Synchrotron-based X-ray analysis of living wheat leaves showed that foliar-applied ZnEDTA is taken up as a ligand complex, and typical Zn agricultural application rates may induce localized toxicity

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Background: The aim of this study was to develop an intelligent system based on a deep learning algorithm for automatically diagnosing fungal keratitis (FK) in in vivo confocal microscopy (IVCM) images.Methods: A total of 2,088 IVCM images were included in the training dataset. The positive group consisted of 688 images with fungal hyphae, and the negative group included 1,400 images without fungal hyphae. A total of 535 images in the testing dataset were not included in the training dataset. Deep Residual Learning for Image Recognition (ResNet) was used to build the intelligent system for diagnosing FK automatically. The system was verified by external validation in the testing dataset using the area under the receiver operating characteristic curve (AUC), accuracy, specificity and sensitivity.Results: In the testing dataset, 515 images were diagnosed correctly and 20 were misdiagnosed (including 6 with fungal hyphae and 14 without). The system achieved an AUC of 0.9875 with an accuracy of 0.9626 in detecting fungal hyphae. The sensitivity of the system was 0.9186, with a specificity of 0.9834. When 349 diabetic patients were included in the training dataset, 501 images were diagnosed correctly and thirtyfour were misdiagnosed (including 4 with fungal hyphae and 30 without). The AUC of the system was 0.9769.The accuracy, specificity and sensitivity were 0.9364, 0.9889 and 0.8256, respectively. Conclusions:The intelligent system based on a deep learning algorithm exhibited satisfactory diagnostic performance and effectively classified FK in various IVCM images. The context of this deep learning automated diagnostic system can be extended to other types of keratitis.

Adjuvant gonadotropin-releasing hormone analogues for the prevention of chemotherapy-induced premature ovarian failure in premenopausal women.

Foliar absorption of zinc (Zn) is limited by several barriers, the first of which is the leaf cuticle. In this study, we investigated the absorption of Zn from Zn oxide nanoparticles (ZnO-NPs) in wheat (Triticum aestivum cv Gladius) and sunflower (Helianthus annuus cv Hyoleic 41) to determine the importance of NP surface coating for Zn absorption. Fourier transform infrared (FTIR) spectroscopy showed a higher polysaccharide content in the wheat cuticle than sunflower, indicated by a more pronounced glycosidic bond at 1020 cm −1 , but wax and cutin content were similar. Scanning electron microscopy (SEM) revealed that trichome density was twice as high in wheat (3600 AE 900 cm −2) as in sunflower (1600 cm −2) and stomatal density four times higher in sunflower (6400 AE 800 cm −2 in wheat and 22 900 cm −2 in sunflower). Suspensions of ZnO-NPs with coatings of different hydrophobicity were applied to leaves to compare Zn absorption using X-ray fluorescence microscopy (XFM) and inductively coupled plasma mass spectroscopy (ICP-MS). Absorption of Zn was similar between wheat and sunflower when Zn was applied at 1000 mg Zn l −1 , but much less Zn was absorbed from all ZnO products than from soluble Zn fertiliser. Particle coating did not affect Zn absorption, but it may facilitate particle adhesion to leaves, providing a longer-term source of resupply of Zn ions to the leaves. Differences in leaf surface characteristics did not affect Zn absorption, indicating that the cuticle is the main pathway of absorption under these conditions.

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Using ECP for the attachment of conjunctival autograft in pterygium surgery is safe, fast, simple, and economical with less postoperative discomfort.

Purpose Infiltration of activated dendritic cells and inflammatory cells in cornea represents an important marker for defining corneal inflammation. Deep transfer learning has presented a promising potential and is gaining more importance in computer assisted diagnosis. This study aimed to develop deep transfer learning models for automatic detection of activated dendritic cells and inflammatory cells using in vivo confocal microscopy images. Methods A total of 3453 images was used to train the models. External validation was performed on an independent test set of 558 images. A ground-truth label was assigned to each image by a panel of cornea specialists. We constructed a deep transfer learning network that consisted of a pre-trained network and an adaptation layer. In this work, five pre-trained networks were considered, namely VGG-16, ResNet-101, Inception V3, Xception, and Inception-ResNet V2. The performance of each transfer network was evaluated by calculating the area under the curve (AUC) of receiver operating characteristic, accuracy, sensitivity, specificity, and G mean. Results The best performance was achieved by Inception-ResNet V2 transfer model. In the validation set, the best transfer system achieved an AUC of 0.9646 (P<0.001) in identifying activated dendritic cells (accuracy, 0.9319; sensitivity, 0.8171; specificity, 0.9517; and G mean, 0.8872), and 0.9901 (P<0.001) in identifying inflammatory cells (accuracy, 0.9767; sensitivity, 0.9174; specificity, 0.9931; and G mean, 0.9545). Conclusions The deep transfer learning models provide a completely automated analysis of corneal inflammatory cellular components with high accuracy. The implementation of such models would greatly benefit the management of corneal diseases and reduce workloads for ophthalmologists.