Notes

Nusinersen in pediatric as well as grownup patients using kind III spinal muscular waste away.
We induced and evaluated different levels of retinal-image degradation using Bangerter foils and fog filters. selleck inhibitor We found increased straylight and an important deterioration in visual performance, assessed by means of visual acuity, contrast threshold, and visual discrimination capacity. Bangerter foils induced forward scattering levels comparable to those observed in mature to severe cataracts, with an important impact of halos and starbursts. Fog filters induced lower levels of intraocular scattering, although luminous veils and circular halos were reported. The visual disturbance index positively correlated with intraocular scattering and straylight. Our results show retinal-image quality has an important influence on night-vision performance.Using near-infrared (NIR) light with 700-1200 nm wavelength, transillumination images of small animals and thin parts of a human body such as a hand or foot can be obtained. They are two-dimensional (2D) images of internal absorbing structures in a turbid medium. A three-dimensional (3D) see-through image is obtainable if one can identify the depth of each part of the structure in the 2D image. Nevertheless, the obtained transillumination images are blurred severely because of the strong scattering in the turbid medium. Moreover, ascertaining the structure depth from a 2D transillumination image is difficult. To overcome these shortcomings, we have developed a new technique using deep learning principles. A fully convolutional network (FCN) was trained with 5,000 training pairs of clear and blurred images. Also, a convolutional neural network (CNN) was trained with 42,000 training pairs of blurred images and corresponding depths in a turbid medium. Numerous training images were provided by the convolution with a point spread function derived from diffusion approximation to the radiative transport equation. The validity of the proposed technique was confirmed through simulation. Experiments demonstrated its applicability. This technique can provide a new tool for the NIR imaging of animal bodies and biometric authentication of a human body.Visual simulators are useful tools to provide patients experience of multifocal vision prior to treatment. In this study, commercially available center-near aspheric multifocal contact lenses (MCLs) of low, medium, and high additions were mapped on a spatial light modulator (SLM) and validated on a bench. Through focus visual acuity (TFVA) was measured in subjects through the SLM and real MCLs on the eye. A correlation metric revealed statistically significant shape similarity between TFVA curves with real and simulated MCLs. A Bland-Altman analysis showed differences within confidence intervals of ±0.01 logMAR for LowAdd/MediumAdd and ±0.06 logMAR for HighAdd. Visual performance with simulated MCLs outperformed real MCLs by ∼20%. In conclusion, SLM captures the profile of center-near MCLs and reproduces vision with real MCLs, revealing that the MCL profile and its interactions with the eye's optics (and not fitting aspects) account for the majority of the contributions to visual performance with MCLs.Pathophysiology of sickle cell disease (SCD) features intermittent vaso-occlusion of microcirculatory networks that facilitate ischemic damage. Past research has, however, relied on static images to characterize this active disease state. This study develops imaging metrics to more fully capture dynamic vascular changes, quantifying intermittent retinal capillary perfusion in unaffected controls and SCD patients using sequential optical coherence tomography angiography (OCT-A) scans. The results reveal significant dynamic variation of capillary perfusion in SCD patients compared to controls. This measurement of vaso-occlusive burden in patients would provide utility in monitoring of the disease state and in evaluating treatment efficacy.High-resolution microendoscopy (HRME) is a low-cost strategy to acquire images of intact tissue with subcellular resolution at frame rates ranging from 11 to 18 fps. Current HRME imaging strategies are limited by the small microendoscope field of view (∼0.5 mm2); multiple images must be acquired and reliably registered to assess large regions of clinical interest. Image mosaics have been assembled from co-registered frames of video acquired as a microendoscope is slowly moved across the tissue surface, but the slow frame rate of previous HRME systems made this approach impractical for acquiring quality mosaicked images from large regions of interest. Here, we present a novel video mosaicking microendoscope incorporating a high frame rate CMOS sensor and optical probe holder to enable high-speed, high quality interrogation of large tissue regions of interest. Microendoscopy videos acquired at >90 fps are assembled into an image mosaic. We assessed registration accuracy and image sharpness across the mosaic for images acquired with a handheld probe over a range of translational speeds. This high frame rate video mosaicking microendoscope enables in vivo probe translation at >15 millimeters per second while preserving high image quality and accurate mosaicking, increasing the size of the region of interest that can be interrogated at high resolution from 0.5 mm2 to >30 mm2. Real-time deployment of this high-frame rate system is demonstrated in vivo and source code made publicly available.A new type of cascaded taper integrated ultra-long-period fiber grating (ULPFG) based immunobiologic sensor has been developed that benefits from the self-assembled monolayer of class I hydrophobin HGFI. Due to the cascaded arc, discharge tapers constitute an ultra-long-period and circular symmetrical refractive index modulation along fiber axial direction, and by local integration in one period, the mode coupling would generate to the higher harmonic of LP02, LP03 and LP04 modes in the wavelength range from 1300 nm to 1620 nm. The hydrophobic characteristic of the ULPFG surface is modified employing the HGFI, and the antibody molecule probes could be absorbed strongly on the HGFI nano-film, furthermore, the performances of immunobiologic sensing are investigated employing multiple control groups of matched and mismatched antigen molecule targets. The results show that it possesses higher sensing sensitivity of 4.5 nm/(µg/ml), faster response time about of 35 min, lower stability error of 8.8%, and excellent immuno-specificity. Moreover, this proposed ULPFG sensor has the advantages of low cost, simple fabrication and label-free, which is a powerful tool in the trace biomedical detection field.Deep neural networks have made incredible progress in many computer vision tasks, owing to access to a great amount of data. However, collecting ground truth for large medical image datasets is extremely inconvenient and difficult to implement in practical applications, due to high professional requirements. Synthesizing can generate meaningful supplement samples to enlarge the insufficient medical image dataset. In this study, we propose a new data augmentation method, Multiple Lesions Insertion (MLI), to simulate new diabetic retinopathy (DR) fundus images based on the healthy fundus images that insert real lesions, such as exudates, hemorrhages, microaneurysms templates, into new healthy fundus images with Poisson editing. The synthetic fundus images can be generated according to the clinical rules, i.e., in different DR grading fundus images, the number of exudates, hemorrhages, microaneurysms are different. The generated DR fundus images by our MLI method are realistic with the real texture features and rich details, without black spots, artifacts, and discontinuities. We first demonstrate the feasibility of this method in a DR computer-aided diagnosis (CAD) system, which judges whether the patient has transferred treatment or not. Our results indicate that the MLI method outperforms most of the traditional augmentation methods, i.e, oversampling, under-sampling, cropping, rotation, and adding other real sample methods in the DR screening task.Chondrocyte viability is a crucial factor in evaluating cartilage health. Most cell viability assays rely on dyes and are not applicable for in vivo or longitudinal studies. We previously demonstrated that two-photon excited autofluorescence and second harmonic generation microscopy provided high-resolution images of cells and collagen structure; those images allowed us to distinguish live from dead chondrocytes by visual assessment or by the normalized autofluorescence ratio. However, both methods require human involvement and have low throughputs. Methods for automated cell-based image processing can improve throughput. Conventional image processing algorithms do not perform well on autofluorescence images acquired by nonlinear microscopes due to low image contrast. In this study, we compared conventional, machine learning, and deep learning methods in chondrocyte segmentation and classification. We demonstrated that deep learning significantly improved the outcome of the chondrocyte segmentation and classification. With appropriate training, the deep learning method can achieve 90% accuracy in chondrocyte viability measurement. The significance of this work is that automated imaging analysis is possible and should not become a major hurdle for the use of nonlinear optical imaging methods in biological or clinical studies.Optical properties, such as the attenuation coefficients of multi-layer tissue samples, could be used as a biomarker for diagnosis and disease progression in clinical practice. In this paper, we present a method to estimate the attenuation coefficients in a multi-layer sample by fitting a single scattering model for the OCT signal to the recorded OCT signal. In addition, we employ numerical simulations to obtain the theoretically achievable precision and accuracy of the estimated parameters under various experimental conditions. Finally, the method is applied to two sets of measurements obtained from a multi-layer phantom by two experimental OCT systems one with a large and one with a small Rayleigh length. Numerical and experimental results show an accurate estimation of the attenuation coefficients when using multiple B-scans.Alloy nanostructures unveil extraordinary plasmonic phenomena that supersede the mono-metallic counterparts. Here we report silver-gold (Ag-Au) alloy nanohole arrays (α-NHA) for ultra-sensitive plasmonic label-free detection of Escherichia Coli (E. coli). Large-area α-NHA were fabricated by using nanoimprint lithography and concurrent thermal evaporation of Ag and Au. The completely miscible Ag-Au alloy exhibits an entirely different dielectric function in the near infra-red wavelength range compared to mono-metallic Ag or Au. The α-NHA demonstrate substantially enhanced refractive index sensitivity of 387 nm/RIU, surpassing those of Ag or Au mono-metallic nanohole arrays by approximately 40%. Moreover, the α-NHA provide highly durable material stability to corrosion and oxidation during over one-month observation. The ultra-sensitive α-NHA allow the label-free detection of E. coli in various concentration levels ranging from 103 to 108 cfu/ml with a calculated limit of detection of 59 cfu/ml. This novel alloy plasmonic material provides a new outlook for widely applicable biosensing and bio-medical applications.
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