A dual network cross-linked hydrogel with multifunctional Bletilla striata polysaccharide/gelatin/tea polyphenol for wound healing promotion.

Wound healing is a dynamic and complex biological process, and traditional biological excipients cannot meet the needs of the wound healing process, and there is an urgent need for a biological dressing with multifunctionality and the ability to participate in all stages of wound healing. This study developed tea polyphenol (TP) incorporated multifunctional hydrogel based on oxidized Bletilla striata polysaccharide (OBSP) and adipic acid dihydrazide modified gelatin (Gel-ADH) with antimicrobial, antioxidant hemostatic, and anti-inflammatory properties to promote wound healing. The composite OBSP, Gel-ADH, TP (OBGTP) hydrogels prepared by double crosslinking between OBSP, TP and Gel-ADH via Schiff base bonding and hydrogen bonding had good rheological and swelling properties. The introduction of TP provided the composite hydrogel with excellent antioxidant antibacterial activities against Staphylococcus aureus (S. aureus) and Escherichia coli (E. coil). In the rat liver hemorrhage model and skin injury model, the OBGTP composite hydrogel had significant (p < 0.001) hemostatic ability, and had the ability to accelerate collagen deposition, reduce the expression of inflammatory factors, and promote rapid wound healing. In addition, OBGTP hydrogels had adhesive properties and good biocompatibility. In conclusion, OBGTP multifunctional composite hydrogels have great potential for wound healing applications.

Multifunctional natural microneedles based methacrylated Bletilla striata polysaccharide for repairing chronic wounds with bacterial infections.

The expeditious healing of chronic wounds with bacterial infections poses a formidable challenge in clinical practice because of the persistent bacterial presence, excessive inflammation, and the accumulation of reactive oxygen species (ROS) in clinical practice. Thus, in this study, natural antimicrobial material microneedles (MNs) with multifunctional properties were prepared by adding peony leaf extract (PLE) into a matrix of methacrylated Bletilla striata polysaccharide (BSPMA) and methacrylated chitosan (CSMA) via cross-linking under ultra-violet light to accelerate the rapid healing of chronic wounds with bacterial infections. Results showed that BCP-MNs effectively inhibited the growth of Escherichia coli, Staphylococcus aureus, and methicillin-resistant S. aureus (MRSA) by disrupting bacterial cell membranes and accelerated the healing of infected wounds by enhancing cell migration, epidermal regeneration, pro-collagen deposition, and angiogenesis and reducing inflammation. Furthermore, BCP-MNs not only possessed good mechanical properties, stability, and biocompatibility but also showed potent antioxidant effects to eliminate excessive ROS accumulation in the wound bed. In conclusion, BCP-MNs possess multifunctional wound-healing properties and can serve as excellent wound dressing in to treat infected wounds.

Plant Species Classification Based on Hyperspectral Imaging via a Lightweight Convolutional Neural Network Model.

In recent years, many image-based approaches have been proposed to classify plant species. Most methods utilized red green blue (RGB) imaging materials and designed custom features to classify the plant images using machine learning algorithms. Those works primarily focused on analyzing single-leaf images instead of live-crown images. Without considering the additional features of the leaves' color and spatial pattern, they failed to handle cases that contained leaves similar in appearance due to the limited spectral information of RGB imaging. To tackle this dilemma, this study proposes a novel framework that combines hyperspectral imaging (HSI) and deep learning techniques for plant image classification. We built a plant image dataset containing 1,500 images of 30 different plant species taken by a 470-900 nm hyperspectral camera and designed a lightweight conventional neural network (CNN) model (LtCNN) to perform image classification. Several state-of-art CNN classifiers are chosen for comparison. The impact of using different band combinations as the network input is also investigated. Results show that using simulated RGB images achieves a kappa coefficient of nearly 0.90 while using the combination of 3-band RGB and 3-band near-infrared images can improve to 0.95. It is also found that the proposed LtCNN can obtain a satisfactory performance of plant classification (kappa = 0.95) using critical spectral features of the green edge (591 nm), red-edge (682 nm), and near-infrared (762 nm) bands. This study also demonstrates the excellent adaptability of the LtCNN model in recognizing leaf features of plant live-crown images while using a relatively smaller number of training samples than complex CNN models such as AlexNet, GoogLeNet, and VGGNet.

Morphological and phylogenetic analyses reveal three new species of Diaporthe from Yunnan, China.

Species of Diaporthe have often been reported as plant pathogens, endophytes or saprobes, commonly isolated from a wide range of plant hosts. Sixteen strains isolated from species of ten host genera in Yunnan Province, China, represented three new species of Diaporthe, D. chrysalidocarpi, D. machili and D. pometiae as well as five known species D. arecae, D. hongkongensis, D. middletonii, D. osmanthi and D. pandanicola. Morphological comparisons with known species and DNA-based phylogenies based on the analysis of a multigene (ITS, TUB, TEF, CAL and HIS) dataset support the establishment of the new species. This study reveals that a high species diversity of Diaporthe with wide host ranges occur in tropical rainforest in Yunnan Province, China.

Morphological and molecular identification of Diaporthe species in south-western China, with description of eight new species.

Diaporthe species have often been reported as plant pathogens, endophytes and saprophytes, commonly isolated from a wide range of infected plant hosts. In the present study, twenty strains obtained from leaf spots of twelve host plants in Yunnan Province of China were isolated. Based on a combination of morphology, culture characteristics and multilocus sequence analysis of the rDNA internal transcribed spacer region (ITS), translation elongation factor 1-α (TEF), ß-tubulin (TUB), calmodulin (CAL), and histone (HIS) genes, these strains were identified as eight new species: Diaporthe camelliae-sinensis, D. grandiflori, D. heliconiae, D. heterostemmatis, D. litchii, D. lutescens, D. melastomatis, D. pungensis and two previously described species, D. subclavata and D. tectonendophytica. This study showed high species diversity of Diaporthe in tropical rain forests and its hosts in south-western China.

Two new species of Microdochium from Indocalamus longiauritus in south-western China.

Microdochium species have often been reported as plant pathogens and saprophytes and are commonly isolated from some diseased plant hosts. The primary aim of the present study was to describe and illustrate two new Microdochium species isolated from the leaf spot of Indocalamus longiauritus in Yunnan Province, China, namely Microdochium yunnanense and M. indocalami, spp. nov., based on their morphology and multilocus phylogenetic analyses of the combined ITS, LSU, TUB2, and RPB2. DNA sequence data indicate that six strains represent three independent groups from related and similar species in Microdochium. Microdochium indocalami sp. nov. clustered with M. fisheri, M. lycopodinum, M. rhopalostylidis, and M. phragmitis. Microdochium yunnanense sp. nov. grouped with M. bolleyi. In addition, the strain SAUCC1017 is recorded as an unidentified species in Microdochium. Descriptions and illustrations of the new species in the genus and Microdochium sp. indet. are provided.