Structural characterization and anti-oxidative activity for a glycopeptide from Ganoderma lucidum fruiting body.

A water-soluble glycopeptide (named GL-PWQ3) with a molecular weight (Mw) of 2.40 × 104 g/mol was isolated from Ganoderma lucidum fruiting body by hot water extraction, membrane ultrafiltration, and gel column chromatography, which mainly consisted of glucose and galactose. Based on the methylation, FT-IR, 1D, and 2D NMR analysis, the polysaccharide portion of GL-PWQ3 was identified as a glucogalactan, which was comprised of unsubstituted (1,6-α-Galp, 1,6-ß-Glcp, 1,4-ß-Glcp) and monosubstituted (1,2,6-α-Galp and 1,3,6-ß-Glcp) in the backbone and possible branches that at the O-3 position of 1,3-Glcp and T-Glcp, and the O-2 position of T-Fucp, T-Manp or T-Glcp. The chain conformational study by SEC-MALLS-RI and AFM revealed that GL-PWQ3 was identified as a highly branched polysaccharide with a polydispersity index of 1.25, and might have compact sphere structures caused by stacked multiple chains. Moreover, the GL-PWQ3 shows strong anti-oxidative activity in NRK-52E cells. This study provides a theoretical basis for further elucidating the structure-functionality relationships of GL-PWQ3 and its potential application as a natural antioxidant in pharmacotherapy as well as functional food additives.

Multisequence magnetic resonance imaging-based radiomics models for the prediction of microsatellite instability in endometrial cancer.

PURPOSE: To evaluate the performance of multisequence magnetic resonance imaging (MRI)-based radiomics models in the assessment of microsatellite instability (MSI) status in endometrial cancer (EC). MATERIALS AND METHODS: This retrospective multicentre study included 338 EC patients with available MSI status and preoperative MRI scans, divided into training (37 MSI, 123 microsatellite stability [MSS]), internal validation (15 MSI, 52 MSS), and external validation cohorts (30 MSI, 81 MSS). Radiomics features were extracted from T2-weighted images, diffusion-weighted images, and contrast-enhanced T1-weighted images. The ComBat harmonisation method was applied to remove intrascanner variability. The Boruta wrapper algorithm was used for key feature selection. Three classification algorithms, logistic regression (LR), random forest (RF), and support vector machine (SVM), were applied to build the radiomics models. The area under the receiver operating characteristic curve (AUC) was calculated to compare the diagnostic performance of the models. Decision curve analysis (DCA) was conducted to determine the clinical usefulness of the models. RESULTS: Among the 1980 features, Boruta finally selected nine radiomics features. A higher MSI prediction performance was achieved after running the ComBat harmonisation method. The SVM algorithm had the best performance, with AUCs of 0.921, 0.903, and 0.937 in the training, internal validation, and external validation cohorts, respectively. The DCA results showed that the SVM algorithm achieved higher net benefits than the other classifiers over a threshold range of 0.581-0.783. CONCLUSION: The multisequence MRI-based radiomics models showed promise in preoperatively predicting the MSI status in EC in this multicentre setting.

Diffusion-weighted imaging-based radiomics in epithelial ovarian tumors: Assessment of histologic subtype.

Background: Epithelial ovarian tumors (EOTs) are a group of heterogeneous neoplasms. It is importance to preoperatively differentiate the histologic subtypes of EOTs. Our study aims to investigate the potential of radiomics signatures based on diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) maps for categorizing EOTs. Methods: This retrospectively enrolled 146 EOTs patients [34 with borderline EOT(BEOT), 30 with type I and 82 with type II epithelial ovarian cancer (EOC)]. A total of 390 radiomics features were extracted from DWI and ADC maps. Subsequently, the LASSO algorithm was used to reduce the feature dimensions. A radiomics signature was established using multivariable logistic regression method with 3-fold cross-validation and repeated 50 times. Patients with bilateral lesions were included in the validation cohort and a heuristic selection method was established to select the tumor with maximum probability for final consideration. A nomogram incorporating the radiomics signature and clinical characteristics was also developed. Receiver operator characteristic, decision curve analysis (DCA), and net reclassification index (NRI) were applied to compare the diagnostic performance and clinical net benefit of predictive model. Results: For distinguishing BEOT from EOC, the radiomics signature and nomogram showed more favorable discrimination than the clinical model (0.915 vs. 0.852 and 0.954 vs. 0.852, respectively) in the training cohort. In classifying early-stage type I and type II EOC, the radiomics signature exhibited superior diagnostic performance over the clinical model (AUC 0.905 vs. 0.735). The diagnostic efficacy of the nomogram was the same as that of the radiomics model with NRI value of -0.1591 (P = 0.7268). DCA also showed that the radiomics model and combined model had higher net benefits than the clinical model. Conclusion: Radiomics analysis based on DWI, and ADC maps serve as an effective quantitative approach to categorize EOTs.