Identification of biomarkers in macrophages of atherosclerosis by microarray analysis.

BACKGROUND: Atherosclerotic cardiovascular disease (ASCVD) refers to a series of diseases caused by atherosclerosis (AS). It is one of the most important causes of death worldwide. According to the inflammatory response theory, macrophages play a critical role in AS. However, the potential targets associated with macrophages in the development of AS are still obscure. This study aimed to use bioinformatics tools for screening and identifying molecular targets in AS macrophages. METHODS: Two expression profiling datasets (GSE7074 and GSE9874) were obtained from the Gene Expression Omnibus dataset, and differentially expressed genes (DEGs) between non-AS macrophages and AS macrophages were identified. Functional annotation of the DEGs was performed by analyzing the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes databases. STRING and Cytoscape were employed for constructing a protein-protein interaction network and analyzing hub genes. RESULTS: A total of 98 DEGs were distinguished between non-AS macrophages and AS macrophages. The functional variations in DEGs were mainly enriched in response to hypoxia, respiratory gaseous exchange, protein binding, and intracellular, ciliary tip, early endosome membrane, and Lys63-specific deubiquitinase activities. Three genes were identified as hub genes, including KDELR3, CD55, and DYNC2H1. CONCLUSION: Hub genes and DEGs identified by using microarray techniques can be used as diagnostic and therapeutic biomarkers for AS.

Histopathologic study of keloid vascular structures shows the vascular origin pattern of keloid subepidermal vascular network flaps.

BACKGROUND: Keloid subepidermal vascular network flaps (KSVNFs) have achieved satisfactory results in clinical practice. Through this retrospective study, we further examined keloid vascular structure to better understand vascular origin pattern in KSVNFs. METHODS: Paraffin-embedded keloid tissues were stained for CD31. Distances from keloid subepidermal capillaries to the skin surface were measured. The included angle between the pedicle vessels and skin surface (angle PV), as well as the included angle between the keloid margin and skin surface (angle KM), were also measured. The major and minor axes of the capillary in the central areas of keloid (KDC), adjacent skin (AS) and marginal areas of keloid (KDM) were analyzed, and the major:minor axis ratios (M/m) were calculated. Vessels in KSVNF pedicle sites (KDP) were compared with vessels in adjacent skin as a subgroup analysis. RESULTS: Twenty-nine keloid specimens in total were collected. Based on 1630 measured data points, the capillary distance to the skin surface was 387.2±96.7 µm. The angle PV was 70.1±36.6°, and the angle KM was 67.0±18.1°. The major axis of the KDM capillaries was significantly longer than that of KDC and AS (both P < 0.001). The major and minor axes were longer in KDP than in AS (both P < 0.001). CONCLUSION: Suprakeloidal blood vessels are mainly distributed at a depth of 387.2±96.7 µm from the skin. The subepidermal plexus in KSVNF pedicle sites enters the skin at an acute angle and runs parallel to the keloid margin layer. Vessels in keloid marginal areas had crushed vascular lumen, but vessels in KSVNF pedicles did not.

A Potential Target for Clinical Atherosclerosis: A Novel Insight Derived from TPM2.

Atherosclerosis (AS) is a potential inducer of numerous cardio-cerebrovascular diseases. However, little research has investigated the expression of TPM2 in human atherosclerosis samples. A total of 34 clinical samples were obtained, including 17 atherosclerosis and 17 normal artery samples, between January 2018 and April 2021. Bioinformatics analysis was applied to explore the potential role of TPM2 in atherosclerosis. Immunohistochemistry, immunofluorescence, and western blotting assays were used to detect the expression of TPM2 and α-SMA proteins. The mRNA expression levels of TPM2 and α-SMA were detected using RT-qPCR. A neural network and intima-media thickness model were constructed. A strong relationship existed between the intima-media thickness and relative protein expression of TPM2 (P<0.001, R=-0.579). The expression of TPM2 was lower in atherosclerosis than normal artery (P<0.05). Univariate logistic regression showed that TPM2 (OR=0.150, 95% CI: 0.026-0.868, P=0.034) had clear correlations with atherosclerosis. A neural network model was successfully constructed with a relativity of 0.94434. TPM2 might be an independent protective factor for arteries, and one novel biomarker of atherosclerosis.

SGLT1/2 as the potential biomarkers of renal damage under Apoe-/- and chronic stress via the BP neural network model and support vector machine.

Background: Chronic stress (CS) could produce negative emotions. The molecular mechanism of SGLT1 and SGLT2 in kidney injury caused by chronic stress combined with atherosclerosis remains unclear. Methods: In total, 60 C57BL/6J mice were randomly divided into four groups, namely, control (CON, n = 15), control diet + chronic stress (CON+CS, n = 15), high-fat diet + Apoe-/- (HF + Apoe-/-, n = 15), and high-fat diet + Apoe-/- + chronic stress (HF+Apoe-/- + CS, n = 15) groups. The elevated plus maze and open field tests were performed to examine the effect of chronic stress. The expression of SGLT1 and SGLT2 in the kidney was detected. The support vector machine (SVM) and back propagation (BP) neural network model were constructed to explore the predictive value of the expression of SGLT1/2 on the renal pathological changes. The receiver operating characteristic (ROC) curve analysis was used. Results: A chronic stress model and atherosclerosis model were constructed successfully. Edema, broken reticular fiber, and increased glycogen in the kidney would be obvious in the HF + Apoe-/- + CS group. Compared with the CON group, the expression of SGLT1/2 in the kidney was upregulated in the HF + Apoe-/- + CS group (P < 0.05). There existed positive correlations among edema, glycogen, reticular fiber, expression of SGLT1/2 in the kidney. There were higher sensitivity and specificity of diagnosis of SGLT1/2 for edema, reticular fiber, and glycogen in the kidney. The result of the SVM and BP neural network model showed better predictive values of SGLT1 and SGLT2 for edema and glycogen in the kidney. Conclusion: In conclusion, SGLT1/2 might be potential biomarkers of renal damage under Apoe-/- and chronic stress, which provided a potential research direction for future related explorations into this mechanism.

Identification of the similarly expressed genes in patients with polycystic ovary syndrome and transsexuals.

ABSTRACT: Polycystic ovary syndrome (PCOS) is a common female infertility, which may be caused by excessive androgen, but its mechanism remains unknown. Transsexuals are women who take androgen drugs for a long time, and gradually have male signs. Their ovaries may have received high concentrations of androgen, which leads to the failure of ovarian reproductive function. Therefore, we searched the relevant data of PCOS and transsexuals in gene expression omnibus database, used limma package to identify the most similarly genes, and then analyzed the possible mechanism of PCOS through gene ontology (GO) and kyoto encyclopedia of genes and genomes (KEGG) pathway analysis. Then, the protein-protein interaction network was constructed by searching the String database, and the top 5 hub genes were identified by the cytohubba plug-in of Cytoscape. Finally, ubiquitin conjugating enzyme E2 E1 (UBE2E1), ubiquitin C (UBC), transcription elongation factor B subunit 1 (TCEB1), ubiquitin conjugating enzyme E2 N (UBE2N), and ring finger protein 7 (RNF7) genes were identified as the most similarly expressed genes between PCOS and Transsexuals. They may cause the ubiquitination of androgen receptor and eventually lead to sinus follicular growth arrest. In conclusion, 5 Central genes were identified in PCOS and transsexuals. These genes can be used as targets for early diagnosis or treatment of PCOS.

MYL2 as a potential predictive biomarker for rhabdomyosarcoma.

ABSTRACT: Rhabdomyosarcoma (RMS) is a common malignant soft tissue sarcoma, which is the third most common soft tissue sarcoma after malignant fibrohistoma and liposarcoma. The discovery of potential postbiomarkers could lead to early and more effective treatment measures to reduce the mortality of RMS. The discovery of biomarker is expected to be the direction of targeted therapy, providing a new direction for the precise treatment of RMS.Gene Expression Omnibus database was used to download the tow gene profiles, GSE28511 and GSE135517. GEO2R was applied to identify differently expressed genes (DEGs) between RMS and normal group. Database for Annotation, Visualization and Integrated Discovery and Metascape can perform the enrichment analysis for the DEGs. Protein-protein interaction network was constructed, and the hub genes was identified by the Cytoscape. Expression and overall survival analysis of hub genes were performed.A total of 15 common DEGs were screened between RMS and normal tissues. The enrichment analysis here showed that the DEGs mainly enriched in the muscle filament sliding, myofibril, protein complex, sarcomere, myosin complex, nuclear chromosome, and tight junction. The 6 hub genes (DNA Topoisomerase II Alpha, Insulin Like Growth Factor 2, HIST1H4C, Cardiomyopathy Associated 5, Myosin Light Chain 2 [MYL2], Myosin Heavy Chain 2) were identified. Compared with the normal tissues, MYL2 were down-regulated in the RMS tissues. RMS patients with low expression level of MYL2 had poorer overall survival times than those with high expression levels (P < .05).In summary, lower expression of MYL2 was 1 prediction for poor prognosis of RMS. MYL2 is hope to be the target of therapy, which leads to more effective treatment and reduces the mortality rate of RMS.

Screening and Identification of Key Biomarkers of Gastric Cancer: Three Genes Jointly Predict Gastric Cancer.

BACKGROUND: Gastric cancer (GC) is one of the most common cancers all over the world, causing high mortality. Gastric cancer screening is one of the effective strategies used to reduce mortality. We expect that good biomarkers can be discovered to diagnose and treat gastric cancer as early as possible. METHODS: We download four gene expression profiling datasets of gastric cancer (GSE118916, GSE54129, GSE103236, GSE112369), which were obtained from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) between gastric cancer and adjacent normal tissues were detected to explore biomarkers that may play an important role in gastric cancer. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of overlap genes were conducted by the Metascape online database; the protein-protein interaction (PPI) network was constructed by the STRING online database, and we screened the hub genes of the PPI network using the Cytoscape software. The survival curve analysis was conducted by km-plotter and the stage plots of hub genes were created by the GEPIA online database. PCR, WB, and immunohistochemistry were used to verify the expression of hub genes. A neural network model was established to quantify the predictors of gastric cancer. RESULTS: The relative expression level of cadherin-3 (CDH3), lymphoid enhancer-binding factor 1 (LEF1), and matrix metallopeptidase 7 (MMP7) were significantly higher in gastric samples, compared with the normal groups (p<0.05). Receiver operator characteristic (ROC) curves were constructed to determine the effect of the three genes' expression on gastric cancer, and the AUC was used to determine the degree of confidence: CDH3 (AUC = 0.800, P<0.05, 95% CI =0.857-0.895), LEF1 (AUC=0.620, P<0.05, 95%CI=0.632-0.714), and MMP7 (AUC=0.914, P<0.05, 95%CI=0.714-0.947). The high-risk warning indicator of gastric cancer contained 8

Bioinformatics analysis of key biomarkers for retinoblastoma.

OBJECTIVE: To identify key genes involved in occurrence and development of retinoblastoma. METHODS: The microarray dataset, GSE5222, was downloaded from the gene expression omnibus (GEO) database. Differentially expressed genes (DEGs) between unilateral and bilateral retinoblastoma were identified and functional enrichment analysis performed. The protein-protein interaction (PPI) network was constructed and analysed by STRING and Cytoscape. RESULTS: DEGs were mainly associated with activation of cysteine-type endopeptidase activity involved in apoptotic process and small molecule catabolic process. Seven genes (WAS, GNB3, PTGER1, TACR1, GPR143, NPFF and CDKN2A) were identified as HUB genes. CONCLUSION: Our research provides more understanding of the mechanisms of the disease at a molecular level and may help in the identification of novel biomarkers for retinoblastoma.

Hyperbaric oxygen treatment on keloid tumor immune gene expression.

BACKGROUND: Hyperbaric oxygen treatment (HBOT) has been demonstrated to influence the keloid recurrence rate after surgery and to relieve keloid symptoms and other pathological processes in keloids. To explore the mechanism of the effect of HBOT on keloids, tumor immune gene expression and immune cell infiltration were studied in this work. METHODS: From February 2021 to April 2021, HBOT was carried out on keloid patients four times before surgery. Keloid tissue samples were collected and divided into an HBOT group (keloid with HBOT before surgery [HK] group, n = 6) and a non-HBOT group (K group, n = 6). Tumor gene expression was analyzed with an Oncomine Immune Response Research Assay kit. Data were mined with R package. The differentially expressed genes between the groups were compared. Hub genes between the groups were determined and verified with Quantitative Real-time PCR. Immune cell infiltration was analyzed based on CIBERSORT deconvolution algorithm analysis of gene expression and verified with immunohistochemistry (IHC). RESULTS: Inflammatory cell infiltration was reduced in the HK group. There were 178 upregulated genes and 217 downregulated genes. Ten hub genes were identified, including Integrin Subunit Alpha M (ITGAM), interleukin (IL)-4, IL-6, IL-2, Protein Tyrosine Phosphatase Receptor Type C (PTPRC), CD86, transforming growth factor (TGF), CD80, CTLA4, and IL-10. CD80, ITGAM, IL-4, and PTPRC with significantly downregulated expression were identified. IL-10 and IL-2 were upregulated in the HK group but without a significant difference. Infiltration differences of CD8 lymphocyte T cells, CD4 lymphocyte T-activated memory cells, and dendritic resting cells were identified with gene CIBERSORT deconvolution algorithm analysis. Infiltration levels of CD4 lymphocyte T cell in the HK group were significantly higher than those of the K group in IHC verification. CONCLUSION: HBOT affected tumor gene expression and immune cell infiltration in keloids. CD4 lymphocyte T cell, especially activated memory CD4+T, might be the key regulatory immune cell, and its related gene expression needs further study.

Citrate Synthase and OGDH as Potential Biomarkers of Atherosclerosis under Chronic Stress.

BACKGROUND: Pathological changes of the adrenal gland and the possible underlying molecular mechanisms are currently unclear in the case of atherosclerosis (AS) combined with chronic stress (CS). METHODS: New Zealand white rabbits were used to construct a CS and AS animal model. Proteomics and bioinformatics were employed to identify hub proteins in the adrenal gland related to CS and AS. Hub proteins were detected using immunohistochemistry, immunofluorescence assays, and Western blotting. Real-time quantitative polymerase chain reaction (RT-qPCR) was used to analyze the expression of genes. In addition, a neural network model was constructed. The quantitative relationships were inferred by cubic spline interpolation. Enzymatic activity of mitochondrial citrate synthase and OGDH was detected by the enzymatic assay kit. Function of citrate synthase and OGDH with knockdown experiments in the adrenal cell lines was performed. Furthermore, target genes-TF-miRNA regulatory network was constructed. Coimmunoprecipitation (IP) assay and molecular docking study were used to detect the interaction between citrate synthase and OGDH. RESULTS: Two most significant hub proteins (citrate synthase and OGDH) that were related to CS and AS were identified in the adrenal gland using numerous bioinformatic methods. The hub proteins were mainly enriched in mitochondrial proton transport ATP synthase complex, ATPase activation, and the AMPK signaling pathway. Compared with the control group, the adrenal glands were larger and more disordered, irregular, and necrotic in the AS+CS group. The expression of citrate synthase and OGDH was higher in the AS+CS group than in the control group, both at the protein and mRNA levels (P < 0.05). There were strong correlations among the cross-sectional areas of adrenal glands, citrate synthase, and OGDH (P < 0.05) via Spearman's rho analysis, receiver operating characteristic curves, a neural network model, and cubic spline interpolation. Enzymatic activity of citrate synthase and OGDH increased under the situation of atherosclerosis and chronic stress. Through the CCK8 assay, the adrenal cell viability was downregulated significantly after the knockdown experiment of citrate synthase and OGDH. Target genes-TF-miRNA regulatory network presented the close interrelations among the predicted microRNA, citrate synthase and OGDH. After Coimmunoprecipitation (IP) assay, the result manifested that the citrate synthase and OGDH were coexpressed in the adrenal gland. The molecular docking study showed that the docking score of optimal complex conformation between citrate synthase and OGDH was -6.15 kcal/mol. CONCLUSION: AS combined with CS plays a significant role on the hypothalamic-pituitary-adrenal (HPA) axis, promotes adrenomegaly, increases the release of glucocorticoid (GC), and might enhance ATP synthesis and energy metabolism in the body through citrate synthase and OGDH gene targets, providing a potential research direction for future related explorations into this mechanism.

Identification of biomarkers for the diagnosis and treatment of primary colorectal cancer based on microarray technology.

BACKGROUND: Primary colorectal cancer (PCRC) is one of the most common malignant tumors in clinic, and is characterized by high heterogeneity occurring between tumors and intracellularly. Therefore, this study aimed to explore potential gene targets for the diagnosis and treatment of PCRC via bioinformatic technology. METHODS: Gene Expression Omnibus (GEO) was used to download the data used in this study. Differently expressed genes (DEGs) were identified with GEO2R, and the gene set enrichment analysis (GSEA) was implemented for enrichment analysis. Then, the researchers constructed a protein-protein interaction (PPI) network, a significant module, and a hub genes network. RESULTS: The GSE81558 dataset was downloaded, and a total of 97 DEGs were found. There were 23 up-regulated DEGs and 74 down-regulated DEGs in the PCRC samples, compared with the control group. The PPI network included a total of 42 nodes and 63 edges. One module network consisted of 11 nodes and 25 edges. Another module network consisted of 4 nodes and 6 edges. The hub genes network was created by cytoHubba using GCG, GUCA2B, CLCA4, ZG16, TMIGD1, GUCA2A, CHGA, PYY, SST, and MS4A12. CONCLUSIONS: Ten hub genes were found from the genomic samples of patients with PCRC and normal controls by bioinformatics analysis. The hub genes might provide novel ideas and evidence for the diagnosis and targeted therapy of PCRC.

Identification and Functional Enrichment Analysis of Potential Diagnostic and Therapeutic Targets in Adamantinomatous Craniopharyngioma.

Adamantinomatous craniopharyngioma (ACP) is a congenital epithelial tumor in the sellar region with benign histological manifestation but invasive. Currently, surgery is the main treatment for it, but its recurrence rate is high. Therefore, it is of great importance to explore the mechanism of occurrence and development of ACP and to identify new molecules. One gene expression profile, GSE94349, was downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified by the limma package. Gene set enrichment analysis was used to make enrichment analysis using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Then, we performed the construction and analysis of the protein-protein interaction (PPI) network and significant module. The analysis of the GSE94349 dataset identified 109 DEGs, consisting of 80 upregulated genes and 29 downregulated genes in ACP samples compared with normal brain tissues. Functional and pathway enrichment analysis of DEGs provided a comprehensive overview of some major pathophysiological mechanisms in ACP: RNA polymerase II promoter, glutamate receptor binding, and so on. A total of 10 hub genes of DEGs were obtained from the PPI network, which provided potential therapeutic targets for the ACP. In summary, there were DEGs between ACP tissues and normal brain tissues, which may be involved in the mechanisms of occurrence and development of ACP, especially via the regulation of RNA polymerase II promoter and glutamate receptor binding. Key genes in DEGs could serve as new research targets for the diagnosis and treatment of ACP.

Screening and Identification of Key Biomarkers in Pancreatic Cancer: Evidence from Bioinformatic Analysis.

Pancreatic cancer (PC) whose mortality is comparable to morbidity is a highly fatal disease. Early approaches of diagnosis and treatment for PC are quite limited, so it is of great urgency to figure out the exact tumorigenesis and development mechanism of PC. To identify the related molecular markers of pancreatic oncogenesis, we downloaded three microarray datasets (GSE63111, GSE101448, and GSE107610) from Gene Expression Omnibus (GEO) database. The common differentially expressed genes (DEGs) among them were identified, and the corresponding function enrichment analyses were accomplished. The protein-protein interaction network was conducted by Search Tool for the Retrieval of Interacting Genes (STRING), and the corresponding module analysis was accomplished by Cytoscape. There were 55 DEGs found in total. The molecular function and biological processes (BP) of these DEGs mainly include cytokinesis, mitotic nuclear division, cell division, cell proliferation, microtubule-based movement, and mineral absorption. Among the 55 DEGs, 14 hub genes were further confirmed and it was concluded that they mainly function in mitotic cytokinesis, microtubule-based movement, mitotic chromosome condensation, and mitotic spindle assembly from the BP analysis. The survival analysis showed that all the 14 hub genes, especially nucleolar and spindle associated protein 1 and abnormal spindle microtubule assembly, may involve in the tumorigenesis and development of PC. And they might be used as new biomarkers for auxiliary diagnosis and potential targets for immunotherapy of PC.

Screening and authentication of molecular markers in malignant glioblastoma based on gene expression profiles.

Glioblastoma (GBM) is a malignant tumor of the central nervous system with high mortality rates. Gene expression profiling may determine the chemosensitivity of GBMs. However, the molecular mechanisms underlying GBM remain to be determined. To screen the novel key genes in its occurrence and development, two glioma databases, GSE122498 and GSE104291, were analyzed in the present study. Bioinformatics analyses were performed using the Database for Annotation, Visualization and Integrated Discovery, the Search Tool for the Retrieval of Interacting Genes, Cytoscape, cBioPortal, and Gene Expression Profiling Interactive Analysis softwares. Patients with recurrent GBM showed worse overall survival rate. Overall, 341 differentially expressed genes (DEGs) were authenticated based on two microarray datasets, which were primarily enriched in 'cell division', 'mitotic nuclear division', 'DNA replication', 'nucleoplasm', 'cytosol, nucleus', 'protein binding', 'ATP binding', 'protein C-terminus binding', 'the cell cycle', 'DNA replication', 'oocyte meiosis' and 'valine'. The protein-protein interaction network was composed of 1,799 edges and 237 nodes. Its significant module had 10 hub genes, and CDK1, BUB1B, NDC80, NCAPG, BUB1, CCNB1, TOP2A, DLGAP5, ASPM and MELK were significantly associated with carcinogenesis and the development of GBM. The present study indicated that the DEGs and hub genes, identified based on bioinformatics analyses, had significant diagnostic value for patients with GBM.

TPM2 as a potential predictive biomarker for atherosclerosis.

Cardiac-cerebral vascular disease (CCVD), is primarily induced by atherosclerosis, and is a leading cause of mortality. Numerous studies have investigated and attempted to clarify the molecular mechanisms of atherosclerosis; however, its pathogenesis has yet to be completely elucidated. Two expression profiling datasets, GSE43292 and GSE57691, were obtained from the Gene Expression Omnibus (GEO) database. The present study then identified the differentially expressed genes (DEGs), and functional annotation of the DEGs was performed. Finally, an atherosclerosis animal model and neural network prediction model was constructed to verify the relationship between hub gene and atherosclerosis. The results identified a total of 234 DEGs between the normal and atherosclerosis samples. The DEGs were mainly enriched in actin filament, actin binding, smooth muscle cells, and cytokine-cytokine receptor interactions. A total of 13 genes were identified as hub genes. Following verification of animal model, the common DEG, Tropomyosin 2 (TPM2), was found, which were displayed at lower levels in the atherosclerosis models and samples. In summary, DEGs identified in the present study may assist clinicians in understanding the pathogenesis governing the occurrence and development of atherosclerosis, and TPM2 exhibits potential as a promising diagnostic and therapeutic biomarker for atherosclerosis.

Pivotal micro factors associated with endothelial cells.

OBJECTIVE: Recent studies have shown the important influence of various micro factors on the general biological activity and function of endothelial cells (ECs). Vascular endothelial growth factor (VEGF) and angiogenin (ANG) are classic micro factors that promote proliferation, differentiation, and migration of ECs. The underlying pathophysiological mechanisms and related pathways of these micro factors remain the focus of current research. DATA SOURCES: An extensive search was undertaken in the PubMed database by using keywords including "micro factors" and "endothelial cell." This search covered relevant research articles published between January 1, 2007 and December 31, 2018. STUDY SELECTION: Original articles, reviews, and other articles were searched and reviewed for content on micro factors of ECs. RESULTS: VEGF and ANG have critical functions in the occurrence, development, and status of the physiological pathology of ECs. Other EC-associated micro factors include interleukin 10, tumor protein P53, nuclear factor kappa B subunit, interleukin 6, and tumor necrosis factor. The results of Gene Ontology analysis revealed that variations were mainly enriched in positive regulation of transcription by the RNA polymerase II promoter, cellular response to lipopolysaccharides, negative regulation of apoptotic processes, external side of the plasma membrane, cytoplasm, extracellular regions, cytokine activity, growth factor activity, and identical protein binding. The results of the Kyoto Encyclopedia of Genes and Genomes analysis revealed that micro factors were predominantly enriched in inflammatory diseases. CONCLUSIONS: In summary, the main mediators, factors, or genes associated with ECs include VEGF and ANG. The effect of micro factors on ECs is complex and multifaceted. This review summarizes the correlation between ECs and several micro factors.

Systematic estimation of BMI: A novel insight into predicting overweight/obesity in undergraduates.

The prevalence of overweight-obesity has increased sharply among undergraduates worldwide. In 2016, approximately 52% of adults were overweight-obese. This cross-sectional study aimed to investigate the prevalence of overweight-obesity and explore in depth the connection between eating habits and overweight-obesity among Chinese undergraduates.The study population included 536 undergraduates recruited in Shijiazhuang, China, in 2017. They were administered questionnaires for assessing demographic and daily lifestyle characteristics, including sex, region, eating speed, number of meals per day, and sweetmeat habit. Anthropometric status was assessed by calculating the body mass index (BMI). The determinants of overweight-obesity were investigated by the Pearson χ test, Spearman rho test, multivariable linear regression, univariate/multivariate logistic regression, and receiver operating characteristic curve analysis.The prevalence of undergraduate overweight-obesity was 13.6%. Sex [male vs female, odds ratio (OR): 1.903; 95% confidence interval (95% CI): 1.147-3.156], region (urban vs rural, OR: 1.953; 95% CI: 1.178-3.240), number of meals per day (3 vs 2, OR: 0.290; 95% CI: 0.137-0.612), and sweetmeat habit (every day vs never, OR: 4.167; 95% CI: 1.090-15.933) were significantly associated with overweight-obesity. Eating very fast was positively associated with overweight-obesity and showed the highest OR (vs very slow/slow, OR: 5.486; 95% CI: 1.622-18.553). However, the results of multivariate logistic regression analysis indicated that only higher eating speed is a significant independent risk factor for overweight/obesity (OR: 17.392; 95% CI, 1.614-187.363; P = .019).Scoremeng = 1.402 × scoresex + 1.269 × scoreregion + 19.004 × scoreeatin speed + 2.546 × scorenumber of meals per day + 1.626 × scoresweetmeat habit and BMI = 0.253 × Scoremeng + 18.592. These 2 formulas can help estimate the weight status of undergraduates and predict whether they will be overweight or obese.

Neutrophils and neutrophil-lymphocyte ratio: Inflammatory markers associated with intimal-media thickness of atherosclerosis.

INTRODUCTION: Cardiac-cerebral vascular diseases (CCVDs) are global health problems due to the characteristic of high mortality. It is found that atherosclerosis (AS), a main cause of CCVDs, is significantly relevant to the change of intimal and media thickness. Neutrophil count (NEU) and neutrophil-lymphocyte ratio (N/L) are recognized possible risk factors for atherosclerosis (AS). However, there are few studies on the separate relationship between carotid intimal thickness, media thickness and NEU, N/L. This study explored the respective effects of NEU and N/L on AS and intimal, media thickness. MATERIALS AND METHODS: The χ2, Spearman's rho test, and multiple linear regression were implemented to analyze the relevance between blood parameters and intimal-media thickness. The potential factors, affecting non-depression time (NDT), is identified by univariate Cox regression. ROC curve was performed to determine the ability of blood parameters to predict intimal-media thickness. Immunohistochemistry was implemented. RESULTS AND CONCLUSION: Based on χ2, Spearman's rho test and multiple linear regression, NEU is related with intimal thickness (P < 0.05). Furthermore, NEU can predict the intimal thickness through the ROC curve. What's more, N/L is a risk factor of carotid media thickness (P < 0.05) by the Spearman's rho test, and is also correlated with poor NDT (P < 0.05) based on univariate Cox proportional regression analysis. Through ROC curve, N/L can predict the carotid media thickness. The carotid atherosclerotic endarterium is richest in macrophagocytes, and the arrangement of endotheliocytes is disordered. In summary, the increased NEU and N/L respectively have a strong correlation and precise predictability for carotid intimal and media thickness of atherosclerosis.