As one of the most ubiquitous diagnostic imaging tests in medical practice, chest radiography requires timely reporting of potential findings and diagnosis of diseases in the images. Automated, fast, ...and reliable detection of diseases based on chest radiography is a critical step in radiology workflow. In this work, we developed and evaluated various deep convolutional neural networks (CNN) for differentiating between normal and abnormal frontal chest radiographs, in order to help alert radiologists and clinicians of potential abnormal findings as a means of work list triaging and reporting prioritization. A CNN-based model achieved an AUC of 0.9824 ± 0.0043 (with an accuracy of 94.64 ± 0.45%, a sensitivity of 96.50 ± 0.36% and a specificity of 92.86 ± 0.48%) for normal versus abnormal chest radiograph classification. The CNN model obtained an AUC of 0.9804 ± 0.0032 (with an accuracy of 94.71 ± 0.32%, a sensitivity of 92.20 ± 0.34% and a specificity of 96.34 ± 0.31%) for normal versus lung opacity classification. Classification performance on the external dataset showed that the CNN model is likely to be highly generalizable, with an AUC of 0.9444 ± 0.0029. The CNN model pre-trained on cohorts of adult patients and fine-tuned on pediatric patients achieved an AUC of 0.9851 ± 0.0046 for normal versus pneumonia classification. Pretraining with natural images demonstrates benefit for a moderate-sized training image set of about 8500 images. The remarkable performance in diagnostic accuracy observed in this study shows that deep CNNs can accurately and effectively differentiate normal and abnormal chest radiographs, thereby providing potential benefits to radiology workflow and patient care.
Advances in flexible and stretchable electronics have enabled an unprecedented level of coupling between electronics and bio-tissues by overcoming obstacles associated with the bio-tissues’ ...curvilinearity, softness, deformability, and wetness. This review begins by detailing the outstanding challenges in achieving body-conformable electronics stemming from the disparate properties of bio-tissues and man-made materials and the complexity of their interfaces. Given tissue properties, an existing mechanics model has revealed how device softness and interfacial adhesion govern the bio-electronics conformability. Therefore, we first summarize methods for improving the mechanical compliance of electronics through both material engineering and structural design. Then, we discuss strategies to enhance bio-electronics adhesion in both dry and wet environments. We point out that innovative bio-electronics integration procedures also have a significant impact on bio-electronics conformability. We conclude by providing an outlook into future opportunities and proposing a holistic approach to strategizing body-conformable electronics.
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Unobstructive, long-term, and high-fidelity human body sensing and stimulation must overcome the challenges of manifold mismatches between bio-tissues and man-made materials. The emergence of body-conformable electronics is a promising solution to these inherent obstacles. In the last two decades, various strategies have been developed to promote bio-electronics conformability by (1) improving device thinness and compliance, (2) enhancing bio-electronics interfacial adhesion, and (3) refining the bio-integration process. A successful body-conformable electronic device can only be created through comprehensive consideration of all three aspects. This review summarizes recent advancements in these three directions and proposes a holistic strategy. We envision that future research efforts in body-conformable electronics will focus on new functionalities, enhanced performance, and personalization. The rapid progress in body-conformable electronics shall meet the ever-growing demands in telemedicine, mobile health, points of care, and human-machine interfaces.
Body-conformable electronics allow for unobstructive, long-term, and high-fidelity human body sensing and stimulation. In this review, challenges and governing parameters of body-conformable electronics are first discussed. Thereafter, strategies to achieve body conformability are summarized from three aspects: improving the mechanical compliance of electronics, enhancing bio-electronics adhesion, and inventing new bio-integration procedures. In the end, we provide an outlook into future opportunities and suggest a holistic view of body-conformable electronics.
Biomass-carbon prepared by Kiwi peel and asymmetric carbon-based nanospheres synthesized by hydrothermally method are used as substrate to decorate NiS2 nanoparticles to obtain NiS2/BC. Dropping the ...NiS2/BC dispersions on the surface of glassy carbon electrode to improve conductivity. Then we modified the electrode with N-GOQDs and AuNPs to improve the electroactive region of the surface and the electron transfer rate. Nicotinamide was used as functional monomer, MIP was prepared on the surface of Au/N-GOQDs/NiS2/BC/GCE by electropolymerization method in presence of DA and CPZ to enhance the selectivity and sensitivity of the sensor. After extraction of the template molecule, DA and CPZ can be specifically recognized by the imprinted cavities formed on the surface of the MIP. Quantitative electrochemical analysis is performed by differential pulse voltammetry (DPV). Demonstrate the prepared Au/N-GOQDs/NiS2/BC/MIP/GCE can be used for sensitive and selective determination of DA and CPZ in human serum, urine and pharmaceutical samples.
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•A novel imprinted polymer electrochemical sensor based on AuNPs and N-GOQDs coated on NiS2/BC was proposed.•Molecularly imprinting approach used for simultaneous determination of DA and CPZ.•Highly sensitive and selective determination of DA and CPZ with LODs in nanomolar range.•This sensor was successful for determination of DA and CPZ in biological samples.
In this study, a dual template molecularly imprinted electrochemical sensor was fabricated for the simultaneous determination of dopamine (DA) and chlorpromazine (CPZ). Nitrogen-doped graphene oxide quantum dots (N-GOQDs) and gold nanoparticles (AuNPs) were deposited on a combination of biomass carbon and biomass-derived asymmetric carbon-based nanospheres decorated with NiS2 nanoparticles (NiS2/BC), and the obtained material was used to modify glass carbon electrode (GCE). The electropolymerization of nicotinamide (NA) on Au/N-GOQDs/NiS2/BC/GCE via cyclic voltammetry (CV) using NA as functional monomer and DA and CPZ as template was used to produce molecularly imprinted polymer (MIP) film. Various experimental parameters, including electropolymerization cycles, template-to-functional monomer ratio, pH value, elution time, and incubation time, were optimized. Differential pulse voltammetry (DPV) response under optimized parameters show two linear ranges for DA (0.05–8 µM and 8–40 µM), and the limit of detection (LOD) is as low as 2.8 nM (S/N = 3). CPZ had a linearity range of 0.005–2 µM with very low LOD value of 0.25 nM (S/N = 3). The prepared MIP electrochemical sensor had good reproducibility and repeatability, acceptable stability, and high selectivity for DA and CPZ. Furthermore, the sensor was used in real sample analysis of human serum, urine and pharmaceutical samples, and the result of recovery (93.9%–106.15%) and relative standard deviation (RSD) (1.5%–6.6%) indicated good practicality.
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•ZnO1-x coatings with different oxygen vacancy concentration were successfully regulated.•Oxygen vacancy played an important role in the optical and electrical properties of ...ZnO.•ZnO0.952 coatings with the highest oxygen vacancy concentration showed the best response to NO2.
Oxygen vacancy concentration of ZnO1-x coatings were regulated by dipping the coatings into hydrogen peroxide solution for 1, 10, 60 and 120min (abbreviated as Vo-1, Vo-10, Vo-60 and Vo-120 samples) with an addition of subsequent annealing. XRD, Raman, PL and XPS characterizations were used to determine the evolution of oxygen vacancy concentration in the treated coatings. The oxygen vacancy concentration for Vo-1, Vo-10, Vo-60 and Vo-120 samples was 1.9%, 3.7%, 4.8% and 4.3%, respectively. Optical and electrical measurement showed that the visible light absorption range was extended and electrical resistance decreased when the oxygen vacancy concentration was increased. The Vo-60 samples possessed the widest range of visible light response. First principle calculation and various experimental characterizations were used to study the role of the oxygen vacancy in bandgap of the coatings and the results showed that the bandgap was narrowed when the oxygen vacancy was increased. The sensors exhibited significant sensing responses to 0.9ppm NO2 at room temperature. The coatings with the highest oxygen vacancy concentration had the best NO2 gas sensing properties. In addition, the Vo-60 coatings showed good selectivity and stability to NO2.
Using phosphorus-doped ZnO nanowire (NW) arrays grown on silicon substrate, energy conversion using the p-type ZnO NWs has been demonstrated for the first time. The p-type ZnO NWs produce positive ...output voltage pulses when scanned by a conductive atomic force microscope (AFM) in contact mode. The output voltage pulse is generated when the tip contacts the stretched side (positive piezoelectric potential side) of the NW. In contrast, the n-type ZnO NW produces negative output voltage when scanned by the AFM tip, and the output voltage pulse is generated when the tip contacts the compressed side (negative potential side) of the NW. In reference to theoretical simulation, these experimentally observed phenomena have been systematically explained based on the mechanism proposed for a nanogenerator.
In healthcare, patients commonly use medical devices that require batteries and wired connections, and do not have timely access to monitoring data. Ambient WiFi backscatter systems are attracting ...attention in healthcare owing to their ultra-low power consumption, wireless communication method, battery-free mode, and the ability for continuous monitoring. However, there is a lack of investigations on it. In this paper, we focus on discovering ambient WiFi backscatter systems’ applications in healthcare. Therefore, in this paper, we provide a comprehensive survey of ambient WiFi backscatter in healthcare. Firstly, we present some existing ambient WiFi backscatter systems to help us better understand the operating principle, and then classify them based on the use of commercial WiFi receivers. After that, we survey their latest applications and discuss the challenges in healthcare, starting from two general directions: smart hospitals and medical devices. Finally, we foresee the development of ambient WiFi backscatter technology and expand it in other fields. The research aims to receive a comprehensive understanding of the applications and challenges of ambient WiFi backscatter systems in healthcare, and thus promoting their further development.
Abstract
The identification and interpretation of genomic variants play a key role in the diagnosis of genetic diseases and related research. These tasks increasingly rely on accessing relevant ...manually curated information from domain databases (e.g. SwissProt or ClinVar). However, due to the sheer volume of medical literature and high cost of expert curation, curated variant information in existing databases are often incomplete and out-of-date. In addition, the same genetic variant can be mentioned in publications with various names (e.g. 'A146T' versus 'c.436G>A' versus 'rs121913527'). A search in PubMed using only one name usually cannot retrieve all relevant articles for the variant of interest. Hence, to help scientists, healthcare professionals, and database curators find the most up-to-date published variant research, we have developed LitVar for the search and retrieval of standardized variant information. In addition, LitVar uses advanced text mining techniques to compute and extract relationships between variants and other associated entities such as diseases and chemicals/drugs. LitVar is publicly available at https://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/LitVar.
Pediatric-onset colitis and inflammatory bowel disease (IBD) have significant effects on the growth of infants and children, but the etiopathogenesis underlying disease subtypes remains incompletely ...understood. Here, we report single-cell clustering, immune phenotyping, and risk gene analysis for children with undifferentiated colitis, Crohn’s disease, and ulcerative colitis. We demonstrate disease-specific characteristics, as well as common pathogenesis marked by impaired cyclic AMP (cAMP)-response signaling. Specifically, infiltration of PDE4B- and TNF-expressing macrophages, decreased abundance of CD39-expressing intraepithelial T cells, and platelet aggregation and release of 5-hydroxytryptamine at the colonic mucosae were common in colitis and IBD patients. Targeting these pathways by using the phosphodiesterase inhibitor dipyridamole restored immune homeostasis and improved colitis symptoms in a pilot study. In summary, comprehensive analysis of the colonic mucosae has uncovered common pathogenesis and therapeutic targets for children with colitis and IBD.
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•Defective cAMP response underlies mucosal immune defects in pediatric colitis or IBD•Platelets are activated at colonic mucosae in pediatric colitis or IBD•Dipyridamole promoted mucosal healing in nine children with colitis in a pilot study•Candidate risk genes are differentially enriched in mucosal cellular subsets
Single-cell and risk gene analysis of children with undifferentiated colitis, Crohn’s disease, and ulcerative colitis identifies common underlying mechanisms of pathogenesis and reveals the potential therapeutic benefit of modulating cAMP signaling via the drug dipyridamole.
Serum amyloid A (SAA) represents an evolutionarily conserved family of inflammatory acute-phase proteins. It is also a major constituent of secondary amyloidosis. To understand its function and ...structural transition to amyloid, we determined a structure of human SAA1.1 in two crystal forms, representing a prototypic member of the family. Native SAA1.1 exists as a hexamer, with subunits displaying a unique four-helix bundle fold stabilized by its long C-terminal tail. Structure-based mutational studies revealed two positive-charge clusters, near the center and apex of the hexamer, that are involved in SAA association with heparin. The binding of high-density lipoprotein involves only the apex region of SAA and can be inhibited by heparin. Peptide amyloid formation assays identified the N-terminal helices 1 and 3 as amyloidogenic peptides of SAA1.1. Both peptides are secluded in the hexameric structure of SAA1.1, suggesting that the native SAA is nonpathogenic. Furthermore, dissociation of the SAA hexamer appears insufficient to initiate amyloidogenic transition, and proteolytic cleavage or removal of the C-terminal tail of SAA resulted in formation of various-sized structural aggregates containing ∼5-nm regular repeating protofibril-like units. The combined structural and functional studies provide mechanistic insights into the pathogenic contribution of glycosaminoglycan in SAA1.1-mediated AA amyloid formation.
3-D convolutional neural networks (3-D CNNs) have been established as a powerful tool to simultaneously learn features from both spatial and temporal dimensions, which is suitable to be applied to ...video-based action recognition. In this paper, we propose not to directly use the activations of fully connected layers of a 3-D CNN as the video feature, but to use selective convolutional layer activations to form a discriminative descriptor for video. It pools the feature on the convolutional layers under the guidance of body joint positions. Two schemes of mapping body joints into convolutional feature maps for pooling are discussed. The body joint positions can be obtained from any off-the-shelf skeleton estimation algorithm. The helpfulness of the body joint guided feature pooling with inaccurate skeleton estimation is systematically evaluated. To make it end-to-end and do not rely on any sophisticated body joint detection algorithm, we further propose a two-stream bilinear model which can learn the guidance from the body joints and capture the spatio-temporal features simultaneously. In this model, the body joint guided feature pooling is conveniently formulated as a bilinear product operation. Experimental results on three real-world datasets demonstrate the effectiveness of body joint guided pooling which achieves promising performance.
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