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Chronic inflammation exerts pleiotropic effects in the etiology and progression of chronic obstructive pulmonary disease (COPD). Glucosamine is widely used in many countries and may have anti-inflammatory properties. We aimed to prospectively evaluate the association of regular glucosamine use with incident COPD risk and explore whether such association could be modified by smoking in the UK Biobank cohort, which recruited more than half a million participants aged 40–69 years from across the UK between 2006 and 2010. Cox proportional hazards models with adjustment for potential confounding factors were used to calculate hazard ratios (HRs) as well as 95% confidence intervals (95% CIs) for the risk of incident COPD. During a median follow-up of 8.96 years (interquartile range 8.29 to 9.53 years), 9016 new-onset events of COPD were documented. We found that regular use of glucosamine was associated with a significantly lower risk of incident COPD with multivariable adjusted HR of 0.80 (95% CI, 0.75 to 0.85; P<0.001). When subgroup analyses were performed by smoking status, the adjusted HRs for the association of regular glucosamine use with incident COPD were 0.84 (0.73 to 0.96), 0.84 (0.77 to 0.92), and 0.71 (0.62 to 0.80) among never smokers, former smokers and current smokers, respectively. No significant interaction was observed between glucosamine use and smoking status (P for interaction=0.078). Incident COPD could be reduced by 14% to 84% through a combination of regular glucosamine use and smoking cessation
The effect of vitamin D (VD) on the risk of preeclampsia (PE) is uncertain. Few of previous studies focused on the relationship between dietary VD intake and PE risk. Therefore, we conducted this 1:1 matched case-control study to explore the association of dietary VD intake and serum VD concentrations with PE risk in Chinese pregnant women. A total of 440 pairs of participants were recruited during March 2016 to June 2019. Dietary information was obtained using a 78-item semi-quantitative food frequency questionnaire. Serum concentrations of 25(OH)D2 and 25(OH)D3 were measured by liquid chromatography–tandem mass spectrometry. Multivariate conditional logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs). Restricted cubic splines (RCS) were plotted to evaluate the dose-response relationship of dietary VD intake and serum VD concentrations with PE risk. Compared with the lowest quartile, the ORs of the highest quartile were 0.45 (95%CI: 0.29-0.71, Ptrend = 0.001) for VD dietary intake and 0.26 (95%CI: 0.11-0.60, Ptrend = 0.003) for serum levels after adjusting for confounders. In addition, the RCS analysis suggested a reverse J-shaped relationship between dietary VD intake and PE risk (P-nonlinearity = 0.02). A similar association was also found between serum concentrations of total 25(OH)D and PE risk (P-nonlinearity = 0.02). In conclusion, this study provides evidence that higher dietary intake and serum levels of VD are associated with the lower risk of PE in Chinese pregnant women.
Type D personality and depression are the independent psychological risk factors for adverse outcomes in cardiovascular patients. The aim of this study was to examine the combined effect of Type D personality and depression on clinical outcomes in patients suffering from acute myocardial infarction (AMI).
This prospective cohort study included 3568 patients diagnosed with AMI between February 2017 and September 2018. Type D personality and depression were assessed at baseline, while the major adverse cardiac event (MACE) rate (cardiac death, recurrent non-fatal myocardial infarction, revascularization, and stroke) and in-stent restenosis (ISR) rate were analyzed after a 2-year follow-up period.
A total of 437 patients developed MACEs and 185 had ISR during the follow-up period. The Type D (+) depression (+) and Type D (+) depression (−) groups had a higher risk of MACE [95% confidence interval (CI) 1.74–6.07] (95% CI 1.25–2.96) and ISR (95% CI 3.09–8.28) (95% CI 1.85–6.22). Analysis of Type D and depression as continuous variables indicated that the main effect of Type D, depression and their combined effect were significantly associated with MACE and ISR. Moreover, Type D (+) depression (+) and Type D (+) depression (−) emerged as significant risk factors for MACE and ISR in males, while only Type D (+) depression (+) was associated with MACE and ISR in female patients.
These findings suggest that patients complicated with depression and Type D personality are at a higher risk of adverse cardiovascular outcomes. Individual assessments of Type D personality and depression, and comprehensive interventions are required.
To explore the feasibility and superiority of applying the WeChat platform in a midterm follow-up of surgical repair for ventricular septal defects in infants.
Eighty-six infants with VSD who underwent surgical repair were divided into an outpatient follow-up group and a WeChat follow-up group. The clinical data, including complications, economic cost, time spent, loss to follow-up rate, and parents’ satisfaction at the 3-month and 1-year follow-ups, were recorded and analysed.
There was no significant difference in the incidence of post-operative complications between the two groups. Although the loss to follow-up rate in the WFU group was lower than that of the OFU group, the difference was not statistically significant. The economic cost and time spent in the 3 months and 1 year after discharge in the WFU group were significantly lower than those in the OFU group. One year after discharge, the PSQ-18 score of the WFU group was significantly higher than that of the OFU group.
Compared with outpatient follow-up, the WeChat platform at the midterm follow-up after surgical repair of VSDs in infants has the advantages of saving time and economic costs and improves parents’ satisfaction.
Users in a social network are usually confronted with decision-making under uncertain network states. While there are some works in the social learning literature on how to construct belief in an uncertain network state, few studies have focused on integrating learning with decision-making for the scenario in which users are uncertain about the network state and their decisions influence each other. Moreover, the population in a social network can be dynamic since users may arrive at or leave the network at any time, which makes the problem even more challenging. In this chapter, we introduce a dynamic Chinese restaurant game to study how a user in a dynamic social network learns about the uncertain network state and makes optimal decisions by taking into account not only the immediate utility, but also subsequent users’ influence. We introduce a Bayesian learning-based method for users to learn the network state and discuss a multidimensional Markov decision process-based approach for users to make optimal decisions. Finally, we apply the dynamic Chinese restaurant game to cognitive radio networks and use simulations to verify the effectiveness of the scheme.
While peer-to-peer (P2P) video streaming systems have achieved promising results, they introduce a large number of unnecessary traverse links, leading to substantial network inefficiency. To address this problem, we discuss how to enable cooperation among “group peers,” which are geographically neighboring peers with large intragroup upload and download bandwidths. Considering the peers’ selfish nature, we formulate the cooperative streaming problem as an evolutionary game and introduce, for every peer, the evolutionarily stable strategy (ESS). Moreover, we discuss a simple and distributed learning algorithm for the peers to converge to the ESSs. With the discussed algorithm, each peer decides whether to be an agent who downloads data from the peers outside the group or a free-rider who downloads data from the agents by simply tossing a coin, where the probability of the coin showing a head is learned from the peer’s own past payoff history. Simulation results show that compared to the traditional noncooperative P2P schemes, the discussed cooperative scheme achieves much better performance in terms of social welfare, probability of real-time streaming, and video quality.
In cognitive networks, how to stimulate cooperation among nodes is very important. However, most existing game-theoretic cooperation stimulation approaches rely on the assumption that the interactions between any pair of players are long-lasting. When this assumption is not true, such as in the well-known Prisoner’s dilemma and the backward induction principle, the unique Nash equilibrium is to always play noncooperatively. In this chapter, we discuss a cooperation stimulation scheme for the scenario in which the number of interactions is finite. This scheme is based on indirect reciprocity game modeling where the key concept is “I help you not because you have helped me but because you have helped others.” The problem of finding the optimal action rule is formulated as a Markov decision process, and a modified value-iteration algorithm is utilized to find the optimal action rule. Using the packet forwarding game as an example, it is shown that with an appropriate cost-to-gain ratio, the strategy of forwarding the number of packets that is equal to the reputation level of the receiver is an evolutionarily stable strategy.
In the third part of this book, the third branch of modern game theory – sequential decision-making – is presented. The important components in sequential decision-making, such as network externality, information asymmetry, and user rationality, are presented and defined. The limitations of the existing approaches, such as social learning and multiarm bandit problems, are also presented.
The viability of cooperative communications depends on the willingness of users to help. Therefore, it is important to study incentive issues when designing such systems. In this chapter, we discuss a cooperation stimulation scheme for multiuser cooperative communications using an indirect reciprocity game. By introducing the notion of reputation and social norms, rational users who care about their future utility are incentivized to cooperate with others. Differently from existing works on reputation-based schemes that mainly rely on experimental verification, the effectiveness of the scheme is demonstrated in two steps. First, we conduct a steady-state analysis of the game and show that cooperating with users who have a good reputation can be sustained as an equilibrium when the cost-to-gain ratio is below a certain threshold. Then, by modeling the action spreading at transient states as an evolutionary game, we show that the equilibria we found in the steady-state analysis are stable and can be reached with proper initial conditions. Moreover, we introduce energy detection to handle the possible cheating behaviors of users and study its impact on the indirect reciprocity game.
A huge amount of information, created and forwarded by millions of people with various characteristics, propagates through online social networks every day. Understanding the mechanisms of information diffusion over social networks is critical to various applications, including online advertisements and website management. Differently from most existing works in this area, we investigate information diffusion from an evolutionary game-theoretic perspective and try to reveal the underlying principles dominating the complex information diffusion process over heterogeneous social networks. Modeling the interactions among the heterogeneous users as a graphical evolutionary game, we derive the evolutionary dynamics and the evolutionarily stable states (ESSs) of the diffusion. The different payoffs of the heterogeneous users lead to different diffusion dynamics and ESSs among them, in accordance with the heterogeneity observed in real-world data sets. The theoretical results are confirmed by simulations. We also test the theory on the Twitter hashtag data set. We observe that the evolutionary dynamics fit the data well and can predict future diffusion data.
Network service acquisition in a wireless environment requires the selection of a wireless access network. A key problem in wireless access network selection is studying rational strategies that consider negative network externality. In this chapter, we formulate the wireless network selection problem as a stochastic game with negative network externality and show that finding the optimal decision rule can be modeled as a multidimensional Markov decision process. A modified value-iteration algorithm is utilized to efficiently obtain the optimal decision rule with a simple threshold structure. We further investigate the mechanism design problem with incentive compatibility constraints, which force the networks to reveal truthful state information. The formulated problem is a mixed-integer programming problem that, in general, lacks an efficient solution. Exploiting the optimality of substructures, we introduce a dynamic programming algorithm that can optimally solve the problem in the two-network scenario. For the multinetwork scenario, the dynamic programming algorithm can outperform the heuristic greedy approach in polynomial-time complexity.
In a social network, agents are intelligent and have the capacity to make decisions so as to maximize their utility. They can either make wise decisions by taking advantages of other agents’ experiences through learning or make decisions earlier to avoid competition from huge crowds. Both of these effects – social learning and negative network externality – play important roles in the decision-making process of an agent. In this chapter, a new game called the Chinese restaurant game is introduced to formulate the social learning problem with negative network externality. Through analyzing the Chinese restaurant game, we derive the optimal strategy of each agent and provide a recursive method to achieve the optimal strategy. How social learning and negative network externality influence each other under various settings is studied through simulations. We also illustrate the spectrum access problem in cognitive radio networks as one application of the Chinese restaurant game. We find that the Chinese restaurant game-theoretic approach indeed helps users make better decisions and improves overall system performance.
Many spectrum sensing methods and dynamic access algorithms have been proposed to improve secondary users’ access opportunities. However, few of them have considered integrating the design of spectrum sensing and access algorithms together by taking into account the mutual influence between them. In this chapter, we focus on jointly analyzing the spectrum sensing and access problem. Due to their selfish nature, secondary users tend to act selfishly to access the channel without contributing to spectrum sensing. Moreover, they may employ out-of-equilibrium strategies because of the uncertainty of others’ strategies. To model the complicated interactions among secondary users, the joint spectrum sensing and access problem is formulated as an evolutionary game and the evolutionarily stable strategy (ESS) that no one will deviate from is studied. Furthermore, a distributed learning algorithm for the secondary users to converge to the ESS is introduced. Simulation results shows that the system can quickly converge to the ESS and such an ESS is robust to the sudden unfavorable deviations of the selfish secondary users.