Alleviation of Salt Stress and Changes in Glycyrrhizic Acid Accumulation by Dark Septate Endophytes in Glycyrrhiza glabra Grown under Salt Stress.

This study aimed to investigate the mechanisms by which dark septate endophytes (DSE) regulate salt tolerance and the accumulation of bioactive constituents in licorice. First, the salt stress tolerance and resynthesis with the plant effect of isolated DSE from wild licorice were tested. Second, the performance of licorice inoculated with DSE, which had the best salt-tolerant and growth-promoting effects, was examined under salt stress. All isolated DSE showed salt tolerance and promoted plant growth, withCurvularia lunata D43 being the most effective. Under salt stress, C. lunata D43 could promote growth, increase antioxidant enzyme activities, enhance glycyrrhizic acid accumulation, improve key enzyme activities in the glycyrrhizic acid synthesis pathway, and induce the expression of the key enzyme gene and salt tolerance gene of licorice. The structural equation model demonstrated that DSE alleviate the negative effects of salt stress through direct and indirect pathways. Variations in key enzyme activities, gene expression, and bioactive constituent concentration can be attributed to the effects of DSE. These results contribute to revealing the value of DSE for cultivating medicinal plants in saline soils.

The responses and detoxification mechanisms of dark septate endophytes (DSE), Exophiala salmonis, to CuO nanoparticles.

To understand the tolerance mechanisms of dark septate endophytes (DSE), Exophiala salmonis, to CuO nanoparticles (CuO-NPs) with different sizes (40 and 150 nm), we investigated the morphology, antioxidant response, Cu subcellular distribution, and the melanin gene expression in the mycelia of E. salmonis. E. salmonis was cultured in liquid and solid media under the stress of increasing CuO-NP concentrations (0, 50, 100, 150, and 250 mg/L). Results showed that (1) E. salmonis showed good CuO-NP tolerance, and the tolerance to CuO-NPs at 150 nm was stronger than that at 40 nm. A large number of agglomeration structures were observed on the mycelia surface with the exception of 50 mg/L CuO-NPs with a diameter of 150 nm. (2) CuO-NP stress significantly stimulated the production of antioxidant enzymes, particularly the CuO-NPs with small particle size (40 nm). (3) Cu uptaken by E. salmonis increased proportionally with the increase of CuO-NP concentration in the medium. More than 80% Cu was absorbed in cell wall of mycelia treated with a small particle size (40 nm). (4) FTIR analysis revealed that hydroxyl, amine, carboxyl, and phosphate groups were associated with CuO-NP binding regardless of particle size. (5) Fungal melanin content increased with the addition of CuO-NPs; the increase of melanin induced by CuO-NPs with small particle size (40 nm) was more significant. (6) The expression of 1,3,6,8-tetrahydroxynaphthalene reductase (Arp2) in the melanin synthesis pathway increased under the stress of CuO-NPs, and CuO-NPs with a small particle size (40 nm) caused a significant change in the expression level of Arp2 gene than those with a large particle size (150 nm). In conclusion, E. salmonis had a strong tolerance to CuO-NPs and mitigated the toxic effects of CuO-NPs through the antioxidant system, the expression of genes related to melanin synthesis, and the synthesis of melanin.

Transcriptome analysis reveals the molecular mechanisms of Phragmites australis tolerance to CuO-nanoparticles and/or flood stress induced by arbuscular mycorrhizal fungi.

The molecular mechanism of arbuscular mycorrhizal fungi (AMF) in vertical flow constructed wetlands (VFCWs) for the purification of copper oxide nanoparticles (CuO-NPs) contaminated wastewater remains unclear. In this study, transcriptome analysis was used to explore the effect of AMF inoculation on the gene expression profile of Phragmites australis roots under different concentrations of CuO-NPs and/or flood stress. 551, 429 and 2281 differentially expressed genes (DEGs) were specially regulated by AMF under combined stresses of CuO-NPs and flood, single CuO-NPs stress and single flood stress, respectively. Based on the results of DEG function annotation and enrichment analyses, AMF inoculation under CuO-NPs and/or flood stress up-regulated the expression of a number of genes involved in antioxidant defense systems, cell wall biosynthesis and transporter protein, which may contribute to plant tolerance. The expression of 30 transcription factors (TFs) was up-regulated by AMF inoculation under combined stresses of CuO-NPs and flood, and 44 and 44 TFs were up-regulated under single CuO-NPs or flood condition, respectively, which may contribute to the alleviating effect of symbiosis on CuO-NPs and/or flood stress. These results provided a theoretical basis for enhancing the ecological restoration function of wetland plants for metallic nanoparticles (MNPs) by mycorrhizal technology in the future.

Artificial Intelligence Clinicians Can Use Chest Computed Tomography Technology to Automatically Diagnose Coronavirus Disease 2019 (COVID-19) Pneumonia and Enhance Low-Quality Images.

PURPOSE: Nowadays, the number of patients with COVID-19 pneumonia worldwide is still increasing. The clinical diagnosis of COVID-19 pneumonia faces challenges, such as the difficulty to perform RT-PCR tests in real time, the lack of experienced radiologists, clinical low-quality images, and the similarity of imaging features of community-acquired pneumonia and COVID-19. Therefore, we proposed an artificial intelligence model GARCD that uses chest CT images to assist in the diagnosis of COVID-19 in real time. It can show better diagnostic performance even facing low-quality CT images. METHODS: We used 14,129 CT images from 104 patients. A total of 12,929 samples were used to build artificial intelligence models, and 1200 samples were used to test its performance. The image quality improvement module is based on the generative adversarial structure. It improves the quality of the input image under the joint drive of feature loss and content loss. The enhanced image is sent to the disease diagnosis model based on residual convolutional network. It automatically extracts the semantic features of the image and then infers the probability that the sample belongs to COVID-19. The ROC curve is used to evaluate the performance of the model. RESULTS: This model can effectively enhance the low-quality image and make the image that is difficult to be recognized become recognizable. The model proposed in this paper reached 97.8% AUC, 96.97% sensitivity and 91.16% specificity in an independent test set. ResNet, GADCD, CNN, and DenseNet achieved 80.9%, 97.3%, 70.7% and 85.7% AUC in the same test set, respectively. By comparing the performance with related works, it is proved that the model proposed has stronger clinical usability. CONCLUSION: The method proposed can effectively assist doctors in real-time detection of suspected cases of COVID-19 pneumonia even faces unclear image. It can quickly isolate patients in a targeted manner, which is of positive significance for preventing the further spread of COVID-19 pneumonia.