Interpretable machine learning based on CT-derived extracellular volume fraction to predict pathological grading of hepatocellular carcinoma.

PURPOSE: To develop a non-invasive auxiliary assessment method based on CT-derived extracellular volume (ECV) to predict the pathological grading (PG) of hepatocellular carcinoma (HCC). METHODS: The study retrospectively analyzed 238 patients who underwent HCC resection surgery between January 2013 and April 2023. Six machine learning algorithms were employed to construct predictive models for HCC PG: logistic regression, extreme gradient boosting, Light Gradient Boosting Machine (LightGBM), random forest, adaptive boosting, and Gaussian naive Bayes. Model performance was evaluated using receiver operating characteristic curve analysis, including area under the curve (AUC), sensitivity, specificity, accuracy, positive predictive value, negative predictive value, and F1 score. Calibration plots were used for visual evaluation of model calibration. Clinical decision curve analysis was performed to assess potential clinical utility by calculating net benefit. RESULTS: 166 patients from Hospital A were allocated to the training set, while 72 patients from Hospital B (constituting 30.25% of the total sample) were assigned to the test set. The model achieved an AUC of 1.000 (95%CI: 1.000-1.000) in the training set and 0.927 (95%CI: 0.837-0.999) in the validation set, respectively. Ultimately, the model achieved an AUC of 0.909 (95%CI: 0.837-0.980) in the test set, with an accuracy of 0.778, sensitivity of 0.906, specificity of 0.789, negative predictive value of 0.556, and F1 score of 0.908. CONCLUSION: This study successfully developed and validated a non-invasive auxiliary assessment method based on CT-derived ECV to predict the HCC PG, providing important supplementary information for clinical decision-making.

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.

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.

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.

A Cox Nomogram for Assessing Recurrence Free Survival in Hepatocellular Carcinoma Following Surgical Resection Using Dynamic Contrast-Enhanced MRI Radiomics.

BACKGROUND: The prognosis of hepatocellular carcinoma (HCC) is difficult to predict and carries high mortality. This study utilized radiomic techniques with clinical examinations to assess recurrence in HCC. PURPOSE: To develop a Cox nomogram to assess the risk of postoperative recurrence in HCC using radiomic features of three volumes of interest (VOIs) in preoperative dynamic contrast-enhanced MRI (DCE-MRI), along with clinical findings. STUDY TYPE: Retrospective. SUBJECTS: 249 patients with pathologically proven HCCs undergoing surgical resection at three institutions were selected. FIELD STRENGTH/SEQUENCE: Fat saturated T2-weighted, Fat saturated T1-weighted, and DCE-MRI performed at 1.5 T and 3.0 T. ASSESSMENT: Three VOIs were generated; the tumor VOI corresponds to the area from the tumor core to the outer perimeter of the tumor, the tumor +10 mm VOI represents the area from the tumor perimeter to 10 mm distal to the tumor in all directions, finally, the background liver parenchyma VOI represents the hepatic tissue outside the tumor. Three models were generated. The total radiomic model combined information from the three listed VOI's above. The clinical-radiological model combines physical examination findings with imaging characteristics such as tumor size, margin features, and metastasis. The combined radiomic model includes features from both models listed above and showed the highest reliability for assessing 24-month survival for HCC. STATISTICAL TESTS: The least absolute shrinkage and selection operator (LASSO) Cox regression, univariable, and multivariable Cox regression, Kmeans clustering, and Kaplan-Meier analysis. The discrimination performance of each model was quantified by the C-index. A P value <0.05 was considered statistically significant. RESULTS: The combined radiomic model, which included features from the radiomic VOI's and clinical imaging provided the highest performance (C-index: training cohort = 0.893, test cohort = 0.851, external cohort = 0.797) in assessing the survival of HCC. CONCLUSION: The combined radiomic model provides superior ability to discern the possibility of recurrence-free survival in HCC over the total radiomic and the clinical-radiological models. EVIDENCE LEVEL: 4. TECHNICAL EFFICACY: Stage 2.

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.

Rational design of a genome-based insulated system in Escherichia coli facilitates heterologous uricase expression for hyperuricemia treatment.

Hyperuricemia is a prevalent disease worldwide that is characterized by elevated urate levels in the blood owing to purine metabolic disorders, which can result in gout and comorbidities. To facilitate the treatment of hyperuricemia through the uricolysis, we engineered a probiotic Escherichia coli Nissle 1917 (EcN) named EcN C6 by inserting an FtsP-uricase cassette into an "insulated site" located between the uspG and ahpF genes. Expression of FtsP-uricase in this insulated region did not influence the probiotic properties or global gene transcription of EcN but strongly increased the enzymatic activity for urate degeneration, suggesting that the genome-based insulated system is an ideal strategy for EcN modification. Oral administration of EcN C6 successfully alleviated hyperuricemia, related symptoms and gut microbiota in a purine-rich food-induced hyperuricemia rat model and a uox-knockout mouse model. Together, our study provides an insulated site for heterologous gene expression in EcN strain and a recombinant EcN C6 strain as a safe and effective therapeutic candidate for hyperuricemia treatment.

GluCEST Imaging and Structural Alterations of the Bilateral Hippocampus in First-Episode and Early-Onset Major Depression Disorder.

BACKGROUND: Glutamate dysregulation is one of the key pathogenic mechanisms of major depressive disorder (MDD), and glutamate chemical exchange saturation transfer (GluCEST) has been used for glutamate measurement in some brain diseases but rarely in depression. PURPOSE: To investigate the GluCEST changes in hippocampus in MDD and the relationship between glutamate and hippocampal subregional volumes. STUDY TYPE: Cross-sectional. SUBJECTS: Thirty-two MDD patients (34% males; 22.03 ± 7.21 years) and 47 healthy controls (HCs) (43% males; 22.00 ± 3.28 years). FIELD STRENGTH/SEQUENCE: 3.0 T; magnetization prepared rapid gradient echo (MPRAGE) for three-dimensional T1-weighted images, two-dimensional turbo spin echo GluCEST, and multivoxel chemical shift imaging (CSI) for proton magnetic resonance spectroscopy (1 H MRS). ASSESSMENT: GluCEST data were quantified by magnetization transfer ratio asymmetry (MTRasym ) analysis and assessed by the relative concentration of 1 H MRS-measured glutamate. FreeSurfer was used for hippocampus segmentation. STATISTICAL TESTS: The independent sample t test, Mann-Whitney U test, Spearman's correlation, and partial correlation analysis were used. P < 0.05 was considered statistically significant. RESULTS: In the left hippocampus, GluCEST values were significantly decreased in MDD (2.00 ± 1.08 [MDD] vs. 2.62 ± 1.41 [HCs]) and showed a significantly positive correlation with Glx/Cr (r = 0.37). GluCEST values were significantly positively correlated with the volumes of CA1 (r = 0.40), subiculum (r = 0.40) in the left hippocampus and CA1 (r = 0.51), molecular_layer_HP (r = 0.50), GC-ML-DG (r = 0.42), CA3 (r = 0.44), CA4 (r = 0.44), hippocampus-amygdala-transition-area (r = 0.46), and the whole hippocampus (r = 0.47) in the right hippocampus. Hamilton Depression Rating Scale scores showed significantly negative correlations with the volumes of the left presubiculum (r = -0.40), left parasubiculum (r = -0.47), and right presubiculum (r = -0.41). DATA CONCLUSION: GluCEST can be used to measure glutamate changes and help to understand the mechanism of hippocampal volume loss in MDD. Hippocampal volume changes are associated with disease severity. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 1.

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.

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.