Identification of WUSCHEL-related homeobox gene and truncated small peptides in transformation efficiency improvement in Eucalyptus.

BACKGROUND: The WUSCHEL-related Homeobox (WOX) genes, which encode plant-specific homeobox (HB) transcription factors, play crucial roles in regulating plant growth and development. However, the functions of WOX genes are little known in Eucalyptus, one of the fastest-growing tree resources with considerable widespread cultivation worldwide. RESULTS: A total of nine WOX genes named EgWOX1-EgWOX9 were retrieved and designated from Eucalyptus grandis. From the three divided clades marked as Modern/WUS, Intermediate and Ancient, the largest group Modern/WUS (6 EgWOXs) contains a specific domain with 8 amino acids: TLQLFPLR. The collinearity, cis-regulatory elements, protein-protein interaction network and gene expression analysis reveal that the WUS proteins in E. grandis involve in regulating meristems development and regeneration. Furthermore, by externally adding of truncated peptides isolated from WUS specific domain, the transformation efficiency in E. urophylla × E. grandis DH32-29 was significant enhanced. The transcriptomics data further reveals that the use of small peptides activates metabolism pathways such as starch and sucrose metabolism, phenylpropanoid biosynthesis and flavonoid biosynthesis. CONCLUSIONS: Peptides isolated from WUS protein can be utilized to enhance the transformation efficiency in Eucalyptus, thereby contributing to the high-efficiency breeding of Eucalyptus.

Integration of a copper-based metal-organic framework with an ionic liquid for electrochemically discriminating cysteine enantiomers.

The identification of cysteine enantiomers is of great significance in the biopharmaceutical industry and medical diagnostics. Herein, we develop an electrochemical sensor to discriminate cysteine (Cys) enantiomers based on the integration of a copper metal-organic framework (Cu-MOF) with an ionic liquid. Because the combine energy of D-cysteine (D-Cys) with Cu-MOF (-9.905 eV) is lower than that of L-cysteine (L-Cys) with Cu-MOF (-9.694 eV), the decrease in the peak current of the Cu-MOF/GCE induced by D-Cys is slightly higher than that induced by L-Cys in the absence of an ionic liquid. In contrast, the combine energy of L-Cys with an ionic liquid (-1.084 eV) is lower than that of D-Cys with an ionic liquid (-1.052 eV), and the ionic liquid is easier to cross-link with L-Cys than with D-Cys. When an ionic liquid is present, the decrease in the peak current of the Cu-MOF/GCE induced by D-Cys is much higher than that induced by L-Cys. Consequently, this electrochemical sensor can efficiently discriminate D-Cys from L-Cys, and it can sensitively detect D-Cys with a detection limit of 0.38 nM. Moreover, this electrochemical sensor exhibits good selectivity, and it can accurately measure the spiked D-Cys in human serum with a recovery ratio of 100.2-102.6%, with wide applications in biomedical research and drug discovery.

Hypophysitis induced by anti-programmed cell death protein 1 immunotherapy in non-small cell lung cancer: Three case reports.

BACKGROUND: Hypophysitis induced by programmed cell death 1 protein (PD-1) immune checkpoint inhibitors is rare and poorly described. We report three patients with non-small cell lung cancer who developed hypophysitis after anti-PD-1 immunotherapy. CASE SUMMARY: Both case 1 and case 2 presented with common symptoms of fatigue, nausea, and vomiting. However, case 3 showed rare acute severe symptoms such as hoarse voice, bucking, and difficulty in breathing even when sitting. Following two cycles of immunotherapy in case 3, the above severe symptoms and pituitary gland enlargement were found on magnetic resonance imaging at the onset of hypophysitis. These symptoms were relieved after 10 d of steroid treatment. Case 3 was the first patient with these specific symptoms, which provided a new insight into the diagnosis of hypophysitis. In addition, we found that the clinical prognosis of patients with hypophysitis was related to the dose of steroid therapy. Case 3 was treated with high-dose hormone therapy and her pituitary-corticotropic axis dysfunction returned to normal after more than 6 mo of steroid treatment. Cases 1 and 2 were treated with the low-dose hormone, and dysfunction of the pituitary-corticotropic axis was still present after up to 7 mo of steroid treatment. CONCLUSION: The clinical symptoms described in this study provide a valuable reference for the diagnosis and treatment of immune-related hypophysitis.

A Deep Learning Based Framework for Diagnosing Multiple Skin Diseases in a Clinical Environment.

Background: Numerous studies have attempted to apply artificial intelligence (AI) in the dermatological field, mainly on the classification and segmentation of various dermatoses. However, researches under real clinical settings are scarce. Objectives: This study was aimed to construct a novel framework based on deep learning trained by a dataset that represented the real clinical environment in a tertiary class hospital in China, for better adaptation of the AI application in clinical practice among Asian patients. Methods: Our dataset was composed of 13,603 dermatologist-labeled dermoscopic images, containing 14 categories of diseases, namely lichen planus (LP), rosacea (Rosa), viral warts (VW), acne vulgaris (AV), keloid and hypertrophic scar (KAHS), eczema and dermatitis (EAD), dermatofibroma (DF), seborrheic dermatitis (SD), seborrheic keratosis (SK), melanocytic nevus (MN), hemangioma (Hem), psoriasis (Pso), port wine stain (PWS), and basal cell carcinoma (BCC). In this study, we applied Google's EfficientNet-b4 with pre-trained weights on ImageNet as the backbone of our CNN architecture. The final fully-connected classification layer was replaced with 14 output neurons. We added seven auxiliary classifiers to each of the intermediate layer groups. The modified model was retrained with our dataset and implemented using Pytorch. We constructed saliency maps to visualize our network's attention area of input images for its prediction. To explore the visual characteristics of different clinical classes, we also examined the internal image features learned by the proposed framework using t-SNE (t-distributed Stochastic Neighbor Embedding). Results: Test results showed that the proposed framework achieved a high level of classification performance with an overall accuracy of 0.948, a sensitivity of 0.934 and a specificity of 0.950. We also compared the performance of our algorithm with three most widely used CNN models which showed our model outperformed existing models with the highest area under curve (AUC) of 0.985. We further compared this model with 280 board-certificated dermatologists, and results showed a comparable performance level in an 8-class diagnostic task. Conclusions: The proposed framework retrained by the dataset that represented the real clinical environment in our department could accurately classify most common dermatoses that we encountered during outpatient practice including infectious and inflammatory dermatoses, benign and malignant cutaneous tumors.

Deep learning-based, computer-aided classifier developed with dermoscopic images shows comparable performance to 164 dermatologists in cutaneous disease diagnosis in the Chinese population.

BACKGROUND: Diagnoses of Skin diseases are frequently delayed in China due to lack of dermatologists. A deep learning-based diagnosis supporting system can facilitate pre-screening patients to prioritize dermatologists' efforts. We aimed to evaluate the classification sensitivity and specificity of deep learning models to classify skin tumors and psoriasis for Chinese population with a modest number of dermoscopic images. METHODS: We developed a convolutional neural network (CNN) based on two datasets from a consecutive series of patients who underwent the dermoscopy in the clinic of the Department of Dermatology, Peking Union Medical College Hospital, between 2016 and 2018, prospectively. In order to evaluate the feasibility of the algorithm, we used two datasets. Dataset I consisted of 7192 dermoscopic images for a multi-class model to differentiate three most common skin tumors and other diseases. Dataset II consisted of 3115 dermoscopic images for a two-class model to classify psoriasis from other inflammatory diseases. We compared the performance of CNN with 164 dermatologists in a reader study with 130 dermoscopic images. The experts' consensus was used as the reference standard except for the cases of basal cell carcinoma (BCC), which were all confirmed by histopathology. RESULTS: The accuracies of multi-class and two-class models were 81.49% ±â€Š0.88% and 77.02% ±â€Š1.81%, respectively. In the reader study, for the multi-class tasks, the diagnosis sensitivity and specificity of 164 dermatologists were 0.770 and 0.962 for BCC, 0.807 and 0.897 for melanocytic nevus, 0.624 and 0.976 for seborrheic keratosis, 0.939 and 0.875 for the "others" group, respectively; the diagnosis sensitivity and specificity of multi-class CNN were 0.800 and 1.000 for BCC, 0.800 and 0.840 for melanocytic nevus, 0.850 and 0.940 for seborrheic keratosis, 0.750 and 0.940 for the "others" group, respectively. For the two-class tasks, the sensitivity and specificity of dermatologists and CNN for classifying psoriasis were 0.872 and 0.838, 1.000 and 0.605, respectively. Both the dermatologists and CNN achieved at least moderate consistency with the reference standard, and there was no significant difference in Kappa coefficients between them (P > 0.05). CONCLUSIONS: The performance of CNN developed with relatively modest number of dermoscopic images of skin tumors and psoriasis for Chinese population is comparable with 164 dermatologists. These two models could be used for screening in patients suspected with skin tumors and psoriasis respectively in primary care hospital.

Analysis of the effect of different doses of oral tranexamic acid on melasma: a multicentre prospective study.

BACKGROUND: Melasma is pale brown to dark brown hyperpigmentation of the facial skin that commonly affects women of reproductive age. Treatment methods for melasma include oral and topical use of vitamin C, hydroquinone ointment, and laser treatment, with unsatisfactory results. Tranexamic acid (TA) has been shown to be effective against melasma, however, the optimal dose has not been investigated. OBJECTIVE: To analyse the therapeutic effect of different doses of oral TA on melasma. MATERIALS & METHODS: Patients with severe melasma were randomised to receive TA at a daily dose of 500 mg, 750 mg, 1,000 mg, or 1,500 mg. Clinical and VISIA photographs of the patients were taken at baseline, four weeks, eight weeks, six months, one year, and two years. The melasma area and severity index (MASI), as well as the melanin index, were measured. Routine blood and coagulation tests were performed at each time point. The photographs were divided into five groups according to level of improvement: apparent improvement, slight improvement, unchanged, and deterioration. RESULTS: Clinical photographs showed that all four doses of TA were effective in treating melasma, and the efficacy correlated with treatment time and dosage. However, there were no significant differences in the MASI or melanin index between the four doses. The treatment was generally safe for most patients and side effects included mild stomach upset and decreased menstruation. CONCLUSION: Oral TA was safe and effective for the treatment of melasma. Patient satisfaction was high and most patients could withstand long-term treatment.

C3: Consensus Cancer Driver Gene Caller.

Next-generation sequencing has allowed identification of millions of somatic mutations in human cancer cells. A key challenge in interpreting cancer genomes is to distinguish drivers of cancer development among available genetic mutations. To address this issue, we present the first web-based application, consensus cancer driver gene caller (C3), to identify the consensus driver genes using six different complementary strategies, i.e., frequency-based, machine learning-based, functional bias-based, clustering-based, statistics model-based, and network-based strategies. This application allows users to specify customized operations when calling driver genes, and provides solid statistical evaluations and interpretable visualizations on the integration results. C3 is implemented in Python and is freely available for public use at http://drivergene.rwebox.com/c3.