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Insulin-like expansion factor-I and injure healing, a possible answer to non-healing wounds: An organized review of the particular literature and future points of views.
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.Structured illumination microscopy (SIM) has become an important technique for optical super-resolution imaging because it allows a doubling of image resolution at speeds compatible with live-cell imaging. However, the reconstruction of SIM images is often slow, prone to artefacts, and requires multiple parameter adjustments to reflect different hardware or experimental conditions. Here, we introduce a versatile reconstruction method, ML-SIM, which makes use of transfer learning to obtain a parameter-free model that generalises beyond the task of reconstructing data recorded by a specific imaging system for a specific sample type. We demonstrate the generality of the model and the high quality of the obtained reconstructions by application of ML-SIM on raw data obtained for multiple sample types acquired on distinct SIM microscopes. ML-SIM is an end-to-end deep residual neural network that is trained on an auxiliary domain consisting of simulated images, but is transferable to the target task of reconstructing experimental SIM images. By generating the training data to reflect challenging imaging conditions encountered in real systems, ML-SIM becomes robust to noise and irregularities in the illumination patterns of the raw SIM input frames. Since ML-SIM does not require the acquisition of experimental training data, the method can be efficiently adapted to any specific experimental SIM implementation. We compare the reconstruction quality enabled by ML-SIM with current state-of-the-art SIM reconstruction methods and demonstrate advantages in terms of generality and robustness to noise for both simulated and experimental inputs, thus making ML-SIM a useful alternative to traditional methods for challenging imaging conditions. Additionally, reconstruction of a SIM stack is accomplished in less than 200 ms on a modern graphics processing unit, enabling future applications for real-time imaging. selleck chemical Source code and ready-to-use software for the method are available at http//ML-SIM.github.io.
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