Impact of primary percutaneous coronary intervention on ST-segment elevation myocardial infarction patients:A comprehensive analysis

BACKGROUND Myocardial infarction,particularly ST-segment elevation myocardial infarction(STEMI),is a key global mortality cause.Our study investigated predictors of mortality in 96 STEMI patients undergoing primary percutaneous coronary intervention at Erbil Cardiac Center.Multiple factors were identified influencing in-hospital mortality.Significantly,time from symptom onset to hospital arrival emerged as a decisive factor.Consequently,our study hypothesis is:"Reducing time from symptom onset to hospital arrival significantly improves STEMI prognosis."AIM To determine the key factors influencing mortality rates in STEMI patients.METHODS We studied 96 consecutive STEMI patients undergoing primary percutaneous coronary intervention(PPCI)at the Erbil Cardiac Center.Their clinical histories were compiled,and coronary evaluations were performed via angiography on admission.Data included comorbid conditions,onset of cardiogenic shock,complications during PPCI,and more.Post-discharge,one-month follow-up assessments were completed.Statistical significance was set at P<0.05.RESULTS Our results unearthed several significant findings.The in-hospital and 30-d mortality rates among the 96 STEMI patients were 11.2%and 2.3%respectively.On the investigation of independent predictors of in-hospital mortality,we identified atypical presentation,onset of cardiogenic shock,presence of chronic kidney disease,Thrombolysis In Myocardial Infarction grades 0/1/2,triple vessel disease,ventricular tachycardia/ventricular fibrillation,coronary dissection,and the no-reflow phenomenon.Specifically,the recorded average time from symptom onset to hospital arrival amongst patients who did not survive was significantly longer(6.92±3.86 h)compared to those who survived(3.61±1.67 h),P<0.001.These findings underscore the critical role of timely intervention in improving the survival outcomes of STEMI patients.CONCLUSION Our results affirm that early hospital arrival after symptom onset significantly improves survival rates in STEMI patients,highlighting the critical need for prompt intervention.

An image segmentation method of pulverized coal for particle size analysis

An important index to evaluate the process efficiency of coal preparation is the mineral liberation degree of pulverized coal,which is greatly influenced by the particle size and shape distribution acquired by image segmentation.However,the agglomeration effect of fine powders and the edge effect of granular images caused by scanning electron microscopy greatly affect the precision of particle image segmentation.In this study,we propose a novel image segmentation method derived from mask regional convolutional neural network based on deep learning for recognizing fine coal powders.Firstly,an atrous convolution is introduced into our network to learn the image feature of multi-sized powders,which can reduce the missing segmentation of small-sized agglomerated particles.Then,a new mask loss function combing focal loss and dice coefficient is used to overcome the false segmentation caused by the edge effect.The final comparative experimental results show that our method achieves the best results of 94.43%and 91.44%on AP50 and AP75 respectively among the comparison algorithms.In addition,in order to provide an effective method for particle size analysis of coal particles,we study the particle size distribution of coal powders based on the proposed image segmentation method and obtain a good curve relationship between cumulative mass fraction and particle size.

Influence of lens position as detected by an anterior segment analysis system on postoperative refraction in cataract surgery

AIM:To predict postoperative intraocular lens(IOL)position using the Sirius anterior segment analysis system and investigate the effect of lens position and IOL type on postoperative refraction.METHODS:A total of 97 patients(102 eyes)were enrolled in the final analysis.An anterior segment biometry measurement was performed preoperatively with Sirius and Lenstar.The results of predicted lens position(PLP)and IOL power were automatically calculated by the software used by the instruments.Effective lens position(ELP)was measured manually using Sirius 3 mo postoperatively.Pearson's correlation analysis and linear regression analysis were used to determine the correlation of lens position to other parameters.RESULTS:PLP and ELP were positively correlated to axial length(AL;r=0.42,P<0.0001 and r=0.49,P<0.0001,respectively).There was a weak correlation between the peLP(ELP-PLP)and the prediction error of spherical refraction(peSR;r=0.34,P<0.0001).The peLP of Softec HD IOL differed statistically from those of both the TECNIS ZCB00 and Sensor AR40E IOLs.Multiple linear regression was used to obtain the prediction formula:ELP=0.66+0.63×[aqueous depth(AQD)+0.6 LT](r=0.61,P<0.0001),and a new variable(AQD+0.6 LT)was found to have the strongest correlation with ELP.CONCLUSION:The Sirius anterior segment analysis system is helpful to predict ELP,which reduces postoperative refraction error.

Study on Segmented Correlation in EEG Based on Principal Component Analysis

In order to explore the correlation between the adjacent segments of a long term EEG, an improved principal component analysis(PCA) method based on mutual information algorithm is proposed. A one-dimension EEG time series is divided equally into many segments, so that each segment can be regarded as an independent variables and multi-segmented EEG can be expressed as a data matrix. Then, we substitute mutual information matrix for covariance matrix in PCA and conduct the relevance analysis of segmented EEG. The experimental results show that the contribution rate of first principal component(FPC) of segmented EEG is more larger than others, which can effectively reflect the difference of epileptic EEG and normal EEG with the change of segment number. In addition, the evolution of FPC conduce to identify the time-segment locations of abnormal dynamic processes of brain activities,these conclusions are helpful for the clinical analysis of EEG.

A novel technique of three-dimensional reconstruction segmentation and analysis for sliced images of biological tissues

A novel technique of three-dimensional (3D) reconstruction, segmentation, display and analysis of series slices of images including microscopic wide field optical sectioning by deconvolution method, cryo-electron microscope slices by Fou-rier-Bessel synthesis and electron tomography (ET), and a series of computed tomography (CT) was developed to perform si-multaneous measurement on the structure and function of biomedical samples. The paper presents the 3D reconstruction seg-mentation display and analysis results of pollen spore, chaperonin, virus, head, cervical bone, tibia and carpus. At the same time, it also puts forward some potential applications of the new technique in the biomedical realm.

Finite Element Analysis of Shrinkage in the Interface of Functionally Graded Concrete Segment Used in Shield Tunneling

In functionally graded materials (FGM), the problem of interface stability caused by the volume deformation is commonly regarded as the key factor for its performance. Based on test results, in terms of finite element method (FEM) this paper analyzed problems in the shrinkage of functionally graded material interface of shield concrete segment, which was designed and produced by the principle of functionally graded materials. In the analysis model, the total shrinkage of concrete was converted into the thermal shrinkage by means of the method of "Equivalent Temperature Difference". Consequently, the shrinkage stress of interface layer was calculated and compared with the bond strength of interface layer.The results indicated that the volume deformation of two-phase materials of functionally graded concrete (FGC) segment, which were the concrete cover and the concrete structure layer, showed better compatibility and the tension stress of interface layer, which was resulted from the shrinkage of concrete and calculated by ANSYS, was less than the bond strength of interface layer. Therefore, the interface stability of functionally graded concrete segment was good and the sliding deformation of interface layer would not generate.

Test and Numerical Analysis on Performance of Reinforced Concrete Segment in Subway Tunnel

To investigate the mechanical behavior of segmental lining,a three-dimensional numerical analysis and test using three actual segments were used to analyze the effects of axial force and reinforcement ratio on the failure mechanism and ultimate bearing capacity of segmental lining.Both numerical and test results confirmed that the cracking load,yield and ultimate load were strongly influenced by axial force,and it was also proved that the yield and ultimate load would increase with the increase of reinforcement ratio,but the cracking load was almost not affected.The cracking load,yield and ultimate load are about 28.7%,500% and 460% larger due to the effect of axial force respectively.The comparison between numerical calculation and test results showed that the finite element analysis results were in good agreement with the test results.

Correlation analysis-based image segmentation approach for automatic agriculture vehicle

It is important to segment image correctly to extract guidance information for automatic agriculture vehicle. If we can make the computer know where the crops are, we can extract the guidance line easily. Images were divided into some rec-tangle small windows, then a pair of 1-D arrays was constructed in each small windows. The correlation coefficients of every small window constructed the features to segment images. The results showed that correlation analysis is a potential approach for processing complex farmland for guidance system, and more correlation analysis methods must be researched.

Vascular Stress Analysis Based on in vivo Intravascular Optical Coherence Tomography Image Segmentation

Identification of carotid artery atherosclerosis is crucial for the diagnosis of the cerebral apoplexy and other vascular diseases.Intravascular optical tomography(IVOCT)has been employed to clinical coronary imaging for several years.Vessel morphological information on IVOCT images together with blood flow information on Doppler OCT(DOCT)images could provide a more accurate internal environment of arteries.Images integrated with fluid-structure interaction(FSI)could obtain the accurate mechanical responses and the quantitative material characters.A porcine carotid artery was imaged with an intravascular system(C7-XR,St.Jude Medical Inc.St.Paul,Minnesota,USA)in vivo,during which 120 images of one section and 600 images of a 5 mm/s pull back were captured within 6 s.Those images were then overlapped with Doppler phase changes to imply the changes in flow profiles.Segmentation and quantification of vessel structure was done in the software(MATLAB 2014b),including specifically the segmentation of lumen,imaging catheter,vessel wall and the guide wire.Appropriate interpolation functions are selected in the coordinate transformation algorithm to have smooth boundaries from images.A set of flow algorithms include image segmentation,three-dimensional/two-dimensional model reconstruction,inversion of material parameters,fitting of experimental velocity data and theoretical derivation based on simulation results is proposed.All steps are programmed to provide a theoretical basis for the future simplified process control.3D-reconstruction FSI model was built in SOLIDWORKS by lofting operation based on the segmentation results.Commercial finite element software(COMSOL 5.3,Sweden)numerically analyzed the entity model to obtain vessel stress/strain and flow shear stress data.Boundary conditions are from the OCT detection.Material of the artery was set to be the modified Mooney-Rivlin constitutive model and the parameters used were adjusted in an algorithm to match an ex vivo experiment.Wall shear stresses(WSS)and vessel deformations were chosen to measure the conditions of the artery and would serve as a target variables for future prediction.Thus,the geometric information together with the data of materials and other mechanical properties are possible to obtain during the imaging process.Segmentation process provided anatomically correct models of a two-layered artery.Numerical simulation permits reliable stress distribution in which the position of catheter and the artery curvature have a neglectable disturbance.Shear stress of the fluid is quite small compared with that of the wall at the same interface,which shows good agreement with the former studies.Moreover,a high flushing speed of 0.1 mps have little impact on the stress distributions and magnitudes,which denotes that the OCT imaging process brings little harm to the vessel.It is the first attempt to combine the OCT imaging and Doppler OCT within a full algorithm and a structural analysis.This study is helpful for the biomechanical property studies of carotid arteries and the development of medical imaging technology.

Additional manual thrombus aspiration for ST-segment elevation myocardial infarction during percutaneous coronary intervention： an updated meta- analysis

BackgroundThe 临床的功效和在有圣片断举起的病人的附属血栓渴望(TA ) 的安全心肌的梗塞(STEMI ) 在经皮的冠的干预(一种总线标准) 期间仍然是 controversial.MethodsTwenty 五合格使随机化的控制试用被包括把血栓渴望(TA ) 的使用与一种总线标准作比较并且为 STEMI 一种总线标准唯一。主要端点是所有原因死亡和死亡。第二等的端点是主要不利心脏的事件(向)，周期性的梗塞( RI )，目标容器 revascularization ( TVR )， stent 血栓(圣)，灌注代理人标记和 stroke.ResultsTIMI 流动分级 3 并且 MBG 2-3 显著地与一种总线标准唯一的手臂相比在 TA 正一种总线标准手臂被增加[相对风险( RR )：1.05， 95% 信心间隔(CI ) ：1.02-1.09， P = 0.004 ] 并且(RR：1.68， 95% CI：1.40-2.00， P &#x0003c；0.001 ) 分别地。在在二个组之间的所有原因死亡，权标， TVR 和圣率没有重要差别。RI 率在有短期的后续持续时间的一种总线标准唯一的手臂比那在 TA 正一种总线标准手臂是更低的(RR：0.60， 95% CI：0.38-0.96， P = 0.03 ) ，但是在在中期或长期的后续时期上的 RI 发生没有重要差别(RR：1.00， 95% CI：0.77-1.29， P = 0.98 ) ，并且(RR：0.96， 95% CI：0.81-1.15， P = 0.69 ) 分别地。在 TA 正一种总线标准在中期或长期的后续时期和粗略的击率上处于粗略的击和击的率有统计上重要的差别(RR：1.60， 95% CI：1.08-2.38， P = 0.02 ) 并且(RR：1.43， 95% CI：1.03-1.98， P = 0.03 ) 分别地；这没在短期的后续时期期间在二只手臂之间被观察(RR：1.47， 95% CI：0.97-2.21， P = 0.07 ).ConclusionsRoutine 在 STEMI 病人的帮助 TA 的一种总线标准能改进心肌的灌注并且得到与临床的端点有关的有限好处，它可以与中风风险被联系。

Seismic Analysis of the Connections of Buried Segmented Pipes

Seismic analysis of buried pipes has been one study focus during the last decades,but the systematic seismic research of pipe connections,especially its relationship with the connected straight pipe,is nearly blank.On the basis,the influence of pipe connections on the joint deformations(JDs)of buried segmented pipes is analyzed in detail by considering different parameters,namely,connection shapes,ground conditions,pipe diameters,branch angles,seismic incident angles,and input ground motions.Moreover,an influence coefficient,which measures the influence of pipe connections on pipe JDs,is calculated.Results show that pipe connections can reduce the JDs of segmented pipes by 40%-50%.Furthermore,the JD is more sensitive to the connection shape,ground condition and pipe diameter than the incident angle and characteristics of seismic waves.An influence coefficient of 0.65 is recommended conservatively for the design of the buried segmented pipes.

Sequence Analysis of Segment 8 of Five Chinese Isolates of Rice Gall Dwarf Virus and Expression of a Main Outer Capsid Protein in Escherichia coli

The rice gall dwarf disease, caused by the Rice gall dwarf virus (RGDV) is a serious disease occurring in rice in many regions of Guangdong province. As a basis to control the disease we have studied the genomic diversity of a variety of isolates from different locations. Genome segment 8(S8), encoding a main outer capsid protein (Pns8) of RGDV five isolates (BL, CH, DQ, GZ, XY) from Guangdong province was cloned and sequenced. The results revealed that all the S8 segments of the five isolates consisted of 1 578 nucleotides and had a single open reading frame (ORF) extending for 1 301 nucleotides from nucleotide 21 which encoded a polypeptide of 426 amino acids with an estimated molecular weight of 47.4 kDa. The S8 full-length sequence and the ORF sequence shared 97.3%-98.8% and 97.3%-99.1% nucleotide sequence identities within the five Chinese isolates, and shared 94.8%-95.6% and 95.0%-96.0% identities with those of the Thailand isolate respectively. The deduced amino acid sequence of Pns8 in GZ isolate was identical to that in the Thailand isolate, while the amino acid sequence variability of Pns8 within five Chinese isolates ranged from 0.5% to 2.1%. These results indicate that the S8 segment of RGDV is highly conserved in different isolates from different locations. The S8 cDNA from the XY isolate was cloned into the plasmid vector pET-28b(+) and a fused expression protein with an apparent molecular mass of 51kDa was specifically detected in an analysis of Escherichia coli Rossetta(DE3)Ⅱcells. To our knowledge, this is the first report on analysis of the RGDV segment 8 sequence and genetic comparison of different RGDV isolates and their protein expression.

Factor and Cluster Analysis as a Tool for Patient Segmentation Applied to Hospital Marketing in Jordan

Hospital marketing is becoming important for the survival and the prosperity of the health service. In addition, it indirectly acts as a formal feedback channel for the customer requirements, preferences, suggestions and complaints. In this work we have undertaken a survey based marketing study for two main objectives: The first being to better understand the patient clusters through k-means clustering and the second to understand customer perception of the different known quality perspectives through factor rotated and unrotated analysis. All of the questionnaires were designed according to international studies. Based on general descriptive statistics, items classified with higher variance but important, are: clean environment, doctors and nurses capabilities, and specialized doctors. Items that are less important with low variance are: food type, lighting and insurance. Also, items classified as more important with low variance are: recommended, no mistakes, and the cost. Using factor analysis rotated and unrotated reduced the variables into five main variables described as: medical aspects, psychological aspects, cost aspects, hospital image and ease of access and procedures. Using k-means clustering, the customers can be clustered into four main clusters with two of them described as general patient with wide variety of interest, serious cases interested in specialized doctors and food, and very serious case with high stress on equipment, no mistakes.

Medical Image Segmentation Based on Wavelet Analysis and Gradient Vector Flow

Medical image segmentation is one of the key technologies in computer aided diagnosis. Due to the complexity and diversity of medical images, the wavelet multi-scale analysis is introduced into GVF (gradient vector flow) snake model. The modulus values of each scale and phase angle values are calculated using wavelet transform, and the local maximum points of modulus values, which are the contours of the object edges, are obtained along phase angle direction at each scale. Then, location of the edges of the object and segmentation is implemented by GVF snake model. The experiments on some medical images show that the improved algorithm has small amount of computation, fast convergence and good robustness to noise.

Deep Learning in Medical Ultrasound Analysis: A Review

Ultrasound (US) has become one of the most commonly performed imaging modalities in clinical practice. It is a rapidly evolving technology with certain advantages and with unique challenges that include low imaging quality and high variability. From the perspective of image analysis, it is essential to develop advanced automatic US image analysis methods to assist in US diagnosis and/or to make such assessment more objective and accurate. Deep learning has recently emerged as the leading machine learning tool in various research fields, and especially in general imaging analysis and computer vision. Deep learning also shows huge potential for various automatic US image analysis tasks. This review first briefly introduces several popular deep learning architectures, and then summarizes and thoroughly discusses their applications in various specific tasks in US image analysis, such as classification, detection, and segmentation. Finally, the open challenges and potential trends of the future application of deep learning in medical US image analysis are discussed.

Semi-supervised learning based probabilistic latent semantic analysis for automatic image annotation

In recent years,multimedia annotation problem has been attracting significant research attention in multimedia and computer vision areas,especially for automatic image annotation,whose purpose is to provide an efficient and effective searching environment for users to query their images more easily. In this paper,a semi-supervised learning based probabilistic latent semantic analysis( PLSA) model for automatic image annotation is presenred. Since it's often hard to obtain or create labeled images in large quantities while unlabeled ones are easier to collect,a transductive support vector machine( TSVM) is exploited to enhance the quality of the training image data. Then,different image features with different magnitudes will result in different performance for automatic image annotation. To this end,a Gaussian normalization method is utilized to normalize different features extracted from effective image regions segmented by the normalized cuts algorithm so as to reserve the intrinsic content of images as complete as possible. Finally,a PLSA model with asymmetric modalities is constructed based on the expectation maximization( EM) algorithm to predict a candidate set of annotations with confidence scores. Extensive experiments on the general-purpose Corel5k dataset demonstrate that the proposed model can significantly improve performance of traditional PLSA for the task of automatic image annotation.

Quality of Service Analysis of Ethernet Network Based on Packet Size

Ethernet network, standardized by IEEE 802.3, is vastly installed in Local Area Network (LAN) for cheaper cost and reliability. With the emergence of cost effective and enhanced user experience needs, the Quality of Service (QoS) of the underlying Ethernet network has become a major issue. A network must provide predictable, reliable and guaranteed services. The required QoS on the network is achieved through managing the end-to-end delay, throughput, jitter, transmission rate and many other network performance parameters. The paper investigates QoS parameters based on packet size to analyze the network performance. Segmentation in packet size larger than 1500 bytes, Maximum Transmission Unit (MTU) of Ethernet, is used to divide the large data into small packets. A simulation process under Riverbed modeler 17.5 initiates several scenarios of the Ethernet network to depict the QoS metrics in the Ethernet topology. For analyzing the result from the simulation process, varying sized packets are considered. Hence, the network performance results in distinct throughput, end-to-end delay, packet loss ratio, bit error rate etc. for varying packet sizes.