Recent progress in the applications of presynaptic dopaminergic positron emission tomography imaging in parkinsonism

Nowadays,presynaptic dopaminergic positron emission tomography,which assesses deficiencies in dopamine synthesis,storage,and transport,is widely utilized for early diagnosis and differential diagnosis of parkinsonism.This review provides a comprehensive summary of the latest developments in the application of presynaptic dopaminergic positron emission tomography imaging in disorders that manifest parkinsonism.We conducted a thorough literature search using reputable databases such as PubMed and Web of Science.Selection criteria involved identifying peer-reviewed articles published within the last 5 years,with emphasis on their relevance to clinical applications.The findings from these studies highlight that presynaptic dopaminergic positron emission tomography has demonstrated potential not only in diagnosing and differentiating various Parkinsonian conditions but also in assessing disease severity and predicting prognosis.Moreover,when employed in conjunction with other imaging modalities and advanced analytical methods,presynaptic dopaminergic positron emission tomography has been validated as a reliable in vivo biomarker.This validation extends to screening and exploring potential neuropathological mechanisms associated with dopaminergic depletion.In summary,the insights gained from interpreting these studies are crucial for enhancing the effectiveness of preclinical investigations and clinical trials,ultimately advancing toward the goals of neuroregeneration in parkinsonian disorders.

Pervasive Attentive Neural Network for Intelligent Image Classification Based on N-CDE’s

The utilization of visual attention enhances the performance of image classification tasks.Previous attentionbased models have demonstrated notable performance,but many of these models exhibit reduced accuracy when confronted with inter-class and intra-class similarities and differences.Neural-Controlled Differential Equations(N-CDE’s)and Neural Ordinary Differential Equations(NODE’s)are extensively utilized within this context.NCDE’s possesses the capacity to effectively illustrate both inter-class and intra-class similarities and differences with enhanced clarity.To this end,an attentive neural network has been proposed to generate attention maps,which uses two different types of N-CDE’s,one for adopting hidden layers and the other to generate attention values.Two distinct attention techniques are implemented including time-wise attention,also referred to as bottom N-CDE’s;and element-wise attention,called topN-CDE’s.Additionally,a trainingmethodology is proposed to guarantee that the training problem is sufficiently presented.Two classification tasks including fine-grained visual classification andmulti-label classification,are utilized to evaluate the proposedmodel.The proposedmethodology is employed on five publicly available datasets,including CUB-200-2011,ImageNet-1K,PASCAL VOC 2007,PASCAL VOC 2012,and MS COCO.The obtained visualizations have demonstrated that N-CDE’s are better appropriate for attention-based activities in comparison to conventional NODE’s.

GAN-DIRNet:A Novel Deformable Image Registration Approach for Multimodal Histological Images

Multi-modal histological image registration tasks pose significant challenges due to tissue staining operations causing partial loss and folding of tissue.Convolutional neural network(CNN)and generative adversarial network(GAN)are pivotal inmedical image registration.However,existing methods often struggle with severe interference and deformation,as seen in histological images of conditions like Cushing’s disease.We argue that the failure of current approaches lies in underutilizing the feature extraction capability of the discriminator inGAN.In this study,we propose a novel multi-modal registration approach GAN-DIRNet based on GAN for deformable histological image registration.To begin with,the discriminators of two GANs are embedded as a new dual parallel feature extraction module into the unsupervised registration networks,characterized by implicitly extracting feature descriptors of specific modalities.Additionally,modal feature description layers and registration layers collaborate in unsupervised optimization,facilitating faster convergence and more precise results.Lastly,experiments and evaluations were conducted on the registration of the Mixed National Institute of Standards and Technology database(MNIST),eight publicly available datasets of histological sections and the Clustering-Registration-Classification-Segmentation(CRCS)dataset on the Cushing’s disease.Experimental results demonstrate that our proposed GAN-DIRNet method surpasses existing approaches like DIRNet in terms of both registration accuracy and time efficiency,while also exhibiting robustness across different image types.

A Spectral Convolutional Neural Network Model Based on Adaptive Fick’s Law for Hyperspectral Image Classification

Hyperspectral image classification stands as a pivotal task within the field of remote sensing,yet achieving highprecision classification remains a significant challenge.In response to this challenge,a Spectral Convolutional Neural Network model based on Adaptive Fick’s Law Algorithm(AFLA-SCNN)is proposed.The Adaptive Fick’s Law Algorithm(AFLA)constitutes a novel metaheuristic algorithm introduced herein,encompassing three new strategies:Adaptive weight factor,Gaussian mutation,and probability update policy.With adaptive weight factor,the algorithmcan adjust theweights according to the change in the number of iterations to improve the performance of the algorithm.Gaussianmutation helps the algorithm avoid falling into local optimal solutions and improves the searchability of the algorithm.The probability update strategy helps to improve the exploitability and adaptability of the algorithm.Within the AFLA-SCNN model,AFLA is employed to optimize two hyperparameters in the SCNN model,namely,“numEpochs”and“miniBatchSize”,to attain their optimal values.AFLA’s performance is initially validated across 28 functions in 10D,30D,and 50D for CEC2013 and 29 functions in 10D,30D,and 50D for CEC2017.Experimental results indicate AFLA’s marked performance superiority over nine other prominent optimization algorithms.Subsequently,the AFLA-SCNN model was compared with the Spectral Convolutional Neural Network model based on Fick’s Law Algorithm(FLA-SCNN),Spectral Convolutional Neural Network model based on Harris Hawks Optimization(HHO-SCNN),Spectral Convolutional Neural Network model based onDifferential Evolution(DE-SCNN),SpectralConvolutionalNeuralNetwork(SCNN)model,and SupportVector Machines(SVM)model using the Indian Pines dataset and PaviaUniversity dataset.The experimental results show that the AFLA-SCNN model outperforms other models in terms of Accuracy,Precision,Recall,and F1-score on Indian Pines and Pavia University.Among them,the Accuracy of the AFLA-SCNN model on Indian Pines reached 99.875%,and the Accuracy on PaviaUniversity reached 98.022%.In conclusion,our proposed AFLA-SCNN model is deemed to significantly enhance the precision of hyperspectral image classification.

Detection of Alzheimer’s disease onset using MRI and PET neuroimaging:longitudinal data analysis and machine learning

The scientists are dedicated to studying the detection of Alzheimer’s disease onset to find a cure, or at the very least, medication that can slow the progression of the disease. This article explores the effectiveness of longitudinal data analysis, artificial intelligence, and machine learning approaches based on magnetic resonance imaging and positron emission tomography neuroimaging modalities for progression estimation and the detection of Alzheimer’s disease onset. The significance of feature extraction in highly complex neuroimaging data, identification of vulnerable brain regions, and the determination of the threshold values for plaques, tangles, and neurodegeneration of these regions will extensively be evaluated. Developing automated methods to improve the aforementioned research areas would enable specialists to determine the progression of the disease and find the link between the biomarkers and more accurate detection of Alzheimer’s disease onset.

ABB工业机器人通过S7-1200 PLC与RFID的数据收发程序设计

西门子S7-1200 PLC与ABB工业机器人通过TCP/IP使用Socket通信,可以实现相互之间数据收发。而西门子S7-1200 PLC与RFID则通过Modbus-TCP通信协议来实现数据收发。通过西门子S7-1200 PLC程序设计和ABB工业机器人程序设计,实现西门子S7-1200 PLC、ABB工业机器人和RFID三者之间的数据收发,方便由ABB工业机器人为主体读取或写入RFID信息,实现智能化工艺流程控制。经在机器人系统集成应用技术平台反复的实验表明,此程序设计方法可靠且稳定性好。

Abbe's瓣修复双侧唇裂术后继发畸形患者围手术期护理效果分析

目的 探讨Abbe’s瓣修复双侧唇裂术后继发畸形患者的围手术期护理效果。方法 选择Abbe’s瓣修复双侧唇裂术后(2021年7月~2022年8月)继发鼻、唇畸形12例患者作为研究对象。Ⅱ期手术为鼻唇部继发畸形的整复,给予患者有效的围术期护理干预,观察患者术后效果及并发症情况。结果 患者Ⅱ期手术后鼻翼—唇峰(11.12±1.75mm)、人中长度(10.91±1.96mm)显著大于术前(14.85±2.03mm、14.69±1.38mm)(P<0.05),人中宽度(16.35±1.25mm)显著小于术前(12.76±1.96mm)。患者术前、术后鼻翼—口角、唇峰—口角和全鼻宽比较,差异无统计学意义。见表1。两组术后鼻唇畸形矫正效果良好,局部功能满意。患者未见术后并发症,伤口愈合良好,术后7天拆线,无明显瘢痕形成。结论 围手术期密切观察患者症状,给予患者综合化、科学化、规范化的护理干预,可帮助患者术后康复,术后继发畸形修复效果良好。

Impact of cognition-related single nucleotide polymorphisms on brain imaging phenotype in Parkinson’s disease

Multiple single nucleotide polymorphisms may contribute to cognitive decline in Parkinson’s disease. However, the mechanism by which these single nucleotide polymorphisms modify brain imaging phenotype remains unclear. The aim of this study was to investigate the potential effects of multiple single nucleotide polymorphisms on brain imaging phenotype in Parkinson’s disease. Forty-eight Parkinson’s disease patients and 39 matched healthy controls underwent genotyping and 7 T magnetic resonance imaging. A cognitive-weighted polygenic risk score model was designed, in which the effect sizes were determined individually for 36 single nucleotide polymorphisms. The correlations between polygenic risk score, neuroimaging features, and clinical data were analyzed. Furthermore, individual single nucleotide polymorphism analysis was performed to explore the main effects of genotypes and their interactive effects with Parkinson’s disease diagnosis. We found that, in Parkinson’s disease, the polygenic risk score was correlated with the neural activity of the hippocampus, parahippocampus, and fusiform gyrus, and with hippocampal-prefrontal and fusiform-temporal connectivity, as well as with gray matter alterations in the orbitofrontal cortex. In addition, we found that single nucleotide polymorphisms in α-synuclein(SNCA) were associated with white matter microstructural changes in the superior corona radiata, corpus callosum, and external capsule. A single nucleotide polymorphism in catechol-O-methyltransferase was associated with the neural activities of the lingual, fusiform, and occipital gyri, which are involved in visual cognitive dysfunction. Furthermore, DRD3 was associated with frontal and temporal lobe function and structure. In conclusion, imaging genetics is useful for providing a better understanding of the genetic pathways involved in the pathophysiologic processes underlying Parkinson’s disease. This study provides evidence of an association between genetic factors, cognitive functions, and multi-modality neuroimaging biomarkers in Parkinson’s disease.

应用Abbe's瓣联合上唇人中皮瓣修复双侧唇裂术后继发畸形

目的:探讨应用Abbe's瓣联合上唇人中皮瓣修复双侧唇裂继发畸形的方法和优缺点。方法:手术分两期进行,第一期将上唇人中皮瓣向上转移以延长鼻小柱,上唇部的缺损用以一侧下唇动脉为蒂的Abbe's瓣来修复,术后10～14天行二期断蒂手术。结果:临床应用12例,其中男5例,女7例,唇瓣完全成活,上唇形态接近正常,鼻畸形矫正满意。结论:应用Abbe's瓣联合上唇人中皮瓣修复双侧唇裂继发畸形可获得满意的上唇形态,但其缺点是需要两次手术及下唇供瓣区遗留切口瘢痕,并且此皮瓣易出现静脉回流问题。因此在上唇组织量足够时,叉状皮瓣法仍是双侧唇裂继发畸形较好的治疗方法。

Abbe's瓣治疗双侧唇裂术后继发唇畸形患者麻醉恢复期的护理

总结Abbe's瓣治疗双侧唇裂术后继发唇畸形患者麻醉恢复期的护理体会,回顾性分析2012~2016年本科治疗的12例临床资料,12例患者未出现术后并发症、唇瓣成活,伤口愈合良好,唇畸形得到矫正,患者对治疗效果满意,认为安全有效的围手术期护理是Abbe's瓣手术成功的重要保证。

ABB机器人与西门子S7-1200 PLC的TCP/IP通信应用研究

在工业生产中ABB机器人IRB 120需要与西门子S7-1200 PLC(可编程逻辑控制器)进行大量的数据传输,根据现场情况可以采用TCP/IP以太网通信方法实现。为了实现ABB机器人与西门子S7-1200PLC两者之间的TCP/IP通信,从通信的硬件配置、机器人与PLC的参数设置、机器人与PLC的通信程序编写等方面进行了详细介绍。从实践效果看,该通信方法能很好地达到项目功能要求。

基于轻量化YOLOX-S与多阈值分割的矿山遥感图像去噪算法

矿山遥感图像普遍存在大量的噪点,给后续图像分析和处理带来了很大困难。提出了一种基于轻量化目标检测模型YOLOX-S和多阈值分割的矿山遥感图像去噪算法。首先使用YOLOX-S模型对矿山遥感图像进行目标检测,得到矿山目标的位置信息。然后针对矿山目标的特点,设计了一种多阈值分割方法消除图像中的噪声点。通过将图像分为若干个子区域,并对每个子区域采用不同的阈值进行二值化处理,最终将各子区域的二值化结果合并得到去噪后的图像。试验结果表明:该算法能够有效地去除矿山遥感图像中的噪声点,并且在保留目标特征的同时,大幅提升了图像质量。此外,由于采用了轻量化模型和多阈值分割算法,使得该算法具有较快的处理速度和较低的计算成本,适用于大规模图像数据的处理任务。

基于MRI影像组学构建PD-1/PD-L1抑制剂治疗dMMR/MSI-H直肠癌疗效的预测模型

目的:探讨MRI影像组学模型在程序性细胞死亡蛋白-1(PD-1)/程序性细胞死亡-配体1(PD-L1)抑制剂联合全程新辅助治疗(TNT)局部进展期直肠癌(LARC)的疗效预测价值。方法:收集河南中医药大学第一附属医院PD-1/PD-L1抑制剂联合TNT治疗的80例错配修复基因缺陷(dMMR)/微卫星高度不稳定(MSI-H)基因型中低位LARC患者的临床和影像资料。将入组患者按7∶3比例分为训练集和测试集,提取影像组学特征,从中筛选并构建影像组学模型。描绘影像组学模型的Rad-score与病理金标准之间的受试者工作特征(ROC)曲线,计算曲线下面积(AUC),并评价模型的诊断效能。采用决策曲线分析(DCA)计算风险阈值的范围,并评估临床获益情况。收集湖南省人民医院25例dMMR/MSI-H基因型LARC患者的影像资料作为外部验证集。结果:训练集、测试集及外部验证集三者之间的临床特征无统计学差异(P>0.05)。经过降维处理、t检验及一致性检验以及LASSO交叉验证后,筛选出一阶偏度特征和体积2个特征构建影像组学模型。训练集、测试集和外部验证集的影像组学预测模型ROC曲线的AUC、灵敏度、特异度、阳性预测值和阴性预测值分别为0.920、97.1%、85.7%、91.9%、94.7%;0.885、80.0%、88.9%、92.3%、72.7%;0.875、87.5%、88.9%、93.3%、80.0%。DCA曲线显示,当风险阈值范围为0%~82%时,采用影像组学模型预测LARC患者为病理完全缓解(pCR)的获益大于将所有患者都视为pCR或者无病理完全缓解(npCR)。结论:基于MRI影像组学构建的dMMR/MSI-H型局部进展期直肠癌PD-1/PD-L1抑制剂联合全程新辅助放化疗疗效预测模型,有较大潜力为不同基因分型的直肠癌患者制定个体化治疗策略提供量化依据。

Dynamic contrast-enhanced magnetic resonance imaging and diffusion-weighted imaging in the activity staging of terminal ileum Crohn's disease

BACKGROUND The activity staging of Crohn’s disease(CD)in the terminal ileum is critical in developing an accurate clinical treatment plan.The activity of terminal ileum CD is associated with the microcirculation of involved bowel walls.Dynamic contrast-enhanced magnetic resonance imaging(DCE-MRI)and diffusionweighted imaging(DWI)can reflect perfusion and permeability of bowel walls by providing microcirculation information.As such,we hypothesize that DCE-MRI and DWI parameters can assess terminal ileum CD,thereby providing an opportunity to stage CD activity.AIM To evaluate the value of DCE-MRI and DWI in assessing activity of terminal ileum CD.METHODS Forty-eight patients with CD who underwent DCE-MRI and DWI were enrolled.The patients’activity was graded as remission,mild and moderate-severe.The transfer constant(Ktrans),wash-out constant(Kep),and extravascular extracellular volume fraction(Ve)were calculated from DCE-MRI and the apparent diffusion coefficient(ADC)was obtained from DWI.Magnetic Resonance Index of Activity(MaRIA)was calculated from magnetic resonance enterography.Differences in these quantitative parameters were compared between normal ileal loop(NIL)and inflamed terminal ileum(ITI)and among different activity grades.The correlations between these parameters,MaRIA,the Crohn’s Disease Activity Index(CDAI),and Crohn’s Disease Endoscopic Index of Severity(CDEIS)were examined.Receiver operating characteristic curve analyses were used to determine the diagnostic performance of these parameters in differentiating between CD activity levels.RESULTS Higher Ktrans(0.07±0.04 vs 0.01±0.01),Kep(0.24±0.11 vs 0.15±0.05)and Ve(0.27±0.07 vs 0.08±0.03),but lower ADC(1.41±0.26 vs 2.41±0.30)values were found in ITI than in NIL(all P<0.001).The Ktrans,Kep,Ve and MaRIA increased with disease activity,whereas the ADC decreased(all P<0.001).The Ktrans,Kep,Ve and MaRIA showed positive correlations with the CDAI(r=0.866 for Ktrans,0.870 for Kep,0.858 for Ve,0.890 for MaRIA,all P<0.001)and CDEIS(r=0.563 for Ktrans,0.567 for Kep,0.571 for Ve,0.842 for MaRIA,all P<0.001),while the ADC showed negative correlations with the CDAI(r=-0.857,P<0.001)and CDEIS(r=-0.536,P<0.001).The areas under the curve(AUC)for the Ktrans,Kep,Ve,ADC and MaRIA values ranged from 0.68 to 0.91 for differentiating inactive CD(CD remission)from active CD(mild to severe CD).The AUC when combining the Ktrans,Kep and Ve was 0.80,while combining DCE-MRI parameters and ADC values yielded the highest AUC of 0.95.CONCLUSION DCE-MRI and DWI parameters all serve as measures to stage CD activity.When they are combined,the assessment performance is improved and better than MaRIA.

Narrow-band imaging endoscopy to assess mucosal angiogenesis in inflammatory bowel disease: A pilot study

AIM: To investigate whether narrow band imaging (NBI) is a useful tool for the in vivo detection of angiogenesis in inflammatory bowel disease (IBD) patients. METHODS: Conventional and NBI colonoscopy was performed in 14 patients with colonic inflammation (8 ulcerative colitis and 6 Crohn’s disease). Biopsy samples were taken and CD31 expression was assayed immuno- histochemically; microvascular density was assessed by vessel count. RESULTS: In areas that were endoscopically normal but positive on NBI, there was a significant (P < 0.05) increase in angiogenesis (12 ± 1 vessels/field vs 18 ± 2 vessels/field) compared with areas negative on NBI. In addition, in areas that were inflamed on white light endoscopy and positive on NBI, there was a significant (P < 0.01) increase in vessel density (24 ± 7 vessels/f ield) compared with NBI-negative areas.CONCLUSION: NBI may allow in vivo imaging of intestinal angiogenesis in IBD patients.

Optimal b value of diffusion-weighted imaging on a 3.0T magnetic resonance scanner in Crohn's disease

AIM: To determine the optimal b value of diffusion-weighted imaging for detecting active inflammation in Crohn&#x02019;s disease.

Cerebellum and neurodegenerative diseases:Beyond conventional magnetic resonance imaging

The cerebellum plays a key role in movement control and in cognition and cerebellar involvement is described in several neurodegenerative diseases.While conventional magnetic resonance imaging(MRI) is widely used for brain and cerebellar morphologic evaluation,advanced MRI techniques allow the investigation of cerebellar microstructural and functional characteristics.Volumetry,voxel-based morphometry,diffusion MRI based fiber tractography,resting state and task related functional MRI,perfusion,and proton MR spectroscopy are among the most common techniques applied to the study of cerebellum.In the present review,after providing a brief description of each technique's advantages and limitations,we focus on their application to the study of cerebellar injury in major neurodegenerative diseases,such as multiple sclerosis,Parkinson's and Alzheimer's disease and hereditary ataxia.A brief introduction to the pathological substrate of cerebellar involvement is provided for each disease,followed by the review of MRI studies exploring structural and functional cerebellar abnormalities and by a discussion of the clinical relevance of MRI measures of cerebellar damage in terms of both clinical status and cognitive performance.

Advanced endoscopic imaging in Barrett's oesophagus:A review on current practice

Over the last few years,improvements in endoscopic imaging technology have enabled identification of dysplasia and early cancer in Barrett's oesophagus.New techniques should exhibit high sensitivities and specificities and have good interobserver agreement.They should also be affordable and easily applicable to the community gastroenterologist.Ideally,these modalities must exhibit the capability of imaging wide areas in real time whilst enabling the endoscopist to specifically target abnormal areas.This review will specifically focus on some of the novel endoscopic imaging modalities currently available in routine practice which includes chromoendoscopy,autofluorescence imaging and narrow band imaging.

What can imaging tell us about cognitive impairment and dementia?

Dementia is a contemporary global health issue with far reaching consequences, not only for affected individuals and their families, but for national and global socio-economic conditions. The hallmark feature of dementia is that of irreversible cognitive decline, usually affecting memory, and impaired activities of daily living. Advances in healthcare worldwide have facilitated longer life spans, increasing the risks of developing cognitive decline and dementia in late life. Dementia remains a clinical diagnosis. The role of structural and molecular neuroimaging in patients with dementia is primarily supportive role rather than diagnostic, American and European guidelines recommending imaging to exclude treatable causes of dementia, such as tumor, hydrocephalus or intracranial haemorrhage, but also to distinguish between different dementia subtypes, the commonest of which is Alzheimer&#x02019;s disease. However, this depends on the availability of these imaging techniques at individual centres. Advanced magnetic resonance imaging (MRI) techniques, such as functional connectivity MRI, diffusion tensor imaging and magnetic resonance spectroscopy, and molecular imaging techniques, such as 18F fluoro-deoxy glucose positron emission tomography (PET), amyloid PET, tau PET, are currently within the realm of dementia research but are available for clinical use. Increasingly the research focus is on earlier identification of at risk preclinical individuals, for example due to family history. Intervention at the preclinical stages before irreversible brain damage occurs is currently the best hope of reducing the impact of dementia.

Diffusion tensor imaging of the brain in patients with Alzheimer's disease and cerebrovascular lesions

Background:Recent autopsy study showed a high incidence of cerebrovascular lesions in Alzheimer's disease(AD).To assess the impact of cerebrovascular pathology in AD,we used diffusion tensor imaging(DTI) to study AD patients with and without cerebrovascular lesions.Materials and Methods:Conventional and DTI scans were obtained from 10 patients with probable AD,10 AD/V patients(probable AD with cerebrovascular lesions) and ten normal controls.Mean diffusivity(D) and fractional anisotropy(FA) values of some structures involved with AD pathology were measured.Results:D value was higher in AD patients than in controls in hippocampus and the cingulate gyrus.In AD/V patients,increased D value was found in the same structures and also in the thalamus and basal ganglia compared to controls.There was a significant difference of D value between AD and AD/V patients.FA value reduced in the white matter of left inferior temporal gyrus and in the bilateral middle cingulate gyrus in patients with AD/V compared with controls.The MMSE(mini-mental state examination) score significantly correlated with FA value in the right hippocampus(r=0.639,P<0.019),in the right anterior cingulate gyrus(r=0.587,P<0.035) and in left parahippocampal gyrus(r=0.559,P<0.047).Conclusion:Cerebrovascular pathology had stronger impact on the D value than the AD pathology alone did.Elevated D value in thalamic and basal ganglia may contribute to cognitive decline in AD/V patients.Reduced FA values in AD/V patients may indicate that cerebrovascular pathology induced more severe white matter damage than the AD pathology alone did.