenous RNA (ceRNA) network was constructed to explore potential regulatory mechanisms. The gene expression profiles of GSE93249, GSE133093, GSE138637, GSE174549, GSE45376, GSE171441_3d and GSE171441_35d were selected in this study. We identified 10 and 12 PRGs in rats and mice datasets respectively. Six common DEGs were identified in the intersection of rats and mice PRGs. Enrichment analysis of these DEGs indicated that GO analysis was mainly focused on inflammation-related factors, while KEGG analysis showed that the most genes were enriched on the NOD-like receptor signaling pathway. We constructed a ceRNA regulatory network that consisted of five important PRGs, as well as 24 miRNAs and 34 lncRNAs. This network revealed potential regulatory mechanisms. Additionally, the three hub genes obtained from the intersection were validated in the rat model, showing high expression of PRGs in SCI. Pyroptosis is involved in secondary SCI and may play a significant role in its pathogenesis. The regulatory mechanisms associated with pyroptosis deserve further in-depth research.
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Affiliation(s)
- Fu-Sheng Liu
- Department of Spine Surgery, The Second Xiangya Hospital, Central South University, 139 Renmin Middle Road, Changsha, 410011, Hunan, China
| | - Hai-Long Huang
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, 410011, China
| | - Lin-Xia Deng
- Department of Pediatrics, The Third Xiangya Hospital, Central South University, Changsha, 410011, China
| | - Qian-Shi Zhang
- Department of Spine Surgery, The Second Xiangya Hospital, Central South University, 139 Renmin Middle Road, Changsha, 410011, Hunan, China
| | - Xiao-Bin Wang
- Department of Spine Surgery, The Second Xiangya Hospital, Central South University, 139 Renmin Middle Road, Changsha, 410011, Hunan, China
| | - Jing Li
- Department of Spine Surgery, The Second Xiangya Hospital, Central South University, 139 Renmin Middle Road, Changsha, 410011, Hunan, China
| | - Fu-Bing Liu
- Department of Spine Surgery, The Second Xiangya Hospital, Central South University, 139 Renmin Middle Road, Changsha, 410011, Hunan, China.
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Chen J, Ou L, Liu W, Gao F. Exploring the molecular mechanisms of ferroptosis-related genes in periodontitis: a multi-dataset analysis. BMC Oral Health 2024; 24:611. [PMID: 38802844 PMCID: PMC11129485 DOI: 10.1186/s12903-024-04342-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Accepted: 05/07/2024] [Indexed: 05/29/2024] Open
Abstract
PURPOSE This study aims to elucidate the biological functions of ferroptosis-related genes in periodontitis, along with their correlation to tumor microenvironment (TME) features such as immune infiltration. It aims to provide potential diagnostic markers of ferroptosis for clinical management of periodontitis. METHODS Utilizing the periodontitis-related microarray dataset GSE16134 from the Gene Expression Omnibus (GEO) and a set of 528 ferroptosis-related genes identified in prior studies, this research unveils differentially expressed ferroptosis-related genes in periodontitis. Subsequently, a protein-protein interaction network was constructed. Subtyping of periodontitis was explored, followed by validation through immune cell infiltration and gene set enrichment analyses. Two algorithms, randomForest and SVM(Support Vector Machine), were employed to reveal potential ferroptosis diagnostic markers for periodontitis. The diagnostic efficacy, immune correlation, and potential transcriptional regulatory networks of these markers were further assessed. Finally, potential targeted drugs for differentially expressed ferroptosis markers in periodontitis were predicted. RESULTS A total of 36 ferroptosis-related genes (30 upregulated, 6 downregulated) were identified from 829 differentially expressed genes between 9 periodontitis samples and the control group. Subsequent machine learning algorithm screening highlighted 4 key genes: SLC1A5(Solute Carrier Family 1 Member 5), SLC2A14(Solute Carrier Family 1 Member 14), LURAP1L(Leucine Rich Adaptor Protein 1 Like), and HERPUD1(Homocysteine Inducible ER Protein With Ubiquitin Like Domain 1). Exploration of these 4 key genes, supported by time-correlated ROC analysis, demonstrated reliability, while immune infiltration results indicated a strong correlation between key genes and immune factors. Furthermore, Gene Set Enrichment Analysis (GSEA) was conducted for the four key genes, revealing enrichment in GO/KEGG pathways that have a significant impact on periodontitis. Finally, the study predicted potential transcriptional regulatory networks and targeted drugs associated with these key genes in periodontitis. CONCLUSIONS The ferroptosis-related genes identified in this study, including SLC1A5, SLC2A14, LURAP1L, and HERPUD1, may serve as novel diagnostic and therapeutic targets for periodontitis. They are likely involved in the occurrence and development of periodontitis through mechanisms such as immune infiltration, cellular metabolism, and inflammatory chemotaxis, potentially linking the ferroptosis pathway to the progression of periodontitis. Targeted drugs such as flurofamide, L-733060, memantine, tetrabenazine, and WAY-213613 hold promise for potential therapeutic interventions in periodontitis associated with these ferroptosis-related genes.
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Affiliation(s)
- Jili Chen
- Department of Periodontics, Panyu Branch, Stomatological Hospital, School of Stomatology, Southern Medical University, No.366 Jiangnan Dadao Nan, Haizhu District, Guangzhou, Guangdong, 510220, China
| | - Lijia Ou
- Department of Histology and Embryology, Xiangya School of Medicine, Central South University, No. 172 Tongzipo Road, Yuelu District, Changsha, 410006, China
| | - Weizhen Liu
- Department of Periodontics, Panyu Branch, Stomatological Hospital, School of Stomatology, Southern Medical University, No.366 Jiangnan Dadao Nan, Haizhu District, Guangzhou, Guangdong, 510220, China
| | - Feng Gao
- Department of Periodontics, Panyu Branch, Stomatological Hospital, School of Stomatology, Southern Medical University, No.366 Jiangnan Dadao Nan, Haizhu District, Guangzhou, Guangdong, 510220, China.
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Feng Z, Wu Z, Zhang Y. Integration of bioinformatics and machine learning approaches for the validation of pyrimidine metabolism-related genes and their implications in immunotherapy for osteoporosis. BMC Musculoskelet Disord 2024; 25:402. [PMID: 38778304 PMCID: PMC11110368 DOI: 10.1186/s12891-024-07512-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Accepted: 05/09/2024] [Indexed: 05/25/2024] Open
Abstract
BACKGROUND Osteoporosis (OP), the "silent epidemic" of our century, poses a significant challenge to public health, predominantly affecting postmenopausal women and the elderly. It evolves from mild symptoms to pronounced severity, stabilizing eventually. Unique among OP's characteristics is the altered metabolic profile of affected cells, particularly in pyrimidine metabolism (PyM), a crucial pathway for nucleotide turnover and pyrimidine decomposition. While metabolic adaptation is acknowledged as a therapeutic target in various diseases, the specific role of PyM genes (PyMGs) in OP's molecular response remains to be clarified. METHODS In pursuit of elucidating and authenticating PyMGs relevant to OP, we embarked on a comprehensive bioinformatics exploration. This entailed the integration of Weighted Gene Co-expression Network Analysis (WGCNA) with a curated list of 37 candidate PyMGs, followed by the examination of their biological functions and pathways via Gene Set Variation Analysis (GSVA). The Least Absolute Shrinkage and Selection Operator (LASSO) technique was harnessed to identify crucial hub genes. We evaluated the diagnostic prowess of five PyMGs in OP detection and explored their correlation with OP's clinical traits, further validating their expression profiles through independent datasets (GSE2208, GSE7158, GSE56815, and GSE35956). RESULTS Our analytical rigor unveiled five PyMGs-IGKC, TMEM187, RPS11, IGLL3P, and GOLGA8N-with significant ties to OP. A deeper dive into their biological functions highlighted their roles in estrogen response modulation, cytosolic calcium ion concentration regulation, and GABAergic synaptic transmission. Remarkably, these PyMGs emerged as potent diagnostic biomarkers for OP, distinguishing affected individuals with substantial accuracy. CONCLUSIONS This investigation brings to light five PyMGs intricately associated with OP, heralding new avenues for biomarker discovery and providing insights into its pathophysiological underpinnings. These findings not only deepen our comprehension of OP's complexity but also herald the advent of more refined diagnostic and therapeutic modalities.
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Affiliation(s)
- Zichen Feng
- Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Zixuan Wu
- Guangzhou University of Chinese Medicine, Guangzhou, Guangdong Province, 510006, China
| | - Yongchen Zhang
- Shandong University of Traditional Chinese Medicine, Jinan, China.
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Zhao Y, Ma Y, Pei J, Zhao X, Jiang Y, Liu Q. Exploring Pyroptosis-related Signature Genes and Potential Drugs in Ulcerative Colitis by Transcriptome Data and Animal Experimental Validation. Inflammation 2024:10.1007/s10753-024-02025-2. [PMID: 38656456 DOI: 10.1007/s10753-024-02025-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2024] [Revised: 04/10/2024] [Accepted: 04/11/2024] [Indexed: 04/26/2024]
Abstract
Ulcerative colitis (UC) is an idiopathic, relapsing inflammatory disorder of the colonic mucosa. Pyroptosis contributes significantly to UC. However, the molecular mechanisms of UC remain unexplained. Herein, using transcriptome data and animal experimental validation, we sought to explore pyroptosis-related molecular mechanisms, signature genes, and potential drugs in UC. Gene profiles (GSE48959, GSE59071, GSE53306, and GSE94648) were selected from the Gene Expression Omnibus (GEO) database, which contained samples derived from patients with active and inactive UC, as well as health controls. Gene Set Enrichment Analysis (GSEA), Weighted Gene Co-expression Network Analysis (WGCNA) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed on microarrays to unravel the association between UC and pyroptosis. Then, differential expressed genes (DEGs) and pyroptosis-related DEGs were obtained by differential expression analyses and the public database. Subsequently, pyroptosis-related DEGs and their association with the immune infiltration landscape were analyzed using the CIBERSORT method. Besides, potential signature genes were selected by machine learning (ML) algorithms, and then validated by testing datasets which included samples of colonic mucosal tissue and peripheral blood. More importantly, the potential drug was screened based on this. And these signature genes and the drug effect were finally observed in the animal experiment. GSEA and KEGG enrichment analyses on key module genes derived from WGCNA revealed a close association between UC and pyroptosis. Then, a total of 20 pyroptosis-related DEGs of UC and 27 pyroptosis-related DEGs of active UC were screened. Next, 6 candidate genes (ZBP1, AIM2, IL1β, CASP1, TLR4, CASP11) in UC and 2 candidate genes (TLR4, CASP11) in active UC were respectively identified using the binary logistic regression (BLR), least absolute shrinkage and selection operator (LASSO), random forest (RF) analysis and artificial neural network (ANN), and these genes also showed high diagnostic specificity for UC in testing sets. Specially, TLR4 was elevated in UC and further elevated in active UC. The results of the drug screen revealed that six compounds (quercetin, cyclosporine, resveratrol, cisplatin, paclitaxel, rosiglitazone) could target TLR4, among which the effect of quercetin on intestinal pathology, pyroptosis and the expression of TLR4 in UC and active UC was further determined by the murine model. These findings demonstrated that pyroptosis may promote UC, and especially contributes to the activation of UC. Pyroptosis-related DEGs offer new ideas for the diagnosis of UC. Besides, quercetin was verified as an effective treatment for pyroptosis and intestinal inflammation. This study might enhance our comprehension on the pathogenic mechanism and diagnosis of UC and offer a treatment option for UC.
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Affiliation(s)
- Yang Zhao
- The Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, 210029, China
| | - Yiming Ma
- Macau University of Science and Technology, Macau, 999078, China
| | - Jianing Pei
- The Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, 210029, China
| | - Xiaoxuan Zhao
- Department of Traditional Chinese Medicine (TCM) Gynecology, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, 310007, China
| | - Yuepeng Jiang
- College of Pharmaceutical Science, Zhejiang Chinese Medical University, Hangzhou, 310053, China
| | - Qingsheng Liu
- Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, 310007, China.
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6
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Shao Z, Gao H, Wang B, Zhang S. Exploring the impact of pathogenic microbiome in orthopedic diseases: machine learning and deep learning approaches. Front Cell Infect Microbiol 2024; 14:1380136. [PMID: 38633744 PMCID: PMC11021578 DOI: 10.3389/fcimb.2024.1380136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Accepted: 03/19/2024] [Indexed: 04/19/2024] Open
Abstract
Osteoporosis, arthritis, and fractures are examples of orthopedic illnesses that not only significantly impair patients' quality of life but also complicate and raise the expense of therapy. It has been discovered in recent years that the pathophysiology of orthopedic disorders is significantly influenced by the microbiota. By employing machine learning and deep learning techniques to conduct a thorough analysis of the disease-causing microbiome, we can enhance our comprehension of the pathophysiology of many illnesses and expedite the creation of novel treatment approaches. Today's science is undergoing a revolution because to the introduction of machine learning and deep learning technologies, and the field of biomedical research is no exception. The genesis, course, and management of orthopedic disorders are significantly influenced by pathogenic microbes. Orthopedic infection diagnosis and treatment are made more difficult by the lengthy and imprecise nature of traditional microbial detection and characterization techniques. These cutting-edge analytical techniques are offering previously unheard-of insights into the intricate relationships between orthopedic health and pathogenic microbes, opening up previously unimaginable possibilities for illness diagnosis, treatment, and prevention. The goal of biomedical research has always been to improve diagnostic and treatment methods while also gaining a deeper knowledge of the processes behind the onset and development of disease. Although traditional biomedical research methodologies have demonstrated certain limits throughout time, they nevertheless rely heavily on experimental data and expertise. This is the area in which deep learning and machine learning approaches excel. The advancements in machine learning (ML) and deep learning (DL) methodologies have enabled us to examine vast quantities of data and unveil intricate connections between microorganisms and orthopedic disorders. The importance of ML and DL in detecting, categorizing, and forecasting harmful microorganisms in orthopedic infectious illnesses is reviewed in this work.
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Affiliation(s)
| | | | | | - Shenqi Zhang
- Department of Joint and Sports Medicine, Zaozhuang Municipal Hospital, Affiliated to Jining Medical University, Zaozhuang, China
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7
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Yang K, Zhang Z, Zhang Q, Zhang H, Liu X, Jia Z, Ying Z, Liu W. Potential diagnostic markers and therapeutic targets for periodontitis and Alzheimer's disease based on bioinformatics analysis. J Periodontal Res 2024; 59:366-380. [PMID: 38189472 DOI: 10.1111/jre.13220] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 11/02/2023] [Accepted: 11/22/2023] [Indexed: 01/09/2024]
Abstract
BACKGROUND AND OBJECTIVE As a chronic inflammatory disease, periodontitis threatens oral health and is a risk factor for Alzheimer's disease (AD). There is growing evidence that these two diseases are closely related. However, current research is still incomplete in understanding the common genes and common mechanisms between periodontitis and AD. In this study, we aimed to identify common genes in periodontitis and AD and analyze the relationship between crucial genes and immune cells to provide new therapeutic targets for clinical treatment. MATERIALS AND METHODS We evaluated differentially expressed genes (DEGs) specific to periodontitis and AD. Co-expressed genes were identified by obtaining gene expression profile data from the Gene Expression Omnibus (GEO) database. Using the STRING database, protein-protein interaction (PPI) networks were constructed, and essential genes were identified. We also used four algorithms to identify critical genes and constructed regulatory networks. The association of crucial genes with immune cells and potential therapeutic effects was also assessed. RESULTS PDGFRB, VCAN, TIMP1, CHL1, EFEMP2, and IGFBP5 were obtained as crucial common genes. Immune infiltration analysis showed that Natural killer cells and Myeloid-derived suppressor cells were significantly differentially expressed in patients with PD and AD compared with the normal group. FOXC1 and GATA2 are important TFs for PD and AD. MiR-23a, miR-23b, miR-23a, and miR-23b were associated with AD and PD. Finally, the hub genes retrieved from the DSigDB database indicate multiple drug molecule and drug-target interactions. CONCLUSION This study reveals commonalities in common hub genes and immune infiltration between periodontitis and AD, and the analysis of six hub genes and immune cells may provide new insights into potential therapeutic directions for the pathogenesis of periodontitis complicated by AD.
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Affiliation(s)
- Kai Yang
- Acupuncture and Moxibustion Massage College, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Zhaoqi Zhang
- The First Clinical Medical College of Shandong University of Chinese Medicine, Jinan, China
| | - Qingyuan Zhang
- The First Clinical Medical College of Shandong University of Chinese Medicine, Jinan, China
| | - Hongyu Zhang
- Rehabilitation Department, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Xiaoju Liu
- The First Clinical Medical College of Shandong University of Chinese Medicine, Jinan, China
| | - Zhicheng Jia
- The First Clinical Medical College of Shandong University of Chinese Medicine, Jinan, China
| | - Zhenhao Ying
- Rehabilitation Department, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Wei Liu
- Department of Neurology, The Second Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, China
- Shandong University of Traditional Chinese Medicine, Jinan, China
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Liu X, Li J, Yue Y, Li J, Wang M, Hao L. Mechanisms of mechanical force aggravating periodontitis: A review. Oral Dis 2024; 30:895-902. [PMID: 36989127 DOI: 10.1111/odi.14566] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Revised: 02/13/2023] [Accepted: 03/10/2023] [Indexed: 03/19/2023]
Abstract
Periodontitis is a widespread oral disease accompanied by uncontrolled inflammation-related tissue destruction. Periodontitis is related to various factors. Among them, occlusal trauma can aggravate the severity of periodontitis and has been attracting a great deal of attention. We systematically searched PubMed and Web of Science databases for related articles. Keywords for the search were "mechanical force", "mechanical stress", "occlusal trauma" and "periodontitis". This review focuses on the effect of mechanical forces on periodontitis and discusses the possible pivotal targets participating in this process. We elucidated and summarized 21 articles that reported on our topic. Several biological processes and pathways that participate in enhancing the inflammatory response to mechanical stress have been studied, including the regulation of osteogenesis and osteoclastic resorption balance, Yes-associated protein signaling, induction of collagen destruction, and regulation of programmed cell death. Mechanical force enhances the process of periodontitis in multiple ways. However, currently, no studies have further examined its underlying mechanism. Understanding the specific roles of mechanical forces may assist in the treatment of periodontitis with traumatic occlusal trauma.
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Affiliation(s)
- Xinran Liu
- The State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, Department of Prosthodontics, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
| | - Jiaxin Li
- The State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, Department of Prosthodontics, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
| | - Yuan Yue
- The State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, Department of Prosthodontics, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
| | - Jinle Li
- The State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, Department of Prosthodontics, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
| | - Min Wang
- The State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, Department of Prosthodontics, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
| | - Liang Hao
- The State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, Department of Prosthodontics, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
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9
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Wu J, Yao L, Liu Y, Zhang S, Wang K. Periodontitis and osteoporosis: a two-sample Mendelian randomization analysis. Braz J Med Biol Res 2024; 57:e12951. [PMID: 38511766 PMCID: PMC10946243 DOI: 10.1590/1414-431x2024e12951] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 01/05/2024] [Indexed: 03/22/2024] Open
Abstract
The incidences of periodontitis and osteoporosis are rising worldwide. Observational studies have shown that periodontitis is associated with increased risk of osteoporosis. We performed a Mendelian randomization (MR) study to genetically investigate the causality of periodontitis on osteoporosis. We explored the causal effect of periodontitis on osteoporosis by MR analysis. A total of 9 single nucleotide polymorphisms (SNP) were related to periodontitis. The primary approach in this MR analysis was the inverse variance-weighted (IVW) method. Simple median, weighted median, and penalized weighted median were used to analyze sensitivity. The fixed-effect IVW model and random-effect IVW model showed no significant causal effect of genetically predicted periodontitis on the risk of osteoporosis (OR=1.032; 95%CI: 0.923-1.153; P=0.574; OR=1.032; 95%CI: 0.920-1.158; P=0.588, respectively). Similar results were observed in simple mode (OR=1.031; 95%CI: 0.780-1.361, P=0.835), weighted mode (OR=1.120; 95%CI: 0.944-1.328, P=0.229), simple median (OR=1.003; 95%CI: 0.839-1.197, P=0.977), weighted median (OR=1.078; 95%CI: 0.921-1.262, P=0.346), penalized weight median (OR 1.078; 95%CI: 0.919-1.264, P=0.351), and MR-Egger method (OR=1.360; 95%CI: 0.998-1.853, P=0.092). There was no heterogeneity in the IVW and MR-Egger analyses (Q=7.454, P=0.489 and Q=3.901, P=0.791, respectively). MR-Egger regression revealed no evidence of a pleiotropic influence through genetic variants (intercept: -0.004; P=0.101). The leave-one-out sensitivity analysis indicated no driven influence of any individual SNP on the association between periodontitis and osteoporosis. The Mendelian randomization analysis did not show a significant detrimental effect of periodontitis on the risk of osteoporosis.
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Affiliation(s)
- Jiale Wu
- Department of Oral and Maxillofacial Surgery, Peking University School of Medicine, Hospital of Stomatology, Beijing, China
| | - Lihui Yao
- Department of Stomatology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Yuchen Liu
- Department of Oral and Maxillofacial Surgery, Peking University School of Medicine, Hospital of Stomatology, Beijing, China
| | - ShuaiShuai Zhang
- Department of Stomatology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Kan Wang
- Department of Oral and Maxillofacial Surgery, Peking University School of Medicine, Hospital of Stomatology, Beijing, China
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10
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Xu G, Zhang W, Yang J, Sun N, Qu X. Identification of neutrophil extracellular traps and crosstalk genes linking inflammatory bowel disease and osteoporosis by integrated bioinformatics analysis and machine learning. Sci Rep 2023; 13:23054. [PMID: 38155235 PMCID: PMC10754907 DOI: 10.1038/s41598-023-50488-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Accepted: 12/20/2023] [Indexed: 12/30/2023] Open
Abstract
Musculoskeletal deficits are among the most common extra-intestinal manifestations and complications of inflammatory bowel disease (IBD). This study aimed to identify crosstalk genes between IBD and osteoporosis (OP) and potential relationships between crosstalk and neutrophil extracellular traps (NETs)-related genes. Three common hub genes from different compared groups are actually the same, namely HDAC6, IL-8, and PPIF. ROC showed that the combined diagnostic value of HDAC6, IL-8, and PPIF was higher than each of the three key hub genes. Immune infiltration results showed that HDAC6 and IL-8 key genes negatively correlated with CD65 bright natural killer cells. USF1 was the common upstream TFs between HDAC6 and PPIF, and MYC was the common upstream TFs between IL-8 and PPIF in RegNetwork. Taken together, this study shows a linked mechanism between IBD and OP via NETs and crosstalk genes. These findings may show light on better diagnosis and treatment of IBD complicated with OP.
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Affiliation(s)
- Gang Xu
- Department of Orthopaedics, First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning Province, China.
- Key Laboratory of Molecular Mechanism for Repair and Remodeling of Orthopaedic Diseases, Dalian, Liaoning Province, China.
| | - Wanhao Zhang
- Department of Orthopaedics, First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning Province, China
| | - Jun Yang
- Department of Orthopaedics, First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning Province, China
| | - Na Sun
- Department of Pharmacy, The Third People's Hospital of Dalian, Dalian, Liaoning Province, China
| | - Xiaochen Qu
- Department of Orthopaedics, First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning Province, China.
- Key Laboratory of Molecular Mechanism for Repair and Remodeling of Orthopaedic Diseases, Dalian, Liaoning Province, China.
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11
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Zhu Y, Liu Y, Wang Q, Niu S, Wang L, Cheng C, Chen X, Liu J, Zhao S. Using machine learning to identify patients at high risk of developing low bone density or osteoporosis after gastrectomy: a 10-year multicenter retrospective analysis. J Cancer Res Clin Oncol 2023; 149:17479-17493. [PMID: 37897658 DOI: 10.1007/s00432-023-05472-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Accepted: 10/10/2023] [Indexed: 10/30/2023]
Abstract
INTRODUCTION Osteoporosis that emerges subsequent to gastrectomy poses a significant threat to the long-term health of patients. The primary objective of this investigation was to formulate a machine learning algorithm capable of identifying substantial preoperative, intraoperative, and postoperative risk factors. This algorithm, in turn, would enable the anticipation of osteoporosis occurrence after gastrectomy. METHODS This research encompassed a cohort of 1125 patients diagnosed with gastric cancer, including 108 individuals with low bone density or osteoporosis. A total of 40 distinct variables were collected, comprising patient demographics, pertinent medical history, medication records, preoperative examination attributes, surgical procedure specifics, and intraoperative details. Four distinct machine learning algorithms-extreme gradient boosting (XGBoost), random forest (RF), support vector machine (SVM), and k-nearest neighbor algorithm (KNN)-were employed to establish the predictive model. Evaluation of the models involved receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Shapley additive explanation (SHAP) was employed for visualization and analysis. RESULTS Among the four prediction models employed, the XGBoost algorithm demonstrated exceptional performance. The ROC analysis yielded excellent predictive accuracy, showcasing area under the curve (AUC) values of 0.957 and 0.896 for training and validation sets, respectively. The calibration curve further confirmed the robust predictive capacity of the XGBoost model. The DCA demonstrated a notably higher benefit rate for patients undergoing intervention based on the XGBoost model. Moreover, the AUC value of 0.73 for the external validation set indicated favorable extrapolation of the XGBoost prediction model. SHAP analysis outcomes unveiled numerous high-risk factors for osteoporosis development after gastrectomy, including a history of chronic obstructive pulmonary disease (COPD), inflammatory bowel disease (IBD), hypoproteinemia, postoperative neutrophil-to-lymphocyte ratio (NLR) exceeding 3, steroid usage history, advanced age, and absence of calcitonin use. CONCLUSION The osteoporosis prediction model derived through the XGBoost machine learning algorithm in this study displays remarkable predictive precision and carries significant clinical applicability.
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Affiliation(s)
- Yanfei Zhu
- Wuxi Medical Center of Nanjing Medical University, Wuxi, China
| | - Yuan Liu
- Wuxi Medical Center of Nanjing Medical University, Wuxi, China
| | - Qi Wang
- Department of Gastroenterology, Affiliated Hospital of Jiangsu University, Zhenjiang, China
| | - Sen Niu
- Wuxi Medical Center of Nanjing Medical University, Wuxi, China
| | - Lanyu Wang
- Wuxi Medical Center of Nanjing Medical University, Wuxi, China
| | - Chao Cheng
- Wuxi Medical Center of Nanjing Medical University, Wuxi, China
| | - Xujin Chen
- Wuxi Medical Center of Nanjing Medical University, Wuxi, China
| | - Jinhui Liu
- Department of Gynecology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
| | - Songyun Zhao
- Wuxi Medical Center of Nanjing Medical University, Wuxi, China.
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12
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Wang L, Deng C, Wu Z, Zhu K, Yang Z. Bioinformatics and machine learning were used to validate glutamine metabolism-related genes and immunotherapy in osteoporosis patients. J Orthop Surg Res 2023; 18:685. [PMID: 37710308 PMCID: PMC10503203 DOI: 10.1186/s13018-023-04152-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/24/2023] [Accepted: 08/30/2023] [Indexed: 09/16/2023] Open
Abstract
BACKGROUND Osteoporosis (OP), often referred to as the "silent disease of the twenty-first century," poses a significant public health concern due to its severity, chronic nature, and progressive course, predominantly affecting postmenopausal women and elderly individuals. The pathogenesis and progression of this disease have been associated with dysregulation in tumor metabolic pathways. Notably, the metabolic utilization of glutamine has emerged as a critical player in cancer biology. While metabolic reprogramming has been extensively studied in various malignancies and linked to clinical outcomes, its comprehensive investigation within the context of OP remains lacking. METHODS This study aimed to identify and validate potential glutamine metabolism genes (GlnMgs) associated with OP through comprehensive bioinformatics analysis. The identification of GlnMgs was achieved by integrating the weighted gene co-expression network analysis and a set of 28 candidate GlnMgs. Subsequently, the putative biological functions and pathways associated with GlnMgs were elucidated using gene set variation analysis. The LASSO method was employed to identify key hub genes, and the diagnostic efficacy of five selected GlnMgs in OP detection was assessed. Additionally, the relationship between hub GlnMgs and clinical characteristics was investigated. Finally, the expression levels of the five GlnMgs were validated using independent datasets (GSE2208, GSE7158, GSE56815, and GSE35956). RESULTS Five GlnMgs, namely IGKC, TMEM187, RPS11, IGLL3P, and GOLGA8N, were identified in this study. To gain insights into their biological functions, particular emphasis was placed on synaptic transmission GABAergic, inward rectifier potassium channel activity, and the cytoplasmic side of the lysosomal membrane. Furthermore, the diagnostic potential of these five GlnMgs in distinguishing individuals with OP yielded promising results, indicating their efficacy as discriminative markers for OP. CONCLUSIONS This study discovered five GlnMgs that are linked to OP. They shed light on potential new biomarkers for OP and tracking its progression.
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Affiliation(s)
- Lei Wang
- Shandong University of Traditional Chinese Medicine, Jinan, China
- Second Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Chaosheng Deng
- The Third People Hospital of Longgang District, Shenzhen, Guangdong Province, China
| | - Zixuan Wu
- Guangzhou University of Chinese Medicine, Guangzhou, Guangdong Province, China
| | - Kaidong Zhu
- Second Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, China.
| | - Zhenguo Yang
- Second Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, China.
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13
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Liu M, Wang Y, Shi W, Yang C, Wang Q, Chen J, Li J, Chen B, Sun G. PCDH7 as the key gene related to the co-occurrence of sarcopenia and osteoporosis. Front Genet 2023; 14:1163162. [PMID: 37476411 PMCID: PMC10354703 DOI: 10.3389/fgene.2023.1163162] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 04/06/2023] [Indexed: 07/22/2023] Open
Abstract
Sarcopenia and osteoporosis, two degenerative diseases in older patients, have become severe health problems in aging societies. Muscles and bones, the most important components of the motor system, are derived from mesodermal and ectodermal mesenchymal stem cells. The adjacent anatomical relationship between them provides the basic conditions for mechanical and chemical signals, which may contribute to the co-occurrence of sarcopenia and osteoporosis. Identifying the potential common crosstalk genes between them may provide new insights for preventing and treating their development. In this study, DEG analysis, WGCNA, and machine learning algorithms were used to identify the key crosstalk genes of sarcopenia and osteoporosis; this was then validated using independent datasets and clinical samples. Finally, four crosstalk genes (ARHGEF10, PCDH7, CST6, and ROBO3) were identified, and mRNA expression and protein levels of PCDH7 in clinical samples from patients with sarcopenia, with osteoporosis, and with both sarcopenia and osteoporosis were found to be significantly higher than those from patients without sarcopenia or osteoporosis. PCDH7 seems to be a key gene related to the development of both sarcopenia and osteoporosis.
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Affiliation(s)
- Mingchong Liu
- Department of Traumatic Surgery, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Yongheng Wang
- Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Wentao Shi
- Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Chensong Yang
- Department of Traumatic Surgery, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Qidong Wang
- Department of Traumatic Surgery, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Jingyao Chen
- Institute for Regenerative Medicine, Shanghai East Hospital, The Institute for Biomedical Engineering and Nano Science, Tongji University School of Medicine, Shanghai, China
| | - Jun Li
- Institute for Regenerative Medicine, Shanghai East Hospital, The Institute for Biomedical Engineering and Nano Science, Tongji University School of Medicine, Shanghai, China
| | - Bingdi Chen
- Institute for Regenerative Medicine, Shanghai East Hospital, The Institute for Biomedical Engineering and Nano Science, Tongji University School of Medicine, Shanghai, China
| | - Guixin Sun
- Department of Traumatic Surgery, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, China
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Hu S, Li S, Ning W, Huang X, Liu X, Deng Y, Franceschi D, Ogbuehi AC, Lethaus B, Savkovic V, Li H, Gaus S, Zimmerer R, Ziebolz D, Schmalz G, Huang S. Identifying crosstalk genetic biomarkers linking a neurodegenerative disease, Parkinson's disease, and periodontitis using integrated bioinformatics analyses. Front Aging Neurosci 2022; 14:1032401. [PMID: 36545026 PMCID: PMC9760933 DOI: 10.3389/fnagi.2022.1032401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 11/16/2022] [Indexed: 12/12/2022] Open
Abstract
Objective To identify the genetic linkage mechanisms underlying Parkinson's disease (PD) and periodontitis, and explore the role of immunology in the crosstalk between both these diseases. Methods The gene expression omnibus (GEO) datasets associated with whole blood tissue of PD patients and gingival tissue of periodontitis patients were obtained. Then, differential expression analysis was performed to identify the differentially expressed genes (DEGs) deregulated in both diseases, which were defined as crosstalk genes. Inflammatory response-related genes (IRRGs) were downloaded from the MSigDB database and used for dividing case samples of both diseases into different clusters using k-means cluster analysis. Feature selection was performed using the LASSO model. Thus, the hub crosstalk genes were identified. Next, the crosstalk IRRGs were selected and Pearson correlation coefficient analysis was applied to investigate the correlation between hub crosstalk genes and hub IRRGs. Additionally, immune infiltration analysis was performed to examine the enrichment of immune cells in both diseases. The correlation between hub crosstalk genes and highly enriched immune cells was also investigated. Results Overall, 37 crosstalk genes were found to be overlapping between the PD-associated DEGs and periodontitis-associated DEGs. Using clustering analysis, the most optimal clustering effects were obtained for periodontitis and PD when k = 2 and k = 3, respectively. Using the LASSO feature selection, five hub crosstalk genes, namely, FMNL1, MANSC1, PLAUR, RNASE6, and TCIRG1, were identified. In periodontitis, MANSC1 was negatively correlated and the other four hub crosstalk genes (FMNL1, PLAUR, RNASE6, and TCIRG1) were positively correlated with five hub IRRGs, namely, AQP9, C5AR1, CD14, CSF3R, and PLAUR. In PD, all five hub crosstalk genes were positively correlated with all five hub IRRGs. Additionally, RNASE6 was highly correlated with myeloid-derived suppressor cells (MDSCs) in periodontitis, and MANSC1 was highly correlated with plasmacytoid dendritic cells in PD. Conclusion Five genes (i.e., FMNL1, MANSC1, PLAUR, RNASE6, and TCIRG1) were identified as crosstalk biomarkers linking PD and periodontitis. The significant correlation between these crosstalk genes and immune cells strongly suggests the involvement of immunology in linking both diseases.
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Affiliation(s)
- Shaonan Hu
- Stomatological Hospital, Southern Medical University, Guangzhou, China,*Correspondence: Shaonan Hu,
| | - Simin Li
- Stomatological Hospital, Southern Medical University, Guangzhou, China
| | - Wanchen Ning
- Stomatological Hospital, Southern Medical University, Guangzhou, China
| | - Xiuhong Huang
- Stomatological Hospital, Southern Medical University, Guangzhou, China
| | - Xiangqiong Liu
- Laboratory of Molecular Cell Biology, Beijing Tibetan Hospital, China Tibetology Research Center, Beijing, China
| | - Yupei Deng
- Laboratory of Molecular Cell Biology, Beijing Tibetan Hospital, China Tibetology Research Center, Beijing, China
| | - Debora Franceschi
- Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy
| | | | - Bernd Lethaus
- Department of Cranio Maxillofacial Surgery, University Clinic Leipzig, Leipzig, Germany
| | - Vuk Savkovic
- Department of Cranio Maxillofacial Surgery, University Clinic Leipzig, Leipzig, Germany
| | - Hanluo Li
- Department of Cranio Maxillofacial Surgery, University Clinic Leipzig, Leipzig, Germany
| | - Sebastian Gaus
- Department of Cranio Maxillofacial Surgery, University Clinic Leipzig, Leipzig, Germany
| | - Rüdiger Zimmerer
- Department of Cranio Maxillofacial Surgery, University Clinic Leipzig, Leipzig, Germany
| | - Dirk Ziebolz
- Department of Cariology, Endodontology and Periodontology, University of Leipzig, Leipzig, Germany
| | - Gerhard Schmalz
- Department of Cariology, Endodontology and Periodontology, University of Leipzig, Leipzig, Germany
| | - Shaohong Huang
- Stomatological Hospital, Southern Medical University, Guangzhou, China,Shaohong Huang,
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