Augmented reality-guided positioning system for radiotherapy patients.

In modern radiotherapy, error reduction in the patients' daily setup error is important for achieving accuracy. In our study, we proposed a new approach for the development of an assist system for the radiotherapy position setup by using augmented reality (AR). We aimed to improve the accuracy of the position setup of patients undergoing radiotherapy and to evaluate the error of the position setup of patients who were diagnosed with head and neck cancer, and that of patients diagnosed with chest and abdomen cancer. We acquired the patient's simulation CT data for the three-dimensional (3D) reconstruction of the external surface and organs. The AR tracking software detected the calibration module and loaded the 3D virtual model. The calibration module was aligned with the Linac isocenter by using room lasers. And then aligned the virtual cube with the calibration module to complete the calibration of the 3D virtual model and Linac isocenter. Then, the patient position setup was carried out, and point cloud registration was performed between the patient and the 3D virtual model, such the patient's posture was consistent with the 3D virtual model. Twenty patients diagnosed with head and neck cancer and 20 patients diagnosed with chest and abdomen cancer in the supine position setup were analyzed for the residual errors of the conventional laser and AR-guided position setup. Results show that for patients diagnosed with head and neck cancer, the difference between the two positioning methods was not statistically significant (P > 0.05). For patients diagnosed with chest and abdomen cancer, the residual errors of the two positioning methods in the superior and inferior direction and anterior and posterior direction were statistically significant (t = -5.80, -4.98, P < 0.05). The residual errors in the three rotation directions were statistically significant (t = -2.29 to -3.22, P < 0.05). The experimental results showed that the AR technology can effectively assist in the position setup of patients undergoing radiotherapy, significantly reduce the position setup errors in patients diagnosed with chest and abdomen cancer, and improve the accuracy of radiotherapy.

Rapid diagnostic testing platform for iron and vitamin A deficiency.

Micronutrient deficiencies such as those of vitamin A and iron affect a third of the world's population with consequences such as night blindness, higher child mortality, anemia, poor pregnancy outcomes, and reduced work capacity. Many efforts to prevent or treat these deficiencies are hampered by the lack of adequate, accessible, and affordable diagnostic methods that can enable better targeting of interventions. In this work, we demonstrate a rapid diagnostic test and mobile enabled platform for simultaneously quantifying iron (ferritin), vitamin A (retinol-binding protein), and inflammation (C-reactive protein) status. Our approach, enabled by combining multiple florescent markers and immunoassay approaches on a single test, allows us to provide accurate quantification in 15 min even though the physiological range of the markers of interest varies over five orders of magnitude. We report sensitivities of 88%, 100%, and 80% and specificities of 97%, 100%, and 97% for iron deficiency (ferritin <15 ng/mL or 32 pmol/L), vitamin A deficiency (retinol-binding protein <14.7 µg/mL or 0.70 µmol/L) and inflammation status (C-reactive protein >3.0 µg/mL or 120 nmol/L), respectively. This technology is suitable for point-of-care use in both resource-rich and resource-limited settings and can be read either by a standard laptop computer or through our previously developed NutriPhone technology. If implemented as either a population-level screening or clinical diagnostic tool, we believe this platform can transform nutritional status assessment and monitoring globally.

Rapid Diagnostic Platform for Colorimetric Differential Detection of Dengue and Chikungunya Viral Infections.

In this work, we demonstrate a rapid diagnostic platform with potential to transform clinical diagnosis of acute febrile illnesses in resource-limited settings. Acute febrile illnesses such as dengue and chikungunya, which pose high burdens of disease in tropical regions, share many nonspecific symptoms and are difficult to diagnose based on clinical history alone in the absence of accessible laboratory diagnostics. Through a unique color-mixing encoding and readout strategy, our platform enabled consistent and accurate multiplexed detection of dengue and chikungunya IgM/IgG antibodies in human clinical samples within 30 min. Our multiplex assay offers several advantages over conventional rapid diagnostic tests deployed in resource-limited settings, including a low sample volume requirement and the ability to concurrently detect four analytes. Our platform is a step toward multiplexed diagnostics that will be transformative for disease management in resource-limited settings by enabling informed treatment decisions through accessible evidence-based diagnosis.

Efficient Single Correspondence Voting for Point Cloud Registration.

3D point cloud registration is a crucial task in a variety of fields, including remote sensing mapping, computer vision, virtual reality, and autonomous driving. However, this task is still challenging due to the challenges of noise, non-uniformity, partial overlap, and repeated local features in large scene point clouds. In this paper, we propose an efficient single correspondence voting method for large scene point cloud registration. Specifically, we first propose an efficient hypothetical transformation prediction method called SCVC, which determines the 5 degrees of freedom of the transformation through one correspondence, and then uses Hough voting to determine the last degree of freedom. This algorithm can significantly improve the accuracy of registration in both indoor and outdoor scenes. On the other hand, we propose a more robust transformation verification function called VDIR, which can obtain the optimal registration result of two raw point clouds. Finally, we conduct a series of experiments that demonstrate that our method achieves state-of-the-art performance on four real-world datasets: 3DMatch, 3DLoMatch, KITTI, and WHU-TLS. Our code is available at https://github.com/xingxuejun1989/SCVC.

SPMLD: A skin pathological image dataset for non-melanoma with detailed lesion area annotation.

Skin tumors are the most common tumors in humans and the clinical characteristics of three common non-melanoma tumors (IDN, SK, BCC) are similar, resulting in a high misdiagnosis rate. The accurate differential diagnosis of these tumors needs to be judged based on pathological images. However, a shortage of experienced dermatological pathologists leads to bias in the diagnostic accuracy of these skin tumors in China. In this paper, we establish a skin pathological image dataset, SPMLD, for three non-melanoma to achieve automatic and accurate intelligent identification for them. Meanwhile, we propose a lesion-area-based enhanced classification network with the KLS module and an attention module. Specifically, we first collect thousands of H&E-stained tissue sections from patients with clinically and pathologically confirmed IDN, SK, and BCC from a single-center hospital. Then, we scan them to construct a pathological image dataset of these three skin tumors. Furthermore, we mark the complete lesion area of the entire pathology image to better learn the pathologist's diagnosis process. In addition, we applied the proposed network for lesion classification prediction on the SPMLD dataset. Finally, we conduct a series of experiments to demonstrate that this annotation and our network can effectively improve the classification results of various networks. The source dataset and code are available at https://github.com/efss24/SPMLD.git.

Pleurotus eryngii polysaccharides alleviate aflatoxin B1-induced liver inflammation in ducks involving in remodeling gut microbiota and regulating SCFAs transport via the gut-liver axis.

Aflatoxin B1 (AFB1) is one of the most widespread contaminants in agricultural commodities. Pleurotus eryngii (PE) is widely used as a feed additive for its anti-inflammatory properties, and its major active substance is believed to be polysaccharides. This study aims to explore the underlying mechanism of dietary PE polysaccharides alleviating AFB1-induced toxicity in ducks. The major monosaccharide components of PE polysaccharides were identified as glucose, mannose, galactose, glucuronic acid, and fucose. The results showed that dietary PE polysaccharides could alleviate liver inflammation, alleviate intestinal barrier dysfunction, and change the imbalanced gut microbiota induced by AFB1 in ducks. However, PE polysaccharides failed to exert protective roles on the liver and intestine injury induced by AFB1 in antibiotic-treated ducks. The PE + AFB1-originated microbiota showed a positive effect on intestinal barrier and inflammation, the SCFAs transport via the gut-liver axis, and liver inflammation compared with the AFB1-originated microbiota in ducks. These findings provided a possible mechanism that PE polysaccharides alleviated AFB1-induced liver inflammation in ducks by remodeling gut microbiota, regulating microbiota-derived SCFAs transport via the gut-liver axis, and inhibiting inflammatory gene expressions in the liver, which may provide new insight for therapeutic methods against AFB1 exposure in animals.

NR-MVSNet: Learning Multi-View Stereo Based on Normal Consistency and Depth Refinement.

Multi-view Stereo (MVS) aims to reconstruct a 3D point cloud model from multiple views. In recent years, learning-based MVS methods have received a lot of attention and achieved excellent performance compared with traditional methods. However, these methods still have apparent shortcomings, such as the accumulative error in the coarse-to-fine strategy and the inaccurate depth hypotheses based on the uniform sampling strategy. In this paper, we propose the NR-MVSNet, a coarse-to-fine structure with the depth hypotheses based on the normal consistency (DHNC) module, and the depth refinement with reliable attention (DRRA) module. Specifically, we design the DHNC module to generate more effective depth hypotheses, which collects the depth hypotheses from neighboring pixels with the same normals. As a result, the predicted depth can be smoother and more accurate, especially in texture-less and repetitive-texture regions. On the other hand, we update the initial depth map in the coarse stage by the DRRA module, which can combine attentional reference features and cost volume features to improve the depth estimation accuracy in the coarse stage and address the accumulative error problem. Finally, we conduct a series of experiments on the DTU, BlendedMVS, Tanks & Temples, and ETH3D datasets. The experimental results demonstrate the efficiency and robustness of our NR-MVSNet compared with the state-of-the-art methods. Our implementation is available at https://github.com/wdkyh/NR-MVSNet.

SPDET: Edge-Aware Self-Supervised Panoramic Depth Estimation Transformer With Spherical Geometry.

Panoramic depth estimation has become a hot topic in 3D reconstruction techniques with its omnidirectional spatial field of view. However, panoramic RGB-D datasets are difficult to obtain due to the lack of panoramic RGB-D cameras, thus limiting the practicality of supervised panoramic depth estimation. Self-supervised learning based on RGB stereo image pairs has the potential to overcome this limitation due to its low dependence on datasets. In this work, we propose the SPDET, an edge-aware self-supervised panoramic depth estimation network that combines the transformer with a spherical geometry feature. Specifically, we first introduce the panoramic geometry feature to construct our panoramic transformer and reconstruct high-quality depth maps. Furthermore, we introduce the pre-filtered depth-image-based rendering method to synthesize the novel view image for self-supervision. Meanwhile, we design an edge-aware loss function to improve the self-supervised depth estimation for panorama images. Finally, we demonstrate the effectiveness of our SPDET with a series of comparison and ablation experiments while achieving the state-of-the-art self-supervised monocular panoramic depth estimation. Our code and models are available at https://github.com/zcq15/SPDET.

FMRNet: A fused network of multiple tumoral regions for breast tumor classification with ultrasound images.

PURPOSE: Recent studies have illustrated that the peritumoral regions of medical images have value for clinical diagnosis. However, the existing approaches using peritumoral regions mainly focus on the diagnostic capability of the single region and ignore the advantages of effectively fusing the intratumoral and peritumoral regions. In addition, these methods need accurate segmentation masks in the testing stage, which are tedious and inconvenient in clinical applications. To address these issues, we construct a deep convolutional neural network that can adaptively fuse the information of multiple tumoral-regions (FMRNet) for breast tumor classification using ultrasound (US) images without segmentation masks in the testing stage. METHODS: To sufficiently excavate the potential relationship, we design a fused network and two independent modules to extract and fuse features of multiple regions simultaneously. First, we introduce two enhanced combined-tumoral (EC) region modules, aiming to enhance the combined-tumoral features gradually. Then, we further design a three-branch module for extracting and fusing the features of intratumoral, peritumoral, and combined-tumoral regions, denoted as the intratumoral, peritumoral, and combined-tumoral module. Especially, we design a novel fusion module by introducing a channel attention module to adaptively fuse the features of three regions. The model is evaluated on two public datasets including UDIAT and BUSI with breast tumor ultrasound images. Two independent groups of experiments are performed on two respective datasets using the fivefold stratified cross-validation strategy. Finally, we conduct ablation experiments on two datasets, in which BUSI is used as the training set and UDIAT is used as the testing set. RESULTS: We conduct detailed ablation experiments about the proposed two modules and comparative experiments with other existing representative methods. The experimental results show that the proposed method yields state-of-the-art performance on both two datasets. Especially, in the UDIAT dataset, the proposed FMRNet achieves a high accuracy of 0.945 and a specificity of 0.945, respectively. Moreover, the precision (PRE = 0.909) even dramatically improves by 21.6% on the BUSI dataset compared with the existing method of the best result. CONCLUSION: The proposed FMRNet shows good performance in breast tumor classification with US images, and proves its capability of exploiting and fusing the information of multiple tumoral-regions. Furthermore, the FMRNet has potential value in classifying other types of cancers using multiple tumoral-regions of other kinds of medical images.

Inpainting the metal artifact region in MRI images by using generative adversarial networks with gated convolution.

PURPOSE: Magnetic resonance imaging (MRI) plays an important role in clinical diagnosis, but it is susceptible to metal artifacts. The generative adversarial network GatedConv with gated convolution (GC) and contextual attention (CA) was used to inpaint the metal artifact region in MRI images. METHODS: MRI images containing or near the teeth of 70 patients were collected, and the scanning sequence was a T1-weighted high-resolution isotropic volume examination sequence. A total of 10 000 slices were obtained after data enhancement, of which 8000 slices were used for training. MRI images were normalized to [-1,1]. Based on the randomly generated mask, U-Net, pix2pix, PConv with partial convolution, and GatedConv were used to inpaint the artifact region of MRI images. The mean absolute error (MAE) and peak signal-to-noise ratio (PSNR) for the mask were used to compare the results of these methods. The inpainting effect on the test dataset using dental masks was also evaluated. Besides, the artifact area of clinical MRI images was inpainted based on the mask sketched by physicians. Finally, the earring artifacts and artifacts caused by abnormal signal foci were inpainted to verify the generalization of the models. RESULTS: GatedConv could directly and effectively inpaint the incomplete MRI images generated by masks in the image domain. For the results of U-Net, pix2pix, PConv, and GatedConv, the masked MAEs were 0.1638, 0.1812, 0.1688, and 0.1596, respectively, and the masked PSNRs were 18.2136, 17.5692, 18.2258, and 18.3035 dB, respectively. Using dental masks, the results of U-Net, pix2pix, and PConv differed more from the real images in terms of alveolar shape and surrounding tissue compared with GatedConv. GatedConv could inpaint the metal artifact region in clinical MRI images more effectively than the other models, but the increase in the mask area could reduce the inpainting effect. Inpainted MRI images by GatedConv and CT images with metal artifact reduction coincided with alveolar and tissue structure, and GatedConv could successfully inpaint artifacts caused by abnormal signal foci, whereas the other models failed. The ablation study demonstrated that GC and CA increased the reliability of the inpainting performance of GatedConv. CONCLUSION: MRI images are affected by metal, and signal void areas appear near metal. GatedConv can inpaint the MRI metal artifact region in the image domain directly and effectively and improve image quality. Medical image inpainting by GatedConv has potential value for tasks, such as positron emission tomography (PET) attenuation correction in PET/MRI and adaptive radiotherapy of synthetic CT based on MRI.

TNSNet: Thyroid nodule segmentation in ultrasound imaging using soft shape supervision.

BACKGROUND AND OBJECTIVES: Thyroid nodules are a common disorder of the endocrine system. Segmentation of thyroid nodules on ultrasound images is an important step in the evaluation and diagnosis of nodules and an initial step in computer-aided diagnostic systems. The accuracy and consistency of segmentation remain a challenge due to the low contrast, speckle noise, and low resolution of ultrasound images. Therefore, the study of deep learning-based algorithms for thyroid nodule segmentation is important. This study utilizes soft shape supervision to improve the performance of detection and segmentation of boundaries of nodules. Soft shape supervision can emphasize the boundary features and assist the network in segmenting nodules accurately. METHODS: We propose a dual-path convolution neural network, including region and shape paths, which use DeepLabV3+ as the backbone. Soft shape supervision blocks are inserted between the two paths to implement cross-path attention mechanisms. The blocks enhance the representation of shape features and add them to the region path as auxiliary information. Thus, the network can accurately detect and segment thyroid nodules. RESULTS: We collect 3786 ultrasound images of thyroid nodules to train and test our network. Compared with the ground truth, the test results achieve an accuracy of 95.81% and a DSC of 85.33. The visualization results also suggest that the network has learned clear and accurate boundaries of the nodules. The evaluation metrics and visualization results demonstrate the superior segmentation performance of the network to other classical deep learning-based networks. CONCLUSIONS: The proposed dual-path network can accurately realize automatic segmentation of thyroid nodules on ultrasound images. It can also be used as an initial step in computer-aided diagnosis. It shows superior performance to other classical methods and demonstrates the potential for accurate segmentation of nodules in clinical applications.

Imaging Study of Pseudo-CT Synthesized From Cone-Beam CT Based on 3D CycleGAN in Radiotherapy.

PURPOSE: To propose a synthesis method of pseudo-CT (CTCycleGAN) images based on an improved 3D cycle generative adversarial network (CycleGAN) to solve the limitations of cone-beam CT (CBCT), which cannot be directly applied to the correction of radiotherapy plans. METHODS: The improved U-Net with residual connection and attention gates was used as the generator, and the discriminator was a full convolutional neural network (FCN). The imaging quality of pseudo-CT images is improved by adding a 3D gradient loss function. Fivefold cross-validation was performed to validate our model. Each pseudo CT generated is compared against the real CT image (ground truth CT, CTgt) of the same patient based on mean absolute error (MAE) and structural similarity index (SSIM). The dice similarity coefficient (DSC) coefficient was used to evaluate the segmentation results of pseudo CT and real CT. 3D CycleGAN performance was compared to 2D CycleGAN based on normalized mutual information (NMI) and peak signal-to-noise ratio (PSNR) metrics between the pseudo-CT and CTgt images. The dosimetric accuracy of pseudo-CT images was evaluated by gamma analysis. RESULTS: The MAE metric values between the CTCycleGAN and the real CT in fivefold cross-validation are 52.03 ± 4.26HU, 50.69 ± 5.25HU, 52.48 ± 4.42HU, 51.27 ± 4.56HU, and 51.65 ± 3.97HU, respectively, and the SSIM values are 0.87 ± 0.02, 0.86 ± 0.03, 0.85 ± 0.02, 0.85 ± 0.03, and 0.87 ± 0.03 respectively. The DSC values of the segmentation of bladder, cervix, rectum, and bone between CTCycleGAN and real CT images are 91.58 ± 0.45, 88.14 ± 1.26, 87.23 ± 2.01, and 92.59 ± 0.33, respectively. Compared with 2D CycleGAN, the 3D CycleGAN based pseudo-CT image is closer to the real image, with NMI values of 0.90 ± 0.01 and PSNR values of 30.70 ± 0.78. The gamma pass rate of the dose distribution between CTCycleGAN and CTgt is 97.0% (2%/2 mm). CONCLUSION: The pseudo-CT images obtained based on the improved 3D CycleGAN have more accurate electronic density and anatomical structure.

Research on obtaining pseudo CT images based on stacked generative adversarial network.

BACKGROUND: To investigate the feasibility of using a stacked generative adversarial network (sGAN) to synthesize pseudo computed tomography (CT) images based on ultrasound (US) images. METHODS: The pre-radiotherapy US and CT images of 75 patients with cervical cancer were selected for the training set of pseudo-image synthesis. In the first stage, labeled US images were used as the first conditional GAN input to obtain low-resolution pseudo CT images, and in the second stage, a super-resolution reconstruction GAN was used. The pseudo CT image obtained in the first stage was used as an input, following which a high-resolution pseudo CT image with clear texture and accurate grayscale information was obtained. Five cross validation tests were performed to verify our model. The mean absolute error (MAE) was used to compare each pseudo CT with the same patient's real CT image. Also, another 10 cases of patients with cervical cancer, before radiotherapy, were selected for testing, and the pseudo CT image obtained using the neural style transfer (NSF) and CycleGAN methods were compared with that obtained using the sGAN method proposed in this study. Finally, the dosimetric accuracy of pseudo CT images was verified by phantom experiments. RESULTS: The MAE metric values between the pseudo CT obtained based on sGAN, and the real CT in five-fold cross validation are 66.82±1.59 HU, 66.36±1.85 HU, 67.26±2.37 HU, 66.34±1.75 HU, and 67.22±1.30 HU, respectively. The results of the metrics, namely, normalized mutual information (NMI), structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR), between the pseudo CT images obtained using the sGAN method and the ground truth CT (CTgt) images were compared with those of the other two methods via the paired t-test, and the differences were statistically significant. The dice similarity coefficient (DSC) measurement results showed that the pseudo CT images obtained using the sGAN method were more similar to the CTgt images of organs at risk. The dosimetric phantom experiments also showed that the dose distribution between the pseudo CT images synthesized by the new method was similar to that of the CTgt images. CONCLUSIONS: Compared with NSF and CycleGAN methods, the sGAN method can obtain more accurate pseudo CT images, thereby providing a new method for image guidance in radiotherapy for cervical cancer.

Generating synthetic CT from low-dose cone-beam CT by using generative adversarial networks for adaptive radiotherapy.

OBJECTIVE: To develop high-quality synthetic CT (sCT) generation method from low-dose cone-beam CT (CBCT) images by using attention-guided generative adversarial networks (AGGAN) and apply these images to dose calculations in radiotherapy. METHODS: The CBCT/planning CT images of 170 patients undergoing thoracic radiotherapy were used for training and testing. The CBCT images were scanned under a fast protocol with 50% less clinical projection frames compared with standard chest M20 protocol. Training with aligned paired images was performed using conditional adversarial networks (so-called pix2pix), and training with unpaired images was carried out with cycle-consistent adversarial networks (cycleGAN) and AGGAN, through which sCT images were generated. The image quality and Hounsfield unit (HU) value of the sCT images generated by the three neural networks were compared. The treatment plan was designed on CT and copied to sCT images to calculated dose distribution. RESULTS: The image quality of sCT images by all the three methods are significantly improved compared with original CBCT images. The AGGAN achieves the best image quality in the testing patients with the smallest mean absolute error (MAE, 43.5 ± 6.69), largest structural similarity (SSIM, 93.7 ± 3.88) and peak signal-to-noise ratio (PSNR, 29.5 ± 2.36). The sCT images generated by all the three methods showed superior dose calculation accuracy with higher gamma passing rates compared with original CBCT image. The AGGAN offered the highest gamma passing rates (91.4 ± 3.26) under the strictest criteria of 1 mm/1% compared with other methods. In the phantom study, the sCT images generated by AGGAN demonstrated the best image quality and the highest dose calculation accuracy. CONCLUSIONS: High-quality sCT images were generated from low-dose thoracic CBCT images by using the proposed AGGAN through unpaired CBCT and CT images. The dose distribution could be calculated accurately based on sCT images in radiotherapy.

CrossModalNet: exploiting quality preoperative images for multimodal image registration.

A long-standing problem in image-guided radiotherapy is that inferior intraoperative images present a difficult problem for automatic registration algorithms. Particularly for digital radiography (DR) and digitally reconstructed radiograph (DRR), the blurred, low-contrast, and noisy DR makes the multimodal registration of DR-DRR challenging. Therefore, we propose a novel CNN-based method called CrossModalNet to exploit the quality preoperative modality (DRR) for handling the limitations of intraoperative images (DR), thereby improving the registration accuracy. The method consists of two parts: DR-DRR contour predictions and contour-based rigid registration. We have designed the CrossModal Attention Module and CrossModal Refine Module to fully exploit the multiscale crossmodal features and implement the crossmodal interactions during the feature encoding and decoding stages. Then, the predicted anatomical contours of DR-DRR are registered by the classic mutual information method. We collected 2486 patient scans to train CrossModalNet and 170 scans to test its performance. The results show that it outperforms the classic and state-of-the-art methods with 95th percentile Hausdorff distance of 5.82 pixels and registration accuracy of 81.2%. The code is available at https://github.com/lc82111/crossModalNet.

Quasi Fourier-Mellin Transform for Affine Invariant Features.

Fourier-Mellin transform (FMT) has been widely used for the extraction of rotation- and scale-invariant features. However, affine transform is a more reasonable approximation model for real viewpoint change. Due to shearing, the integral along the angular direction in the calculation of FMT cannot be used to extract the inherent features of an image undergoing affine transform. To eliminate the effect of shearing, whitening transform should be conducted on the integral along the radial direction. FMT can hardly be modified by conventional whitening-based methods with low computational cost due to additional processes. In this paper, two factors are constructed and embedded into FMT. Quasi Fourier-Mellin transform (QFMT) is proposed. The embedding of these factors is equivalent to whitening transform and can eliminate the effect of shearing in the affine transform. In particular, QFMT can also be calculated by integrating along the radial direction followed by integrating along the angular direction, as in FMT. Based on QFMT, the quasi Fourier-Mellin descriptor (QFMD) is constructed for the extraction of affine invariant features. Some experiments have also been conducted to test the performance of the proposed method.

High-yield paper-based quantitative blood separation system.

Interest in developing paper-based devices for point-of-care diagnostics in resource-limited settings has risen remarkably in recent decades. In this paper, we demonstrate what we refer to as "high yield passive rrythrocyte removal" (HYPER) technology, which utilizes capillary forces in a unique cross-flow filtration for the separation of whole blood with performance comparable to centrifuges. As we will demonstrate, state-of-the-art passive blood separation methods implemented in paper-based systems exhibit rapid blood cell clogging on the filtration media or serum outlet and yield only about 10-30% of the total serum present in the sample. Our innovation results from the inclusion of a differentiation pad, which exploits hydrodynamic effects to reduce the formation of a fouling layer on the blood filtration membrane resulting in more than 60% serum yield with undiluted whole blood as direct input. To demonstrate the effectiveness of the HYPER technology we implement it in a lateral flow system and demonstrate the accurate quantification of vitamin A and iron levels in whole blood samples in 15 minutes.

Enhancing the Usability of an Optical Reader System to Support Point-of-Care Rapid Diagnostic Testing: An Iterative Design Approach.

BACKGROUND: In today's health care environment, increasing costs and inadequate medical resources have created a worldwide need for more affordable diagnostic tools that are also portable, fast, and easy to use. To address this issue, numerous research and commercial efforts have focused on developing rapid diagnostic technologies; however, the efficacy of existing systems has been hindered by usability problems or high production costs, making them infeasible for deployment in at-home, point-of-care (POC), or resource-limited settings. OBJECTIVE: The aim of this study was to create a low-cost optical reader system that integrates with any smart device and accepts any type of rapid diagnostic test strip to provide fast and accurate data collection, sample analysis, and diagnostic result reporting. METHODS: An iterative design methodology was employed by a multidisciplinary research team to engineer three versions of a portable diagnostic testing device that were evaluated for usability and overall user receptivity. RESULTS: Repeated design critiques and usability studies identified a number of system requirements and considerations (eg, software compatibility, biomatter contamination, and physical footprint) that we worked to incrementally incorporate into successive system variants. Our final design phase culminated in the development of Tidbit, a reader that is compatible with any Wi-Fi-enabled device and test strip format. The Tidbit includes various features that support intuitive operation, including a straightforward test strip insertion point, external indicator lights, concealed electronic components, and an asymmetric shape, which inherently signals correct device orientation. Usability testing of the Tidbit indicates high usability for potential user communities. CONCLUSIONS: This study presents the design process, specification, and user reception of the Tidbit, an inexpensive, easy-to-use, portable optical reader for fast, accurate quantification of rapid diagnostic test results. Usability testing suggests that the reader is usable among and can benefit a wide group of potential users, including in POC contexts. Generally, the methodology of this study demonstrates the importance of testing these types of systems with potential users and exemplifies how iterative design processes can be employed by multidisciplinary research teams to produce compelling technological solutions.

Solar-thermal complex sample processing for nucleic acid based diagnostics in limited resource settings.

The use of point-of-care (POC) devices in limited resource settings where access to commonly used infrastructure, such as water and electricity, can be restricted represents simultaneously one of the best application fits for POC systems as well as one of the most challenging places to deploy them. Of the many challenges involved in these systems, the preparation and processing of complex samples like stool, vomit, and biopsies are particularly difficult due to the high number and varied nature of mechanical and chemical interferents present in the sample. Previously we have demonstrated the ability to use solar-thermal energy to perform PCR based nucleic acid amplifications. In this work demonstrate how the technique, using similar infrastructure, can also be used to perform solar-thermal based sample processing system for extracting and isolating Vibrio Cholerae nucleic acids from fecal samples. The use of opto-thermal energy enables the use of sunlight to drive thermal lysing reactions in large volumes without the need for external electrical power. Using the system demonstrate the ability to reach a 95°C threshold in less than 5 minutes and maintain a stable sample temperature of +/- 2°C following the ramp up. The system is demonstrated to provide linear results between 10(4) and 10(8) CFU/mL when the released nucleic acids were quantified via traditional means. Additionally, we couple the sample processing unit with our previously demonstrated solar-thermal PCR and tablet based detection system to demonstrate very low power sample-in-answer-out detection.

Enhanced performance of nano-Bi2WO6-graphene as pseudocapacitor electrodes by charge transfer channel.

The nano-Bi2WO6/reduced graphene oxide composite obtained by a simple hydrothermal reaction demonstrates a larger specific capacitance of 922 F/g at a charge and discharge currents of 3 A/g with longer cycle life. The As comparison, pristine Bi2WO6 nanoparticles have poor specific capacitance of 574 F/g at a charge and discharge currents of 2 A/g with weak cycle life. Though analyzing the Cyclic voltammetry curves, it is found that there are two oxidation reaction occurring in the materials: oxidation of Bi (III) to Bi (IV) and Bi (III) to Bi (V). The oxidation of Bi (III) to Bi (IV) is reversible while Bi (III) to Bi (V) will cause nonreversible destroy on structure. In this nano-Bi2WO6/reduced graphene oxide composite, graphene with well conductivity will enhance the electrically conducting as charge transfer channel, so that electrons will be transfer much faster in oxidation and most Bi (III) is oxidized to be Bi (IV) which ensure larger specific capacitance and long cycle life. This nano-Bi2WO6/reduced graphene oxide composite has application prospect in high-performance pseudo-capacitors.