A novel mouse model of calcific aortic valve stenosis

Background:Calcific aortic valve stenosis(CAVS)is one of the most challenging heart diseases in clinical with rapidly increasing prevalence.However,study of the mecha-nism and treatment of CAVS is hampered by the lack of suitable,robust and efficient models that develop hemodynamically significant stenosis and typical calcium deposi-tion.Here,we aim to establish a mouse model to mimic the development and features of CAVS.Methods:The model was established via aortic valve wire injury(AVWI)combined with vitamin D subcutaneous injected in wild type C57/BL6 mice.Serial transthoracic echocardiography was applied to evaluate aortic jet peak velocity and mean gradi-ent.Histopathological specimens were collected and examined in respect of valve thickening,calcium deposition,collagen accumulation,osteogenic differentiation and inflammation.Results:Serial transthoracic echocardiography revealed that aortic jet peak velocity and mean gradient increased from 7 days post model establishment in a time depend-ent manner and tended to be stable at 28 days.Compared with the sham group,sim-ple AVWI or the vitamin D group,the hybrid model group showed typical pathological features of CAVS,including hemodynamic alterations,increased aortic valve thicken-ing,calcium deposition,collagen accumulation at 28 days.In addition,osteogenic dif-ferentiation,fibrosis and inflammation,which play critical roles in the development of CAVS,were observed in the hybrid model.Conclusions:We established a novel mouse model of CAVS that could be induced efficiently,robustly and economically,and without genetic intervention.It provides a fast track to explore the underlying mechanisms of CAVS and to identify more effec-tive pharmacological targets.

A Positive Influence Maximization Algorithm in Signed Social Networks

The influence maximization(IM)problem aims to find a set of seed nodes that maximizes the spread of their influence in a social network.The positive influence maximization(PIM)problem is an extension of the IM problem,which consider the polar relation of nodes in signed social networks so that the positive influence of seeds can be the most widely spread.To solve the PIM problem,this paper proposes the polar and decay related independent cascade(IC-PD)model to simulate the influence propagation of nodes and the decay of information during the influence propagation in signed social networks.To overcome the low efficiency of the greedy based algorithm,this paper defines the polar reverse reachable(PRR)set and devises a signed reverse influence sampling(SRIS)algorithm.The algorithm utilizes the ICPD model as well as the PRR set to select seeds.There are two phases in SRIS.One is the sampling phase,which utilizes the IC-PD model to generate the PRR set and a binary search algorithm to calculate the number of needed PRR sets.The other is the node selection phase,which uses a greedy coverage algorithm to select optimal seeds.Finally,Experiments on three real-world polar social network datasets demonstrate that SRIS outperforms the baseline algorithms in effectiveness.Especially on the Slashdot dataset,SRIS achieves 24.7% higher performance than the best-performing compared algorithm under the weighted cascade model when the seed set size is 25.

An Influence Maximization Algorithm Based on Improved K-Shell in Temporal Social Networks

Influence maximization of temporal social networks(IMT)is a problem that aims to find the most influential set of nodes in the temporal network so that their information can be the most widely spread.To solve the IMT problem,we propose an influence maximization algorithm based on an improved K-shell method,namely improved K-shell in temporal social networks(KT).The algorithm takes into account the global and local structures of temporal social networks.First,to obtain the kernel value Ks of each node,in the global scope,it layers the network according to the temporal characteristic of nodes by improving the K-shell method.Then,in the local scope,the calculation method of comprehensive degree is proposed to weigh the influence of nodes.Finally,the node with the highest comprehensive degree in each core layer is selected as the seed.However,the seed selection strategy of KT can easily lose some influential nodes.Thus,by optimizing the seed selection strategy,this paper proposes an efficient heuristic algorithm called improved K-shell in temporal social networks for influence maximization(KTIM).According to the hierarchical distribution of cores,the algorithm adds nodes near the central core to the candidate seed set.It then searches for seeds in the candidate seed set according to the comprehensive degree.Experiments showthatKTIMis close to the best performing improved method for influence maximization of temporal graph(IMIT)algorithm in terms of effectiveness,but runs at least an order of magnitude faster than it.Therefore,considering the effectiveness and efficiency simultaneously in temporal social networks,the KTIM algorithm works better than other baseline algorithms.

Maximizing Influence in Temporal Social Networks:A Node Feature-Aware Voting Algorithm

Influence Maximization(IM)aims to select a seed set of size k in a social network so that information can be spread most widely under a specific information propagation model through this set of nodes.However,most existing studies on the IM problem focus on static social network features,while neglecting the features of temporal social networks.To bridge this gap,we focus on node features reflected by their historical interaction behavior in temporal social networks,i.e.,interaction attributes and self-similarity,and incorporate them into the influence maximization algorithm and information propagation model.Firstly,we propose a node feature-aware voting algorithm,called ISVoteRank,for seed nodes selection.Specifically,before voting,the algorithm sets the initial voting ability of nodes in a personalized manner by combining their features.During the voting process,voting weights are set based on the interaction strength between nodes,allowing nodes to vote at different extents and subsequently weakening their voting ability accordingly.The process concludes by selecting the top k nodes with the highest voting scores as seeds,avoiding the inefficiency of iterative seed selection in traditional voting-based algorithms.Secondly,we extend the Independent Cascade(IC)model and propose the Dynamic Independent Cascade(DIC)model,which aims to capture the dynamic features in the information propagation process by combining node features.Finally,experiments demonstrate that the ISVoteRank algorithm has been improved in both effectiveness and efficiency compared to baseline methods,and the influence spread through the DIC model is improved compared to the IC model.

The decision for hospice care in patients with terminal illness in Shanghai:A mixed-method study

Objectives To investigate why patients with terminal illness and their families in Shanghai choose the hospice ward and their decision-making process.Methods This was a mixed-method study consisting of a cross-sectional survey and a descriptive qualitative study.Medical decision-makers for patients hospitalized in hospice wards were recruited between September 2019 and July 2021.A medical decision-maker is a family member who makes medical decisions for a patient.All 146 participants completed a self-developed 10-item questionnaire that included five items about their demographic characteristics and five items about the decision-making process.The semi-structured interviews were conducted with nine participants to understand the family’s decision-making process when they chose a hospice ward.The interviews were analyzed using qualitative content analysis.Results The mean age of the 146 participants was 57.6 years old.Of the decision-makers,56.85%were the patients’children.Family-dominated discussions involving other family members were the main decision-making mode(84.93%).Patient participation in the decision-making process was reported in 43.15%of families.The participation of doctors(17.81%)and nurses(2.05%)were reported in a small number of families.The most common reason for choosing the hospice ward was the inability to find any other hospital for the patients(82.19%).The most common ways to learn about the service were neighbors and friends(38.36%)and social media(28.77%).Two themes and six categories emerged from the interviews.The first theme was reasons for choosing hospice wards.The reasons included being unable to care for the patients at home,staying in a hospice ward could reduce the psychological stress for home care,being unable to be admitted into tertiary/secondary hospitals,and thinking a hospice ward was a suitable place for the family.The second theme was the decision process of choosing a hospice ward.This theme included the following two categories,i.e.,ways to learn about the hospice ward and family-discussion decision mode.Conclusion To most families having dying patients,a hospice ward is a reasonable and balanced choice after the families experience huge care stress and practical difficulties.The participation of patients should be encouraged in the family discussion so that their wishes can be known.More efforts will be needed to guide the families with dying patients to make reasonable medical choices.Social media can be a good way to improve public awareness of hospice services in the future.Meanwhile,healthcare providers should be more involved in the decision-making process.