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.

Synthesis of amide derivatives containing the imidazole moiety and evaluation of their anti-cardiac fibrosis activity.

Three series of N-{[4-([1,2,4]triazolo[1,5-α]pyridin-6-yl)-5-(6-methylpyridin-2-yl)-1H-imidazol-2-yl]methyl}acetamides (14a-d, 15a-n, and 16a-f) were synthesized and evaluated for activin receptor-like kinase 5 (ALK5) inhibitory activities in an enzymatic assay. The target compounds showed high ALK5 inhibitory activity and selectivity. The half maximal inhibitory concentration (IC50) for phosphorylation of ALK5 of 16f (9.1 nM), the most potent compound, was 2.7 times that of the clinical candidate EW-7197 (vactosertib) and 14 times that of the clinical candidate LY-2157299. The selectivity index of 16f against p38α mitogen-activated protein kinase was >109, which was much higher than that of positive controls (EW-7197: >41, and LY-2157299: 4). Furthermore, a molecular docking study provided the interaction modes between the target compounds and ALK5. Compounds 14c, 14d, and 16f effectively inhibited the protein expression of α-smooth muscle actin (α-SMA), collagen I, and tissue inhibitor of metalloproteinase 1 (TIMP-1)/matrix metalloproteinase 13 (MMP-13) in transforming growth factor-ß-induced human umbilical vein endothelial cells. Compounds 14c and 16f showed especially high activity at low concentrations, which suggests that these compounds could inhibit myocardial cell fibrosis. Compounds 14c, 14d, and 16f are potential preclinical candidates for the treatment of cardiac fibrosis.

Synthesis and Evaluation of Imidazole Derivatives Bearing Imidazo[2,1-b] [1,3,4]thiadiazole Moiety as Antibacterial Agents.

BACKGROUND: Drug-resistant infections kill hundreds of thousands of people globally every year. In previous work, we found that tri-methoxy- and pyridine-substituted imidazoles show strong antibacterial activities. OBJECTIVE: The aim of this work was to investigate the antibacterial activities and bacterial resistances of imidazoles bearing an aromatic heterocyclic, alkoxy, or polycyclic moiety on the central ring. METHODS: Three series of 2-cyclopropyl-5-(5-(6-methylpyridin-2-yl)-2-substituted-1H-imidazol-4- yl)-6-phenylimidazo[2,1-b][1,3,4]thiadiazoles (13a-e, 14a-d, and 15a-f) were synthesized and their antibacterial activity was evaluated. The structures were confirmed by their 1H NMR, 13C NMR, and HRMS spectra. All the synthesized compounds were screened against Gram-positive, Gramnegative, and multidrug-resistant bacterial strains. RESULTS: More than half of the compounds showed moderate or strong antibacterial activity. Among them, compound 13e (MICs = 1-4 µg/mL) showed the strongest activity against Gram-positive and drug-resistant bacteria as well as high selectivity against Gram-negative bacteria. Furthermore, it showed no cytotoxicity against HepG2 cells, even at 100 µM, and no hemolysis at 20 µM. CONCLUSION: These results indicate that compound 13e is excellent candicate for further study as a potential antibacterial agent.

Synthesis and Antibacterial Activity Evaluation of Imidazole Derivatives Containing 6-Methylpyridine Moiety.

A series of 2-cyclopropyl-5-(5-(6-methylpyridin-2-yl)-2-substituted-1H-imidazol-4-yl)-6-phenylimidazo[2,1-b][1,3,4]thiadiazoles (15a-t and 16a-f) were synthesized and their antibacterial activities were evaluated. More than half of the compounds showed moderate or strong antibacterial activity. Among them, compounds 15t (MIC=1-2 µg/mL) and 16d (MIC=0.5 µg/mL) showed the strongest antibacterial activities. Notably, compound 16d did not exhibit cytotoxicity in HepG2 cells and did not show hemolysis like the positive control compound Gatifloxacin. The results suggest that compound 16d should be further investigated as a candidate antibacterial agent.

Effects and neuroprotective mechanisms of vagus nerve stimulation on cognitive impairment with traumatic brain injury in animal studies: A systematic review and meta-analysis.

Introduction: Cognitive impairment is the main clinical feature after traumatic brain injury (TBI) and is usually characterized by attention deficits, memory loss, and decreased executive function. Vagus nerve stimulation (VNS) has been reported to show potential improvement in the cognition level after traumatic brain injury in clinical and preclinical studies. However, this topic has not yet been systematically reviewed in published literature. In this study, we present a systematic review and meta-analysis of the effects of VNS on cognitive function in animal models of TBI and their underlying mechanisms. Methods: We performed a literature search on PubMed, PsycINFO, Web of Science, Embase, Scopus, and Cochrane Library from inception to December 2021 to identify studies describing the effects of VNS on animal models of TBI. Results: Overall, nine studies were identified in animal models (36 mice, 268 rats, and 27 rabbits). An analysis of these studies showed that VNS can improve the performance of TBI animals in behavioral tests (beam walk test: SMD: 4.95; 95% confidence interval [CI]: 3.66, 6.23; p < 0.00001) and locomotor placing tests (SMD: -2.39; 95% CI: -4.07, -0.71; p = 0.005), whereas it reduced brain edema (SMD: -1.58; 95% CI: -2.85, -0.31; p = 0. 01) and decrease TNF-α (SMD: -3.49; 95% CI: -5.78, -1.2; p = 0.003) and IL-1ß (SMD: -2.84; 95% CI: -3.96, -1.71; p < 0.00001) expression level in the brain tissue. However, the checklist for SYRCLE showed a moderate risk of bias (quality score between 30% and 60%), mainly because of the lack of sample size calculation, random assignment, and blinded assessment. Conclusion: The present review showed that VNS can effectively promote cognitive impairment and neuropathology in animal models of TBI. We hope that the results of this systematic review can be applied to improve the methodological quality of animal experiments on TBI, which will provide more important and conclusive evidence on the clinical value of VNS. To further confirm these results, there is a need for high-quality TBI animal studies with sufficient sample size and a more comprehensive outcome evaluation. Systematic review registration: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021290797, identifier: CRD42021290797.

Brain-machine interface-based training for improving upper extremity function after stroke: A meta-analysis of randomized controlled trials.

Background: Upper extremity dysfunction after stroke is an urgent clinical problem that greatly affects patients' daily life and reduces their quality of life. As an emerging rehabilitation method, brain-machine interface (BMI)-based training can extract brain signals and provide feedback to form a closed-loop rehabilitation, which is currently being studied for functional restoration after stroke. However, there is no reliable medical evidence to support the effect of BMI-based training on upper extremity function after stroke. This review aimed to evaluate the efficacy and safety of BMI-based training for improving upper extremity function after stroke, as well as potential differences in efficacy of different external devices. Methods: English-language literature published before April 1, 2022, was searched in five electronic databases using search terms including "brain-computer/machine interface", "stroke" and "upper extremity." The identified articles were screened, data were extracted, and the methodological quality of the included trials was assessed. Meta-analysis was performed using RevMan 5.4.1 software. The GRADE method was used to assess the quality of the evidence. Results: A total of 17 studies with 410 post-stroke patients were included. Meta-analysis showed that BMI-based training significantly improved upper extremity motor function [standardized mean difference (SMD) = 0.62; 95% confidence interval (CI) (0.34, 0.90); I 2 = 38%; p < 0.0001; n = 385; random-effects model; moderate-quality evidence]. Subgroup meta-analysis indicated that BMI-based training significantly improves upper extremity motor function in both chronic [SMD = 0.68; 95% CI (0.32, 1.03), I 2 = 46%; p = 0.0002, random-effects model] and subacute [SMD = 1.11; 95%CI (0.22, 1.99); I 2 = 76%; p = 0.01; random-effects model] stroke patients compared with control interventions, and using functional electrical stimulation (FES) [SMD = 1.11; 95% CI (0.67, 1.54); I 2 = 11%; p < 0.00001; random-effects model]or visual feedback [SMD = 0.66; 95% CI (0.2, 1.12); I 2 = 4%; p = 0.005; random-effects model;] as the feedback devices in BMI training was more effective than using robot. In addition, BMI-based training was more effective in improving patients' activities of daily living (ADL) than control interventions [SMD = 1.12; 95% CI (0.65, 1.60); I 2 = 0%; p < 0.00001; n = 80; random-effects model]. There was no statistical difference in the dropout rate and adverse effects between the BMI-based training group and the control group. Conclusion: BMI-based training improved upper limb motor function and ADL in post-stroke patients. BMI combined with FES or visual feedback may be a better combination for functional recovery than robot. BMI-based trainings are well-tolerated and associated with mild adverse effects.

Metabolomic Mechanisms of Radix Fici Hirtae against Carbon Tetrachloride-Induced Acute Liver Damage in Mice.

Background: Radix Fici Hirtae (RFH), known as Cantonese ginseng, is an alternative folk medicine that is widely used to treat various diseases in southern China. The aim of this study was to investigate the effect and metabolic mechanisms of pretreatment with RFH on the serum metabolic profiles of carbon tetrachloride (CCl4) induced acute liver injury in mice. Methods: Mice fed with the water extract of RFH at a dose of 1.5 g/kg and 0.75 g/kg for consecutive 7 days, and then serum samples were taken for the metabolomic analysis. Furthermore, the bioinformatics and pathways analysis were measured. Results: The UHPLC-Orbitrap/MS based-metabolomic analysis identified 20 differential metabolic markers in serum of CCl4-induced liver injury mice compared to that of the normal controls, which were mainly related to the metabolism of amino acids and fatty acids. Furthermore, most of these biomarkers contributing to CCl4 induction were ameliorated by RFH, and the bioinformatics and pathways analysis revealed that therapeutic actions of RFH were mainly involved in the regulation of the oxidative stress responses and energy homeostasis. Conclusion: These findings provide potential metabolic mechanism for future study and allow for hypothesis generation about the hepatoprotective effects of Radix Fici Hirtae.

Prism adaptation combined with eye movement training for unilateral spatial neglect after stroke: Study protocol for a single-blind prospective, randomized controlled trial.

Background: Unilateral spatial neglect (USN) is a complex neurological syndrome that often reduces rehabilitation outcomes, prolongs patients' hospital stays, and decreases their quality of life. However, the current therapies for USN have varying efficacy. We will explore a new treatment option that combines prism adaptation (PA) with eye movement training (EMT) for the treatment of USN after stroke. Methods: We will conduct a single-blind, prospective, randomized controlled trial to assess the efficacy of the combined intervention (PA & EMT) on USN in an inpatient rehabilitation setting. The study aims to recruit 88 patients with USN after an ischemic or hemorrhagic stroke. Participants will be randomly assigned to the following four groups: (1) PA group (n = 22), (2) EMT group (n = 22), (3) PA and EMT group (n = 22), and (4) control group (n = 22). All groups will receive 10 sessions of interventions over 2 weeks, 5 times per week. Blinded assessors will conduct a baseline assessment, a post-intervention assessment, and a follow-up assessment (2 weeks post-intervention). The primary outcome measure will use the Behavioral Inattention Test-Conventional Subset (BIT-C) and Catherine Bergego Scale (CBS) to assess the levels of USN. Secondary outcome measures will assess the patient's ability to perform activities of daily living using the Modified Barthel Index (MBI). Patients who completed all treatment and assessment sessions will be included in the final analysis. Discussion: This study will explore the effects of 10 sessions of combined interventions (PA & EMT) on USN and functional capacity. This study has the potential to identify a new, evidence-based treatment option and provide new ideas for the treatment of USN. Ethics and dissemination: The study protocol has been approved by the Nanchong Central Hospital. Written informed consent will be obtained from all the participants. The results of this study will be disseminated to the public through scientific conferences and a peer-reviewed journal. Trial registration: ChiCTR, ChiCTR2100049482. Registered on 2 August 2021, http://www.chictr.org.cn/showproj.aspx?proj=130823.

A recurrence network-based convolutional neural network for fatigue driving detection from EEG.

Driver fatigue is an important cause of traffic accidents, which has triggered great concern for detecting drivers' fatigue. Numerous methods have been proposed to fulfill this challenging task, including feature methods and machine learning methods. Recently, with the development of deep learning techniques, many studies achieved better results than traditional feature methods, and the combination of traditional methods and deep learning techniques gradually received attention. In this paper, we propose a recurrence network-based convolutional neural network (RN-CNN) method to detect fatigue driving. To be specific, we first conduct a simulated driving experiment to collect electroencephalogram (EEG) signals of subjects under alert state and fatigue state. Then, we construct the multiplex recurrence network (RN) from EEG signals to fuse information from the original time series. Finally, CNN is employed to extract and learn the features of a multiplex RN for realizing a classification task. The results indicate that the proposed RN-CNN method can achieve an average accuracy of 92.95%. To verify the effectiveness of our method, some existing competitive methods are compared with ours. The results show that our method outperforms the existing methods, which demonstrate the effect of the RN-CNN method.

Multiplex Limited Penetrable Horizontal Visibility Graph from EEG Signals for Driver Fatigue Detection.

Driver fatigue is an important contributor to road accidents, and driver fatigue detection has attracted a great deal of attention on account of its significant importance. Numerous methods have been proposed to fulfill this challenging task, though, the characterization of the fatigue mechanism still, to a large extent, remains to be investigated. To address this problem, we, in this work, develop a novel Multiplex Limited Penetrable Horizontal Visibility Graph (Multiplex LPHVG) method, which allows in not only detecting fatigue driving but also probing into the brain fatigue behavior. Importantly, we use the method to construct brain networks from EEG signals recorded from different subjects performing simulated driving tasks under alert and fatigue driving states. We then employ clustering coefficient, global efficiency and characteristic path length to characterize the topological structure of the networks generated from different brain states. In addition, we combine average edge overlap with the network measures to distinguish alert and mental fatigue states. The high-accurate classification results clearly demonstrate and validate the efficacy of our multiplex LPHVG method for the fatigue detection from EEG signals. Furthermore, our findings show a significant increase of the clustering coefficient as the brain evolves from alert state to mental fatigue state, which yields novel insights into the brain behavior associated with fatigue driving.

Multivariate weighted recurrence network analysis of EEG signals from ERP-based smart home system.

Smart home has been widely used to improve the living quality of people. Recently, the brain-computer interface (BCI) contributes greatly to the smart home system. We design a BCI-based smart home system, in which the event-related potentials (ERP) are induced by the image interface based on the oddball paradigm. Then, we investigate the influence of mental fatigue on the ERP classification by the Fisher linear discriminant analysis. The results indicate that the classification accuracy of ERP decreases as the brain evolves from the normal stage to the mental fatigue stage. In order to probe into the difference of the brain, cognitive process between mental fatigue and normal states, we construct multivariate weighted recurrence networks and analyze the variation of the weighted clustering coefficient and weighted global efficiency corresponding to these two brain states. The findings suggest that these two network metrics allow distinguishing normal and mental fatigue states and yield novel insights into the brain fatigue behavior resulting from a long use of the ERP-based smart home system. These properties render the multivariate recurrence network, particularly useful for analyzing electroencephalographic recordings from the ERP-based smart home system.

A recurrence quantification analysis-based channel-frequency convolutional neural network for emotion recognition from EEG.

Constructing a reliable and stable emotion recognition system is a critical but challenging issue for realizing an intelligent human-machine interaction. In this study, we contribute a novel channel-frequency convolutional neural network (CFCNN), combined with recurrence quantification analysis (RQA), for the robust recognition of electroencephalogram (EEG) signals collected from different emotion states. We employ movie clips as the stimuli to induce happiness, sadness, and fear emotions and simultaneously measure the corresponding EEG signals. Then the entropy measures, obtained from the RQA operation on EEG signals of different frequency bands, are fed into the novel CFCNN. The results indicate that our system can provide a high emotion recognition accuracy of 92.24% and a relatively excellent stability as well as a satisfactory Kappa value of 0.884, rendering our system particularly useful for the emotion recognition task. Meanwhile, we compare the performance of the entropy measures, extracted from each frequency band, in distinguishing the three emotion states. We mainly find that emotional features extracted from the gamma band present a considerably higher classification accuracy of 90.51% and a Kappa value of 0.858, proving the high relation between emotional process and gamma frequency band.

[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.

Visibility Graph from Adaptive Optimal Kernel Time-Frequency Representation for Classification of Epileptiform EEG.

Detecting epileptic seizure from EEG signals constitutes a challenging problem of significant importance. Combining adaptive optimal kernel time-frequency representation and visibility graph, we develop a novel method for detecting epileptic seizure from EEG signals. We construct complex networks from EEG signals recorded from healthy subjects and epilepsy patients. Then we employ clustering coefficient, clustering coefficient entropy and average degree to characterize the topological structure of the networks generated from different brain states. In addition, we combine energy deviation and network measures to recognize healthy subjects and epilepsy patients, and further distinguish brain states during seizure free interval and epileptic seizures. Three different experiments are designed to evaluate the performance of our method. The results suggest that our method allows a high-accurate classification of epileptiform EEG signals.

Multiscale limited penetrable horizontal visibility graph for analyzing nonlinear time series.

Visibility graph has established itself as a powerful tool for analyzing time series. We in this paper develop a novel multiscale limited penetrable horizontal visibility graph (MLPHVG). We use nonlinear time series from two typical complex systems, i.e., EEG signals and two-phase flow signals, to demonstrate the effectiveness of our method. Combining MLPHVG and support vector machine, we detect epileptic seizures from the EEG signals recorded from healthy subjects and epilepsy patients and the classification accuracy is 100%. In addition, we derive MLPHVGs from oil-water two-phase flow signals and find that the average clustering coefficient at different scales allows faithfully identifying and characterizing three typical oil-water flow patterns. These findings render our MLPHVG method particularly useful for analyzing nonlinear time series from the perspective of multiscale network analysis.

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.