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The taste of tea is one of the key indicators in the evaluation of its quality and is a key factor in its grading and market pricing. To objectively and digitally evaluate the taste quality of tea leaves, miniature near-infrared (NIR) spectroscopy and electronic tongue (ET) sensors are considered effective sensor signals for the characterization of the taste quality of tea leaves. This study used micro-NIR spectroscopy and ET sensors in combination with data fusion strategies and chemometric tools for the taste quality assessment and prediction of multiple grades of black tea. Using NIR features and ET sensor signals as fused information, the data optimization based on grey wolf optimization, ant colony optimization (ACO), particle swarm optimization, and non-dominated sorting genetic algorithm II were employed as modeling features, combined with support vector machine (SVM), extreme learning machine and K-nearest neighbor algorithm to build the classification models. The results obtained showed that the ACO−SVM model had the highest classification accuracy with a discriminant rate of 93.56%. The overall results reveal that it is feasible to qualitatively distinguish black tea grades and categories by NIR spectroscopy and ET techniques.

Overexpression of reactive oxygen species in the substantia nigra pars compacta destroys dopaminergic neurons and accelerates the pathological process of Parkinson's disease (PD). In this study, a new hydrophilic nano-bioconjugate, lactoferrin (Lf)-modified Au-Bi2 Se3 nanodot (ND) for efficient PD therapy is developed. In particular, the Lf-Au-Bi2 Se3 NDs exhibit strong blood-brain barrier (BBB) permeation. The Lf-Au-Bi2 Se3 NDs can also serve as multiple enzymes such as superoxide dismutase, catalase, glutathione peroxidase, and peroxide. These NDs are located close to the mitochondria and thus maintain the mitochondrial membrane potential as well as control the cellular ROS level. In vivo results further demonstrate that the NDs can improve the memory and mobility of PD mice; permeation of the BBB, protection of mitochondria, and suppression of dopaminergic neuron loss in the substantia nigra pars compacta are also observed. It is anticipated that these new Lf-Au-Bi2 Se3 NDs with excellent biocompatibility, multienzyme functionality, and BBB permeability will open new opportunities for PD therapy.

Pancreatic stellate cells (PSCs) and other pancreatic components that play a critical role in exocrine pancreatic diseases are generally identified separately by conventional studies, which provide indirect links between these components. Here, nonlinear optical microscopy was evaluated for simultaneous characterization of these components within a three-dimensional (3-D) tissue environment, primarily based on multichannel detection of intrinsic optical emissions and cell morphology. Fresh rat pancreatic tissues harvested at 1 day, 7 days, and 28 days after induction of chronic pancreatitis were imaged, respectively. PSCs, inflammatory cells, blood vessels, and collagen fibers were identified simultaneously. The PSCs at day 1 of chronic pancreatitis showed significant enlargement compared with those in normal pancreas ( p 0.001 , analysis of variance linear contrast; n = 8 for each group). Pathological events relating to these components were observed, including presence of inflammatory cells, deposited collagen, and phenotype conversion of PSCs. We demonstrate that label-free nonlinear optical microscopy is an efficient tool for dissecting PSCs and other pancreatic components coincidently within 3-D pancreatic tissues. It is a prospect for intravital observation of dynamic events under natural physiological conditions, and might help uncover the key mechanisms of exocrine pancreatic diseases, leading to more effective treatments.

In routine clinical work, classical histology is regarded as the gold standard for the discrimination of healthy and pathological tissue. However, the whole process can be tediously long and has some restraints. As a result, under most conditions, doctors have to wait for the histological results until decisions can be made. In this paper, a practical procedure of tissue preprocess, serial section, label-free imaging and image analysis is demonstrated, which costs less time and could give sufficiently good contrast to obtain information about cellular structure and organization of biological tissues.

Background Pancreatic cancer is a lethal disease with a 5-year survival rate of only 1–5%. The acceleration of intraoperative histological examination would be beneficial for better management of pancreatic cancer, suggesting an improved survival. Nonlinear optical methods based on two-photon excited fluorescence (TPEF) and second harmonic generation (SHG) of intrinsic optical biomarkers show the ability to visualize the morphology of fresh tissues associated with histology, which is promising for real-time intraoperative evaluation of pancreatic cancer. Methodology/Principal Findings In order to investigate whether the nonlinear optical imaging methods have the ability to characterize pancreatic histology at cellular resolution, we studied different types of pancreatic tissues by using label-free TPEF and SHG. Compared with other routine methods for the preparation of specimens, fresh tissues without processing were found to be most suitable for nonlinear optical imaging of pancreatic tissues. The detailed morphology of the normal rat pancreas was observed and related with the standard histological images. Comparatively speaking, the preliminary images of a small number of chemical-induced pancreatic cancer tissues showed visible neoplastic differences in the morphology of cells and extracellular matrix. The subcutaneous pancreatic tumor xenografts were further observed using the nonlinear optical microscopy, showing that most cells are leucocytes at 5 days after implantation, the tumor cells begin to proliferate at 10 days after implantation, and the extracellular collagen fibers become disordered as the xenografts grow. Conclusions/Significance In this study, nonlinear optical imaging was used to characterize the morphological details of fresh pancreatic tissues for the first time. We demonstrate that it is possible to provide real-time histological evaluation of pancreatic cancer by the nonlinear optical methods, which present an opportunity for the characterization of the progress of spontaneous pancreatic cancer and further application in a non-invasive manner.

Collagen is the most prominent protein in the human body, making up 30% of the total protein content. Quantitative studies have shown structural differences between collagen fibers of the normal and diseased tissues, due to the remodeling of the extracellular matrix during the pathological process. The dominant orientation, which is an important characteristic of collagen fibers, has not been taken into consideration for quantitative collagen analysis. Based on the conventional gray level co-occurrence matrix (GLCM) method, the authors proposed the orientation-dependent GLCM (OD-GLCM) method by estimating the dominant orientation of collagen fibers. The authors validated the utility of the OD-GLCM method on second harmonic generation (SHG) microscopic images of tendons from rats with different ages. Compared with conventional GLCM method, the authors' method has not only improved the discrimination between different tissues but also provided additional texture information of the orderliness of collagen fibers and the fiber size. The OD-GLCM method was further applied to the differentiation of the preliminary SHG images of normal and cancerous human pancreatic tissues. The combination of SHG microscopy and the OD-GLCM method might be helpful for the evaluation of diseases marked with abnormal collagen morphology.