流产后宫内胎物组织残留的超声诊断

回顾性分析了４６例临床被证实为宫内胎物组织残留的病例。结果表明，流产后较长时间有不规则阴道流血，而作Ｂ超检查能发现特征性改变。声像图显示，子宫增大，宫内有异常光团和不规则液性暗区或宫腔局部膨隆，周边见很亮光条。认为诊断宫内残留组织，可以结合病只与声像技术较易诊断出来。本组超声诊断符合率达９８％。对减少因宫内残留造成的子宫收缩不良、不规则阴道流血等现象，避免并发感染及导致绒癌发生等不良后果，有重要的     

A Small-Molecule Diketopyrrolopyrrole-Based Dye for in vivo NIR-IIa Fluorescence Bioimaging

Organic small-molecule fluorophores with near-infrared IIa (NIR-IIa) emission have great potential in pre-clinical detection and inoperative imaging due to the high-spatial resolution and deep penetration. However, developments of the NIR-IIa fluorophores are still facing considerable challenges. In this work, a series of diketopyrrolopyrrole (DPP)-based fluorophores were designed and synthesized. Subsequently, nanomaterial T25@F127 with significant NIR-IIa emission properties was rationally prepared by encapsulating DPP-based fluorophore T25 , and was selected for fluorescence angiography and cerebral vascular microscopic imaging with nearly 800 μm penetrating depth and excellent signal-background ratio of 4.07 and 2.26 (at 250 and 400 μm), respectively. Furthermore, the nanomaterial T25@cRGD with tumor targeting ability can image tiny metastatic tumor on intestine with a small size of 0.3 mm×1.0 mm and high-spatial resolution (SBR=3.84). This study demonstrates that the nanomaterials which encapsulated T25 behave as excellent NIR-IIa fluorescence imaging agents and have a great potential for in vivo biological application.     

Mechanism of interaction of pressure waves at a discrete partial blockage

This paper analyses the mechanism of interaction between an incident pressure wave and blockages of different geometrical characteristics (i.e., a butterfly and a ball valves, two short stretches of pipe with a reduced diameter, and a device simulating a longitudinal body blockage) by means of laboratory and numerical tests. Experiments have shown that the mechanism of interaction with pressure waves is influenced by their path through the device: sinuous because of the device body for partially closed in-line valves (type I mechanism), and straight for the small bore pipe devices (type II mechanism). Type I mechanism is characterized by a rise followed by an almost constant value whereas in type II one a drop occurs after the rise. To complete the investigation the effect of the pre-transient condition is discussed.     

Application of nanodiamonds in human body fluid analysis by matrix-assisted laser desorption/ionization mass spectrometry

Direct mass spectrometric analysis of complex biological samples is very important and challenging. In this paper, nanodiamonds have been successfully used in matrix-assisted laser desorption/ionization mass spectrometric analysis of human serum and urine. As a practical tool and platform, it can be widely used in the field of humoral proteomics, and it plays a very promising role in clinical diagnosis, including identification of novel disease-associated biomarkers.     

Laser absorption spectroscopy for combustion diagnosis in reactive flows: A review

Laser absorption spectroscopy (LAS) has been rapidly developed and widely applied to combustion diagnosis in recent decades. As a cost-effective tool for measuring multiple combustion parameters, LAS provides unique properties in terms of accuracy and sensitivity for understanding the reactions and kinetics in reactive flows. Line-of-sight and tomographic LAS techniques have stimulated numerous applications and been proved to be robust for in situ combustion diagnosis in uniform and non-uniform combustion fields, respectively. This review highlights the breakthroughs in the evolution of LAS techniques from the viewpoints of key principles, sensors and instrumentations developed for combustion diagnosis, with particular emphasis on a series of spatially-resolved LAS techniques with their recent applications on obtaining high-fidelity measurement results with minimal intrusion to the practical combustors. Along the way, we note some challenges and requirements for further development of the LAS-based combustion diagnosis.     

An Interpretation Architecture for Deep Learning Models with the Application of COVID-19 Diagnosis

The Coronavirus disease 2019 (COVID-19) has become one of the threats to the world. Computed tomography (CT) is an informative tool for the diagnosis of COVID-19 patients. Many deep learning approaches on CT images have been proposed and brought promising performance. However, due to the high complexity and non-transparency of deep models, the explanation of the diagnosis process is challenging, making it hard to evaluate whether such approaches are reliable. In this paper, we propose a visual interpretation architecture for the explanation of the deep learning models and apply the architecture in COVID-19 diagnosis. Our architecture designs a comprehensive interpretation about the deep model from different perspectives, including the training trends, diagnostic performance, learned features, feature extractors, the hidden layers, the support regions for diagnostic decision, and etc. With the interpretation architecture, researchers can make a comparison and explanation about the classification performance, gain insight into what the deep model learned from images, and obtain the supports for diagnostic decisions. Our deep model achieves the diagnostic result of 94.75%, 93.22%, 96.69%, 97.27%, and 91.88% in the criteria of accuracy, sensitivity, specificity, positive predictive value, and negative predictive value, which are 8.30%, 4.32%, 13.33%, 10.25%, and 6.19% higher than that of the compared traditional methods. The visualized features in 2-D and 3-D spaces provide the reasons for the superiority of our deep model. Our interpretation architecture would allow researchers to understand more about how and why deep models work, and can be used as interpretation solutions for any deep learning models based on convolutional neural network. It can also help deep learning methods to take a step forward in the clinical COVID-19 diagnosis field.     

Automated Spleen Injury Detection Using 3D Active Contours and Machine Learning

The spleen is one of the most frequently injured organs in blunt abdominal trauma. Computed tomography (CT) is the imaging modality of choice to assess patients with blunt spleen trauma, which may include lacerations, subcapsular or parenchymal hematomas, active hemorrhage, and vascular injuries. While computer-assisted diagnosis systems exist for other conditions assessed using CT scans, the current method to detect spleen injuries involves the manual review of scans by radiologists, which is a time-consuming and repetitive process. In this study, we propose an automated spleen injury detection method using machine learning. CT scans from patients experiencing traumatic injuries were collected from Michigan Medicine and the Crash Injury Research Engineering Network (CIREN) dataset. Ninety-nine scans of healthy and lacerated spleens were split into disjoint training and test sets, with random forest (RF), naive Bayes, SVM, k-nearest neighbors (k-NN) ensemble, and subspace discriminant ensemble models trained via 5-fold cross validation. Of these models, random forest performed the best, achieving an Area Under the receiver operating characteristic Curve (AUC) of 0.91 and an F1 score of 0.80 on the test set. These results suggest that an automated, quantitative assessment of traumatic spleen injury has the potential to enable faster triage and improve patient outcomes.     

A novel anthracene-based fluorescent colorimetric sensor for the simple naked-eye diagnosis of methylmalonic aciduria

A novel molecular sensor using anthracene bearing two amidopyridines emits blue fluorescence in the presence of succinic acid and green fluorescence in the presence of malonic acid, and its fluorescence intensity increased upon binding. Using this molecular sensor, we succeeded in detecting the difference of one carbon atom between succinic acid and malonic acid with the naked-eye. Furthermore, when methylmalonic acid was dissolved in urine to provide a model system for methylmalonic aciduria, the fluorescence changed from blue to green, and methylmalonic acid was successfully detected with the naked-eye.     

基于振动监测的静载荷滑动轴承接触摩擦故障诊断实验研究

采用静载荷滑动轴承试验台模拟轴颈-轴承从液体润滑状态逐步向干摩擦状态过渡时的接触摩擦故障的发生和发展过程,进而利用振动信号诊断滑动轴承的接触摩擦故障;通过对特征参数进行归一化处理得到无量纲特征参数,进而通过无量纲特征参数的适当数学组合得到无量纲诊断准则,并探讨了无量纲诊断准则的适用性.结果表明:利用所得到的无量纲诊断准则进行轴颈-轴承接触摩擦故障诊断时无须建立用于模式识别的标准模,可实现快捷方便的故障诊断;无量纲诊断准则对工况不敏感,而对故障更敏感,适用于可变工况下滑动轴承的故障诊断,且诊断成本较低.