Synthesis and biological evaluation of sulfonamide derivatives containing imidazole moiety as ALK5 inhibitors.

Four series of sulfonamide derivatives (13a-b, 14a-d, 15a-b, and 16a-d) were synthesized and evaluated for their activin receptor-like kinase 5 (ALK5) inhibitory activities. Of these, compounds 13b (IC50 = 0.130 µM) and 15a (IC50 = 0.130 µM) showed the highest inhibitory activities against ALK5 kinase, with activities similar to the positive control LY-2157299. Notably, we discovered that introduction of sulfonamide group at the 2-position of the central imidazole ring significantly increased ALK5 inhibitory activity. Compounds 13b and 15a did not show toxicity in A549 cells up to the maximum concentration of 50 µM, and effectively inhibited TGF-ß1-induced Smad-signaling and cell motility in A549 cells. The results indicate that compounds 13b and 15a are worth of further development as anticancer agents.

The association between diabetes mellitus and prostate cancer: a meta-analysis and Mendelian randomization.

BACKGROUND: Prostate cancer is one of the most common types of cancer in the US, and it has a high mortality rate. Diabetes mellitus is also a dangerous health condition. While some studies have examined the relationship between diabetes mellitus and the risk of prostate cancer, there is still some debate on the matter. This study aims to carefully assess the relationship between prostate cancer and diabetes from both real-world and genetic-level data. METHODS: This meta-analysis was conducted following the PRISMA 2020 reporting guidelines. The study searched three databases including Medline, Embase and Cochrane. The studies about the incidence risk of prostate cancer with diabetes mellitus were included and used to evaluate the association. The odds ratio (OR), risk ratio (RR) and 95% confidence intervals (95% CI) were estimated using Random Effects models and Fixed Effects models. Mendelian randomization study using genetic variants was also conducted. RESULTS: A total of 72 articles were included in this study. The results showed that risk of prostate cancer decreased in diabetes patients. And the influence was different in different regions. This study also estimated the impact of body mass index (BMI) in the diabetes populations and found that the risk decreased in higher BMI populations. The MR analysis found that diabetes mellitus exposure reduced the risk of prostate cancer in the European population and Asia populations. Conclusions The diabetes mellitus has a protective effect on prostate cancer. And the influence of obesity in diabetes mellitus plays an important role in this effect.

[Nicorandil improves cognitive dysfunction in mice with streptozotocin-induced diabetes].

OBJECTIVE: To observe the protective effects of potassium channel opener nicorandil against cognitive dysfunction in mice with streptozotocin (STZ)-induced diabetes. METHODS: C57BL/6J mouse models of type 1 diabetes mellitus (T1DM) were established by intraperitoneal injection of STZ and received daily treatment with intragastric administration of nicorandil or saline (model group) for 4 consecutive weeks, with normal C57BL/6J mice serving as control. Fasting blood glucose level was recorded every week and Morris water maze was used to evaluate the cognitive behavior of the mice in the 4th week. At the end of the experiment, the mice were sacrificed to observe the ultrastructural changes in the hippocampus and pancreas under transmission electron microscopy; the contents of glucose-dependent insulinotropic polypeptide (GIP) and glucagon-like peptide-1 (GLP-1) in the hippocampus and SOD activity and MDA level in the brain tissue were determined. RESULTS: Compared with the control group, the model group showed significantly increased fasting blood glucose (P<0.001), significantly prolonged escape latency (P<0.05) and increased swimming distance (P<0.01) with ultrastructural damage of pancreatic ß cells and in the hippocampus; GIP and GLP-1 contents in the hippocampus (P<0.01) and SOD activity in the brain were significantly decreased (P<0.05) and MDA content was significantly increased in the model group (P<0.05). Compared with the model group, nicorandil treatment did not cause significant changes in fasting blood glucose, but significantly reduced the swimming distance (P<0.05); nicorandil did not improve the ultrastructural changes in pancreatic ß cells but obviously improved the ultrastructures of hippocampal neurons and synapses. Nicorandil also significantly increased the contents of GIP and GLP-1 in the hippocampus (P<0.05), enhanced SOD activity (P<0.05) and decreased MDA level (P<0.01) in the brain tissue. CONCLUSION: Nicorandil improves cognitive dysfunction in mice with STZ-induced diabetes by increasing GIP and GLP-1 contents in the hippocampus and promoting antioxidation to relieve hippocampal injury.

PageRank versatility analysis of multilayer modality-based network for exploring the evolution of oil-water slug flow.

Numerous irregular flow structures exist in the complicated multiphase flow and result in lots of disparate spatial dynamical flow behaviors. The vertical oil-water slug flow continually attracts plenty of research interests on account of its significant importance. Based on the spatial transient flow information acquired through our designed double-layer distributed-sector conductance sensor, we construct multilayer modality-based network to encode the intricate spatial flow behavior. Particularly, we calculate the PageRank versatility and multilayer weighted clustering coefficient to quantitatively explore the inferred multilayer modality-based networks. Our analysis allows characterizing the complicated evolution of oil-water slug flow, from the opening formation of oil slugs, to the succedent inter-collision and coalescence among oil slugs, and then to the dispersed oil bubbles. These properties render our developed method particularly powerful for mining the essential flow features from the multilayer sensor measurements.

Multiplex multivariate recurrence network from multi-channel signals for revealing oil-water spatial flow behavior.

The exploration of the spatial dynamical flow behaviors of oil-water flows has attracted increasing interests on account of its challenging complexity and great significance. We first technically design a double-layer distributed-sector conductance sensor and systematically carry out oil-water flow experiments to capture the spatial flow information. Based on the well-established recurrence network theory, we develop a novel multiplex multivariate recurrence network (MMRN) to fully and comprehensively fuse our double-layer multi-channel signals. Then we derive the projection networks from the inferred MMRNs and exploit the average clustering coefficient and the spectral radius to quantitatively characterize the nonlinear recurrent behaviors related to the distinct flow patterns. We find that these two network measures are very sensitive to the change of flow states and the distributions of network measures enable to uncover the spatial dynamical flow behaviors underlying different oil-water flow patterns. Our method paves the way for efficiently analyzing multi-channel signals from multi-layer sensor measurement system.

Multivariate weighted recurrence network inference for uncovering oil-water transitional flow behavior in a vertical pipe.

Exploring the dynamical behaviors of high water cut and low velocity oil-water flows remains a contemporary and challenging problem of significant importance. This challenge stimulates us to design a high-speed cycle motivation conductance sensor to capture spatial local flow information. We systematically carry out experiments and acquire the multi-channel measurements from different oil-water flow patterns. Then we develop a novel multivariate weighted recurrence network for uncovering the flow behaviors from multi-channel measurements. In particular, we exploit graph energy and weighted clustering coefficient in combination with multivariate time-frequency analysis to characterize the derived complex networks. The results indicate that the network measures are very sensitive to the flow transitions and allow uncovering local dynamical behaviors associated with water cut and flow velocity. These properties render our method particularly useful for quantitatively characterizing dynamical behaviors governing the transition and evolution of different oil-water flow patterns.

Complex network analysis of phase dynamics underlying oil-water two-phase flows.

Characterizing the complicated flow behaviors arising from high water cut and low velocity oil-water flows is an important problem of significant challenge. We design a high-speed cycle motivation conductance sensor and carry out experiments for measuring the local flow information from different oil-in-water flow patterns. We first use multivariate time-frequency analysis to probe the typical features of three flow patterns from the perspective of energy and frequency. Then we infer complex networks from multi-channel measurements in terms of phase lag index, aiming to uncovering the phase dynamics governing the transition and evolution of different oil-in-water flow patterns. In particular, we employ spectral radius and weighted clustering coefficient entropy to characterize the derived unweighted and weighted networks and the results indicate that our approach yields quantitative insights into the phase dynamics underlying the high water cut and low velocity oil-water flows.

Multivariate multiscale complex network analysis of vertical upward oil-water two-phase flow in a small diameter pipe.

High water cut and low velocity vertical upward oil-water two-phase flow is a typical complex system with the features of multiscale, unstable and non-homogenous. We first measure local flow information by using distributed conductance sensor and then develop a multivariate multiscale complex network (MMCN) to reveal the dispersed oil-in-water local flow behavior. Specifically, we infer complex networks at different scales from multi-channel measurements for three typical vertical oil-in-water flow patterns. Then we characterize the generated multiscale complex networks in terms of network clustering measure. The results suggest that the clustering coefficient entropy from the MMCN not only allows indicating the oil-in-water flow pattern transition but also enables to probe the dynamical flow behavior governing the transitions of vertical oil-water two-phase flow.

Multi-frequency complex network from time series for uncovering oil-water flow structure.

Uncovering complex oil-water flow structure represents a challenge in diverse scientific disciplines. This challenge stimulates us to develop a new distributed conductance sensor for measuring local flow signals at different positions and then propose a novel approach based on multi-frequency complex network to uncover the flow structures from experimental multivariate measurements. In particular, based on the Fast Fourier transform, we demonstrate how to derive multi-frequency complex network from multivariate time series. We construct complex networks at different frequencies and then detect community structures. Our results indicate that the community structures faithfully represent the structural features of oil-water flow patterns. Furthermore, we investigate the network statistic at different frequencies for each derived network and find that the frequency clustering coefficient enables to uncover the evolution of flow patterns and yield deep insights into the formation of flow structures. Current results present a first step towards a network visualization of complex flow patterns from a community structure perspective.