CT-based radiomics for predicting pathological grade in hepatocellular carcinoma.

Objective: To construct and validate radiomics models for hepatocellular carcinoma (HCC) grade predictions based on contrast-enhanced CT (CECT). Methods: Patients with pathologically confirmed HCC after surgery and underwent CECT at our institution between January 2016 and December 2020 were enrolled and randomly divided into training and validation datasets. With tumor segmentation and feature extraction, radiomic models were constructed using univariate analysis, followed by least absolute shrinkage and selection operator (LASSO) regression. In addition, combined models with clinical factors and radiomics scores (Radscore) were constructed using logistic regression. Finally, all models were evaluated using the receiver operating characteristic (ROC) curve with the area under the curve (AUC), calibration curve, and decision curve analysis (DCA). Results: In total 242 patients were enrolled in this study, of whom 170 and 72 formed the training and validation datasets, respectively. The arterial phase and portal venous phase (AP+VP) radiomics model were evaluated as the best for predicting HCC pathological grade among all the models built in our study (AUC = 0.981 in the training dataset; AUC = 0.842 in the validation dataset) and was used to build a nomogram. Furthermore, the calibration curve and DCA indicated that the AP+VP radiomics model had a satisfactory prediction efficiency. Conclusions: Low- and high-grade HCC can be distinguished with good diagnostic performance using a CECT-based radiomics model.

Multi-omics Analysis Reveals the Crucial Mediators of DJB in the Treatment of Type 2 Diabetes.

PURPOSE: Duodenal-jejunal bypass (DJB) has a definite hypoglycemic effect; however, the intrinsic mechanisms remain unclear. The purpose of this study was to determine whether DJB may cause changes in the gut microbiota and metabolite of portal venous blood and to explore the effects of DJB on blood glucose metabolism. METHODS: T2DM was induced in rats with a high-fat diet and a low dose of streptozotocin, which were randomly divided into two groups: Sham operation and DJB. RESULTS: DJB significantly improved several diabetic parameters. 16S rRNA analyses showed that the compositions of the gut microbiota were significantly different between the two groups. The results of metabolomics showed that DJB could significantly regulate the metabolites, among which diaminopimelic acid and isovaleric acid had a significant down-regulation in the DJB group. Transcriptomic analysis showed that DJB can regulate the expression of hepatic genes related to abnormal glucose metabolism, such as Ltc4s, Alox15, Ggt1, Gpat3, and Cyp2c24. Correlation analyses showed that diaminopimelic acid was positively associated with Allobaculum, Serratia, and Turicibacter. There was a significant correlation between diaminopimelic acid and Gpat3, and its Spearman correlation coefficient was the highest among metabolite-DEG pairs (ρ=0.97). DISCUSSIONS: These results suggest an important cue of the relation between the diaminopimelic acid, Gpat3, and gut microbiome in the mechanism by which DJB can improve glucose metabolism.

Estimation of source infrared spectra profiles of acetylspiramycin active components from troches using kernel independent component analysis.

Kernel independent component analysis (KICA), a kind of independent component analysis (ICA) algorithms based on kernel, was preliminarily investigated for blind source separation (BSS) of source spectra profiles from troches. The robustness of different ICA algorithms (KICA, FastICA and Infomax) was first checked by using them in the retrieval of source infrared (IR), ultraviolet (UV) and mass spectra (MS) from synthetic mixtures. It was found that KICA is the most robust method for retrieval of source spectra profiles. KICA algorithm is subsequently adopted in the analysis of diffuse reflection IR of acetylspiramycin (ASPM) troches. It is observed that KICA is able to isolate the theoretically predicted spectral features corresponding to the ASPM active components, excipients and other minor components as different independent (spectral) component. A troche can be authenticated and semi-quantified using the estimated ICs. KICA is an useful method for estimation of source spectral features of molecules with different geometry and stoichiometry, while features belonging to very similar molecules remain grouped.

Estimation of source spectra profiles and simultaneous determination of polycomponent in mixtures from ultraviolet spectra data using kernel independent component analysis and support vector regression.

A method that use kernel independent component analysis (KICA) and support vector regression (SVR) was proposed for estimation of source ultraviolet (UV) spectra profiles and simultaneous determination of polycomponents in mixtures. In KICA-SVR procedure, the UV source spectra profiles were estimated using KICA, then the mixing matrix of the components were calculated using the estimated sources, and the calibration model was build using SVR based on the calculated mixing matrix. A simulated UV dataset of three-component mixtures was used to test the ability of KICA for estimating source spectra profiles from spectra data of mixtures. It was found that KICA has the potential power to estimate pure UV spectra profiles, and correlation coefficient of estimated sources correspond to the real adopted ones are better compared with that by FastICA and Infomax ICA. An UV dataset of polycomponent vitamin B was processed using the proposed KICA-SVR method. The results show that the estimated source spectra profiles are correlative with the real UV spectra of the components and chemically interpretable, and accurate results were obtained.