(1-3)-β-D葡聚糖联合降钙素原、CD4^(+)T淋巴细胞多指标在艾滋病患者马尔尼菲篮状菌感染早期诊断临床研究

目的探讨(1-3)-β-D葡聚糖联合降钙素原(procalcitonin,PCT)、CD4^(+)T淋巴细胞多指标在艾滋病患者马尔尼菲篮状菌感染早期诊断临床研究。方法回顾性选取我院2020年1月—2022年6月住院的120例艾滋病患者为研究对象。依据实验室结果,将其分为马尔尼菲篮状菌感染确诊组(血或组织液培育养出马尔尼菲篮状菌),简称A组(62例),及马尔尼菲篮状菌感染临床诊断组[根据临床症状、体征、血常规及(1-3)-β-D葡聚糖、PCT、CD4^(+)T淋巴细胞多指标诊断],简称B组(58例)。检测患者(1-3)-β-D葡聚糖、PCT、CD4^(+)T淋巴细胞的表达水平,采用受试者工作特征(receiver-operating characteristic,ROC)曲线下面积(area under the curve,AUC)评估上述指标联合检测对艾滋病患者感染马尔尼菲篮状菌的诊断效能。结果A组的(1-3)-β-D葡聚糖和PCT水平均高于B组,CD4^(+)T淋巴细胞个数低于B组(P<0.05);(1-3)-β-D葡聚糖、PCT、CD4^(+)T淋巴细胞联合检测的AUC为0.933,(1-3)-β-D葡聚糖单独检测的AUC是0.812,PCT单独检测的AUC为0.883,CD4^(+)T淋巴细胞单独检测的AUC是0.810,(1-3)-β-D葡聚糖、PCT和CD4^(+)T淋巴细胞联合检测的AUC皆优于三项单独检测,表明(1-3)-β-D葡聚糖、PCT和CD4^(+)T淋巴细胞联合检测的诊断价值皆优于单一指标诊断,且联合检测的特异度、约登指数分别为92.43%和0.580,均高于三项单独检测。结论(1-3)-β-D葡聚糖联合PCT和CD4^(+)T淋巴细胞多指标对艾滋病马尔尼菲篮状菌感染具有非常高的临床诊断价值,能够帮助医生分析出高危风险患者,及时制定治疗方案,同时也承担预后效果的判断依据,对治疗艾滋病马尔尼菲篮状菌感染具有非常重要的研究价值。

1例RHD-CE(3-7)-D基因重组与RHCE变异型患者的血清学与分子生物学分析

目的 研究分析1例Rh血型弱D、弱cE患者的血清学与分子生物学特征,为该类患者的临床安全输血提供实验依据。方法 采用微柱凝胶卡法对患者红细胞进行ABO、RhDCcEe抗原的鉴定,同时采用试管法进行血型复核,抗人球蛋白卡法筛查不规则抗体;采用PCR-SSP法对RhDCcEe(RhD、RhC、Rhc、RhE、Rhe)基因型进行检测;三代全长测序技术对RHD/RHCE基因序列进行测序分析。结果 微柱凝胶卡法鉴定ABO、RhD、RhCcEe血型抗原的结果为:A抗原(-)、B抗原(-)、RhD(1+)、RhC(4+)、Rhc(1+)、RhE(1+)、Rhe(4+)、对照孔(-);试管法ABO、RhD、RhCcEe抗原鉴定该患者表型为:A抗原(-)、B抗原(-)、RhD(w+)、RhC(4+)、Rhc(w+)、RhE(w+)、Rhe(4+),对照管(-);抗人球蛋白卡法筛查患者不规则抗体阴性;PCR-SSP法血型基因分型RhDCcEe结果:RhD(+)、RhC(+)、Rhc(+)、RhE(+)、Rhe(+);RHD/RHCE基因结果:RHD单倍体1为外显子1-10全缺失,而单倍体2为外显子RHD-CE基因重组融合,且确认其重组类型为RHD-CE(3-7)-D,起点在外显子2(g.20238-20312之间),终点在外显子8(g49184-50480之间),同时RHCE基因第6外显子存在新碱基点突变RHCE*cE(827C>A)。结论RHD-CE(3-7)-D基因重组融合与RHCE*cE(827C>A)新等位基因突变可能引起D、cE血型抗原弱表达,为临床安全输血提供了重要的实验数据支持。

Estimation of the anisotropy of hydraulic conductivity through 3D fracture networks using the directional geological entropy

With an extension of the geological entropy concept in porous media,the approach called directional entrogram is applied to link hydraulic behavior to the anisotropy of the 3D fracture networks.A metric called directional entropic scale is used to measure the anisotropy of spatial order in different directions.Compared with the traditional connectivity indexes based on the statistics of fracture geometry,the directional entropic scale is capable to quantify the anisotropy of connectivity and hydraulic conductivity in heterogeneous 3D fracture networks.According to the numerical analysis of directional entrogram and fluid flow in a number of the 3D fracture networks,the hydraulic conductivities and entropic scales in different directions both increase with spatial order(i.e.,trace length decreasing and spacing increasing)and are independent of the dip angle.As a result,the nonlinear correlation between the hydraulic conductivities and entropic scales from different directions can be unified as quadratic polynomial function,which can shed light on the anisotropic effect of spatial order and global entropy on the heterogeneous hydraulic behaviors.

3D Road Network Modeling and Road Structure Recognition in Internet of Vehicles

Internet of Vehicles (IoV) is a new system that enables individual vehicles to connect with nearby vehicles,people, transportation infrastructure, and networks, thereby realizing amore intelligent and efficient transportationsystem. The movement of vehicles and the three-dimensional (3D) nature of the road network cause the topologicalstructure of IoV to have the high space and time complexity.Network modeling and structure recognition for 3Droads can benefit the description of topological changes for IoV. This paper proposes a 3Dgeneral roadmodel basedon discrete points of roads obtained from GIS. First, the constraints imposed by 3D roads on moving vehicles areanalyzed. Then the effects of road curvature radius (Ra), longitudinal slope (Slo), and length (Len) on speed andacceleration are studied. Finally, a general 3D road network model based on road section features is established.This paper also presents intersection and road section recognition methods based on the structural features ofthe 3D road network model and the road features. Real GIS data from a specific region of Beijing is adopted tocreate the simulation scenario, and the simulation results validate the general 3D road network model and therecognitionmethod. Therefore, thiswork makes contributions to the field of intelligent transportation by providinga comprehensive approach tomodeling the 3Droad network and its topological changes in achieving efficient trafficflowand improved road safety.

Numerical Study of the Biomechanical Behavior of a 3D Printed Polymer Esophageal Stent in the Esophagus by BP Neural Network Algorithm

Esophageal disease is a common disorder of the digestive system that can severely affect the quality of life andprognosis of patients. Esophageal stenting is an effective treatment that has been widely used in clinical practice.However, esophageal stents of different types and parameters have varying adaptability and effectiveness forpatients, and they need to be individually selected according to the patient’s specific situation. The purposeof this study was to provide a reference for clinical doctors to choose suitable esophageal stents. We used 3Dprinting technology to fabricate esophageal stents with different ratios of thermoplastic polyurethane (TPU)/(Poly-ε-caprolactone) PCL polymer, and established an artificial neural network model that could predict the radial forceof esophageal stents based on the content of TPU, PCL and print parameter. We selected three optimal ratios formechanical performance tests and evaluated the biomechanical effects of different ratios of stents on esophagealimplantation, swallowing, and stent migration processes through finite element numerical simulation and in vitrosimulation tests. The results showed that different ratios of polymer stents had different mechanical properties,affecting the effectiveness of stent expansion treatment and the possibility of postoperative complications of stentimplantation.

Luminescence regulation of Sb^(3+)in 0D hybrid metal halides by hydrogen bond network for optical anti-counterfeiting

The Sb^(3+) doping strategy has been proven to be an effective way to regulate the band gap and improve the photophysical properties of organic-inorganic hybrid metal halides(OIHMHs).However,the emission of Sb^(3+) ions in OIHMHs is primarily confined to the low energy region,resulting in yellow or red emissions.To date,there are few reports about green emission of Sb^(3+)-doped OIHMHs.Here,we present a novel approach for regulating the luminescence of Sb^(3+) ions in 0D C_(10)H_(2)_(2)N_(6)InCl_(7)·H_(2)O via hydrogen bond network,in which water molecules act as agents for hydrogen bonding.Sb^(3+)-doped C_(10)H_(2)2N_(6)InCl_(7)·H_(2)O shows a broadband green emission peaking at 540 nm and a high photoluminescence quantum yield(PLQY)of 80%.It is found that the intense green emission stems from the radiative recombination of the self-trapped excitons(STEs).Upon removal of water molecules with heat,C_(10)H_(2)_(2)N_(6)In_(1-x)Sb_(x)Cl_(7) generates yellow emis-sion,attributed to the breaking of the hydrogen bond network and large structural distortions of excited state.Once water molecules are adsorbed by C_(10)H_(2)_(2)N_(6)In_(1-x)Sb_(x)Cl_(7),it can subsequently emit green light.This water-induced reversible emission switching is successfully used for optical security and information encryption.Our findings expand the under-standing of how the local coordination structure influences the photophysical mechanism in Sb^(3+)-doped metal halides and provide a novel method to control the STEs emission.

SGT-Net: A Transformer-Based Stratified Graph Convolutional Network for 3D Point Cloud Semantic Segmentation

In recent years,semantic segmentation on 3D point cloud data has attracted much attention.Unlike 2D images where pixels distribute regularly in the image domain,3D point clouds in non-Euclidean space are irregular and inherently sparse.Therefore,it is very difficult to extract long-range contexts and effectively aggregate local features for semantic segmentation in 3D point cloud space.Most current methods either focus on local feature aggregation or long-range context dependency,but fail to directly establish a global-local feature extractor to complete the point cloud semantic segmentation tasks.In this paper,we propose a Transformer-based stratified graph convolutional network(SGT-Net),which enlarges the effective receptive field and builds direct long-range dependency.Specifically,we first propose a novel dense-sparse sampling strategy that provides dense local vertices and sparse long-distance vertices for subsequent graph convolutional network(GCN).Secondly,we propose a multi-key self-attention mechanism based on the Transformer to further weight augmentation for crucial neighboring relationships and enlarge the effective receptive field.In addition,to further improve the efficiency of the network,we propose a similarity measurement module to determine whether the neighborhood near the center point is effective.We demonstrate the validity and superiority of our method on the S3DIS and ShapeNet datasets.Through ablation experiments and segmentation visualization,we verify that the SGT model can improve the performance of the point cloud semantic segmentation.

基于二萘并[2,3-B∶2′,3′-D]呋喃基团的高效窄发射蓝光OLED器件

高效率窄光谱蓝色有机电致发光器件(OLED)是柔性显示领域的研究重点之一。本文以二萘并[2,3-B∶2′,3′-D]呋喃为弱电子受体、N-(4-联苯基)-1-萘胺作为电子给体设计合成了一种D-A-D型蓝光分子DPF-NA,其在正己烷溶液中的发射峰位于441 nm。理论计算与光物理测试结果显示DPF-NA具有杂化局域电荷转移激发态(HLCT)特性,兼具局域态(LE)高发光效率与电荷转移态(CT)高激子利用率特征,在二氯甲烷溶液中的光致发光量子效率(PLQY)为81.2%。基于质量分数3%DPF-NA掺杂浓度的OLED器件电致发光(EL)峰位于455 nm,半峰宽(FWHM)仅为26 nm,CIE(x,y)坐标为(0.14,0.08),最大外量子效率(EQEmax)为6.76%。

Fang-Xia-Dihuang decoction inhibits breast cancer progression induced by psychological stress via down-regulation of PI3K/AKT and JAK2/STAT3 pathways:An in vivo and a network pharmacology assessment

Background:The development and prognosis of breast cancer are intricately linked to psychological stress.In addition,depression is the most common psychological comorbidity among breast cancer survivors,and reportedly,Fang-Xia-Dihuang decoction(FXDH)can effectively manage depression in such patients.However,its pharmacological and molecular mechanisms remain obscure.Methods:Public databases were used for obtaining active components and related targets.Main active components were further verified by ultra-high-performance liquid chromatography-high-resolution mass spectrometry(UPLC-HRMS).Protein–protein interaction and enrichment analyses were taken to predict potential hub targets and related pathways.Molecule docking was used to understand the interactions between main compounds and hub targets.In addition,an animal model of breast cancer combined with depression was established to evaluate the intervention effect of FXDH and verify the pathways screened by network pharmacology.Results:174 active components of FXDH and 163 intersection targets of FXDH,breast cancer,and depression were identified.Quercetin,methyl ferulate,luteolin,ferulaldehyde,wogonin,and diincarvilone were identified as the principal active components of FXDH.Protein–protein interaction and KEGG enrichment analyses revealed that the phosphoinositide-3-kinase–protein kinase B(PI3K/AKT)and Janus kinase/signal transducer and activator of transcription(JAK2/STAT3)signaling pathways played a crucial role in mediating the efficacy of FXDH for inhibiting breast cancer progression induced by depression.In addition,in vivo experiments revealed that FXDH ameliorated depression-like behavior in mice and inhibited excessive tumor growth in mice with breast cancer and depression.FXDH treatment downregulated the expression of epinephrine,PI3K,AKT,STAT3,and JAK2 compared with the control treatment(p<0.05).Molecular docking verified the relationship between the six primary components of FXDH and the three most important targets,including phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha(PIK3CA),AKT,and STAT3.Conclusion:This study provides a scientific basis to support the clinical application of FXDH for improving depression-like behavior and inhibiting breast cancer progression promoted by chronic stress.The therapeutic effects FXDH may be closely related to the PI3K/AKT and JAK2/STAT3 pathways.This finding helps better understand the regulatory mechanisms underlying the efficacy of FXDH.

Review of Artificial Intelligence for Oil and Gas Exploration: Convolutional Neural Network Approaches and the U-Net 3D Model

Deep learning, especially through convolutional neural networks (CNN) such as the U-Net 3D model, has revolutionized fault identification from seismic data, representing a significant leap over traditional methods. Our review traces the evolution of CNN, emphasizing the adaptation and capabilities of the U-Net 3D model in automating seismic fault delineation with unprecedented accuracy. We find: 1) The transition from basic neural networks to sophisticated CNN has enabled remarkable advancements in image recognition, which are directly applicable to analyzing seismic data. The U-Net 3D model, with its innovative architecture, exemplifies this progress by providing a method for detailed and accurate fault detection with reduced manual interpretation bias. 2) The U-Net 3D model has demonstrated its superiority over traditional fault identification methods in several key areas: it has enhanced interpretation accuracy, increased operational efficiency, and reduced the subjectivity of manual methods. 3) Despite these achievements, challenges such as the need for effective data preprocessing, acquisition of high-quality annotated datasets, and achieving model generalization across different geological conditions remain. Future research should therefore focus on developing more complex network architectures and innovative training strategies to refine fault identification performance further. Our findings confirm the transformative potential of deep learning, particularly CNN like the U-Net 3D model, in geosciences, advocating for its broader integration to revolutionize geological exploration and seismic analysis.

New 4-imino-4H-Chromeno[2,3-d]Pyrimidin-3(5H)-Amine: Synthesis, Cytotoxic Effects on Tumoral Cell Lines and in Silico ADMET Properties

The synthesis of new 4-imino-4H-chromeno[2,3-d]pyrimidin-3(5H)-amine in four steps including one step under microwave dielectric heating is reported. The structural identity of the synthesized compounds was established according to their spectroscopic analysis, such as FT-IR, NMR and mass spectroscopy. These new compounds were tested for their antiproliferative activities on seven representative human tumoral cell lines (Huh7 D12, Caco2, MDA-MB231, MDA-MB468, HCT116, PC3 and MCF7) and also on fibroblasts. Among them, only the compounds 6c showed micromolar cytotoxic activity on tumor cell lines (1.8 50 50 > 25 μM). Finally, in silico ADMET studies ware performed to investigate the possibility of using of the identified compound 6c as potential anti-tumor compound.

血浆(1-3)-β-D葡聚糖诊断新生儿侵袭性真菌感染的价值

探讨在针对新生儿侵袭性真菌感染的诊断中使用血浆(1-3)-β-D葡聚糖能够产生的诊断效能。方法 选取2021年1月到2022年2月因为侵袭性真菌感染于医院寻求医疗干预的新生儿总共60例作为本次研究的一部分研究对象记为1组;另外选取60例健康婴儿作为2组;统计、对比两组患者的血浆(1-3)-β-D葡聚糖水平、CD4+T淋巴细胞数水平;整理PCR技术针对1组患者诊断结果,将其与血浆(1-3)-β-D葡聚糖诊断侵袭性真菌感染的诊断效能进行对比。结果 研究结果显示1组患者的血浆(1-3)-β-D葡聚糖水平、CD4+T淋巴细胞数水平较之于2组患者存在明显差异,1组患者血浆(1-3)-β-D葡聚糖水平更高,而CD4+T淋巴细胞数水平明显更低;P<0.05。在针对诊断效能的考察上,血浆(1-3)-β-D葡聚糖诊断效能和PCR诊断结果之间在灵敏度、特异度方面差异不大,P>0.05。结论 针对罹患侵袭性真菌感染的新生儿使用血浆(1-3)-β-D葡聚糖进行检查,一定程度上能够确保诊断结果的严谨程度,可使用。

血浆(1,3)-β-D葡聚糖检测在侵袭性真菌感染诊断中的价值分析

研究血浆(1,3)-β-D葡聚糖检测技术(G实验)应用于侵袭性真菌感染(IFI)诊断中的应用价值。方法 选择我院在2023年1月至2024年1月期间收治的100例高危型侵袭型真菌感染患者,应用MB-80微生物快速检测动态系统以及对应的试剂对患者的血浆(1,3)-β-D葡聚糖进行检测,分析真菌培养、G实验和联合检测结果;三种方法的诊断效能;三种方法的ROC曲线。结果 确诊IFI阳性共28例,阴性共72例;联合检测的诊断效能高于真菌培养或者G实验。联合检测面积高于单一检测。结论 在侵袭性真菌感染的诊断中,应用血浆(1,3)-β-D葡聚糖检测,诊断效能良好,在和传统真菌培养技术联合检测下,可进一步提升诊断效能。

Parallel computing approach for efficient 3-D X-ray-simulated image reconstruction

Accurate 3-dimensional(3-D)reconstruction technology for nondestructive testing based on digital radiography(DR)is of great importance for alleviating the drawbacks of the existing computed tomography(CT)-based method.The commonly used Monte Carlo simulation method ensures well-performing imaging results for DR.However,for 3-D reconstruction,it is limited by its high time consumption.To solve this problem,this study proposes a parallel computing method to accelerate Monte Carlo simulation for projection images with a parallel interface and a specific DR application.The images are utilized for 3-D reconstruction of the test model.We verify the accuracy of parallel computing for DR and evaluate the performance of two parallel computing modes-multithreaded applications(G4-MT)and message-passing interfaces(G4-MPI)-by assessing parallel speedup and efficiency.This study explores the scalability of the hybrid G4-MPI and G4-MT modes.The results show that the two parallel computing modes can significantly reduce the Monte Carlo simulation time because the parallel speedup increment of Monte Carlo simulations can be considered linear growth,and the parallel efficiency is maintained at a high level.The hybrid mode has strong scalability,as the overall run time of the 180 simulations using 320 threads is 15.35 h with 10 billion particles emitted,and the parallel speedup can be up to 151.36.The 3-D reconstruction of the model is achieved based on the filtered back projection(FBP)algorithm using 180 projection images obtained with the hybrid G4-MPI and G4-MT.The quality of the reconstructed sliced images is satisfactory because the images can reflect the internal structure of the test model.This method is applied to a complex model,and the quality of the reconstructed images is evaluated.

血清25-羟维生素-D3 超敏C反应蛋白及心电图参数与ST段抬高型心肌梗死患者介入治疗后发生恶性心律失常的关系

目的:探讨血清25-羟维生素-D3(25-hydroxyvitamin D3,25-(OH)-D3)、超敏C反应蛋白(High sensitivity C-reactive protein,hs-CRP)、心电图参数变化与ST段抬高型心肌梗死(ST segment elevation myocardial infarction,STEMI)患者经皮冠脉介入术(Percutaneous coronary intervention,PCI)后发生恶性心律失常的关系。方法:选取我院2020年1月至2022年6月实施PCI治疗的193例STEMI患者作为研究对象,根据患者术后72h内是否发生恶性心律失常分为心律失常组38例、对照组155例,对比两组患者入院12h内的25-(OH)-D3、hs-CRP、血清电解质、血常规指标、血脂指标、心肌酶学指标,并对患者PCI术后实施连续动态心电图监测,对比两组患者心电图碎裂QRS波、校正Tp-e间期(Corrected Tp-e interval,Tp-ec)、QT离散度(QT dispersion,Qtd),并采用Logistic回归模型分析上述指标与STEMI患者PCI术后发生恶性心律失常的关系。结果:心律失常组患者的血清25-(OH)-D3水平低于对照组,心律失常组患者的血清hs-CRP、Tp-ec测定值、Qtd测定值、碎裂QRS波检出率均高于对照组,上述指标组间差异均具有统计学意义(P<0.05);心律失常组患者的白细胞(White blood cell,WBC)、肌钙蛋白I(Troponin I,cTnI)、肌酸激酶同工酶(Creatine kinase isoenzyme,CK-MB)、肌红蛋白(Myoglobin,MYO)、K+、Mg 2+水平均高于对照组患者,差异具有统计学意义(P<0.05);Logistic回归模型结果显示:cTnI、CK-MB、K+、hs-CRP、Tp-ec、Qtd增高,碎裂QRS波阳性会显著增大STEMI患者PCI后发生恶性心律失常的风险(P<0.05);血清25-(OH)-D3水平增高会降低STEMI患者PCI后发生恶性心律失常的风险(P<0.05)。结论:STEMI患者血清25-(OH)-D3水平降低、hs-CRP增高、PCI术后Tp-ec、Qtd增高、出现碎裂QRS波会增大患者出现恶性心律失常的风险。

Fe_(3)O_(4)/BiOBr/BiOI的制备及降解2,4-D的性能研究

采用水热法制备了微米球花状结构的Fe_(3)O_(4)/BiOBr/BiOI复合材料,通过SEM、XRD、XPS和UV-Vis等手段对催化剂进行表征分析。结果表明,该催化剂对2,4-D具有高效的光降解性能和光催化效率(80 min,88%)。降解速率常数为2.498×10^(-2)min^(-1),分别是Br∶I=5∶5(K=2.004×10^(-2)min^(-1))、BiOBr(K=1.289×10^(-2)min^(-1))和BiOI(K=6.98×10-3min-1)的1.25倍、1.94倍与3.58倍。这是由于Fe_(3)O_(4)/BiOBr/BiOI复合材料内部形成了异质结,利于光生载流子的分离。此外,5次循环实验后,降解效率保持在82.9%,证明该催化剂稳定、可回收、可磁分离。

3-D fracture network dynamic simulation based on error analysis in rock mass of dam foundation

Accurate 3-D fracture network model for rock mass in dam foundation is of vital importance for stability,grouting and seepage analysis of dam foundation.With the aim of reducing deviation between fracture network model and measured data,a 3-D fracture network dynamic modeling method based on error analysis was proposed.Firstly,errors of four fracture volume density estimation methods(proposed by ODA,KULATILAKE,MAULDON,and SONG)and that of four fracture size estimation methods(proposed by EINSTEIN,SONG and TONON)were respectively compared,and the optimal methods were determined.Additionally,error index representing the deviation between fracture network model and measured data was established with integrated use of fractal dimension and relative absolute error(RAE).On this basis,the downhill simplex method was used to build the dynamic modeling method,which takes the minimum of error index as objective function and dynamically adjusts the fracture density and size parameters to correct the error index.Finally,the 3-D fracture network model could be obtained which meets the requirements.The proposed method was applied for 3-D fractures simulation in Miao Wei hydropower project in China for feasibility verification and the error index reduced from 2.618 to 0.337.

Zuo Gui Wan Promotes Osteogenesis via PI3K/AKT Signaling Pathway:Network Pharmacology Analysis and Experimental Validation

Objective Osteogenesis is vitally important for bone defect repair,and Zuo Gui Wan(ZGW)is a classic prescription in traditional Chinese medicine(TCM)for strengthening bones.However,the specific mechanism by which ZGW regulates osteogenesis is still unclear.The current study is based on a network pharmacology analysis to explore the potential mechanism of ZGW in promoting osteogenesis.Methods A network pharmacology analysis followed by experimental validation was applied to explore the potential mechanisms of ZGW in promoting the osteogenesis of bone marrow mesenchymal stem cells(BMSCs).Results In total,487 no-repeat targets corresponding to the bioactive components of ZGW were screened,and 175 target genes in the intersection of ZGW and osteogenesis were obtained.And 28 core target genes were then obtained from a PPI network analysis.A GO functional enrichment analysis showed that the relevant biological processes mainly involve the cellular response to chemical stress,metal ions,and lipopolysaccharide.Additionally,KEGG pathway enrichment analysis revealed that multiple signaling pathways,including the phosphatidylinositol-3-kinase/protein kinase B(PI3K/AKT)signaling pathway,were associated with ZGW-promoted osteogensis.Further experimental validation showed that ZGW could increase alkaline phosphatase(ALP)activity as well as the mRNA and protein levels of ALP,osteocalcin(OCN),and runt related transcription factor 2(Runx 2).What’s more,Western blot analysis results showed that ZGW significantly increased the protein levels of p-PI3K and p-AKT,and the increases of these protein levels significantly receded after the addition of the PI3K inhibitor LY294002.Finally,the upregulated osteogenic-related indicators were also suppressed by the addition of LY294002.Conclusion ZGW promotes the osteogenesis of BMSCs via PI3K/AKT signaling pathway.

Building 3-D Human Data Based on Handed Measurement and CNN

3-dimension(3-D)printing technology is growing strongly with many applications,one of which is the garment industry.The application of human body models to the garment industry is necessary to respond to the increasing personalization demand and still guarantee aesthetics.This paper proposes amethod to construct 3-D human models by applying deep learning.We calculate the location of the main slices of the human body,including the neck,chest,belly,buttocks,and the rings of the extremities,using pre-existing information.Then,on the positioning frame,we find the key points(fixed and unaltered)of these key slices and update these points tomatch the current parameters.To add points to a star slice,we use a deep learning model tomimic the form of the human body at that slice position.We use interpolation to produce sub-slices of different body sections based on the main slices to create complete body parts morphologically.We combine all slices to construct a full 3-D representation of the human body.

Short‐term and long‐term memory self‐attention network for segmentation of tumours in 3D medical images

Tumour segmentation in medical images(especially 3D tumour segmentation)is highly challenging due to the possible similarity between tumours and adjacent tissues,occurrence of multiple tumours and variable tumour shapes and sizes.The popular deep learning‐based segmentation algorithms generally rely on the convolutional neural network(CNN)and Transformer.The former cannot extract the global image features effectively while the latter lacks the inductive bias and involves the complicated computation for 3D volume data.The existing hybrid CNN‐Transformer network can only provide the limited performance improvement or even poorer segmentation performance than the pure CNN.To address these issues,a short‐term and long‐term memory self‐attention network is proposed.Firstly,a distinctive self‐attention block uses the Transformer to explore the correlation among the region features at different levels extracted by the CNN.Then,the memory structure filters and combines the above information to exclude the similar regions and detect the multiple tumours.Finally,the multi‐layer reconstruction blocks will predict the tumour boundaries.Experimental results demonstrate that our method outperforms other methods in terms of subjective visual and quantitative evaluation.Compared with the most competitive method,the proposed method provides Dice(82.4%vs.76.6%)and Hausdorff distance 95%(HD95)(10.66 vs.11.54 mm)on the KiTS19 as well as Dice(80.2%vs.78.4%)and HD95(9.632 vs.12.17 mm)on the LiTS.