应用ECHO模型评价药物治疗管理服务在儿童支气管哮喘治疗中的效果

目的探究药物治疗管理(MTM)服务对儿童哮喘治疗的应用效果。方法选取2022年7—12月在河北省人民医院咳喘药学门诊就诊的5~11岁哮喘患儿107例,随机分成对照组(50例)和干预组(52例)。对照组患儿首诊时给予单次吸入制剂教育服务;干预组患儿给予全程规范化MTM服务。应用经济、临床与人文结果(ECHO)模型,分别于干预前、干预3个月及干预6个月后,统计2组患儿在经济(药物成本、成本-效用比),临床(ACT评分、吸入制剂使用正确率、哮喘发作次数)和人文(EQ-5D-5L效用值、Morisky用药依从性、患者满意度)结果中的差异,评价MTM服务在哮喘患儿中的应用效果。结果干预前2组各项评分指标差异无统计学意义;干预后干预组患儿经济结果、临床结果、人文结果3方面均优于对照组,差异有统计学意义(P<0.05)。结论药师主导的MTM服务可使哮喘患儿在经济、临床、人文多维度获益,既使哮喘患儿疾病得到长期有效控制,又提升药师药学专科服务能力。

Exploring the associations between skeletal muscle echogenicity and physical function in aging adults:A systematic review with meta-analyses

Background:Assessment and quantification of skeletal muscle within the aging population is vital for diagnosis,treatment,and injury/disease prevention.The clinical availability of assessing muscle quality through diagnostic ultrasound presents an opportunity to be utilized as a screening tool for function-limiting diseases.However,relationships between muscle echogenicity and clinical functional assessments require authoritative analysis.Thus,we aimed to(a) synthesize the literature to assess the relationships between skeletal muscle echogenicity and physical function in older adults(≥60 years),(b) perform pooled analyses of relationships between skeletal muscle echogenicity and physical function,and(c) perform sub-analyses to determine between-muscle relationships.Methods:CINAHL,Embase,MEDLINE,PubMed,and Web of Science databases were systematically searched to identify articles relating skeletal muscle echogenicity to physical function in older adults.Risk-of-bias assessments were conducted along with funnel plot examination.Meta-analyses with and without sub-analyses for individual muscles were performed utilizing Fisher's Z transformation for the most common measures of physical function.Fisher's Z was back-transformed to Pearson's r for interpretation.Results:Fifty-one articles(n=5095,female=~2759,male=~2301,72.5± 5.8 years,mean±SD(1 study did not provide sex descriptors))were extracted for review,with previously unpublished data obtained from the authors of 13 studies.The rectus femoris(n=34) and isometric knee extension strength(n=22) were the most accessed muscle and physical qualities,respectively.The relationship between quadriceps echogenicity and knee extensor strength was moderate(n=2924,r=-0.36(95% confidence interval:-0.38 to-0.32),p <0.001),with all other meta-analyses(grip strength,walking speed,sit-to-stand,timed up-and-go) resulting in slightly weaker correlations(r:-0.34 to-0.23,all p <0.001).Sub-analyses determined minimal differences in predictive ability between muscle groups,although combining muscles(e.g.,rectus femoris+vastus lateralis) often re sulted in stronger correlations with maximal strength.Conclusion:While correlations are modest,the affordable,portable,and noninvasive ultrasonic assessment of muscle quality is a consistent predictor of physical function in older adults.Minimal between-muscle differences suggest that echogenicity estimates of muscle quality are systemic.Therefore,practitioners may be able to scan a single muscle to estimate full-body skeletal muscle quality/composition,while researchers should consider combining multiple muscles to strengthen the model.

A Novel On-Site-Real-Time Method for Identifying Characteristic Parameters Using Ultrasonic Echo Groups and Neural Network

On-site and real-time non-destructive measurement of elastic constants for materials of a component in a in-service structure is a challenge due to structural complexities,such as ambiguous boundary,variable thickness,nonuniform material properties.This work develops for the first time a method that uses ultrasound echo groups and artificial neural network(ANN)for reliable on-site real-time identification of material parameters.The use of echo groups allows the use of lower frequencies,and hence more accommodative to structural complexity.To train the ANNs,a numerical model is established that is capable of computing the waveform of ultrasonic echo groups for any given set of material properties of a given structure.The waveform of an ultrasonic echo groups at an interest location on the surface the structure with material parameters varying in a predefined range are then computed using the numerical model.This results in a set of dataset for training the ANN model.Once the ANN is trained,the material parameters can be identified simultaneously using the actual measured echo waveform as input to the ANN.Intensive tests have been conducted both numerically and experimentally to evaluate the effectiveness and accuracy of the currently proposed method.The results show that the maximum identification error of numerical example is less than 2%,and the maximum identification error of experimental test is less than 7%.Compared with currently prevailing methods and equipment,the proposefy the density and thickness,in addition to the elastic constants.Moreover,the reliability and accuracy of inverse prediction is significantly improved.Thus,it has broad applications and enables real-time field measurements,which has not been fulfilled by any other available methods or equipment.

Enhanced air-coupled impact echo technique by phase analysis of signals from multiple sensors

This paper presents an air-coupled impact echo(IE)technique that relies on the phase spectrum of the collected data to find the frequencies corresponding to the reflections from delaminations.The proposed technique takes advantage of the fact that the IE compression wave is not a propagating wave,but it is the 1st order symmetrical(S1)mode Lamb wave at zero group velocity(S1-ZGV).Therefore,it searches the phase spectra of the data collected by multiple sensors to locate the frequency corresponding to the lowest phase difference.As a result,the technique reduces the effect of propagating waves,including the direct acoustic wave and ambient noise.It is named the Constant Phase IE(CPIE).The performance of the CPIE is experimentally compared with the regular amplitude spectrum-based IE technique and two other multisensor IE techniques.The CPIE shows a performance advantage,especially in a noisy environment.

Research on Sleeve Grouting Density Detection Based on the Impact Echo Method

Grouting defects are an inherent challenge in construction practices,exerting a considerable impact on the operational structural integrity of connections.This investigation employed the impact-echo technique for the detection of grouting anomalies within connections,enhancing its precision through the integration of wavelet packet energy principles for damage identification purposes.A series of grouting completeness assessments were meticulously conducted,taking into account variables such as the divergent material properties of the sleeves and the configuration of adjacent reinforcement.The findings revealed that:(i)the energy distribution for the highstrength concrete cohort predominantly occupied the frequency bands 42,44,45,and 47,whereas for other groups,it was concentrated within the 37 to 40 frequency band;(ii)the delineation of empty sleeves was effectively discernible by examining the wavelet packet energy ratios across the spectrum of frequencies,albeit distinguishing between sleeves with 50%and full grouting density proved challenging;and(iii)the wavelet packet energy analysis yielded variable detection outcomes contingent on the material attributes of the sleeves,demonstrating heightened sensitivity when applied to ultrahigh-performance concrete matrices and GFRP-reinforced steel bars.

Relationship of inflammatory indices with left atrial appendage thrombus or spontaneous echo contrast in patients with atrial fibrillation

BACKGROUND Inflammatory indices derived from complete blood tests have been reported to be associated with poor outcomes in patients with atrial fibrillation(AF).The data about the relationship between inflammatory indices and left atrial appendage thrombus(LAAT)or dense spontaneous echo contrast(SEC)are limited.AIM To explore the value of inflammatory indices for predicting the presence of LAAT or dense SEC in nonvalvular AF patients.METHODS A total of 406 patients with nonvalvular AF who underwent transesophageal echocardiography were included and divided into two groups based on the presence(study group)or absence(control group)of LAAT or dense SEC.Inflammatory indices,including the neutrophil-to-lymphocyte ratio(NLR),platelet–tolymphocyte ratio(PLR),and lymphocyte-to-monocyte ratio(LMR),were calculated from complete blood analysis.The associations of inflammatory indices RESULTS LAAT and dense SEC were detected in 11(2.7%)and 42(10.3%)patients,respectively.The PLR only showed an association with LAAT/dense SEC in the univariate model.Elevated NLR(odds ratio[OR]=1.48,95%confidence interval[CI]:1.11-1.98,P=0.007)and reduced LMR(OR=0.59,95%CI:0.41-0.83,P=0.003)were found to be independent risk factors for the presence of LAAT/dense SEC.The areas under the NLR and LMR curves for predicting LAAT/dense SEC were 0.73(95%CI:0.66-0.80,P<0.001)and 0.73(95%CI:0.65-0.81,P<0.001),respectively,while the cutoff values were 2.8(sensitivity:69.8%;specificity:64.0%)and 2.4(sensitivity:71.7%;specificity:60.6%),respectively.CONCLUSION Increased NLR and decreased LMR may predict LAAT/dense SEC in patients with nonvalvular AF.

Comparison of the reaction of polar mesosphere winter echoes and polar mesosphere summer echoes to high-frequency heating in terms of modulated characteristics

In this work,for the first time,we have analyzed and compared the responses of polar mesosphere winter echoes(PMWE)and their summer counterpart,polar mesosphere summer echoes(PMSE),to high-frequency(HF)heating in terms of modulated characteristics(i.e.,backscatter intensity reduction,recovery,and overshoot).Both PMWE and PMSE observations were from the same site(Tromsφ,Norway;69.6°N,19.2°E)and radar(EISCAT[European Incoherent Scatter Scientific Association]very high frequency,224 MHz).The heating patterns of both PMWE and PMSE were found to be similar;however,PMSE was more greatly affected by HF heating.Polar mesosphere summer echoes showed recovery and overshoot more frequently than did PMWE.In addition,the mean recovery and overshoot of PMSE were greater than those of PMWE.The associated electron temperature enhancement was estimated for both PMWE and PMSE and showed that,compared with PMWE,the electron temperature enhancement was more significant in PMSE.The strong heating effects on PMSE may be due to the considerable increase in electron temperature.

CEMA-LSTM:Enhancing Contextual Feature Correlation for Radar Extrapolation Using Fine-Grained Echo Datasets

Accurate precipitation nowcasting can provide great convenience to the public so they can conduct corresponding arrangements in advance to deal with the possible impact of upcoming heavy rain.Recent relevant research activities have shown their concerns on various deep learning models for radar echo extrapolation,where radar echo maps were used to predict their consequent moment,so as to recognize potential severe convective weather events.However,these approaches suffer from an inaccurate prediction of echo dynamics and unreliable depiction of echo aggregation or dissipation,due to the size limitation of convolution filter,lack of global feature,and less attention to features from previous states.To address the problems,this paper proposes a CEMA-LSTM recurrent unit,which is embedded with a Contextual Feature Correlation Enhancement Block(CEB)and a Multi-Attention Mechanism Block(MAB).The CEB enhances contextual feature correlation and supports its model to memorize significant features for near-future prediction;the MAB uses a position and channel attention mechanism to capture global features of radar echoes.Two practical radar echo datasets were used involving the FREM and CIKM 2017 datasets.Both quantification and visualization of comparative experimental results have demonstrated outperformance of the proposed CEMA-LSTMover recentmodels,e.g.,PhyDNet,MIM and PredRNN++,etc.In particular,compared with the second-rankedmodel,its average POD,FAR and CSI have been improved by 3.87%,1.65%and 1.79%,respectively on the FREM,and by 1.42%,5.60%and 3.16%,respectively on the CIKM 2017.

Echo State Network With Probabilistic Regularization for Time Series Prediction

Recent decades have witnessed a trend that the echo state network(ESN)is widely utilized in field of time series prediction due to its powerful computational abilities.However,most of the existing research on ESN is conducted under the assumption that data is free of noise or polluted by the Gaussian noise,which lacks robustness or even fails to solve real-world tasks.This work handles this issue by proposing a probabilistic regularized ESN(PRESN)with robustness guaranteed.Specifically,we design a novel objective function for minimizing both the mean and variance of modeling error,and then a scheme is derived for getting output weights of the PRESN.Furthermore,generalization performance,robustness,and unbiased estimation abilities of the PRESN are revealed by theoretical analyses.Finally,experiments on a benchmark dataset and two real-world datasets are conducted to verify the performance of the proposed PRESN.The source code is publicly available at https://github.com/LongJinlab/probabilistic-regularized-echo-state-network.

Analysis of echo signal modulation characteristic parameters on aerial and space targets

Based on the scattering characteristic,the comparison of RCS(radar cross-section)at different positions of a target in the same direction of incidence can be obtained first by extruding or deleting part of the entity.A simulation method of aerial&space targets echo characteristics(A&STEC)is proposed that is universal to aerial and space targets.We utilize a fixed-wing UAV(unmanned aerial vehicle)and typical missiles in simulation.The echo signal modulation characteristic parameters are calculated theoretically by the atmospheric attenuation model,the finite element method and a MUMPS solver.The verification simulations show that this method can analyze the influence of the target shape,incident direction,detection position and detection frequency on echo waveform,intensity and energy distribution.The results show that the profile of echo waveform can invert the general shape of the target.The relationship between time and intensity can determine whether the target is moving towards or away from the detector in addition.These conclusions can provide a reference for the ballistic missile target tracking and the defense against UVA intrusion in theory.

Gravitational wave echoes from strange quark stars in the equation of state with density-dependent quark masses

According to the recent studies,the gravitational wave(GW)echoes are expected to be generated by quark stars composed of ultrastiff quark matter.The ultrastiff equations of state(EOS)for quark matter were usually obtained either by a simple bag model with artificially assigned sound velocity or by employing interacting strange quark matter(SQM)depicted by simple reparameterization and rescaling.In this study,we investigate GW echoes with EOSs for SQM in the framework of the equivparticle model with density-dependent quark masses and pairing effects.We conclude that strange quark stars(SQSs)can be sufficiently compact to possess a photon sphere capable of generating GW echoes with frequencies in the range of approximately 20 kHz.However,SQSs cannot account for the observed 72 Hz signal in GW170817 event.Furthermore,we determined that quark-pairing effects play a crucial role in enabling SQSs to satisfy the necessary conditions for producing these types of echoes.

Improved Weather Radar Echo Extrapolation Through Wind Speed Data Fusion Using a New Spatiotemporal Neural Network Model

Weather radar echo extrapolation plays a crucial role in weather forecasting.However,traditional weather radar echo extrapolation methods are not very accurate and do not make full use of historical data.Deep learning algorithms based on Recurrent Neural Networks also have the problem of accumulating errors.Moreover,it is difficult to obtain higher accuracy by relying on a single historical radar echo observation.Therefore,in this study,we constructed the Fusion GRU module,which leverages a cascade structure to effectively combine radar echo data and mean wind data.We also designed the Top Connection so that the model can capture the global spatial relationship to construct constraints on the predictions.Based on the Jiangsu Province dataset,we compared some models.The results show that our proposed model,Cascade Fusion Spatiotemporal Network(CFSN),improved the critical success index(CSI)by 10.7%over the baseline at the threshold of 30 dBZ.Ablation experiments further validated the effectiveness of our model.Similarly,the CSI of the complete CFSN was 0.004 higher than the suboptimal solution without the cross-attention module at the threshold of 30 dBZ.

Echo state network based symbol detection in chaotic baseband wireless communication

The Chaotic Baseband Wireless Communication System(CBWCS)is expected to eliminate the Inter-Symbol Interference(ISI)caused by multipath propagation by using the optimal decoding threshold that is the sum of the ISI caused by past decoded bits and the ISI caused by future transmitting bits.However,the current technique is only capable of removing partial effects of the ISI,because only past decoded bits are available for the suboptimal decoding threshold calculation.The unavailability of the future information needed for the optimal decoding threshold is an obstacle to further improve the Bit Error Rate(BER)performance.In contrast to the previous method using Echo State Network(ESN)to predict one future bit,the proposed method in this paper predicts the optimal decoding threshold directly using ESN.The proposed ESN-based threshold prediction method simplifies the symbol decoding operation by avoiding the iterative prediction of the output waveform points using ESN and accumulated error caused by the iterative operation.With this approach,the calculation complexity is reduced compared to the previous ESN-based approach.The proposed method achieves better BER performance compared to the previous method.The reason for this superior result is twofold.First,the proposed ESN is capable of using more future symbols information conveyed by the ESN input to obtain more accurate threshold rather than the previous method in which only one future symbol was available.Second,the proposed method here does not need to estimate the channel information using Least Squared(LS)method,which avoids the extra error caused by inaccurate channel information estimation.Simulation results and experiment based on a wireless open-access research platform under a practical wireless channel show the effectiveness and superiority of the proposed method.

福建省2011年病毒性脑炎暴发病原Echo30分子流行病学分析

目的了解福建省2011年病毒性脑炎暴发的病原特点及福建省近10年Echo30分子流行病学特征。方法应用细胞培养法获得病毒株,微量血清中和试验或RT-PCR序列分析鉴定Echo30血清型,提取病毒RNA核酸,逆转录为cD-NA,分段扩增VP1基因序列全长,测定并分析扩增产物的序列。结果 2011年4月1日-6月2日,从福建省4个县(市)区病毒性脑炎病例标本中分离的病毒经鉴定均为Echo30;VP1基因序列分析显示,泉州市安溪和德化两地流行株高度同源(核苷酸与氨基酸序列同源性分别为99.0%和99.6%),同福安和厦门流行株(核苷酸与氨基酸序列同源性分别为98.5%和100%)核苷酸差异约9%,3个位点氨基酸出现了变异。近10年福建省Echo30分子流行病学分析显示,2011年泉州流行株并非由2005年当地流行株进化而来,而与2008年河南省分离株(HQ625646-Henan/04/2008)同源性最高;近10年福建省Echo30存在多基因型、多谱系的同时传播,并在2006年发现了两株第III新基因型病毒。结论造成2011年福建省病毒性脑炎暴发的病原为Echo30,且至少存在两条不同传播链病毒同时流行。Echo30福建株呈多基因型、多谱系分布流行特点。

连云港市一起ECHO_(30)病毒性脑膜炎爆发流行病学调查

目的分析2006年江苏省连云港市病毒性脑膜炎爆发的流行特征、影响因素和病因。方法采用统一的流行病学调查表对病例进行调查,收集2004年以来全市各县区病毒性脑膜炎住院病例资料,采用1:2配比病例对照研究的方法分析流行因素;24份脑脊液标本送江苏省疾病控制中心检测,并进行病毒分离。结果2004-2005年全市各县区病毒性脑膜炎病例呈现散发状态;2006年1～7月份共查出病例数为968例,从6月份起出现疫情上升,1县2区出现爆发,7月份达高峰,爆发病例为473例,占全市总病例数的48.86%;主要累及15岁以下儿童,占94.44%;男性多于女性,男女比为2.65:1;主要临床表现为发热、头痛、呕吐、脑膜刺激征阳性、脑脊液有核细胞增多。饭前洗手与多吃瓜果为保护因素,说明该病与不良卫生习惯有关系。23份脑脊液经PCR检测,16份ECHO_(30)肠道病毒阳性。结论2006年夏季江苏省连云港市病毒性脑膜炎出现一次爆发并波及1县2区,致病病原体为埃可30型肠道病毒(Echovirus 30,ECHO_(30))肠道病毒,不良卫生习惯可能是导致该病发生流行的危险因素。

急性弛缓性麻痹病例中ECHO11病毒的基因特征分析

该文首次分析了我国ECHO11病毒的分子流行病学资料。1999～2004年，ECHO11病毒是山东省急性弛缓性麻痹（AFP）病例中分离到的优势毒株，2003年从山东省482例AFP病例中共分离到11株ECHO11病毒，其中相关的10例病例分布跨越山东省大部分地区，但发病日期集中在7月和12月。该研究试图通过对VP1编码基因全序列的测定和分析，为探讨ECHO11病毒与AFP之间的病因关系提供线索。分子流行病学研究提示，11株ECHO11毒株都位于同一传播链，核苷酸同源性为97．2％～100％，氨基酸同源性为99．6％～100％，其中7月和12月的分离株之间相层8～9个核苷酸，氨基酸序列一致。这说明山东省2003年7月和12月分别发生了ECHO11病毒流行，但这些毒株与AFP的病因关系尚需进一步研究。11株病毒组成了A基因型中的一个新亚型，在进化树上单独呈密切相关的一簇，与同基因型内的其它亚型的核苷酸同源性为82．2％～84．7％，氨基酸同源性为94.8％～97．6％．

ECHO 19引起龙岩市儿童病毒性脑炎的流行病学调查

[目的]探讨龙岩市2004年儿童病毒性脑炎流行的病因、流行规律及特征.[方法]对142例病毒性脑炎病例进行流行病学调查,对其中30例的脑脊液标本进行病毒分离和型别鉴定.[结果]142例患者分布在我市的6个县(市、区),其中新罗区占(66.2%),另有广东省大埔县2例;年龄以5～12岁为多;男性68.3%,女性31.7%;5～6月为发病高峰.临床主要表现为突然发病、发热(92.4%)、头痛(83.1%)、呕吐(68.6%)等;脑电图以轻中度异常为主;67.8%血常规正常;脑脊液常规87.5%颅内压力增高,56.8%细胞数增加.疗程10天左右.从30份脑脊液中检出16株ECHO 19病毒.[结论]流行病学调查及实验室病原检测确认,龙岩市儿童病毒性脑炎病原为ECHO 19.

一起ECHO 30病毒引发游泳队脑炎爆发调查

目的分析ECHO30病毒性脑炎在冬季体校游泳队爆发的特征。方法应用流行病学描述方法,对体校学生进行筛查;采集典型病例脑脊液(CSF)、血液样本和泳池水样进行实验室检测。结果病例均为体校游泳队学生,共发病37例,罹患率25.18%,性别间罹患率差异无统计学意义(x2=0.03,P>0.50),流行9d;游泳池水污染严重,6份CSF样本肠道病毒核糖核酸阳性,2份分离到ECHO30肠道病毒。结论游泳池循环净水和消毒设备应正常运转,日常监督监测应加强。

上海市人群Echo30病毒性脑膜炎隐性感染调查

为了了解上海市不同年龄组人群的Echo30病毒隐性感染情况及IgG抗体阳性率分布。采集上海市412份不同年龄组人群血清,用间接酶联免疫吸附试验(ELISA)检测血清中的Echo30IgG抗体。发现受检普通人群血清中Echo30IgG抗体阳性率为25.8%。其中1岁以下婴幼儿中未见抗体阳性者,15岁以下儿童抗体阳性率较低(10%～16.7%),15岁以上人群抗体阳性率水平明显升高(45.0%～46.7%)。孕妇与普通人群抗体阳性率无显著性差异。研究结果提示上海市人群中存在隐性感染者,人群通过自然感染获得免疫保护,15岁以下儿童为Echo30感染及发病的高危人群,母体传递给婴幼儿的抗体水平较低,不能为婴幼儿提供先天性免疫保护。