802 research outputs found

    Nanoscale modification of porous gelatin scaffolds with chondroitin sulfate for corneal stromal tissue engineering

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    Recent studies reflect the importance of using naturally occurring biopolymers as three-dimensional corneal keratocyte scaffolds and suggest that the porous structure of gelatin materials may play an important role in controlling nutrient uptake. In the current study, the authors further consider the application of carbodiimide cross-linked porous gelatin as an alternative to collagen for corneal stromal tissue engineering. The authors developed corneal keratocyte scaffolds by nanoscale modification of porous gelatin materials with chondroitin sulfate (CS) using carbodiimide chemistry. Scanning electron microscopy/energy dispersive X-ray spectroscopy and Fourier transform infrared spectroscopy showed that the amount of covalently incorporated polysaccharide was significantly increased when the CS concentration was increased from 0% to 1.25% (w/v). In addition, as demonstrated by dimethylmethylene blue assays, the CS content in these samples was in the range of 0.078–0.149 nmol per 10 mg scaffold. When compared with their counterparts without CS treatment, various CS-modified porous gelatin membranes exhibited higher levels of water content, light transmittance, and amount of permeated nutrients but possessed lower Young’s modulus and resistance against protease digestion. The hydrophilic and mechanical properties of scaffolds modified with 0.25% CS were comparable with those of native corneas. The samples from this group were biocompatible with the rabbit corneal keratocytes and showed enhanced proliferative and biosynthetic capacity of cultured cells. In summary, the authors found that the nanoscale-level modification has influence on the characteristics and cell-material interactions of CS-containing gelatin hydrogels. Porous membranes with a CS content of 0.112 ± 0.003 nmol per 10 mg scaffold may hold potential for use in corneal stromal tissue engineering

    Combined Tractional and Rhegmatogenous Retinal Detachment in Proliferative Diabetic Retinopathy in the Anti-VEGF Era

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    Purpose. To investigate the clinical features, surgical outcomes, and prognostic factors of combined rhegmatogenous and tractional detachment (combined RD) in proliferative diabetic retinopathy (PDR) in recent years. Methods. Medical records of PDR and combined RD treated with vitrectomy from 2008 to 2013 were retrospectively reviewed. Results. A total of 57 eyes from 49 patients were included. Nine eyes had received panretinal photocoagulation (PRP) and 7 eyes had intravitreal bevacizumab (IVB) within 3 months before RD developed. Thirty-eight eyes (66.7%) had ≧3 sites of broad adhesion of fibrovascular proliferation (FVP). Thirty-three eyes (57.9%) showed active FVP. Thirty-four eyes (59.6%) had extent of RD involving 3 or 4 quadrants. The primary reattachment rate was 93.0%, and the final visual acuity (VA) improved by more than 3 lines in 80.7% of eyes. Neovascular glaucoma occurred in 4 eyes postoperatively. Poor preoperative VA, severe vitreoretinal adhesion, and broad extent of RD had significant correlation with poor visual outcomes. Conclusion. PRP or IVB might play a role in provoking combined RD. The anatomical and functional success rates of surgery were high. Poor preoperative VA and severe proliferations predicted poor visual outcomes

    Effect and Evaluation of an Ultrasonic Atomizer With Large Vibration Amplitude

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    An ultrasonic atomizer can produce large vibration amplitude is designed. Different from the structure of the usually seen ultrasonic spray nozzle, the atomizer is fundamentally constructed with a hollow tube encircled with several pieces of sectional type piezoelectric actuators, which can radially oscillate the tube to generate desired vibration profile. Atomization is formed on the surface around the liquid outlet of the tube where maximum vibration amplitude occurs. In search of resonance frequency and vibration amplitude, modal and harmonic analyses of the ultrasonic atomizer are carried out by ANSYS. In comparison the simulated results with the experimental results, both are in good agreement. A measurement system is set up for detecting the atomization droplets and calculating the droplet size and distribution. An attempt is to design an ultrasonic atomizer can produce high distribution and small diameter droplets for some application-level requirements, droplet diameter around 20�60 lm is assumed to be the specification for performance verification of the proposed atomizer. In experiment, it is found nearly 90% of atomized droplets fit for the requirement. Besides the most important factor of operating frequency, a relation of amplitude is found to include in the well-known Lang and Rayleigh�s equation

    PGT-Net: Progressive Guided Multi-task Neural Network for Small-area Wet Fingerprint Denoising and Recognition

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    Fingerprint recognition on mobile devices is an important method for identity verification. However, real fingerprints usually contain sweat and moisture which leads to poor recognition performance. In addition, for rolling out slimmer and thinner phones, technology companies reduce the size of recognition sensors by embedding them with the power button. Therefore, the limited size of fingerprint data also increases the difficulty of recognition. Denoising the small-area wet fingerprint images to clean ones becomes crucial to improve recognition performance. In this paper, we propose an end-to-end trainable progressive guided multi-task neural network (PGT-Net). The PGT-Net includes a shared stage and specific multi-task stages, enabling the network to train binary and non-binary fingerprints sequentially. The binary information is regarded as guidance for output enhancement which is enriched with the ridge and valley details. Moreover, a novel residual scaling mechanism is introduced to stabilize the training process. Experiment results on the FW9395 and FT-lightnoised dataset provided by FocalTech shows that PGT-Net has promising performance on the wet-fingerprint denoising and significantly improves the fingerprint recognition rate (FRR). On the FT-lightnoised dataset, the FRR of fingerprint recognition can be declined from 17.75% to 4.47%. On the FW9395 dataset, the FRR of fingerprint recognition can be declined from 9.45% to 1.09%
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