Oxfendazole Induces Apoptosis in Ovarian Cancer Cells by Activating JNK/MAPK Pathway and Inducing Reactive Oxygen Species Generation.

Ovarian cancer (OC) is one of the most common and high mortality type of cancer among women worldwide. The majority of patients with OC respond to chemotherapy initially; however, most of them become resistant to chemotherapy and results in a high level of treatment failure in OC. Therefore, novel agents for the treatment of OC are urgently required. Benzimidazole anthelmintics might have the promising efficacy for cancer therapy as their selectively binding activity to ß-tubulin. Recent study has shown that one of the benzimidazole anthelmintics oxfendazole inhibited cell growth of non-small cell lung cancer cells, revealing its anti-cancer activity; however, the pharmacological action and detailed mechanism underlying the effects of oxfendazole on OC cells remain unclear. Therefore, the present study investigated the cytotoxic effects of oxfendazole on OC cells. Our results demonstrated that oxfendazole significantly decreased the viability of OC cells. Oxfendazole inhibited the proliferation, induced G2/M phase arrest and apoptotic cell death in A2780 cells. The c-Jun N-terminal kinase (JNK)/mitogen-activated protein kinase (MAPK) pathway was activated and reactive oxygen species (ROS) generation was increased in OC cells treated with oxfendazole; oxfendazole-induced apoptosis was notably abrogated when co-treated with JNK inhibitor SP600125 and ROS scavenger N-acetyl-L-cysteine (NAC), indicating that JNK/MAPK pathway activation and ROS accumulation was associated with the oxfendazole-induced apoptosis of OC cells. Moreover, oxfendazole could also induce the proliferation inhibition and apoptosis of cisplatin resistant cells. Collectively, these results revealed that oxfendazole may serve as a potential therapeutic agent for the treatment of OC.

Double resonance induced by group coupling with quenched disorder.

Results show that the astrocytes can not only listen to the talk of large assemble of neurons but also give advice to the conversations and are significant sources of heterogeneous couplings as well. In the present work, we focus on such regulation character of astrocytes and explore the role of heterogeneous couplings among interacted neuron-astrocyte components in a signal response. We consider reduced dynamics in which the listening and advising processes of astrocytes are mapped into the form of group coupling, where the couplings are normally distributed. In both globally coupled overdamped bistable oscillators and an excitable FitzHugh-Nagumo (FHN) neuron model, we numerically and analytically demonstrate that two types of bell-shaped collective response curves can be obtained as the ensemble coupling strength or the heterogeneity of group coupling rise, respectively, which can be seen as a new type of double resonance. Furthermore, through the bifurcation analysis, we verify that these resonant signal responses stem from the competition between dispersion and aggregation induced by heterogeneous group and positive pairwise couplings, respectively. Our results contribute to a better understanding of the signal propagation in coupled systems with quenched disorder.

Diversity-induced resonance in a globally coupled bistable system with diversely distributed heterogeneity.

A moderate degree of diversity, in form of quenched noise or intrinsic heterogeneity, can significantly strengthen the collective response of coupled extended systems. As yet, related discoveries on diversity-induced resonance are mainly concentrated on symmetrically distributed heterogeneity, e.g., the Gaussian or uniform distributions with zero-mean. The necessary conditions that guarantee the arise of resonance phenomenon in heterogeneous oscillators remain largely unknown. In this work, we show that the standard deviation and the ratio of negative entities of a given distribution jointly modulate diversity-induced resonance and the concomitance of negative and positive entities is the prerequisite for this resonant behavior emerging in diverse symmetrical and asymmetrical distributions. Particularly, for a proper degree of diversity of a given distribution, the collective signal response behaves like a bell-shaped curve as the ratio of negative oscillator increases, which can be termed negative-oscillator-ratio induced resonance. Furthermore, we analytically reveal that the ratio of negative oscillators plays a gating role in the resonance phenomenon on the basis of a reduced equation. Finally, we examine the robustness of these results in globally coupled bistable elements with asymmetrical potential functions. Our results suggest that the phenomenon of diversity-induced resonance can arise in arbitrarily distributed heterogeneous bistable oscillators by regulating the ratio of negative entities appropriately.

Advantage of Being Multicomponent and Spatial: Multipartite Viruses Colonize Structured Populations with Lower Thresholds.

Multipartite viruses have a genome divided into different disconnected viral particles. A majority of multipartite viruses infect plants; very few target animals. To understand why, we use a simple, network-based susceptible-latent-infectious-recovered model. We show both analytically and numerically that, provided that the average degree of the contact network exceeds a critical value, even in the absence of an explicit microscopic advantage, multipartite viruses have a lower threshold to colonizing network-structured populations compared to a well-mixed population. We further corroborate this finding on two-dimensional lattice networks, which better represent the typical contact structures of plants.

Fragility and robustness of self-sustained oscillations in an epidemiological model on small-world networks.

We investigate the susceptible-infected-recovered-susceptible epidemic model, typical of mathematical epidemiology, with the diversity of the durations of infection and recovery of the individuals on small-world networks. Infection spreads from infected to healthy nodes, whose infection and recovery periods denoted by τI and τR, respectively, are either fixed or uniformly distributed around a specified mean. Whenever τI and τR are narrowly distributed around their mean values, the epidemic prevalence in the stationary state is found to reach its maximal level in the typical small-world region. This non-monotonic behavior of the final epidemic prevalence is thought to be similar to the efficient navigation in small worlds with cost minimization. Besides, pronounced oscillatory behavior of the fraction of infected nodes emerges when the number of shortcuts on the underlying network become sufficiently large. Remarkably, we find that the synchronized oscillation of infection incidences is quite fragile to the variability of the two characteristic time scales τI and τR. Specifically, even in the limit of a random network (where the amplest oscillations are expected to arise for fixed τI and τR), increasing the variability of the duration of the infectious period and/or that of the refractory period will push the system to change from a self-sustained oscillation to a fixed point with negligible fluctuations in the steady state. Interestingly, negative correlation between τI and τR can give rise to the robustness of the self-sustained oscillatory phenomenon. Our findings thus highlight the pivotal role of, apart from the external seasonal driving force and demographic stochasticity, the intrinsic characteristic of the system itself in understanding the cycle of outbreaks of recurrent epidemics.

Heterogeneous cooperative leadership structure emerging from random regular graphs.

This paper investigates the evolution of cooperation and the emergence of hierarchical leadership structure in random regular graphs. It is found that there exist different learning patterns between cooperators and defectors, and cooperators are able to attract more followers and hence more likely to become leaders. Hence, the heterogeneous distributions of reputation and leadership can emerge from homogeneous random graphs. The important directed game-learning skeleton is then studied, revealing some important structural properties, such as the heavy-tailed degree distribution and the positive in-in degree correlation.

Solving the Dynamic Correlation Problem of the Susceptible-Infected-Susceptible Model on Networks.

The susceptible-infected-susceptible (SIS) model is a canonical model for emerging disease outbreaks. Such outbreaks are naturally modeled as taking place on networks. A theoretical challenge in network epidemiology is the dynamic correlations coming from that if one node is infected, then its neighbors are likely to be infected. By combining two theoretical approaches-the heterogeneous mean-field theory and the effective degree method-we are able to include these correlations in an analytical solution of the SIS model. We derive accurate expressions for the average prevalence (fraction of infected) and epidemic threshold. We also discuss how to generalize the approach to a larger class of stochastic population models.

Selfish punishment with avoiding mechanism can alleviate both first-order and second-order social dilemma.

Punishment, especially selfish punishment, has recently been identified as a potent promoter in sustaining or even enhancing the cooperation among unrelated individuals. However, without other key mechanisms, the first-order social dilemma and second-order social dilemma are still two enduring conundrums in biology and the social sciences even with the presence of punishment. In the present study, we investigate a spatial evolutionary four-strategy prisoner׳s dilemma game model with avoiding mechanism, where the four strategies are cooperation, defection, altruistic and selfish punishment. By introducing the low level of random mutation of strategies, we demonstrate that the presence of selfish punishment with avoiding mechanism can alleviate the two kinds of social dilemmas for various parametrizations. In addition, we propose an extended pair approximation method, whose solutions can essentially estimate the dynamical behaviors and final evolutionary frequencies of the four strategies. At last, considering the analogy between our model and the classical Lotka-Volterra system, we introduce interaction webs based on the spatial replicator dynamics and the transformed payoff matrix to qualitatively characterize the emergent co-exist strategy phases, and its validity are supported by extensive simulations.

Effect of vaccination strategies on the dynamic behavior of epidemic spreading and vaccine coverage.

The transmission of infectious, yet vaccine-preventable, diseases is a typical complex social phenomenon, where the increasing level of vaccine update in the population helps to inhibit the epidemic spreading, which in turn, however, discourages more people to participate in vaccination campaigns, due to the "externality effect" raised by vaccination. We herein study the impact of vaccination strategies, pure, continuous (rather than adopt vaccination definitely, the individuals choose to taking vaccine with some probabilities), or continuous with randomly mutation, on the vaccination dynamics with a spatial susceptible-vaccinated-infected-recovered (SVIR) epidemiological model. By means of extensive Monte-Carlo simulations, we show that there is a crossover behavior of the final vaccine coverage between the pure-strategy case and the continuous-strategy case, and remarkably, both the final vaccination level and epidemic size in the continuous-strategy case are less than them in the pure-strategy case when vaccination is cheap. We explain this phenomenon by analyzing the organization process of the individuals in the continuous-strategy case in the equilibrium. Our results are robust to the SVIR dynamics defined on other spatial networks, like the Erdos-Rényi and Barabási-Albert networks.

Developing Deep LSTMs With Later Temporal Attention for Predicting COVID-19 Severity, Clinical Outcome, and Antibody Level by Screening Serological Indicators Over Time.

OBJECTIVE: The clinical course of COVID-19, as well as the immunological reaction, is notable for its extreme variability. Identifying the main associated factors might help understand the disease progression and physiological status of COVID-19 patients. The dynamic changes of the antibody against Spike protein are crucial for understanding the immune response. This work explores a temporal attention (TA) mechanism of deep learning to predict COVID-19 disease severity, clinical outcomes, and Spike antibody levels by screening serological indicators over time. METHODS: We use feature selection techniques to filter feature subsets that are highly correlated with the target. The specific deep Long Short-Term Memory (LSTM) models are employed to capture the dynamic changes of disease severity, clinical outcome, and Spike antibody level. We also propose deep LSTMs with a TA mechanism to emphasize the later blood test records because later records often attract more attention from doctors. RESULTS: Risk factors highly correlated with COVID-19 are revealed. LSTM achieves the highest classification accuracy for disease severity prediction. Temporal Attention Long Short-Term Memory (TA-LSTM) achieves the best performance for clinical outcome prediction. For Spike antibody level prediction, LSTM achieves the best permanence. CONCLUSION: The experimental results demonstrate the effectiveness of the proposed models. The proposed models can provide a computer-aided medical diagnostics system by simply using time series of serological indicators.

Association between polymorphisms of cytokine genes and endometriosis: A comprehensive systematic review and meta-analysis.

INTRODUCTION: There is abundant evidence to suggest that cytokines play a part in the mechanisms responsible for the formation of endometrium heterotopy. Cytokine synthesis is not only determined by the body's immunological reactivity but also by polymorphisms in the immune regulatory genes. The study of these polymorphisms in the immune regulatory genes offers up new possibilities in terms of prognosticating the risk of endometriosis and susceptibility to its treatment. The purpose of this comprehensive systematic review and meta-analysis was to investigate whether or not cytokine gene polymorphisms were linked to an increased chance of endometriosis. METHODS: By searching MEDLINE, Scopus, and Web of Science databases, the relevant studies were identified. The odds ratio (OR) with 95% confidence interval (CI) was used to assess the association between TNF-α/IL-10/IL-6/TGF-ß/IFN-γ/IL-1ß gene polymorphisms and endometriosis risk. RESULTS: A total of 5128 cases and 5334 controls in 32 eligible studies were included in the meta-analysis. Overall, results indicated the negative association between the cytokine gene polymorphisms and endometriosis in the dominant model of TNF-α (rs1799964): [OR] = 0.64, [CI]: 0.46-0.89) and a positive association in IFN-γ a13 allele: OR= 1.45, [CI]: 1.07-1.98; and IL-10 (rs1800872): [OR]= 1.60, [CI]: 1.21-2.12). CONCLUSION: The present study suggests that IL-10 (rs1800872) and IFN-γ a13 allele may be a risk factors for endometriosis. Also, TNF-α (rs1799964) is associated with decreased susceptibility to endometriosis.

Nonmonotonic enhancement of diversity-induced resonance in systems of mobile oscillators.

Diversity is omnipresent in natural and synthetic extended systems, the phenomenon of diversity-induced resonance (DIR), wherein a moderate degree of the diversity can provoke an optimal collective response, provides researchers a brand-new strategy to amplify and utilize the weak signal. As yet the relevant advances focus mostly on the ideal situations where the interactions among elements are uncorrelated with the physical proximity of agents. Such a consideration overlooks interactions mediated by the motion of agents in space. Here, we investigate the signal response of an ensemble of spatial mobile heterogeneous bistable oscillators with two canonical interacting modes: dynamic and preset. The oscillators are considered as mass points and perform random walks in a two-dimensional square plane. Under the dynamic scheme, the oscillators can only interact with other oscillators within a fixed vision radius. For the preset circumstance, the interaction among oscillators occurs only when all of them are in a predefined region at the same moment. We find that the DIR can be obtained in both situations. Additionally, the strength of resonance nonmonotonically rises with respect to the increase of moving speed, and the optimal resonance is acquired by an intermediate magnitude of speed. Finally, we propose reduced equations to guarantee the occurrence of such mobility-optimized DIR on the basis of the fast switching approximation theory and also examine the robustness of such phenomenon through the excitable FitzHugh-Nagumo model and a different spatial motion mechanism. Our results reveal for the first time that the DIR can be optimized by the spatial mobility and thus has promising potential application in the communication of mobile agents.

Intercellular competitive growth dynamics with microenvironmental feedback.

Normal life activities between cells rely crucially on the homeostasis of the cellular microenvironment, but aging and cancer will upset this balance. In this paper we introduce the microenvironmental feedback mechanism to the growth dynamics of multicellular organisms, which changes the cellular competitive ability and thereby regulates the growth of multicellular organisms. We show that the presence of microenvironmental feedback can effectively delay aging, but cancer cells may grow uncontrollably due to the emergence of the tumor microenvironment (TME). We study the effect of the fraction of cancer cells relative to that of senescent cells on the feedback rate of the microenvironment on the lifespan of multicellular organisms and find that the average lifespan shortened is close to the data for non-Hodgkin's lymphoma in Canada from 1980 to 2015. We also investigate how the competitive ability of cancer cells affects the lifespan of multicellular organisms and reveal that there is an optimal value of the competitive ability of cancer cells allowing the organism to survive longest. Interestingly, the proposed microenvironmental feedback mechanism can give rise to the phenomenon of Parrondo's paradox: When the competitive ability of cancer cells switches between a too-high and a too-low value, multicellular organisms are able to live longer than in each case individually. Our results may provide helpful clues for targeted therapies aimed at the TME.

Emergent hierarchical structures in multiadaptive games.

We investigate a game-theoretic model of a social system where both the rules of the game and the interaction structure are shaped by the behavior of the agents. We call this type of model, with several types of feedback couplings from the behavior of the agents to their environment, a multiadaptive game. Our model has a complex behavior with several regimes of different dynamic behavior accompanied by different network topological properties. Some of these regimes are characterized by heterogeneous, hierarchical interaction networks, where cooperation and network topology coemerge from the dynamics.

Multistage onset of epidemics in heterogeneous networks.

We develop a theory for the susceptible-infected-susceptible (SIS) epidemic model on networks that incorporate both network structure and dynamic correlations. This theory can account for the multistage onset of the epidemic phase in scale-free networks. This phenomenon is characterized by multiple peaks in the susceptibility as a function of the infection rate. It can be explained by that, even under the global epidemic threshold, a hub can sustain the epidemics for an extended period. Moreover, our approach improves theoretical calculations of prevalence close to the threshold in heterogeneous networks and also can predict the average risk of infection for neighbors of nodes with different degree and state on uncorrelated static networks.

Twofold effect of self-interest in pedestrian room evacuation.

Evacuation dynamics of pedestrians in a square room with one exit is studied. The movement of the pedestrians is guided by the static floor field model. Whenever multiple pedestrians are trying to move to the same target position, a game theoretical framework is introduced to address the conflict. Depending on the payoff matrix, the game that the pedestrians are involved in may be either hawk-dove or prisoner's dilemma, from which the reaped payoffs determine the capacities, or probabilities, of the pedestrians occupying the preferred vacant sites. The pedestrians are allowed to adjust their strategies when competing with others, and a parameter κ is utilized to characterize the extent of their self-interest. It is found that self-interest may induce either positive or negative impacts on the evacuation dynamics depending on whether it can facilitate the formation of collective cooperation in the population or not. Particularly, a resonance-like performance of evacuation is realized in the regime of prisoner's dilemma. The effects of placing an obstacle in front of the exit and the diversity of responses of the pedestrians to the space competition on the evacuation dynamics are also discussed.

Collective firing patterns of neuronal networks with short-term synaptic plasticity.

We investigate the occurrence of synchronous population activities in a neuronal network composed of both excitatory and inhibitory neurons and equipped with short-term synaptic plasticity. The collective firing patterns with different macroscopic properties emerge visually with the change of system parameters, and most long-time collective evolution also shows periodic-like characteristics. We systematically discuss the pattern-formation dynamics on a microscopic level and find a lot of hidden features of the population activities. The bursty phase with power-law distributed avalanches is observed in which the population activity can be either entire or local periodic-like. In the purely spike-to-spike synchronous regime, the periodic-like phase emerges from the synchronous chaos after the backward period-doubling transition. The local periodic-like population activity and the synchronous chaotic activity show substantial trial-to-trial variability, which is unfavorable for neural code, while they are contrary to the stable periodic-like phases. We also show that the inhibitory neurons can promote the generation of cluster firing behavior and strong bursty collective firing activity by depressing the activities of postsynaptic neurons partially or wholly.

Optimal Organization of Functional Connectivity Networks for Segregation and Integration With Large-Scale Critical Dynamics in Human Brains.

The optimal organization for functional segregation and integration in brain is made evident by the "small-world" feature of functional connectivity (FC) networks and is further supported by the loss of this feature that has been described in many types of brain disease. However, it remains unknown how such optimally organized FC networks arise from the brain's structural constrains. On the other hand, an emerging literature suggests that brain function may be supported by critical neural dynamics, which is believed to facilitate information processing in brain. Though previous investigations have shown that the critical dynamics plays an important role in understanding the relation between whole brain structural connectivity and functional connectivity, it is not clear if the critical dynamics could be responsible for the optimal FC network configuration in human brains. Here, we show that the long-range temporal correlations (LRTCs) in the resting state fMRI blood-oxygen-level-dependent (BOLD) signals are significantly correlated with the topological matrices of the FC brain network. Using structure-dynamics-function modeling approach that incorporates diffusion tensor imaging (DTI) data and simple cellular automata dynamics, we showed that the critical dynamics could optimize the whole brain FC network organization by, e.g., maximizing the clustering coefficient while minimizing the characteristic path length. We also demonstrated with a more detailed excitation-inhibition neuronal network model that loss of local excitation-inhibition (E/I) balance causes failure of critical dynamics, therefore disrupting the optimal FC network organization. The results highlighted the crucial role of the critical dynamics in forming an optimal organization of FC networks in the brain and have potential application to the understanding and modeling of abnormal FC configurations in neuropsychiatric disorders.

Analytical treatment for cyclic three-state dynamics on static networks.

Whenever a dynamical process unfolds on static networks, the dynamical state of any focal individual will be exclusively influenced by directly connected neighbors, rather than by those unconnected ones, hence the arising of the dynamical correlation problem, where mean-field-based methods fail to capture the scenario. The dynamic correlation coupling problem has always been an important and difficult problem in the theoretical field of physics. The explicit analytical expressions and the decoupling methods often play a key role in the development of corresponding field. In this paper, we study the cyclic three-state dynamics on static networks, which include a wide class of dynamical processes, for example, the cyclic Lotka-Volterra model, the directed migration model, the susceptible-infected-recovered-susceptible epidemic model, and the predator-prey with empty sites model. We derive the explicit analytical solutions of the propagating size and the threshold curve surface for the four different dynamics. We compare the results on static networks with those on annealed networks and made an interesting discovery: for the symmetrical dynamical model (the cyclic Lotka-Volterra model and the directed migration model, where the three states are of rotational symmetry), the macroscopic behaviors of the dynamical processes on static networks are the same as those on annealed networks; while the outcomes of the dynamical processes on static networks are different with, and more complicated than, those on annealed networks for asymmetric dynamical model (the susceptible-infected-recovered-susceptible epidemic model and the predator-prey with empty sites model). We also compare the results forecasted by our theoretical method with those by Monte Carlo simulations and find good agreement between the results obtained by the two methods.

Pre-treatment thrombocytosis predicts prognosis of endometrial cancer: A meta-analysis of 11 studies.

The aim of the present meta-analysis study was to determine the association between pre-treatment thrombocytosis and prognosis of patients with endometrial cancer. Articles published prior to December 2018 containing information on platelet count and endometrial cancer were searched in the PubMed, Embase and the Cochrane Library databases. A platelet count of ≥350 or >400×109/l was considered to indicate thrombocytosis. Hazard ratios (HRs) with 95% CI were calculated using a random- or fixed-effects model to assess the strength of the associations. A Funnel plot and Egger's test were used to evaluate the publication bias and sensitivity analyses were performed to estimate the robustness of the present results by using Stata 13.0 software. A pooled analysis of 11 studies that met the inclusion criteria was performed, involving a total of 2,590 patients with endometrial cancer. The overall survival (OS), progression-free survival (PFS) and disease-free survival (DFS) time of patients with endometrial cancer who exhibited pre-treatment thrombocytosis were shorter than those in patients without pre-treatment thrombocytosis (OS, HR=2.25, 95% CI=1.26-4.00; PFS, HR=2.60, 95% CI=1.23-5.50; DFS, HR=2.23, 95% CI=1.45-3.42). However, pre-treatment thrombocytosis was not associated with disease-specific survival time in patients with endometrial cancer (HR=2.17, 95% CI=0.51-9.27; P=0.296). Subgroup analysis indicated that pre-treatment thrombocytosis was not associated with OS time in patients of Asian and European ethnicity. Furthermore, pre-treatment thrombocytosis (platelet count >400×109/l) was an independent predictor of OS, PFS and DFS regardless of the clinical stage.