Discussion on the relationship between gut microbiota and glioma through Mendelian randomization test based on the brain gut axis.

BACKGROUND: In the realm of Gut-Brain axis research, existing evidence points to a complex bidirectional regulatory mechanism between gut microbiota and the brain. However, the question of whether a causal relationship exists between gut microbiota and specific types of brain tumors, such as gliomas, remains unresolved. To address this gap, we employed publicly available Genome-Wide Association Study (GWAS) and MIOBEN databases, conducting an in-depth analysis using Two-Sample Mendelian Randomization (MR). METHOD: We carried out two sets of MR analyses. The preliminary analysis included fewer instrumental variables due to a high genome-wide statistical significance threshold (5×10-8). To enable a more comprehensive and detailed analysis, we adjusted the significance threshold to 1×10-5. We performed linkage disequilibrium analysis (R2 <0.001, clumping distance = 10,000kb) and detailed screening of palindromic SNPs, followed by MR analysis and validation through sensitivity analysis. RESULTS: Our findings reveal a causal relationship between gut microbiota and gliomas. Further confirmation via Inverse Variance Weighting (IVW) identified eight specific microbial communities related to gliomas. Notably, the Peptostreptococcaceae and Olsenella communities appear to have a protective effect, reducing glioma risk. CONCLUSION: This study not only confirms the causal link between gut microbiota and gliomas but also suggests a new avenue for future glioma treatment.

Causal relationships of Helicobacter pylori and related gastrointestinal diseases on Type 2 diabetes: Univariable and Multivariable Mendelian randomization.

BACKGROUND: Previous observational studies have demonstrated a connection between the risk of Type 2 diabetes mellitus (T2DM) and gastrointestinal problems brought on by Helicobacter pylori (H. pylori) infection. However, little is understood about how these factors impact on T2DM. METHOD: This study used data from the GWAS database on H. pylori antibodies, gastroduodenal ulcers, chronic gastritis, gastric cancer, T2DM and information on potential mediators: obesity, glycosylated hemoglobin (HbA1c) and blood glucose levels. Using univariate Mendelian randomization (MR) and multivariate MR (MVMR) analyses to evaluate the relationship between H. pylori and associated gastrointestinal diseases with the risk of developing of T2DM and explore the presence of mediators to ascertain the probable mechanisms. RESULTS: Genetic evidence suggests that H. pylori IgG antibody (P = 0.006, b = 0.0945, OR = 1.0995, 95% CI = 1.023-1.176), H. pylori GroEL antibody (P = 0.028, OR = 1.033, 95% CI = 1.004-1.064), gastroduodenal ulcers (P = 0.019, OR = 1.036, 95% CI = 1.006-1.068) and chronic gastritis (P = 0.005, OR = 1.042, 95% CI = 1.012-1.074) are all linked to an increased risk of T2DM, additionally, H. pylori IgG antibody is associated with obesity (P = 0.034, OR = 1.03, 95% CI = 1.002-1.055). The results of MVMR showed that the pathogenic relationship between H. pylori GroEL antibody and gastroduodenal ulcer in T2DM is mediated by blood glucose level and obesity, respectively. CONCLUSION: Our study found that H. pylori IgG antibody, H. pylori GroEL antibody, gastroduodenal ulcer and chronic gastritis are all related to t T2DM, and blood glucose level and obesity mediate the development of H. pylori GroEL antibody and gastroduodenal ulcer on T2DM, respectively. These findings may inform new prevention and intervention strategies for T2DM.

Peripheral artery disease mediating the effect of metabolic syndrome related diseases on lower limb ulcers: Mendelian randomization analysis.

Background: Previous observational studies have demonstrated a correlation between metabolic syndrome related diseases and an elevated susceptibility to ulcers of lower limb. It has been suggested that this causal relationship may be influenced by the presence of peripheral artery disease (PAD). Nevertheless, the precise contribution of these factors as determinants of ulcers of lower limb remains largely unexplored. Method: This research incorporated information on hypertension, BMI, hyperuricemia, type 2 diabetes, PAD, and ulcers of lower limb sourced from the GWAS database. Univariate Mendelian randomization (SVMR) and multivariate Mendelian randomization (MVMR) methods were employed to assess the association between metabolic syndrome related diseases, including hypertension, obesity, hyperuricemia, and type 2 diabetes, as well as to investigate whether this association was influenced by PAD. Results: Univariate Mendelian randomization analysis showed that genetically predicted hypertension, BMI, and type 2 diabetes were associated with an increased risk of PAD and ulcers of lower limb, and PAD was associated with an increased risk of ulcers of lower limb, but there is no causal relationship between hyperuricemia and ulcers of lower limb. The results of multivariate Mendelian randomization showed that PAD mediated the causal relationship between hypertension, obesity and ulcers of lower limb, but the relationship between type 2 diabetes and ulcers of lower limb was not mediated by PAD. Conclusion: Hypertension, BMI and type 2 diabetes can increase the risk of ulcers of lower limb, and PAD can be used as a mediator of hypertension and obesity leading to ulcers of lower limb, These findings may inform prevention and intervention strategies directed toward metabolic syndrome and ulcers of lower limb.

Integrated genetic analysis of diabetic complications: Bioinformatics insights into foot ulcers, neuropathy and peripheral artery disease.

Diabetic foot ulcers (DFU), diabetic peripheral neuropathy (DPN) and peripheral arterial disease (PAD) are common complications of diabetes mellitus, while diabetic peripheral neuropathy and peripheral arterial disease contribute to the pathogenesis of diabetic foot ulcers, and the pathogenic mechanisms between these three diseases still need further investigation. The keywords 'diabetic foot ulcer', 'diabetic peripheral neuropathy' and 'atherosclerosis' were used to search for related gene sets in the GEO database. Differentially expressed genes (DEGs) were screened and analysed for GO, KEGG and enrichR functional enrichment. Potential three disease biomarkers were identified by SVM-SVM-RFE and LASSO regression analysis. The results were also validated using external datasets and discriminability was measured by area under the ROC curve (AUC). Finally, biomarkers and co-upregulated genes were analysed through the GSEA and Attie Laboratories diabetes databases. A total of 11 shared genes (KRT16, CD24, SAMD9L, SRGAP2, FGL2, GPR34, DDIT4, NFE2L3, FBLN5, ANXA3 and CPA3), two biomarkers (SAMD9L and FGL2) and one co-upregulated gene (CD24) were screened. GO and KEGG pathway analysis of DEGs, enrichr enrichment analysis of shared differential genes and GSEA analysis of biomarkers showed that these significant genes were mainly focused on vasoregulatory, inflammatory-oxidative stress and immunomodulatory pathways. In this study, we used bioinformatics to investigate the intrinsic relationship and potential mechanisms of three common lower extremity complications of diabetes and identified two pivotal genes using the LASSO model and the SVM-RFE algorithm, which will further help clinicians to understand the relationship between diabetic complications, improve the diagnosis and treatment of diabetic foot problems and help doctors to identify the potential risk factors of diabetic foot.

Establishment of skin cutaneous melanoma prognosis model based on vascular mimicry risk score.

Studies have indicated that Vascular mimicry (VM) could contribute to the unfavorable prognosis of skin cutaneous melanoma (SKCM). Thus, the objective of this study was to identify therapeutic targets associated with VM in SKCM and develop a novel prognostic model. Gene expression data from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) were utilized to identify differentially expressed genes (DEGs). By intersecting these DEGs with VM genes, we acquired VM-related DEGs specific to SKCM, and then identified prognostic-related VM genes. A VM risk score system was established based on these prognosis-associated VM genes, and patients were then categorized into high- and low-score groups using the median score. Subsequently, differences in clinical characteristics, gene set enrichment analysis (GSEA), and other analyses were further presented between the 2 groups of patients. Finally, a novel prognostic model for SKCM was established using the VM score and clinical characteristics. 26 VM-related DEGs were identified in SKCM, among the identified DEGs associated with VM in SKCM, 5 genes were found to be prognostic-related. The VM risk score system, comprised of these genes, is an independent prognostic risk factor. There were significant differences between the 2 patient groups in terms of age, pathological stage, and T stage. VM risk scores are associated with epithelial biological processes, angiogenesis, regulation of the SKCM immune microenvironment, and sensitivity to targeted drugs. The novel prognostic model demonstrates excellent predictive ability. Our study identified VM-related prognostic markers and therapeutic targets for SKCM, providing novel insights for clinical diagnosis and treatment.

Identification of angiogenesis-related genes in diabetic foot ulcer using machine learning algorithms.

Background: Diabetic foot ulcers (DFUs) are among the most prevalent and dangerous complications of diabetes. Angiogenesis is pivotal for wound healing; however, its role in the chronic wound healing process in DFU requires further investigation. We aimed to investigate the pathogenic processes of angiogenesis in DFU from a molecular biology standpoint and to offer insightful information about DFU prevention and therapy. Methods: Differential gene and weighted gene co-expression network analyses (WGCNA) were employed to screen for genes related to DFU using the downloaded and collated GSES147890 datasets. With the goal of identifying hub genes, an interaction among proteins (PPI) network was constructed, and enrichment analysis was carried out. Utilizing a variety of machine learning techniques, including Boruta, Support Vector Machine Recursive Feature Elimination (SVM-RFE), and Least Absolute Shrinkage and Selection Operator (LASSO), we were able to determine which hub genes most strongly correspond to DFU. This allowed us to create an ideally suited DFU forecasting model that was validated via an external dataset. Finally, by merging 36 angiogenesis-related genes (ARGs) and machine learning models, we identified the genes involved in DFU-related angiogenesis. Results: By merging 260 genes located in the green module and 59 differentially expressed genes (DEGs), 35 candidate genes highly associated with DFU were found for more investigation. 35 candidate genes were enriched in epidermal growth factor receptor binding, nuclear division regulation, fluid shear stress, atherosclerosis, and negative regulation of chromosomal structure for the enrichment study. Fifteen hub genes were found with the aid of the CytoHubba plug. The LASSO method scored better in terms of prediction performance (GSE134341) (LASSO:0.89, SVM:0.65, Boruta:0.66) based on the validation of the external datasets. We identified thrombomodulin (THBD) as a key target gene that potentially regulates angiogenesis during DFU development. Based on the external validation dataset (GSE80178 and GSE29221), receiver operating characteristic (ROC) curves with higher efficiency were generated to confirm the potential of THBD as a biomarker of angiogenesis in DFU. Furthermore supporting this finding were the results of Western blot and real-time quantitative polymerase chain reaction (RT-qPCR), which showed decreased THBD expression in human umbilical vein endothelial cells (HUVECs) cultivated under high glucose. Conclusions: The findings implicate that THBD may influence DFU progression as a potential target for regulating angiogenesis, providing a valuable direction for future studies.

The awareness and determinants of diabetic foot ulcer prevention among diabetic patients: Insights from NHANES (2011-2018).

The prevention of diabetic foot ulcers (DFU) precedes treatment, in that early prevention significantly reduces the incidence of foot ulcers. The main objectives of this study were to examine the current prevalence of proactive foot ulcer examinations among diabetic patients and analyze influencing factors, in order to provide a scientific reference for the prevention of DFU in diabetic patients. The National Health and Nutrition Examination Survey (NHANES) 2011-2018 (n = 1278) data were utilized in this cross-sectional study. The dependent variable was whether patients underwent self-initiated foot ulcer inspections; risk factors that may lead to foot ulcers were included as independent variables. To explore the connection between the patient's subjective motivation to inspect foot ulcers and risk variables, the weighted logistic regression model was further carried out. Among all risk factors, race, body mass index (BMI) and hypertension were statistically significant between whether patients were examined for foot ulcers or not. In the fully adjusted logistic regression model, only hypertension was positively correlated with diabetic patient-initiated examination for foot ulcers. This study suggests that there is still room for improvement in the knowledge and behavior of diabetic patients to be proactive in preventing DFU. Health care and community workers should conduct targeted training on diabetic foot prevention to reduce and prevent DFU by reinforcing knowledge to build positive attitudes and drive preventive behavior change.

Identification of ferroptosis-related molecular clusters and genes for diabetic osteoporosis based on the machine learning.

Background: Diabetic osteoporosis exhibits heterogeneity at the molecular level. Ferroptosis, a controlled form of cell death brought on by a buildup of lipid peroxidation, contributes to the onset and development of several illnesses. The aim was to explore the molecular subtypes associated with ferroptosis in diabetic osteoporosis at the molecular level and to further elucidate the potential molecular mechanisms. Methods: Integrating the CTD, GeneCards, FerrDb databases, and the microarray data of GSE35958, we identified ferroptosis-related genes (FRGs) associated with diabetic osteoporosis. We applied unsupervised cluster analysis to divide the 42 osteoporosis samples from the GSE56814 microarray data into different subclusters based on FRGs. Subsequently, FRGs associated with two ferroptosis subclusters were obtained by combining database genes, module-related genes of WGCNA, and differentially expressed genes (DEGs). Eventually, the key genes from FRGs associated with diabetic osteoporosis were identified using the least absolute shrinkage and selection operator (LASSO), Boruta, support vector machine recursive feature elimination (SVM - RFE), and extreme gradient boosting (XGBoost) machine learning algorithms. Based on ROC curves of external datasets (GSE56815), the model's efficiency was examined. Results: We identified 15 differentially expressed FRGs associated with diabetic osteoporosis. In osteoporosis, two distinct molecular clusters related to ferroptosis were found. The expression results and GSVA analysis indicated that 15 FRGs exhibited significantly different biological functions and pathway activities in the two ferroptosis subclusters. Therefore, we further identified 17 FRGs associated with diabetic osteoporosis between the two subclusters. The results of the comprehensive analysis of 17 FRGs demonstrated that these genes were heterogeneous and had a specific interaction between the two subclusters. Ultimately, the prediction model had a strong foundation and excellent AUC values (0.84 for LASSO, 0.84 for SVM - RFE, 0.82 for Boruta, and 0.81 for XGBoost). IDH1 is a common gene to all four algorithms thus being identified as a key gene with a high AUC value (AUC = 0.698). Conclusions: As a ferroptosis regulator, IDH1 is able to distinguish between distinct molecular subtypes of diabetic osteoporosis, which may offer fresh perspectives on the pathogenesis of the disease's clinical symptoms and prognostic heterogeneity.

Anti-inflammatory and immunomodulatory effects of polysaccharide extracted from Wuguchong (maggot) on 2,4-dinitrochlorobenzene-induced atopic dermatitis in mice.

Atopic dermatitis (AD) is an inflammatory, heterogeneous, chronic skin disorder characterized by recurrent eczematous lesions and intense pruritus, and the pathophysiology mechanism of AD is known for immune dysregulation and inflammatory responses. Wuguchong (maggot) has been widely used in the wound field and found with pharmacological properties of the anti-inflammatory and immunomodulatory function. Recently, some polysaccharides were proven to have beneficial effects on AD skin lesions in mice and humans. However, the effect of the polysaccharide extracted from Wuguchong (PEW) on AD remains to be investigated. In the present study, we examined the anti-inflammatory and immunomodulatory effects of PEW on AD and explored the potential mechanisms. Balb/c mice were orally administrated with PEW to evaluate the therapeutic effect of PEW on 2,4-dinitrochlorobenzene (DNCB)-induced AD. Oral PEW administration significantly ameliorated the lesions and symptoms in AD mice, such as the ear thickness and ear swelling degree, epidermal and dermal thickness, and the infiltration of mast cells. In addition, PEW treatment decreased the levels of serum IgE and histamine, the frequencies of Th1 and Th17 cells, as well as the mRNA expression levels of Th1 and Th17 cytokines and nuclear transcript factors (IFN-γ, T-bet, IL-17A, and ROR-rt). Furthermore, the activation of the NF-κB pathway and the phosphorylation of MAPKs (p38, ERK, and JNK) were significantly suppressed by PEW treatment. Taken together, our study suggests that PEW exerts anti-inflammatory and immunomodulatory effects through inhibition of Th1 and Th17 responses and downregulation of NF-κB and MAPK pathways, PEW would be developed as a promising immune therapy for AD.

Transcriptome analysis based on machine learning reveals a role for autoinflammatory genes of chronic nonbacterial osteomyelitis (CNO).

Chronic nonbacterial osteomyelitis (CNO) is an autoinflammatory bone disorder. The origin and development of CNO involve many complex immune processes, resulting in delayed diagnosis and a lack of effective treatment. Although bioinformatics analysis has been utilized to seek key genes and pathways in CNO, only a few bioinformatics studies that focus on CNO pathogenesis and mechanisms have been reported. This study aimed to identify key biomarkers that could serve as early diagnostic or therapeutic markers for CNO. Two RNA-seq datasets (GSE133378 and GSE187429) were obtained from the Gene Expression Omnibus (GEO). Weighted gene coexpression network analysis (WGCNA) and differentially expressed gene (DEG) analysis were conducted to identify the genes associated with CNO. Then, the autoinflammatory genes most associated with CNO were identified based on the GeneCards database and a CNO prediction model, which was created by the LASSO machine learning algorithm. The accuracy of the model and effects of the autoinflammatory genes according to receiver operating characteristic (ROC) curves were verified in external datasets (GSE7014). Finally, we performed clustering analysis with ConsensusClusterPlus. In total, eighty CNO-related genes were identified and were significantly enriched in the biological processes regulation of actin filament organization, cell-cell junction organization and gamma-catenin binding. The main enriched pathways were adherens junctions, viral carcinogenesis and systemic lupus erythematosus. Two autoinflammatory genes with high expression in CNO samples were identified by combining an optimal machine learning algorithm (LASSO) with the GeneCards database. An external validation dataset (GSE187429) was utilized for ROC analysis of the prediction model and two genes, and the results indicated good efficiency. Then, based on consensus clustering analysis, we found that the expression of UTS2 and MPO differed between clusters. Finally, the ceRNA network of lncRNAs and the small molecule compounds targeting the two autoinflammatory genes were predicted. The identification of two autoinflammatory genes, the HCG18/has-mir-147a/UTS2/MPO axis and signalling pathways in this study can help us understand the molecular mechanism of CNO formation and provides candidate targets for the diagnosis and treatment of CNO.

Revealing novel pyroptosis-related therapeutic targets for sepsis based on machine learning.

BACKGROUND: Sepsis is one of the most lethal diseases worldwide. Pyroptosis is a unique form of cell death, and the mechanism of interaction with sepsis is not yet clear. The aim of this study was to uncover pyroptosis genes associated with sepsis and to provide early therapeutic targets for the treatment of sepsis. METHODS: Based on the GSE134347 dataset, sepsis-related genes were mined by differential expression analysis and weighted gene coexpression network analysis (WGCNA). Subsequently, the sepsis-related genes were analysed for enrichment, and a protein‒protein interaction (PPI) network was constructed. We performed unsupervised consensus clustering of sepsis patients based on 33 pyroptosis-related genes (PRGs) provided by prior reviews. We finally obtained the PRGs mostly associated with sepsis by machine learning prediction models combined with prior reviews. The GSE32707 dataset served as an external validation dataset to validate the model and PRGs via receiver operating characteristic (ROC) curves. The NetworkAnalyst online tool was utilized to create a ceRNA network of lncRNAs and miRNAs around PRGs mostly associated with sepsis. RESULTS: A total of 170 genes associated with sepsis and 13 hub genes were acquired by WGCNA and PPI network analysis. The results of the enrichment analysis implied that these genes were mainly involved in the regulation of the inflammatory response and the positive regulation of bacterial and fungal defence responses. The prolactin signalling pathway and IL-17 signalling pathway were the primary enrichment pathways. Thirty-three PRGs can effectively classify septic patients into two subtypes, implying that there is a reciprocal relationship between sepsis and pyroptosis. Eventually, NLRC4 was considered the PRG most strongly associated with sepsis. The validation results of the prediction model and NLRC4 based on ROC curves were 0.74 and 0.67, respectively, both of which showed better predictive values. Meanwhile, the ceRNA network consisting of 6 lncRNAs and 2 miRNAs was constructed around NLRC4. CONCLUSION: NLRC4, as the PRG mostly associated with sepsis, could be considered a potential target for treatment. The 6 lncRNAs and 2 miRNAs centred on NLRC4 could serve as a further research direction to uncover the deeper pathogenesis of sepsis.

Revealing the novel ferroptosis-related therapeutic targets for diabetic foot ulcer based on the machine learning.

Objectives: DFU is a serious chronic disease with high disability and fatality rates, yet there is no completely effective therapy. While ferroptosis is integrated to inflammation and infection, its involvement in DFU is still unclear. The study aimed to identify ferroptosis-related genes in DFU, providing potential therapeutic targets. Methods: In the GEO database, two DFU microarray datasets (GSE147890 and GSE80178) were collected. WGCNA was conducted to identify the modular genes most involved in DFU. Subsequently, enrichment analysis and PPI analysis were performed. To yield the DFU-associated ferroposis genes, the ferroposis genes were retrieved from the FerrDb database and overlapped with the modular genes. Eventually, an optimal DFU prediction model was created by combining multiple machine learning algorithms (LASSO, SVM-RFE, Boruta, and XGBoost) to detect ferroposis genes most closely associated with DFU. The accuracy of the model was verified by utilizing external datasets (GSE7014) based on ROC curves. Results: WGCNA yielded seven modules in all, and 1223 DFU-related modular genes were identified. GO analysis revealed that inflammatory response, decidualization, and protein binding were the most highly enriched terms. These module genes were also enriched in the ErbB signaling, IL-17 signaling, MAPK signaling, growth hormone synthesis, secretion and action, and tight junction KEGG pathways. Twenty-five DFU-associated ferroposis genes were obtained by cross-linking with modular genes, which could distinguish DFU patients from controls. Ultimately, the prediction model based on machine learning algorithms was well established, with high AUC values (0.79 of LASSO, 0.80 of SVM, 0.75 of Boruta, 0.70 of XGBoost). MAFG and MAPK3 were identified by the prediction model as the most highly associated ferroposis-genes in DFU. Furthermore, the external dataset (GSE29221) validation revealed that MAPK3 (AUC = 0.81) had superior AUC values than MAFG (AUC = 0.62). Conclusion: As the most related ferroptosis-genes with DFU, MAFG and MAPK3 may be employed as potential therapeutic targets for DFU patients. Moreover, MAPK3, with higher accuracy, could be the more potential ferroptosis-related biomarker for further experimental validation.

Bioinformatics Analysis Screened and Identified Key Genes as Potential Biomarkers for Progression of Lung Cancer.

OBJECTIVE: To screen and identify key genes as potential biomarkers of lung cancer using bioinformatics analysis. STUDY DESIGN: Observational study. PLACE AND DURATION OF STUDY: Department of Critical Care Medicine, the First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning Province, China, from August 2018 to April 2021. METHODOLOGY: Independent microarray datasets (GSE85841 and GSE118370) were downloaded from the Gene Expression Omnibus (GEO) database and the differentially expressed genes (DEGs) were screened using GEO2R. Cytohubba was employed to identify the hub genes. Cellular component analysis, hierarchical clustering, and survival analyses of hub genes were performed via BiNGO, UCSC, and cBioPorta. A series of analyses of FGF2 and PIK3R1 were conducted using Oncomine. RESULTS: A total of 463 DEGs were identified and 11 hub genes were determined. BDNF, FGF2, JAK2, NCAM1, CAV1, TJP1, and PIK3R1 may affect the survival probability and life expectancy of lung cancer patients, but the p-values were not statistically significant. FGF2 and PIK3R1 had the highest node degrees, 40 and 32 respectively. The expression of FGF2 and PIK3R1 were significantly lower in the 4 lung cancer data sets compared with non-lung cancer tissues. And the low expression of FGF2 and PIK3R1 is related to tumor grades, family history of cancer, multiple tumors present, and prior therapy of lung cancer. CONCLUSION: Evaluation of FGF2 and PIK3R1 as potential biomarkers can contribute to the subsequent theoretical analysis of potential molecular mechanisms and development of lung cancer, so that the diagnosis of lung cancer may be more accurate, and it is possible to provide therapeutic and prognostic medicine targets. KEY WORDS: Lung neoplasms, Differentially expressed genes, Bioinformatical analysis, Microarray analysis, biomarkers.

Protective effect of homogeneous polysaccharides of Wuguchong (HPW) on intestinal mucositis induced by 5-fluorouracil in mice.

BACKGROUND: In hospitalized patients, drug side effects usually trigger intestinal mucositis (IM), which in turn damages intestinal absorption and reduces the efficacy of treatment. It has been discovered that natural polysaccharides can relieve IM. In this study, we extracted and purified homogenous polysaccharides of Wuguchong (HPW), a traditional Chinese medicine, and explored the protective effect of HPW on 5-fluorouracil (5-FU)-induced IM. METHODS AND RESULTS: First, we identified the physical and chemical properties of the extracted homogeneous polysaccharides. The molecular weight of HPW was 616 kDa, and it was composed of 14 monosaccharides. Then, a model of small IM induced by 5-FU (50 mg/kg) was established in mice to explore the effect and mechanism of HPW. The results showed that HPW effectively increased histological indicators such as villus height, crypt depth and goblet cell count. Moreover, HPW relieved intestinal barrier indicators such as D-Lac and diamine oxidase (DAO). Subsequently, western blotting was used to measure the expression of Claudin-1, Occludin, proliferating cell nuclear antigen, and inflammatory proteins such as NF-κB (P65), tumour necrosis factor-α (TNF-α), and COX-2. The results also indicated that HPW could reduce inflammation and protect the barrier at the molecular level. Finally, we investigated the influence of HPW on the levels of short-chain fatty acids, a metabolite of intestinal flora, in the faeces of mice. CONCLUSIONS: HPW, which is a bioactive polysaccharide derived from insects, has protective effects on the intestinal mucosa, can relieve intestinal inflammation caused by drug side effects, and deserves further development and research.

Stress distribution of different lumbar posterior pedicle screw insertion techniques: a combination study of finite element analysis and biomechanical test.

At present, the pedicle screw is the most commonly used internal fixation device. However, there are many kinds of common posterior pedicle screw insertion techniques performed to reconstruct the lumbar stability. Therefore, spinal surgeons often face a difficult choice. The stress distribution of internal fixation system is an important index for evaluating safety. Unfortunately, little had been known about the difference of stress distribution of screw-rod systems that established by Roy-Camille, Magerl and Krag insertion techniques. Here, combination of finite element analysis and model measurement research was adopted to evaluate the difference of stress. Following different pedicle screw insertion techniques, three lumbar posterior surgery models were established after modeling and validation of the L1-S1 vertebrae finite element model. By analyzing the data, we found that stress concentration phenomenon was in all the postoperative models. Roy-Camille and Magerl insertion techniques led to the great stress on screw-rod systems. Then, fresh frozen calf spines were selected as a model for subsequent measurements. Fitted with a specially designed test pedicle screw, L5-L6 vertebrae were selected to repeat and verify the results of the finite element analysis. With the aid of universal testing machine and digital torque wrench, models simulated flexion, extension, lateral bending and rotation. Finally, the strain value was captured by the strain gauge and was then calculated as the stress value. Krag and Magerl were found to be the safer choice for pedicle screw insertion. Overall, our combination method obtained the reliable result that Krag insertion technique was the safer approach for pedicle screw implantation due to its relatively dispersive stress. Therefore, without the consideration of screw size, pedicle fill, bone density, and bone structures, we recommend the Krag insertion technique as the first choice to reconstruction of lumbar stability. Additionally, the combination method of finite element analysis and strain gauge measurement can provide a feasible way to study the stress distribution of spinal internal fixation.

Brachial-Ankle Pulse Wave Velocity as a Novel Modality for Detecting Early Diabetic Nephropathy in Type 2 Diabetes Patients.

Brachial-ankle pulse wave velocity (baPWV) has been shown to correlate with a host of disorders associated with arterial stiffness. Type 2 diabetes is associated with the involvement of both small vessels and large vessels. Studies on the relevance of baPWV to early diabetic nephropathy are scarce. This retrospective observational case-control study enrolled 120 patients with type 2 diabetes from our medical records. We classified patients into two groups depending on the magnitude of albuminuria: 60 patients with microalbuminuria were classified as the early diabetic nephropathy group (EDN group) and 60 patients without albuminuria were classified as the diabetes without nephropathy group (DWN group). An additional 30 nondiabetic age- and sex-matched controls were also enrolled. Data regarding the lipid profile, blood pressure, baPWV, high-sensitivity C reactive protein (hs-CRP) level, anthropometric measurements, urine albumin/creatinine ratio (UACR), serum creatinine level, and glycemic control indices (i.e., fasting plasma glucose (FPG), postprandial glucose (PPG), and glycosylated hemoglobin (hemoglobin A1c, HbA1c)) were recorded for all enrolled participants. baPWV was significantly higher in the EDN group than in the DWN group. Moreover, baPWV was positively correlated with age, duration of diabetes, obesity, poor glycemic control, and high serum levels of triglycerides (TG), hs-CRP, creatinine, and uric acid as well as a high UACR (all P < 0.01). A significant negative correlation was found between baPWV and high-density lipoprotein levels (P < 0.05). Multivariate regression analysis showed that the hs-CRP level and duration of diabetes most strongly influenced baPWV. baPWV may be a convenient, noninvasive, and reproducible method for detecting early diabetic nephropathy.

Antibacterial and anti-biofilm effects of fatty acids extract of dried Lucilia sericata larvae against Staphylococcus aureus and Streptococcus pneumoniae in vitro.

Development of new effective antimicrobial drugs is still a big challenge to date due to microbial infection remains an inevitable problem against human health. In this study, fatty acids extract of Lucilia sericata larvae (LFAs) was obtained and evaluated by gas chromatograph-mass spectrometry (GC-MS), and its antibacterial activity against Staphylococcus aureus (S. aureus) and Streptococcus pneumoniae (S. pneumoniae) was investigated. We found that LFAs exhibited effective antibacterial activity against S. aureus and S. pneumoniae with minimal inhibitory concentrations (MICs) of 125 µg/mL and 100 µg/mL, respectively. The bacterial wall and membrane were the main targets, which was confirmed by fluorescence microscopy, scanning electron microscopy and transmission electron microscopy. Furthermore, a notable anti-biofilm activity against S. aureus and S. pneumoniae was also observed, which was able to both prevent biofilm formation and eradicate mature biofilms of these bacteria. As a promising antibacterial agent, LFAs showed good application prospects in clinical practice.

Polysaccharide extracted from WuGuChong reduces high-fat diet-induced obesity in mice by regulating the composition of intestinal microbiota.

BACKGROUND: Obesity is a severe public health threat worldwide. Emerging evidence suggests that gut microbiota dysbiosis is closely associated with obesity and its related metabolic complications. The aim of the present study is to investigate the effects of polysaccharide extracted from WuGuChong (PEW) on high-fat diet (HFD)-induced obesity, and the potential mechanisms involving modulation of the gut microbiota composition. METHODS: Mice were fed a normal chow diet and a high-fat diet with or without PEW (300 mg/kg/day) by oral gavage for 8 weeks. Body weight, obesity-related metabolic disorders, and gut microbiota were examined at the end of the experiment. RESULTS: PEW supplementation reduces body weight, adipose hypertrophy, liver steatosis, insulin resistance and systemic inflammation in HFD-fed mice, as well as maintains intestinal epithelium integrity. High-throughput 16S rRNA analysis demonstrates that PEW supplementation alters the composition of gut microbiota. The Firmicutes to Bacteroidetes ratio and the relative abundance of Proteobacteria are increased in HFD-fed mice, which are reversed by PEW supplementation to approximately the control levels. CONCLUSIONS: Our results suggest that PEW may be used as a bioactive ingredient to prevent obesity and its related metabolic disorders by modulating the composition of gut microbiota.

Fatty acid extracts facilitate cutaneous wound healing through activating AKT, ERK, and TGF-ß/Smad3 signaling and promoting angiogenesis.

Fatty acids (FAs) are potential therapeutic agents for cutaneous wound healing; however, the mechanisms underlying this effect have not been clearly defined. In this study, we extracted and characterized FAs from dried Lucilia sericata larvae and investigated the molecular basis by which FAs promote cutaneous wound healing. We first confirmed that FA sodium salts (FASSs) stimulated proliferation, migration, and tube formation of cultured human umbilical vein endothelial cells (HUVECs) in a dose-dependent manner. We then showed that FASSs promoted endothelial-to-mesenchymal transition (EndMT), which plays an important role in stabilizing the neovasculature during angiogenesis. Mechanistically, FASSs up-regulated the expression of angiogenesis-related growth factors, platelet-derived growth factor (PDGF), transforming growth factor-ß1 (TGF-ß1), and vascular endothelial growth factor A (VEGFA), and activated angiogenesis-related signaling pathways, AKT, ERK, and TGF-ß/Smad3. In a rat acute cutaneous-wound model, FAs promoted wound healing. Following treatment, we further found that expression of anti-apoptosis-related factors (c-Myc and Bcl-2) was up-regulated and expression of apoptosis-related factors (p53 and Bad) was down-regulated. Our findings suggest that FAs can promote cutaneous wound healing by inducing angiogenesis, partly by activating AKT, ERK, and TGF-ß/Smad3 signaling.

Pharmacological Properties of the Medical Maggot: A Novel Therapy Overview.

In the last decade, maggot has been hailed as the miraculous "medicinal maggot" for its diverse properties, including antimicrobial, antibiofilm, anti-inflammatory, and wound healing activities. The fact that maggots show so many beneficial properties has increased the interest in these tiny larvae dramatically. Whilst there is relatively abundant clinical evidence to demonstrate the success of maggots as debridement agents, not so much emphasis has been placed on the basic science evidence, which was a combination of physical and biochemical actions. This review differs from those earlier works in that it is undertaken to provide an update of the latest scientific basis published on maggot, particularly active ingredients within maggot excretions/secretions (ES). Further investigations should focus on the isolation, identification, recombination, transgenosis, and mass production of the beneficial molecules within maggots.