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Rationale and objectivesTo use a rapid gas-challenge blood oxygen-level dependent magnetic resonance imaging exam to evaluate changes in tumor hypoxia after 90Y radioembolization (Y90) in the VX2 rabbit model.Materials and methodsWhite New Zealand rabbits (n = 11) provided a Y90 group (n = 6 rabbits) and untreated control group (n = 5 rabbits). R2* maps were generated with gas-challenges (O2/room air) at baseline, 1 week, and 2 weeks post-Y90. Laboratory toxicity was evaluated at baseline, 24 hours, 72 hours, 1 hours, and 2 weeks. Histology was used to evaluate tumor necrosis on hematoxylin and eosin and immunofluorescence imaging was used to assess microvessel density (CD31) and proliferative index (Ki67).ResultsAt baseline, median tumor volumes and time to imaging were similar between groups (p = 1.000 and p = 0.4512, respectively). The median administered dose was 50.4 Gy (95% confidence interval:44.8-55.9). At week 2, mean tumor volumes were 5769.8 versus 643.7 mm3 for control versus Y90 rabbits, respectively (p = 0.0246). At two weeks, ΔR2* increased for control tumors to 12.37 ± 12.36sec-1 and decreased to 4.48 ± 9.00sec-1 after Y90. The Pearson correlation coefficient for ΔR2* at baseline and percent increase in tumor size by two weeks was 0.798 for the Y90 group (p = 0.002). There was no difference in mean microvessel density for control versus Y90 treated tumors (p = 0.6682). The mean proliferative index was reduced in Y90 treated tumors at 30.5% versus 47.5% for controls (p = 0.0071).ConclusionThe baseline ΔR2* of tumors prior to Y90 may be a predictive imaging biomarker of tumor response and treatment of these tumors with Y90 may influence tumor oxygenation over time.

PurposeTo evaluate the combination of 90Y radioembolization (Y90) and drug-eluting bead irinotecan (DEBIRI) microspheres in the VX2 rabbit model.Materials and methodsAn initial dose finding study was performed in 6 White New Zealand rabbits to identify a therapeutic but subcurative dose of Y90. In total, 29 rabbits were used in four groups: Y90 treatment (n = 8), DEBIRI treatment (n = 6), Y90 + DEBIRI treatment (n = 7), and an untreated control group (n = 8). Hepatic toxicity was evaluated at baseline, 24 h, 72 h, 1 week, and 2 weeks. MRI tumor volume (TV) and enhancing tumor volume were assessed baseline and 2 weeks. Tumor area and necrosis were evaluated on H&E for pathology.ResultsInfused activities of 84.0-94.4 MBq (corresponding to 55.1-72.7 Gy) were selected based on the initial dose finding study. Infusion of DEBIRI after Y90 was technically feasible in all cases (7/7). Overall, 21/29 animals survived to 2 weeks, and the remaining animals had extrahepatic disease on necropsy. Liver transaminases were elevated with Y90, DEBIRI, and Y90 + DEBIRI compared to control at 24 h, 72 h, and 1 week post-treatment and returned to baseline by 2 weeks. By TV, Y90 + DEBIRI was the only treatment to show statistically significant reduction at 2 weeks compared to the control group (p = 0.012). The change in tumor volume (week 2-baseline) for both Y90 + DEBIRI versus control (p = 0.002) and Y90 versus control (p = 0.014) was significantly decreased. There were no statistically significant differences among groups on pathology.ConclusionIntra-arterial Y90 + DEBIRI was safe and demonstrated enhanced antitumor activity in rabbit VX2 tumors. This combined approach warrants further investigation.

A genetic Gaussian network of multiple phenotypes, constructed through the inverse matrix of the genetic correlation matrix, is informative for understanding the biological dependencies of the phenotypes. However, its estimation may be challenging because the genetic correlation estimates are biased due to estimation errors and idiosyncratic pleiotropy inherent in GWAS summary statistics. Here, we introduce a novel approach called estimation of genetic graph (EGG), which eliminates the estimation error bias and idiosyncratic pleiotropy bias with the same techniques used in multivariable Mendelian randomization. The genetic network estimated by EGG can be interpreted as shared common biological contributions between phenotypes, conditional on others. We use both simulations and real data to demonstrate the superior efficacy of our novel method in comparison with the traditional network estimators. [ABSTRACT FROM AUTHOR]

Mutant Kras and chronic pancreatitis are the most common pathological events involved in human pancreatic cancer. It has been demonstrated that c-Raf is responsible for transmitting signals from mutant Ras to its downstream signals including MEK-ERK and for initiating carcinogenesis. The soluble epoxide hydrolase (sEH), a pro-inflammatory enzyme, generally inactivates anti-inflammatory and anti-pain epoxyeicosatrienoic acids (EETs). Herein, we have synthesized a novel compound of trans-4-{4-[3-(4-chloro-3-trifluoromethyl-phenyl)-ureido]-cyclohexyloxy}-pyridine-2-carboxylic acid methylamide (t-CUPM) via modifying the central phenyl ring of sorafenib and confirmed its dual inhibition of sEH and c-Raf by recombinant kinase activity assay. Pharmacokinetic analysis revealed that oral dosing of t-CUPM resulted in higher blood levels than that of sorafenib throughout the complete time course (48 h). The effect of t-CUPM on the inhibition of mutant Kras(G12D)-initiated murine pancreatic cancer cell growth was determined using the mouse pancreatic carcinoma cell model obtained from LSL-Kras(G12D)/Pdx1-Cre mice and showed that t-CUPM significantly inhibited this murine pancreatic carcinoma cell growth both in vitro and in mice in vivo. Inhibition of mutant Kras-transmitted phosphorylations of cRAF/MEK/ERK was demonstrated in these pancreatic cancer cells using Western blot assay and immunohistochemical approach. Modulation of oxylipin profile, particularly increased EETs/DHET ratio by sEH inhibition, was observed in mice treated with t-CUPM. These results indicate that t-CUPM is a highly potential agent to treat pancreatic cancer via simultaneously targeting c-Raf and sEH.

Carbon price fluctuations significantly impact the development of industries, energy, agriculture, and stock investments. The carbon price possesses the features of nonlinearity, non-stationarity, and high complexity as a time series. To overcome the negative impact of these characteristics on prediction and to improve the prediction accuracy of carbon price series, a combination prediction model named Lp-CNN-LSTM, which utilizes both convolutional neural networks and long short-term memory networks, has been proposed. Strategy one involved establishing distinct models of CNN-LSTM and LSTM to analyze high-frequency and low-frequency carbon price sequences; the combination of output was integrated to predict carbon prices more precisely. Strategy two comprehensively considered the economic and technical indicators of carbon price sequences based on the Pearson correlation coefficient, while the Multi-CNN-LSTM model selected explanatory variables that strongly correlated with carbon prices. Finally, a predictive model for a combination of carbon prices was developed using Lp-norm. The empirical study focused on China's major carbon markets, including Hubei, Guangdong, and Shanghai. According to the error indicators, the performance of the Lp-CNN-LSTM model was superior to individual strategy prediction models. The Lp-CNN-LSTM model has excellent accuracy, superiority, and robustness in predicting carbon prices, which can provide a necessary basis for revising carbon pricing strategies, regulating carbon trading markets, and making investment decisions. [ABSTRACT FROM AUTHOR]