BACKGROUND:Knee osteoarthritis(KOA)is a common chronic inflammatory disease that causes damage to joint cartilage and surrounding tissues.Immune cells play an important role in the immune-inflammatory response in knee osteoarthritis,but the specific mechanisms involved are still not fully understood. OBJECTIVE:To evaluate the potential causal relationship between 731 immune cell phenotypes and the risk of knee osteoarthritis using Mendelian randomization. METHODS:Summary statistics of genome-wide association studies(GWAS)for 731 immune cell phenotypes(from GCST0001391 to GCST0002121)obtained from the GWAS catalog and GWAS data for knee osteoarthritis from the IEUGWAS database(ebi-a-GCST007090)were used.Inverse variance-weighted method,MR-Egger regression,weighted median method,weighted mode method,and simple mode method were employed to investigate the causal relationship between immune cells and knee osteoarthritis.Sensitivity analyses were conducted to assess the reliability of the Mendelian randomization results.Reverse Mendelian randomization analysis was also performed using the same methods. RESULTS AND CONCLUSION:The forward MR analysis indicated significant causal relationships(FDR<0.20)between knee osteoarthritis and four immune cell phenotypes,namely CD27 on CD24+CD27+in B cells(OR=1.026,P=0.000 26,Pfdr=0.18),CD33 on CD33dim HLA DR-in myeloid cells(OR=1.014,P=0.000 50,Pfdr=0.18),and CD45RA+CD28-CD8br%CD8br in Treg cells(OR=1.001,P=0.000 78,Pfdr=0.18),and PDL-1 on monocytes in mononuclear cells(OR=0.952,P=0.000 98,Pfdr=0.18).These immune cell phenotypes showed direct positive or negative causal associations with the risk of knee osteoarthritis.Reverse Mendelian randomization analysis revealed no significant causal relationships(FDR<0.20)between knee osteoarthritis as exposure and any of the 731 immune cell phenotypes.The results of sensitivity analysis show that the P-values of the Cochran's Q test and the MR-Egger regression method for bidirectional Mendelian randomization were both greater than 0.05,indicating that there is no significant heterogeneity and pleiotropy in the causal effect analysis between immune cell phenotypes and knee osteoarthritis.To conclude,there may be four potential causal relationships between immune cell phenotypes,such as CD27 on CD24+CD27+cells,CD33 on CD33dim HLA DR-cells,CD45RA+CD28-CD8br%CD8br cells,and PDL-1 on monocytes,and knee osteoarthritis.These findings provide valuable clues for studying the biological mechanisms of knee osteoarthritis and exploring early prevention and treatment strategies.They also offer new directions for the development of intervention drugs.

The high incidence and mortality rates of gastric cancer pose a significant burden on human health and public health systems. Peritoneal metastasis is one of the main routes of metastasis in gastric cancer, and patients with this condition have poor prognosis. With advancements in medicine and sociology, the concept of integrated medicine emerged in the late 1980s. It emphasizes treating patients as holistic entities, integrating various medical fields multidimensionally, and combining medicine with other fields. This article organizes the management of gastric cancer peritoneal metastasis around the core concept of "Prevention, Screening, Diagnosis, Treatment, and Rehabilitation": Prevention relies on standardized tumor-free surgical techniques and intraoperative interventions. Screening involves early detection of peritoneal metastasis through integrated technologies. Diagnosis combines multiple methods, including laparoscopic exploration with cytological and histopathological examination, imaging studies, peritoneal fluid cytology, and artificial intelligence. Treatment encompasses comprehensive strategies such as intraperitoneal and systemic chemotherapy, cytoreductive surgery combined with hyperthermic intraperitoneal chemotherapy, pressurized intraperitoneal aerosolized chemotherapy, dendritic cell vaccines combined with nivolumab, and supportive care for managing ascites, malignant bowel obstruction, and nutritional support. This integrated medical perspective offers new insights into the management of gastric cancer peritoneal metastasis.

The high incidence and mortality rates of gastric cancer pose a significant burden on human health and public health systems. Peritoneal metastasis is one of the main routes of metastasis in gastric cancer, and patients with this condition have poor prognosis. With advancements in medicine and sociology, the concept of integrated medicine emerged in the late 1980s. It emphasizes treating patients as holistic entities, integrating various medical fields multidimensionally, and combining medicine with other fields. This article organizes the management of gastric cancer peritoneal metastasis around the core concept of "Prevention, Screening, Diagnosis, Treatment, and Rehabilitation": Prevention relies on standardized tumor-free surgical techniques and intraoperative interventions. Screening involves early detection of peritoneal metastasis through integrated technologies. Diagnosis combines multiple methods, including laparoscopic exploration with cytological and histopathological examination, imaging studies, peritoneal fluid cytology, and artificial intelligence. Treatment encompasses comprehensive strategies such as intraperitoneal and systemic chemotherapy, cytoreductive surgery combined with hyperthermic intraperitoneal chemotherapy, pressurized intraperitoneal aerosolized chemotherapy, dendritic cell vaccines combined with nivolumab, and supportive care for managing ascites, malignant bowel obstruction, and nutritional support. This integrated medical perspective offers new insights into the management of gastric cancer peritoneal metastasis.

Diabetic foot ulcers (DFUs) is one of the severe complications of diabetes, which is difficult to treat and associated with a high risk of amputation. Current guidelines recommend that the plane of amputation be comprehensively assessed based on the degree of tissue necrosis and plane of vascular occlusion in DFUs, the age and systemic organ function of patients. However, the survival and quality of life of DFU patients suffering from amputation will be significantly affected. After reviewing the national and international literature and summarizing the experience of limb-salvage treatment of DFUs, we believe that free flap transplantation is of great significance for limb-salvage treatment to some patients with severe DFUs. In addition, this article also focus on analyzing the indications and perioperative precautions for patients with DFUs undergoing free flap transplantation.

Objectives:To investigate the effect of the expression of low-density lipoprotein receptor associated protein (LDLR) on the vascular abnormalities in hepatocellular carcinoma (HCC) and its mechanisms.Methods:Based on the information of Oncomine Cancer GeneChip database, we analyzed the correlation between the expression level of LDLR and the expression level of carcinoembryonic antigen (CEA) and CD31 in hepatocellular carcinoma tissues. Lentiviral transfection of short hairpin RNA target genes was used to construct LDLR-knockdown MHCC-97H and HLE hepatocellular carcinoma cells. The differential genes and their expression level changes in LDLR-knockdown hepatocellular carcinoma cells were detected by transcriptome sequencing, real-time fluorescence quantitative polymerase chain reaction, and protein immunoblotting. The gene-related signaling pathways that involve LDLR were clarified by enrichment analysis. The effect of LDLR on CEA was assessed by the detection of CEA content in conditioned medium of hepatocellular carcinoma cells. Angiogenesis assay was used to detect the effect of LDLR on the angiogenic capacity of human umbilical vein endothelial cells, as well as the role of CEA in the regulation of angiogenesis by LDLR. Immunohistochemical staining was used to detect the expression levels of LDLR in 176 hepatocellular carcinoma tissues, and CEA and CD31 in 146 hepatocellular carcinoma tissues, and analyze the correlations between the expression levels of LDLR, CEA, and CD31 in the tissues, serum CEA, and alanine transaminase (ALT).Results:Oncomine database analysis showed that the expressions of LDLR and CEA in the tissues of hepatocellular carcinoma patients with portal vein metastasis were negatively correlated ( r=-0.64, P=0.001), whereas the expressions of CEA and CD31 in these tissues were positively correlated ( r=0.46, P=0.010). The transcriptome sequencing results showed that there were a total of 1 032 differentially expressed genes in the LDLR-knockdown group and the control group of MHCC-97H cells, of which 517 genes were up-regulated and 515 genes were down-regulated. The transcript expression level of CEACAM5 was significantly up-regulated in the cells of the LDLR-knockdown group. The Gene Ontology (GO) function enrichment analysis showed that the differential genes were most obviously enriched in the angiogenesis function. The Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway enrichment analysis showed that the relevant pathways involved mainly included the cellular adhesion patch, the extracellular matrix receptor interactions, and the interactions with the extracellular matrix receptors. The CEA content in the conditioned medium of the LDLR-knockdown group was 43.75±8.43, which was higher than that of the control group (1.15±0.14, P＜0.001). The results of angiogenesis experiments showed that at 5 h, the number of main junctions, the number of main segments, and the total area of the lattice formed by HUVEC cells cultured with the conditioned medium of MHCC-97H cells in the LDLR-knockdown group were 295.3±26.4, 552.5±63.8, and 2 239 781.0±13 8211.9 square pixels, which were higher than those of the control group (113.3±23.5, 194.8±36.5, and 660 621.0±280 328.3 square pixels, respectively, all P＜0.01).The number of vascular major junctions, the number of major segments, and the total area of the lattice formed by HUVEC cells cultured in conditioned medium with HLE cells in the LDLR-knockdown group were 245.3±42.4, 257.5±20.4, and 2 535 754.5±249 094.2 square pixels, respectively, which were all higher than those of the control group (113.3±23.5, 114.3±12.2, and 1 565 456.5±219 259.7 square pixels, respectively, all P＜0.01). In the conditioned medium for the control group of MHCC-97H cells,the number of main junctions, the number of main segments, and the total area of the lattice formed by the addition of CEA to cultured HUVEC cells were 178.9±12.0, 286.9±12.3, and 1 966 990.0±126 249.5 spixels, which were higher than those in the control group (119.7±22.1, 202.7±33.7, and 1 421 191.0±189 837.8 square pixels, respectively). The expression of LDLR in hepatocellular carcinoma tissues was not correlated with the expression of CEA, but was negatively correlated with the expression of CD31 ( r=-0.167, P=0.044), the level of serum CEA ( r=-0.061, P=0.032), and the level of serum ALT (r=-0.147, P=0.05). The expression of CEA in hepatocellular carcinoma tissues was positively correlated with the expression of CD31 ( r=0.192, P=0.020). The level of serum CEA was positively correlated with the level of serum ALT ( r=0.164, P=0.029). Conclusion:Knocking down LDLR can promote vascular abnormalities in HCC by releasing CEA.

BACKGROUND:Rapid developments in the field of bioinformatics have provided new methods for the diagnosis of osteoarthritis.Artificial neural networks have powerful data computing and classification capabilities,which have shown better performance in disease diagnosis. OBJECTIVE:To establish a new diagnostic predictive model of osteoarthritis based on artificial neural network and to verify the diagnostic value of the model in osteoarthritis with an external dataset. METHODS:The eligible osteoarthritis-related data sets were downloaded through GEO database search and divided into Train group and Test group.The gene expression matrix of the Train group was analyzed to screen the differentially expressed genes.GO and KEGG enrichment analyses were performed on the differentially expressed genes.Through Lasso regression model,support vector machine model and random forest tree model,the key genes of osteoarthritis were further identified from the differentially expressed genes.The R software"Neuralnet"package was then used to construct the osteoarthritis diagnosis model based on artificial neural network,and the model performance was evaluated by the five-fold cross-validation.Two independent data sets in the Test group were used to verify their diagnostic results. RESULTS AND CONCLUSION:A total of 90 differentially expressed genes related to osteoarthritis were obtained by differential analysis,of which 33 were down-regulated and 57 were up-regulated.GO enrichment analysis showed that the differentially expressed genes were mainly involved in the following biological processes,including leukocyte-mediated immunity,leukocyte migration in bone marrow and chemokine production.KEGG enrichment analysis showed that these genes were mainly enriched in rheumatoid arthritis,interleukin-17 signaling pathway and osteoclast differentiation pathway.Five key genes for the diagnosis of osteoarthritis,HMGB2,GADD45A,SLC19A2,TPPP3 and FOLR2,were identified by three machine learning methods.The artificial neural network model of five key genes in the Train group showed that the accuracy was 96.36%and the area under the curve was 0.997.The five-fold cross validation of the neural network model showed that the average area under the curve was greater than 0.9 and the model was of robustness.Two independent data sets in the Test group showed its area under the curve was 0.814 and 0.788 respectively.Therefore,the establishment of an artificial neural network model for the diagnosis of osteoarthritis has a certain diagnostic value.

BACKGROUND:Rheumatoid arthritis is a chronic systemic autoimmune disease.It is important to study the immunological changes involved in it for diagnosis and treatment. OBJECTIVE:To identify immune-related biomarkers associated with rheumatoid arthritis utilizing bioinformatics techniques and examine alterations in immune cell infiltration as well as the relationship between immune cells and biomarkers. METHODS:Differential expression analysis was used to identify the immune-related genes that were up-regulated in rheumatoid arthritis based on the GEO and Immport databases.Kyoto encyclopedia of genes and genomes(KEGG)and gene ontology(GO)enrichment analyses were used to investigate the possible function of these elevated genes.The immunological characteristic genes associated with rheumatoid arthritis were screened using least absolute shrinkage and selection operator(Lasso)and support vector machine recursive feature elimination(SVM-RFE).Independent datasets were used for difference validation,and the diagnostic performance was evaluated by plotting receiver operating characteristic curves for feature genes.Immune cell infiltration was used to analyze the differential profile of immune cells in rheumatoid arthritis and the correlation between the characterized genes and immune cells.In order to ascertain the causal relationship between monocytes and rheumatoid arthritis in immune cells,Mendelian randomization analysis was ultimately employed. RESULTS AND CONCLUSION:There were 39 upregulated differentially expressed genes in rheumatoid arthritis.The genes were primarily enriched in chemotaxis,cytokine activity,and immune receptor activity,according to GO enrichment analysis,while kEGG enrichment analysis revealed that the genes were considerably enriched in the tumor necrosis factor signaling pathway and peripheral leukocyte migration.Lasso and SVM-RFE identified five feature genes:CXCL13,SDC1,IGLC1,PLXNC1,and SLC29A3.Independent dataset validation of the feature genes found them to be similarly highly expressed in rheumatoid arthritis samples,with area under the curve values greater than 0.8 for all five feature genes in both datasets.Immune cell infiltration indicated that most immune cells,including natural killer cells and monocytes,exhibited increased levels of infiltration in rheumatoid arthritis samples.The correlation analysis revealed a significant positive correlation between memory B cells and immature B cells and these five feature genes.Correlation analysis showed that the five feature genes were positively correlated with memory B cells and immature B cells.The inverse variance weighting method revealed that monocytes were associated with the risk of developing rheumatoid arthritis.

Objectives:To investigate the effect of the expression of low-density lipoprotein receptor associated protein (LDLR) on the vascular abnormalities in hepatocellular carcinoma (HCC) and its mechanisms.Methods:Based on the information of Oncomine Cancer GeneChip database, we analyzed the correlation between the expression level of LDLR and the expression level of carcinoembryonic antigen (CEA) and CD31 in hepatocellular carcinoma tissues. Lentiviral transfection of short hairpin RNA target genes was used to construct LDLR-knockdown MHCC-97H and HLE hepatocellular carcinoma cells. The differential genes and their expression level changes in LDLR-knockdown hepatocellular carcinoma cells were detected by transcriptome sequencing, real-time fluorescence quantitative polymerase chain reaction, and protein immunoblotting. The gene-related signaling pathways that involve LDLR were clarified by enrichment analysis. The effect of LDLR on CEA was assessed by the detection of CEA content in conditioned medium of hepatocellular carcinoma cells. Angiogenesis assay was used to detect the effect of LDLR on the angiogenic capacity of human umbilical vein endothelial cells, as well as the role of CEA in the regulation of angiogenesis by LDLR. Immunohistochemical staining was used to detect the expression levels of LDLR in 176 hepatocellular carcinoma tissues, and CEA and CD31 in 146 hepatocellular carcinoma tissues, and analyze the correlations between the expression levels of LDLR, CEA, and CD31 in the tissues, serum CEA, and alanine transaminase (ALT).Results:Oncomine database analysis showed that the expressions of LDLR and CEA in the tissues of hepatocellular carcinoma patients with portal vein metastasis were negatively correlated ( r=-0.64, P=0.001), whereas the expressions of CEA and CD31 in these tissues were positively correlated ( r=0.46, P=0.010). The transcriptome sequencing results showed that there were a total of 1 032 differentially expressed genes in the LDLR-knockdown group and the control group of MHCC-97H cells, of which 517 genes were up-regulated and 515 genes were down-regulated. The transcript expression level of CEACAM5 was significantly up-regulated in the cells of the LDLR-knockdown group. The Gene Ontology (GO) function enrichment analysis showed that the differential genes were most obviously enriched in the angiogenesis function. The Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway enrichment analysis showed that the relevant pathways involved mainly included the cellular adhesion patch, the extracellular matrix receptor interactions, and the interactions with the extracellular matrix receptors. The CEA content in the conditioned medium of the LDLR-knockdown group was 43.75±8.43, which was higher than that of the control group (1.15±0.14, P＜0.001). The results of angiogenesis experiments showed that at 5 h, the number of main junctions, the number of main segments, and the total area of the lattice formed by HUVEC cells cultured with the conditioned medium of MHCC-97H cells in the LDLR-knockdown group were 295.3±26.4, 552.5±63.8, and 2 239 781.0±13 8211.9 square pixels, which were higher than those of the control group (113.3±23.5, 194.8±36.5, and 660 621.0±280 328.3 square pixels, respectively, all P＜0.01).The number of vascular major junctions, the number of major segments, and the total area of the lattice formed by HUVEC cells cultured in conditioned medium with HLE cells in the LDLR-knockdown group were 245.3±42.4, 257.5±20.4, and 2 535 754.5±249 094.2 square pixels, respectively, which were all higher than those of the control group (113.3±23.5, 114.3±12.2, and 1 565 456.5±219 259.7 square pixels, respectively, all P＜0.01). In the conditioned medium for the control group of MHCC-97H cells,the number of main junctions, the number of main segments, and the total area of the lattice formed by the addition of CEA to cultured HUVEC cells were 178.9±12.0, 286.9±12.3, and 1 966 990.0±126 249.5 spixels, which were higher than those in the control group (119.7±22.1, 202.7±33.7, and 1 421 191.0±189 837.8 square pixels, respectively). The expression of LDLR in hepatocellular carcinoma tissues was not correlated with the expression of CEA, but was negatively correlated with the expression of CD31 ( r=-0.167, P=0.044), the level of serum CEA ( r=-0.061, P=0.032), and the level of serum ALT (r=-0.147, P=0.05). The expression of CEA in hepatocellular carcinoma tissues was positively correlated with the expression of CD31 ( r=0.192, P=0.020). The level of serum CEA was positively correlated with the level of serum ALT ( r=0.164, P=0.029). Conclusion:Knocking down LDLR can promote vascular abnormalities in HCC by releasing CEA.

Objective:To develop a deep learning model which can automatically and accurately detect osteoporotic vertebral compression fractures (OVCF) based on artificial intelligence.Methods:MRI images of 500 patients diagnosed with OVCF at The First People＇s Hospital of Guangzhou from January 2019 to October 2021 were collected retrospectively. There were 396 males and 204 females, with an age of (74.5±6.0) years. The T value of bone mineral density was -2.9±0.8. The fracture segments were L1 in 128 cases, L2 in 113 cases, L3 in 109 cases, L4 in 115 cases, and L5 in 108 cases. The multimodal layered converged network was used to train, test, and verify the robustness and generalization ability of a deep learning model based on MRI images of OVCF. The grad-cam was applied to visualize the results. The diagnostic value of the model for OVCF was assessed by comparing the diagnoses between the artificial intelligence model and 2 senior spinal surgeons on the MRI images of 30 OVCF patients randomized from the 500 ones.Results:Of the precise auxiliary diagnosis model for OVCF based on MRI images, the diagnostic accuracy was 96.7%, the sensitivity 93.5%, the specificity 88.9%, the positive predictive value 100.0%, and the negative predictive value 86.6%, all significantly higher than those of the 2 senior spinal surgeons (70.0%, 72.7%, 28.6%, 82.1%, and 28.6%) ( P<0.05). Conclusion:The present study has successfully established a deep learning model which can automatically and accurately diagnose OVCF based on MRI images, showing a high diagnostic efficiency than human spinal surgeons.

Metabolic reprogramming is a common phenomenon in cancer, with aerobic glycolysis being one of its important characteristics. Hypoxia-inducible factor-1α (HIF1Α) is thought to play an important role in aerobic glycolysis. Meanwhile, naringin is a natural flavanone glycoside derived from grapefruits and many other citrus fruits. In this work, we identified glycolytic genes related to HIF1Α by analyzing the colon cancer database. The analysis of extracellular acidification rate and cell function verified the regulatory effects of HIF1Α overexpression on glycolysis, and the proliferation and migration of colon cancer cells. Moreover, naringin was used as an inhibitor of colon cancer cells to illustrate its effect on HIF1Α function. The results showed that the HIF1Α and enolase 2 (ENO2) levels in colon cancer tissues were highly correlated, and their high expression indicated a poor prognosis for colon cancer patients. Mechanistically, HIF1Α directly binds to the DNA promoter region and upregulates the transcription of ENO2; ectopic expression of ENO2 increased aerobic glycolysis in colon cancer cells. Most importantly, we found that the appropriate concentration of naringin inhibited the transcriptional activity of HIF1Α, which in turn decreased aerobic glycolysis in colon cancer cells. Generally, naringin reduces glycolysis in colon cancer cells by reducing the transcriptional activity of HIF1Α and the proliferation and invasion of colon cancer cells. This study helps to elucidate the relationship between colon cancer progression and glucose metabolism, and demonstrates the efficacy of naringin in the treatment of colon cancer.
Glycolysis ; Colonic Neoplasms/metabolism* ; Humans ; Hypoxia-Inducible Factor 1, alpha Subunit/metabolism* ; Phosphopyruvate Hydratase/metabolism* ; Flavanones/pharmacology* ; Cell Line, Tumor ; Databases, Genetic ; Cell Proliferation/drug effects* ; Transfection ; Warburg Effect, Oncologic