Physical basis of tap test as a quantitative imaging tool for composite structures on aircraft

Author(s):  
David K. Hsu
2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Qingqing Yin ◽  
Anni Pan ◽  
Binlong Chen ◽  
Zenghui Wang ◽  
Mingmei Tang ◽  
...  

AbstractNanoparticle internalisation is crucial for the precise delivery of drug/genes to its intracellular targets. Conventional quantification strategies can provide the overall profiling of nanoparticle biodistribution, but fail to unambiguously differentiate the intracellularly bioavailable particles from those in tumour intravascular and extracellular microenvironment. Herein, we develop a binary ratiometric nanoreporter (BiRN) that can specifically convert subtle pH variations involved in the endocytic events into digitised signal output, enabling the accurately quantifying of cellular internalisation without introducing extracellular contributions. Using BiRN technology, we find only 10.7–28.2% of accumulated nanoparticles are internalised into intracellular compartments with high heterogeneity within and between different tumour types. We demonstrate the therapeutic responses of nanomedicines are successfully predicted based on intracellular nanoparticle exposure rather than the overall accumulation in tumour mass. This nonlinear optical nanotechnology offers a valuable imaging tool to evaluate the tumour targeting of new nanomedicines and stratify patients for personalised cancer therapy.


1998 ◽  
Vol 4 (S2) ◽  
pp. 1004-1005
Author(s):  
David W. Piston ◽  
George H. Patterson ◽  
Susan M. Knobel

The cloning and expression of GFP in heterologous systems introduced a fantastic tool for studying specific gene expression and protein localization inside living cells. However, one aspect of GFP that has not been exploited to its full potential is its use as a quantitative imaging tool. To determine its quantitative usefulness, we have addressed five points that are important in GFP imaging: detectable signal over background, photostability, pH stability of the molecule, temperature dependence of chromophore formation, and estimation and normalization of GFP levels.To determine the quantitative limits of GFP in cells, several GFP versions (wtGFP, αGFP (F99S/M153T/V163A), S65T, EGFP (F64L/S65T), and a blue-shifted variant, EBFP (F64L/S65T/Y66H/Y145F)) were compared by imaging of GFP expressing cells or by spectroscopic measurements of purified proteins. When imaged, the GFP signals are contaminated by the naturally occurring background autofluorescence, but improved detection can be achieved for each green GFP by combination of confocal microscopy using 488 nm excitation, a rapid cut-on dichroic mirror, and a narrow bandpass emission filter (Figure l).


2021 ◽  
pp. 219256822110574
Author(s):  
Allan R. Martin ◽  
Lindsay Tetreault ◽  
Benjamin M. Davies ◽  
Armin Curt ◽  
Patrick Freund ◽  
...  

Study Design Narrative review. Objective The current review aimed to describe the role of existing techniques and emerging methods of imaging and electrophysiology for the management of degenerative cervical myelopathy (DCM), a common and often progressive condition that causes spinal cord dysfunction and significant morbidity globally. Methods A narrative review was conducted to summarize the existing literature and highlight future directions. Results Anatomical magnetic resonance imaging (MRI) is well established in the literature as the key imaging tool to identify spinal cord compression, disc herniation/bulging, and inbuckling of the ligamentum flavum, thus facilitating surgical planning, while radiographs and computed tomography (CT) provide complimentary information. Electrophysiology techniques are primarily used to rule out competing diagnoses. However, signal change and measures of cord compression on conventional MRI have limited utility to characterize the degree of tissue injury, which may be helpful for diagnosis, prognostication, and repeated assessments to identify deterioration. Early translational studies of quantitative imaging and electrophysiology techniques show potential of these methods to more accurately reflect changes in spinal cord microstructure and function. Conclusion Currently, clinical management of DCM relies heavily on anatomical MRI, with additional contributions from radiographs, CT, and electrophysiology. Novel quantitative assessments of microstructure, perfusion, and function have the potential to transform clinical practice, but require robust validation, automation, and standardization prior to uptake.


2019 ◽  
Vol 12 (7) ◽  
pp. 714-719 ◽  
Author(s):  
Mohammad Mahdi Shiraz Bhurwani ◽  
Muhammad Waqas ◽  
Alexander R Podgorsak ◽  
Kyle A Williams ◽  
Jason M Davies ◽  
...  

BackgroundAngiographic parametric imaging (API), based on digital subtraction angiography (DSA), is a quantitative imaging tool that may be used to extract contrast flow parameters related to hemodynamic conditions in abnormal pathologies such as intracranial aneurysms (IAs).ObjectiveTo investigate the feasibility of using deep neural networks (DNNs) and API to predict IA occlusion using pre- and post-intervention DSAs.MethodsWe analyzed DSA images of IAs pre- and post-treatment to extract API parameters in the IA dome and the corresponding main artery (un-normalized data). We implemented a two-step correction to account for injection variability (normalized data) and projection foreshortening (relative data). A DNN was trained to predict a binary IA occlusion outcome: occluded/unoccluded. Network performance was assessed with area under the receiver operating characteristic curve (AUROC) and classification accuracy. To evaluate the effect of the proposed corrections, prediction accuracy analysis was performed after each normalization step.ResultsThe study included 190 IAs. The mean and median duration between treatment and follow-up was 9.8 and 8.0 months, respectively. For the un-normalized, normalized, and relative subgroups, the DNN average prediction accuracies for IA occlusion were 62.5% (95% CI 60.5% to 64.4%), 70.8% (95% CI 68.2% to 73.4%), and 77.9% (95% CI 76.2% to 79.6%). The average AUROCs for the same subgroups were 0.48 (0.44–0.52), 0.67 (0.61–0.73), and 0.77 (0.74–0.80).ConclusionsThe study demonstrated the feasibility of using API and DNNs to predict IA occlusion using only pre- and post-intervention angiographic information.


2019 ◽  
Author(s):  
Jason Causey ◽  
Jake Qualls ◽  
Jason H. Moore ◽  
Fred Prior ◽  
Xiuzhen Huang

AbstractBackgroundLung CT scans are widely used for lung cancer screening and diagnosis. Current research focuses on quantitative analytics (radiomics) to improve screening and detection accuracy. However there are very limited numbers of portable software tools for automatic lung CT image analysis.ResultsHere we build a Docker container, CNNcon, as a quantitative imaging tool for analyzing lung CT image features. CNNcon is developed from our recently published algorithm for nodule analysis, based on convolutional neural networks (CNN). When provided with a list of the centroid coordinates of regions of interest (ROI) in a volumetric CT study containing potential lung nodules, CNNcon can automatically generate highly accurate malignancy prediction of each ROI. CNNcon can also generate a vector of image features of each ROI, to facilitate further analyses by combining image features and other clinical features. As a Docker container, CNNcon is portable to various computer systems, convenient to install, and easy to use. CNNcon was tested on different computer systems and generated identical results.ConclusionsWe anticipate that CNNcon will be a useful tool and broadly acceptable to the research community interested in quantitative image analysis.AvailabilityCNNcon and document are publicly available and can be downloaded from the website: http://bioinformatics.astate.edu/CNN-Container/


2018 ◽  
Vol 294 ◽  
pp. 122-135 ◽  
Author(s):  
Filipa Bouçanova ◽  
André Filipe Maia ◽  
Andrea Cruz ◽  
Val Millar ◽  
Inês Mendes Pinto ◽  
...  

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