Dissolvable polymeric microneedles loaded with aspirin for antiplatelet aggregation

To reduce mucosal damage in the gastrointestinal tract caused by aspirin,we developed a dissolvable polymeric microneedle(MN)patch loaded with aspirin.Biodegradable polymers provide mechanical strength to the MNs.The MN tips punctured the cuticle of the skin and dissolved when in contact with the subcutaneous tissue.The aspirin in the MN patch is delivered continuously through an array of micropores created by the punctures,providing a stable plasma concentration of aspirin.The factors affecting the stability of aspirin during MNs fabrication were comprehensively analyzed,and the hydrolysis rate of aspirin in the MNs was less than 2%.Compared to oral administration,MN administration not only had a smoother plasma concentration curve but also resulted in a lower effective dose of antiplatelet aggregation.Aspirin-loaded MNs were mildly irritating to the skin,causing only slight erythema on the skin and recovery within 24 h.In summary,aspirin-loaded MNs provide a new method to reduce gastrointestinal adverse effects in patients requiring aspirin regularly.

Antiplatelet Aggregation Effects of YIGSK and RGD Containing Peptides

观察了ＹＩＧＳＫ，ＲＧＤＳ，ＲＧＤＶ，ＲＧＤＦ，ＹＩＧＳＫＲＧＤＳ，ＹＩＧＳＫＲＧＤＶ和ＹＩＧＳＫＲＧＤＦ的抗血小板聚集作用，通过比较它们的活性。

Impacts of Aggregation Methods and Trophospecies Number on the Structure and Function of Marine Food Webs

Aggregation of species with similar ecological properties is one of the effective methods to simplify food web researches.However,species aggregation will affect not only the complexity of modeling process but also the accuracy of models’outputs.Selection of aggregation methods and the number of trophospecies are the keys to study the simplification of food web.In this study,three aggregation methods,including taxonomic aggregation(TA),structural equivalence aggregation(SEA),and self-organizing maps(SOM),were analyzed and compared with the linear inverse model–Markov Chain Monte Carlo(LIM-MCMC)model.Impacts of aggregation methods and trophospecies number on food webs were evaluated based on the robustness and unitless of ecological net-work indices.Results showed that aggregation method of SEA performed better than the other two methods in estimating food web structure and function indices.The effects of aggregation methods were driven by the differences in species aggregation principles,which will alter food web structure and function through the redistribution of energy flow.According to the results of mean absolute percentage error(MAPE)which can be applied to evaluate the accuracy of the model,we found that MAPE in food web indices will increase with the reducing trophospecies number,and MAPE in food web function indices were smaller and more stable than those in food web structure indices.Therefore,trade-off between simplifying food webs and reflecting the status of ecosystem should be con-sidered in food web studies.These findings highlight the importance of aggregation methods and trophospecies number in the analy-sis of food web simplification.This study provided a framework to explore the extent to which food web models are affected by dif-ferent species aggregation,and will provide scientific basis for the construction of food webs.

Early antiplatelet therapy used for acute ischemic stroke and intracranial hemorrhage

In this editorial we comment on the article published by Zhang et al in the recent issue of World Journal of Clinical Cases.We evaluate their claims on the benefit of use of Aspirin in the early management of patients with ischemic stroke.We also comment on their contention of using aspirin in the early management of patients with intracranial hemorrhage,a practice not seen in modern medicine.Large clinical trials such as the International Stroke Trial and the Chinese Acute Stroke Trial have shown the benefit of Aspirin use within 48 h of patients with Acute Ischemic Stroke.The findings were corroborated in the open-label trial performed by Zhang et al in a smaller sample group of 25 patients where they showed improvement in functional scores at 90 days without an increase in adverse events.As such,this intervention is also recommended by the American Heart Association stroke guidelines from 2021.With regard to Intracranial hemorrhage,traditional practice has been to discontinue or avoid antiplatelet therapy in these patient groups.However,no studies have been done to evaluate this management strategy that is more borne out of the mechanism behind Aspirin’s effect on the coagulation pathway.Zhang et al evaluate the benefits of Aspirin on patients with low-volume intracranial hemorrhage,i.e.,less than 30 mL on computed tomo-graphy imaging,and show no increase in mortality.The caveat of this finding is that all outcomes were pooled into one group for results,and the number of patients was low.While more studies with larger patient groups are required,the data from Zhang et al suggests that patients with small-volume intracranial hemorrhages may benefit from Aspirin administration in the acute phase of management.

Distribution of gene polymorphisms associated with aspirin antiplatelet in the Han NSTEMI population

Objective:To analyze the genotype and allele distribution characteristics of GPⅢa PLA2(rs5918),PEAR1(rs12041331),and PTGS1(rs10306114)genes related to the antiplatelet pharmacological effects of aspirin,providing reference for individualized treatment of Chinese Han NSTEMI patients.Methods:A total of 107 Han patients with NSTEMI in Beijing Luhe Hospital affiliated to Capital Medical University from January 2016 to December 2022 were selected as the research subjects.The genotypes of GPⅢa PLA2(rs5918),PEAR1(rs12041331)and PTGS1(rs10306114)were detected by fluorescence staining in situ hybridization.The frequency distribution and allele distribution of genotype were analyzed.The results were analyzed whether there were statistical differences in the distribution of related alleles between the Han NSTEMI population and some populations in the 1000 Genomes database.Results:In the Han NSTEMI population,the genotype frequencies of GPⅢa PLA2(rs5918)locus were TT 97.20%,TC 2.80%and CC 0%,the allele frequencies were T 98.60%and C 1.40%.The genotype frequencies of PEAR1(rs12041331)locus were GG 42.06%,GA 44.86%and AA 13.08%,the allele frequencies were G 64.49%and A 35.51%.The genotypes at the PTGS1(rs10306114)locus were all AA(100%),no AG or GG genotype was found.Conclusion:In the NSTEMI population of Han nationality,the mutation at GPⅢa PLA2(rs5918)site related to aspirin antiplatelet pharmacology is rare,and there is no mutation at PTGS1(rs10306114)site.Wild homozygotes are dominant in these two gene loci,while mutations in PEAR1(rs12041331)are more common.Some of the findings in this study are similar to those in previous reports or other populations included in the relevant database;however,some results differ from previous reports or other populations。

Differentially Private Support Vector Machines with Knowledge Aggregation

With the widespread data collection and processing,privacy-preserving machine learning has become increasingly important in addressing privacy risks related to individuals.Support vector machine(SVM)is one of the most elementary learning models of machine learning.Privacy issues surrounding SVM classifier training have attracted increasing attention.In this paper,we investigate Differential Privacy-compliant Federated Machine Learning with Dimensionality Reduction,called FedDPDR-DPML,which greatly improves data utility while providing strong privacy guarantees.Considering in distributed learning scenarios,multiple participants usually hold unbalanced or small amounts of data.Therefore,FedDPDR-DPML enables multiple participants to collaboratively learn a global model based on weighted model averaging and knowledge aggregation and then the server distributes the global model to each participant to improve local data utility.Aiming at high-dimensional data,we adopt differential privacy in both the principal component analysis(PCA)-based dimensionality reduction phase and SVM classifiers training phase,which improves model accuracy while achieving strict differential privacy protection.Besides,we train Differential privacy(DP)-compliant SVM classifiers by adding noise to the objective function itself,thus leading to better data utility.Extensive experiments on three high-dimensional datasets demonstrate that FedDPDR-DPML can achieve high accuracy while ensuring strong privacy protection.

Small molecules to target tau amyloid aggregation

Protein aggregation has been linked with many neurodegenerative diseases,such as Alzheimer’s disease(AD)or Parkinson’s disease.AD belongs to a group of heterogeneous and incurable neurodegenerative disorders collectively known as tauopathies.They comprise frontotemporal dementia,Pick’s disease,or corticobasal degeneration,among others.The symptomatology varies with the specific tau protein variant involved and the affected brain region or cell type.However,they share a common neuropathological hallmark-the formation of proteinaceous deposits named neurofibrillary tangles.Neurofibrillary tangles,primarily composed of aggregated tau(Zhang et al.,2022),disrupt normal neuronal functions,leading to cell death and cognitive decline.

A novel approach to Parkinson's disease treatment with a potentially dual-acting therapeutic agent that targetsα-synuclein aggregation and neuron death

Parkinson's disease(PD),a prevalent neurodegenerative disorder,is chara cterized by the loss of dopaminergic neurons and the aggregation ofα-synuclein protein into Lewy bodies.While the current standards of therapy have been successful in providing some symptom relief,they fail to address the underlying pathophysiology of PD and as a result,they have no effect on disease progression.

Straw return increases crop production by improving soil organic carbon sequestration and soil aggregation in a long-term wheat-cotton cropping system

Straw return is a promising strategy for managing soil organic carbon(SOC)and improving yield stability.However,the optimal straw return strategy for sustainable crop production in the wheat(Triticum aestivum L.)-cotton(Gossypium hirsutum L.)cropping system remains uncertain.The objective of this study was to quantify the long-term(10 years)impact of carbon(C)input on SOC sequestration,soil aggregation and crop yields in a wheat-cotton cropping system in the Yangtze River Valley,China.Five treatments were arranged with a single-factor randomized design as follows:no straw return(Control),return of wheat straw only(Wt),return of cotton straw only(Ct),return of 50%wheat and 50%cotton straw(Wh-Ch)and return of 100%wheat and 100%cotton straw(Wt-Ct).In comparison to the Control,the SOC content increased by 8.4 to 20.2%under straw return.A significant linear positive correlation between SOC sequestration and C input(1.42-7.19 Mg ha^(−1)yr^(−1))(P<0.05)was detected.The percentages of aggregates of sizes>2 and 1-2 mm at the 0-20 cm soil depth were also significantly elevated under straw return,with the greatest increase of the aggregate stability in the Wt-Ct treatment(28.1%).The average wheat yields increased by 12.4-36.0%and cotton yields increased by 29.4-73.7%,and significantly linear positive correlations were also detected between C input and the yields of wheat and cotton.The average sustainable yield index(SYI)reached a maximum value of 0.69 when the C input was 7.08 Mg ha^(−1)yr^(−1),which was close to the maximum value(SYI of 0.69,C input of 7.19 Mg ha^(−1)yr^(-1))in the Wt-Ct treatment.Overall,the return of both wheat and cotton straw was the best strategy for improving SOC sequestration,soil aggregation,yields and their sustainability in the wheat-cotton rotation system.

A non-probabilistic reliability topology optimization method based on aggregation function and matrix multiplication considering buckling response constraints

A non-probabilistic reliability topology optimization method is proposed based on the aggregation function and matrix multiplication.The expression of the geometric stiffness matrix is derived,the finite element linear buckling analysis is conducted,and the sensitivity solution of the linear buckling factor is achieved.For a specific problem in linear buckling topology optimization,a Heaviside projection function based on the exponential smooth growth is developed to eliminate the gray cells.The aggregation function method is used to consider the high-order eigenvalues,so as to obtain continuous sensitivity information and refined structural design.With cyclic matrix programming,a fast topology optimization method that can be used to efficiently obtain the unit assembly and sensitivity solution is conducted.To maximize the buckling load,under the constraint of the given buckling load,two types of topological optimization columns are constructed.The variable density method is used to achieve the topology optimization solution along with the moving asymptote optimization algorithm.The vertex method and the matching point method are used to carry out an uncertainty propagation analysis,and the non-probability reliability topology optimization method considering buckling responses is developed based on the transformation of non-probability reliability indices based on the characteristic distance.Finally,the differences in the structural topology optimization under different reliability degrees are illustrated by examples.

Quantum homomorphic broadcast multi-signature based on homomorphic aggregation

Quantum multi-signature has attracted extensive attention since it was put forward.Beside its own improvement,related research is often combined with other quantum signature.However,this type of quantum signature has one thing in common,that is,the generation and verification of signature depend heavily on the shared classical secret key.In order to increase the reliability of signature,the homomorphic aggregation technique is applied to quantum multi-signature,and then we propose a quantum homomorphic multi-signature protocol.Unlike previous quantum multi-signature protocols,this protocol utilizes homomorphic properties to complete signature generation and verification.In the signature generation phase,entanglement swapping is introduced,so that the individual signatures of multiple users are aggregated into a new multi-signature.The original quantum state is signed by the shared secret key to realize the verification of the signature in the verification phase.The signature process satisfies the homomorphic property,which can improve the reliability of the signature.

Distributed Weighted Data Aggregation Algorithm in End-to-Edge Communication Networks Based on Multi-armed Bandit

As a combination of edge computing and artificial intelligence,edge intelligence has become a promising technique and provided its users with a series of fast,precise,and customized services.In edge intelligence,when learning agents are deployed on the edge side,the data aggregation from the end side to the designated edge devices is an important research topic.Considering the various importance of end devices,this paper studies the weighted data aggregation problem in a single hop end-to-edge communication network.Firstly,to make sure all the end devices with various weights are fairly treated in data aggregation,a distributed end-to-edge cooperative scheme is proposed.Then,to handle the massive contention on the wireless channel caused by end devices,a multi-armed bandit(MAB)algorithm is designed to help the end devices find their most appropriate update rates.Diffe-rent from the traditional data aggregation works,combining the MAB enables our algorithm a higher efficiency in data aggregation.With a theoretical analysis,we show that the efficiency of our algorithm is asymptotically optimal.Comparative experiments with previous works are also conducted to show the strength of our algorithm.

Efficacy and safety of aspirin antiplatelet therapy within 48 h of symptom onset in patients with acute stroke

BACKGROUND Aspirin is a widely used antiplatelet agent that reduces the risk of recurrent ischemic stroke and other vascular events.However,the optimal timing and dose of aspirin initiation after an acute stroke remain controversial.AIM To evaluate the efficacy and safety of aspirin antiplatelet therapy within 48 h of symptom onset in patients with acute stroke.METHODS We conducted a randomized,open-label,controlled trial in 60 patients with acute ischemic or hemorrhagic stroke who were admitted to our hospital within 24 h of symptom onset.Patients were randomly assigned to receive either aspirin 300 mg daily or no aspirin within 48 h of stroke onset.The primary outcome was the occurrence of recurrent stroke,myocardial infarction,or vascular death within 90 d.The secondary outcomes were functional outcomes at 90 d measured using the modified Rankin Scale(mRS),incidence of bleeding complications,and mortality rate.RESULTS The mean age of the patients was 67.8 years and 55%of them were male.The median time from stroke onset to randomization was 12 h.The baseline characteristics were well balanced between the two groups.The primary outcome occurred in 6.7%of patients in the aspirin group and 16.7%of patients in the no aspirin group(relative risk=0.40,95%confidence interval:0.12-1.31,P=0.13).The mRS score at 90 d was significantly lower in the aspirin group than in the no aspirin group(median,2 vs 3,respectively;P=0.04).The incidence of bleeding complications was similar between the groups(6.7%vs 6.7%,P=1.00).The mortality rates were also comparable between the two groups(10%vs 13.3%,P=0.69).CONCLUSION Aspirin use is associated with favorable functional outcomes but does not significantly reduce the risk of recurrent vascular events.Its acceptable safety profile is comparable to that of no aspirin.Further studies with larger sample sizes and longer follow-up periods are needed to confirm these findings.

Prolonged dual antiplatelet therapy after drug-eluting stent implantation improves long-term prognosis for acute coronary syndrome:five-year results from a large cohort study

BACKGROUND:To investigate the most appropriate dual antiplatelet therapy(DAPT)duration for patients with acute coronary syndrome(ACS)after drug-eluting stent(DES)implantation in the largest cardiovascular center of China.METHODS:We enrolled 5,187 consecutive patients with ACS who received DES from January to December 2013.Patients were divided into four groups based on DAPT duration:standard DAPT group(11-13 months,n=1,568)and prolonged DAPT groups(13-18 months[n=308],18-24 months[n=2,125],and>24 months[n=1,186]).Baseline characteristics and 5-year clinical outcomes were recorded.RESULTS:Baseline characteristics were similar across the four groups.Among the four groups,those with prolonged DAPT(18-24 months)had the lowest incidence of major adverse cardiovascular and cerebrovascular events(MACCEs)(14.1%vs.11.7%vs.9.6%vs.24.2%,P<0.001),all-cause death(4.8%vs.3.9%vs.2.1%vs.2.6%,P<0.001),cardiac death(3.1%vs.2.6%vs.1.4%vs.1.9%,P=0.004),and myocardial infarction(MI)(3.8%vs.4.2%vs.2.5%vs.5.8%,P<0.001).The incidence of bleeding was not different among the four groups(9.9%vs.9.4%vs.11.0%vs.9.4%,P=0.449).Cox multivariable analysis showed that prolonged DAPT(18-24 months)was an independent protective factor for MACCEs(hazard ratio[HR]0.802,95%confidence interval[CI]0.729-0.882,P<0.001),all-cause death(HR 0.660,95%CI 0.547-0.795,P<0.001),cardiac death(HR 0.663,95%CI 0.526-0.835,P<0.001),MI(HR 0.796,95%CI 0.662-0.957,P=0.015),and target vessel revascularization(HR 0.867,95%CI 0.755-0.996,P=0.044).Subgroup analysis for high bleeding risk showed that prolonged DAPT remained an independent protective factor for all-cause death and MACCEs.CONCLUSION:For patients with ACS after DES,appropriately prolonging the DAPT duration may be associated with a reduced risk of adverse ischemic events without increasing the bleeding risk.

Assessing edge-coupled interdependent network disintegration via rank aggregation and elite enumeration

The disintegration of networks is a widely researched topic with significant applications in fields such as counterterrorism and infectious disease control. While the traditional approaches for achieving network disintegration involve identifying critical sets of nodes or edges, limited research has been carried out on edge-based disintegration strategies. We propose a novel algorithm, i.e., a rank aggregation elite enumeration algorithm based on edge-coupled networks(RAEEC),which aims to implement tiling for edge-coupled networks by finding important sets of edges in the network while balancing effectiveness and efficiency. Our algorithm is based on a two-layer edge-coupled network model with one-to-one links, and utilizes three advanced edge importance metrics to rank the edges separately. A comprehensive ranking of edges is obtained using a rank aggregation approach proposed in this study. The top few edges from the ranking set obtained by RAEEC are then used to generate an enumeration set, which is continuously iteratively updated to identify the set of elite attack edges.We conduct extensive experiments on synthetic networks to evaluate the performance of our proposed method, and the results indicate that RAEEC achieves a satisfactory balance between efficiency and effectiveness. Our approach represents a significant contribution to the field of network disintegration, particularly for edge-based strategies.

Turing/Turing-like patterns:Products of random aggregation of spatial components

Turing patterns are typical spatiotemporal ordered structures in various systems driven far from thermodynamic equilibrium.Turing’s reaction-diffusion theory,containing a long-range inhibiting agent and a local catalytic agent,has provided an explanation for the formation of some patterns in nature.Numerical,experimental and theoretical studies about Turing/Turing-like patterns have been generally focused on systems driven far from thermodynamic equilibrium.The local dynamics of these systems are commonly very complex,which brings great difficulties to understanding of formation of patterns.Here,we investigate a type of Turing-like patterns in a near-equilibrium thermodynamic system experimentally and theoretically,and put forward a new formation mechanism and a quantitative method for Turing/Turing-like patterns.Specifically,we observe a type of Turing-like patterns in starch solutions,and study the effect of concentration on the structure of patterns.The experimental results show that,with the increase of concentration,patterns change from spots to inverse spots,and labyrinthine stripe patterns appear in the region of intermediate concentration.We analyze and model the formation mechanism of these patterns observed in experiments,and the simulation results agree with the experimental results.Our conclusion indicates that the random aggregation of spatial components leads to formation of these patterns,and the proportion of spatial components determines the structures.Our findings shed light on the formation mechanism for Turing/Turing-like patterns.

High-Performance Transmission Mechanism Design of Multi-Stream Carrier Aggregation for 5G Non-Standalone Network

Multi-stream carrier aggregation is a key technology to expand bandwidth and improve the throughput of the fifth-generation wireless communication systems.However,due to the diversified propagation properties of different frequency bands,the traffic migration task is much more challenging,especially in hybrid sub-6 GHz and millimeter wave bands scenario.Existing schemes either neglected to consider the transmission rate difference between multistream carrier,or only consider simple low mobility scenario.In this paper,we propose a low-complexity traffic splitting algorithm based on fuzzy proportional integral derivative control mechanism.The proposed algorithm only relies on the local radio link control buffer information of sub-6 GHz and mmWave bands,while frequent feedback from user equipment(UE)side is minimized.As shown in the numerical examples,the proposed traffic splitting mechanism can achieve more than 90%link resource utilization ratio for different UE transmission requirements with different mobilities,which corresponds to 10%improvement if compared with conventional baselines.

Automatic Aggregation Enhanced Affinity Propagation Clustering Based on Mutually Exclusive Exemplar Processing

Affinity propagation(AP)is a widely used exemplar-based clustering approach with superior efficiency and clustering quality.Nevertheless,a common issue with AP clustering is the presence of excessive exemplars,which limits its ability to perform effective aggregation.This research aims to enable AP to automatically aggregate to produce fewer and more compact clusters,without changing the similarity matrix or customizing preference parameters,as done in existing enhanced approaches.An automatic aggregation enhanced affinity propagation(AAEAP)clustering algorithm is proposed,which combines a dependable partitioning clustering approach with AP to achieve this purpose.The partitioning clustering approach generates an additional set of findings with an equivalent number of clusters whenever the clustering stabilizes and the exemplars emerge.Based on these findings,mutually exclusive exemplar detection was conducted on the current AP exemplars,and a pair of unsuitable exemplars for coexistence is recommended.The recommendation is then mapped as a novel constraint,designated mutual exclusion and aggregation.To address this limitation,a modified AP clustering model is derived and the clustering is restarted,which can result in exemplar number reduction,exemplar selection adjustment,and other data point redistribution.The clustering is ultimately completed and a smaller number of clusters are obtained by repeatedly performing automatic detection and clustering until no mutually exclusive exemplars are detected.Some standard classification data sets are adopted for experiments on AAEAP and other clustering algorithms for comparison,and many internal and external clustering evaluation indexes are used to measure the clustering performance.The findings demonstrate that the AAEAP clustering algorithm demonstrates a substantial automatic aggregation impact while maintaining good clustering quality.

SFSDA:Secure and Flexible Subset Data Aggregation with Fault Tolerance for Smart Grid

Smart grid(SG)brings convenience to users while facing great chal-lenges in protecting personal private data.Data aggregation plays a key role in protecting personal privacy by aggregating all personal data into a single value,preventing the leakage of personal data while ensuring its availability.Recently,a flexible subset data aggregation(FSDA)scheme based on the Pail-lier homomorphic encryption was first proposed by Zhang et al.Their scheme can dynamically adjust the size of each subset and obtain the aggregated data in the corresponding subset.In this paper,firstly,an efficient attack with both theorems proving and experimentative verification is launched.We find that in a specific scenario where the encrypted data constructed by a smart meter(SM)exceeds the size of one Paillier ciphertext,the malicious fog node(FN)may use the received ciphertext to obtain the reading of the SM.Secondly,to avoid the possibility of privacy disclosure under certain circumstances,additional hash functions are added to the individual encryption process.In addition,fault tolerance is very important to aggregation schemes in practical scenarios.In most of the current schemes,once some SMs failed,then they will not work.As far as we know,there is no multi-subset aggregation scheme both supports flexible subset data aggregation and fault tolerance.Finally,we construct the first secure flexible subset data aggregation(SFSDA)scheme with fault tolerance by combining the fault tolerance method with the flexible multi-subset aggregation,where FN enables the control server(CS)to finally decrypt the aggregated ciphertext by recovering equivalent ciphertexts when some SMs fail to submit their ciphertexts.Experiments show that our SFSDA scheme keeps the efficiency in implementing a flexible multi-subset aggregation function,and only has a small delay in implementing fault-tolerant data aggregation.

Data Aggregation-based Transmission Method in Ultra-Dense Wireless Networks

As the Internet of Things(IoT)advances,machine-type devices are densely deployed and massive networks such as ultra-dense networks(UDNs)are formed.Various devices attend to the network to transmit data using machine-type communication(MTC),whereby numerous,various are generated.MTC devices generally have resource constraints and use wireless communication.In this kind of network,data aggregation is a key function to provide transmission efficiency.It can reduce the number of transmitted data in the network,and this leads to energy saving and reducing transmission delays.In order to effectively operate data aggregation in UDNs,it is important to select an aggregation point well.The total number of transmitted data may vary,depending on the aggregation point to which the data are delivered.Therefore,in this paper,we propose a novel data aggregation scheme to select the appropriate aggregation point and describe the data transmission method applying the proposed aggregation scheme.In addition,we evaluate the proposed scheme with extensive computer simulations.Better performances in the proposed scheme are achieved compared to the conventional approach.