ATP citrate lyase ablation hampers exocrine regeneration via TLR4/NF-kappaB signaling after acute pancreatitis in mice.

BACKGROUND: ATP citrate lyase (Acly) is widely expressed in many tissues, has been proved to be involved in the pathogenesis of many inflammatory diseases. So far, the importance of Acly in acute pancreatitis(AP) has not been clearly determined. The purpose of this study is to clarify whether Acly can evoke inflammatory cascades in the progression of AP and hamper the subsequent regeneration process of pancreas. METHODS: Experimental pancreatitis in mice with a specific deficiency of Acly in the pancreas and in control mice through repetitive cerulein injections in vivo. The pancreas pathological grading, cell proliferative potential and the formation of acinar-to-ductal metaplasia (ADM) were evaluated. The levels of inflammatory cytokines in plasma were qualified by enzyme-linked immuno sorbent assay (ELISA). Pancreatic malondialdehyde (MDA), superoxide dismutase (SOD) activity and reduced glutathione (GSH) contents were measured for oxidative stress. The infiltration of macrophages and neutrophils, the expression of Toll like receptor 4 (TLR4), tumor necrosis factor (TNF)-α, interleukin (IL)-1ß, and the activation of nuclear factor kappaB (NF-κB) and cleaved Caspase-3, were measured using immunostaining. The mRNA transcription levels of TLR4, TNF-α, and IL-1ß in pancreatic tissues were detected by quantitative real-time PCR as well. Additionally, inhibition of TLR4 signaling by TAK-242 in AP mice with a pancreas-specific deletion of Acly was conducted in vivo. RESULTS: The results demonstrated that the elimination of pancreatic Acly not only exacerbated the severity of pancreatitis in mice during the initial inflammatory phase, as evidenced by more severe pathological damage, but also impeded the healing process of the exocrine pancreas by enhancing the formation of ADM and decreasing the ability of acinar cells to proliferate. In addition, deficiency of Acly increased the circulating TNF-α, IL-1ß and IL-6, the infiltration of macrophages and neutrophils, agumented the activation of nuclear factor kappaB (NF-κB) p65, the expression of TLR4, TNF-α, IL-1ß and cleaved Caspase-3, and exacerbated excessive oxidative stress in the pancreas at specific time points of AP mice. However, TLR4 inhibition significantly attenuated the structural and functional damage of the pancreas induced by AP in mice with a pancreas-specific deletion of Acly, as indicated by improvement of the above indexes. CONCLUSIONS: The present study demonstrated that ablation of pancreatic Acly intensified inflammatory reaction and cell death, and dampened exocrine regeneration following AP, due to the positive regulation of TLR4/NF-κB signaling activation.

Contrast-enhanced CT radiomics combined with multiple machine learning algorithms for preoperative identification of lymph node metastasis in pancreatic ductal adenocarcinoma.

Objectives: This research aimed to assess the value of radiomics combined with multiple machine learning algorithms in the diagnosis of pancreatic ductal adenocarcinoma (PDAC) lymph node (LN) metastasis, which is expected to provide clinical treatment strategies. Methods: A total of 128 patients with pathologically confirmed PDAC and who underwent surgical resection were randomized into training (n=93) and validation (n=35) groups. This study incorporated a total of 13 distinct machine learning algorithms and explored 85 unique combinations of these algorithms. The area under the curve (AUC) of each model was computed. The model with the highest mean AUC was selected as the best model which was selected to determine the radiomics score (Radscore). The clinical factors were examined by the univariate and multivariate analysis, which allowed for the identification of factors suitable for clinical modeling. The multivariate logistic regression was used to create a combined model using Radscore and clinical variables. The diagnostic performance was assessed by receiver operating characteristic curves, calibration curves, and decision curve analysis (DCA). Results: Among the 233 models constructed using arterial phase (AP), venous phase (VP), and AP+VP radiomics features, the model built by applying AP+VP radiomics features and a combination of Lasso+Logistic algorithm had the highest mean AUC. A clinical model was eventually constructed using CA199 and tumor size. The combined model consisted of AP+VP-Radscore and two clinical factors that showed the best diagnostic efficiency in the training (AUC = 0.920) and validation (AUC = 0.866) cohorts. Regarding preoperative diagnosis of LN metastasis, the calibration curve and DCA demonstrated that the combined model had a good consistency and greatest net benefit. Conclusions: Combining radiomics and machine learning algorithms demonstrated the potential for identifying the LN metastasis of PDAC. As a non-invasive and efficient preoperative prediction tool, it can be beneficial for decision-making in clinical practice.

Dihydroartemisinin inhibited vasculogenic mimicry in gastric cancer through the FGF2/FGFR1 signaling pathway.

Vasculogenic mimicry (VM) is a novel model for supplying blood to multiple tumors, including gastric cancer (GC), and is a potential target for its treatment. Dihydroartemisinin (DHA) is a potential natural antitumor substance that inhibits the progression of tumors in many ways. The research aimed to evaluate the impact of DHA on VM formation and its mechanisms. The IC50 of DHA, DHA's effect on proliferation, invasion, and migration in GC cells and VM formation in both cell and animal models were determined through wound healing, MTT, EdU, colony formation, and Transwell assays. Genomics was employed to identify genes related to DHA inhibition of VM formation, and to analyze their relationship to VM formation. qRT‒PCR and western blot (WB) analysis were carried out to analyze the changes in protein and mRNA levels after DHA treatment and the changes in VM-associated protein biomarkers after blocking target gene-related pathways. The mechanism by which DHA inhibits VM in GC was elucidated in vivo. DHA reduced the invasion, proliferation, and migration of GC cells and inhibited VM in cells and in vivo. A total of 220 DEGs were identified in the DHA-treated HGC-27 cells. Among the 146 downregulated genes, fibroblast growth Factor 2 (FGF2) was most closely associated with angiogenesis and VM. The level of FGF2 in GC tissues with VM was markedly greater than in VM lacking tissues. Treatment with DHA or FGFR1 blockade suppressed VM formation and reduced VM-related biomarker proteins. DHA suppressed tumor progression and VM formation by reducing FGF2 in xenograft mouse models. Per our knowledge, this is the first study to demonstrate the inhibitory effect of DHA on VM, providing a novel strategy for the treatment of GC.

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.