- 标题
- 摘要
- 关键词
- 实验方案
- 产品
-
Intelligent Quantification of Picomolar Protein Concentration in Serum by Functionalized Nanopores
摘要： Nanopores have been well established as a promising platform for real time stochastic detection of single biomolecules and have made sufficient commercial progress in terms of DNA sequencing. Amongst the various strategies for specific protein estimation in physiological analyte, aptamer functionalized nanopores have been reported to quantify proteins down to few picomolars in control solution. In this paper, we explore the quantification of target protein in serum down to picomolar concentration using aptamer functionalized nanopores. For such cases, the current settles to a new value in multiple steps due to the low dissociation constants of the receptors and the final current blockade sensitivity is the primary indicator of target protein concentration. It has been observed that the current sensitivity histograms not only have a statistical variation (due to the fluctuations in the device fabrication) but also overlap significantly between the different concentration ranges in the picomolar regime, which makes quantification challenging. Here, we introduce probabilistic fuzzy model based on Monte Carlo simulation and demonstrate its ability by quantifying thrombin down to 50-pM concentration in undiluted serum. The method has been verified with 25 test solutions and the results reveal the potential of this computational approach toward lowering the detection limit by three orders of magnitude compared with the existing status, thus enabling the functionalized glass nanopore platform makes great progress toward clinical testing.
关键词： Real time,picomolar quantification,functionalized nanopores,probabilistic fuzzy model
更新于2025-09-09 09:28:46
-
Novel Four Layer Metal Sensing in Portable SPR Sensor Platform for Viral Particles Quantification
摘要： Label-free, direct, rapid and real-time quantification method for human enterovirus 71 (EV71) using surface plasmon resonance (SPR) sensor is presented in this study. The performance of the four layer sensor chip was compared with the conventional Au monolayer sensor chip. Four layer sensor chip gave optimum resonance wavelength around 610 nm, which was same with the OLED light source peak wavelength and gave better quality factor (Q = DIP/FWHM). The result showed that the structure with 3 nm Al, 10 nm Au, 20 nm Ag, 10 nm Au layer sensor chip gave better limit of detection than the 3 nm Cr, and 47 nm Au layer sensor chip. The detection limit of direct quantification of EV71 particles is improved to 43 vp/mL of EV71 in DMEM medium. The results proved that the SPR biosensor with four layer sensor chip structure demonstrated great performance in the quantification of EV71 virus species.
关键词： OLED,quantification,SPR sensor,EV71 virus,four layer sensor chip
更新于2025-09-09 09:28:46
-
Visible-light optical coherence tomography-based multimodal system for quantitative fundus autofluorescence imaging
摘要： Fundus autofluorescence (FAF) imaging is commonly used in ophthalmic clinics for diagnosis and monitoring of retinal diseases. Lipofuscin in the retinal pigment epithelium (RPE), with A2E as its most abundant component and a visual cycle by-product, is the major fluorophore of FAF. Lipofuscin accumulates with age and is implicated in degenerative retinal diseases. The amount of lipofuscin in RPE can be assessed by quantitative measurement of FAF. However, the currently available FAF imaging technologies are not capable of quantifying the absolute intensity of FAF, which is essential for comparing images from different individuals, and from the same individual over time. One major technical difficulty is to compensate the signal attenuation by ocular media anterior to the RPE (pre-RPE media). FAF intensity is also influenced by fluctuations in imaging conditions such as illumination power and detector sensitivity, all of which need to be compensated. In this review, we present the concept and research progress of using visible-light optical coherence tomography-based simultaneous multimodal retinal imaging to compensate signal attenuation by pre-RPE media and the influence of parameters of the acquisition system for accurate measurement of FAF intensities.
关键词： fundus autofluorescence imaging,multimodal imaging,retinal pigment epithelium lipofuscin,Visible-light optical coherence tomography,retinal imaging,fluorescence quantification
更新于2025-09-09 09:28:46
-
[IEEE 2018 25th IEEE International Conference on Image Processing (ICIP) - Athens, Greece (2018.10.7-2018.10.10)] 2018 25th IEEE International Conference on Image Processing (ICIP) - Robust Blood Flow Velocity Estimation from 3D Rotational Angiography
摘要： Blood flow velocity estimation techniques from 2D fluoroscopy and more recently rotational angiography images represent a topic of wide interest in various clinical research areas. In particular, they can be an important step towards patient-specific flow simulations. Additionally, it can be of diagnostic interest to evaluate volumetric blood flow in stenotic vessel segments; e.g. in a patient’s brain. In this work, we present a robust optimization-based approach to estimate mean blood flow velocities from rotational digital subtraction angiography (DSA) images. Our method was extensively evaluated on 70 simulated datasets and 6 clinical datasets with MR phase contrast ground truth data. Our evaluation explores the limitations of image-based velocity estimation; i.e., measurements over short or small vessel segments. Overall, we were able to estimate the mean velocity with average errors as little as 4% for simulation studies, if the vessel segment is sufficiently long, and achieved results within the confines of the MR phase contrast ground truth data for our clinical data, with an average relative error to the centerline measurement of 9.5% ± 10.5%. The achieved accuracy enables patient-specific hemodynamic simulations and may also be of immediate diagnostic interest.
关键词： velocity estimation,flow quantification,contrast medium,angiography
更新于2025-09-09 09:28:46
-
A Novel Method for Quantitative Serial Autofluorescence Analysis in Retinitis Pigmentosa Using Image Characteristics
摘要： Identifying potential biomarkers for disease progression in retinitis pigmentosa (RP) is highly relevant now that gene therapy and other treatments are in clinical trial. Here we report a novel technique for analysis of short-wavelength autofluorescence (AF) imaging to quantify defined regions of AF in RP patients. Methods: Fifty-five–degree AF images were acquired from 12 participants with RP over a 12-month period. Of these, five were identified as having a hyperfluorescent annulus. A standard Cartesian coordinate system was superimposed on images with the fovea as the origin and eight bisecting lines traversing the center at 45 degrees to each other. Spatial extraction software was programmed to highlight pixels corresponding to varying degrees of percentile fluorescence such that the parafoveal AF ring was mapped. Distance between the fovea and midpoint of the AF ring was measured. Percentage of low luminance areas was utilized as a measure of atrophy. Results: The hyperfluorescent ring was most accurately mapped using the 70th percentile of fluorescence. Both the AF ring and peripheral hypofluorescence showed robust repeatability at all time points noted (P ? 0.93). Conclusions: Both a hypofluorescent ring and retinal pigment epithelium atrophy were present on a significant proportion of RP patients and were consistently mapped over a 12-month period. There is potential extrapolation of this methodology to wide-field imaging as well as other retinal dystrophies. This anatomical change may provide a useful anatomical biomarker for assessing treatment end points in RP. Translational Relevance: Spatial extraction software can be a valuable tool in the assessment of ophthalmic imaging data.
关键词： autofluorescence,retinal degeneration,quantification,retinitis pigmentosa
更新于2025-09-04 15:30:14
-
Note: Plasma optical emission spectroscopy for water vapor quantification and detection during vacuum drying process
摘要： A methodology involving plasma optical emission spectroscopy driven by a direct current (dc) plasma source is developed to quantify water vapor concentration in a gaseous stream. The experimental setup consists of a dc driven low-pressure plasma cell in which the emission from the plasma discharge is measured by using an optical emission spectrometer. The emission from Hα at 656.2 nm—the first transition in the Balmer series, was found to be the most sensitive to the water vapor concentration in the gas stream. Consistent linear trends of the emission signals with respect to variation in concentration of water are observed for multiple combinations of operating parameters. This method has been applied to a vacuum drying process of a mock nuclear fuel assembly to quantify the concentration of water vapor during the drying process.
关键词： Hα emission,direct current plasma source,plasma optical emission spectroscopy,vacuum drying process,water vapor quantification
更新于2025-09-04 15:30:14
-
Deep learning-based automatic volumetric damage quantification using depth camera
摘要： A depth camera or 3-dimensional scanner was used as a sensor for traditional methods to quantify the identified concrete spalling damage in terms of volume. However, to quantify the concrete spalling damage automatically, the first step is to detect (i.e., identify) the concrete spalling. The multiple spots of spalling can be possible within a single structural element or in multiple structural elements. However, there is, as of yet, no method to detect concrete spalling automatically using deep learning methods. Therefore, in this paper, a faster region-based convolutional neural network (Faster R-CNN)-based concrete spalling damage detection method is proposed with an inexpensive depth sensor to quantify multiple instances of spalling simultaneously in the same surface separately and consider multiple surfaces in structural elements. A database composed of 1091 images (with 853 × 1440 pixels) labeled for volumetric damage is developed, and the deep learning network is then modified, trained, and validated using the proposed database. The damage quantification is automatically performed by processing the depth data, identifying surfaces, and isolating the damage after merging the output from the Faster R-CNN with the depth stream of the sensor. The trained Faster R-CNN presented an average precision (AP) of 90.79%. Volume quantifications show a mean precision error (MPE) of 9.45% when considering distances from 100 cm to 250 cm between the element and the sensor. Also, an MPE of 3.24% was obtained for maximum damage depth measurements across the same distance range.
关键词： Convolutional neural network,Deep learning,Concrete spalling,Depth sensor,Volume quantification
更新于2025-09-04 15:30:14