Arabidopsis defense against the pathogenic fungus drechslera gigantea is dependent on the integrity of the unfolded protein response

Drechslera gigantea Heald & Wolf is a worldwide-spread necrotrophic fungus closely related to the Bipolaris genus, well-known because many member species provoke severe diseases in cereal crops and studied because they produce sesterpenoid phytoxins named ophiobolins which possess interesting biological properties. The unfolded protein response (UPR) is a conserved mechanism protecting eukaryotic cells from the accumulation of unfolded/misfolded proteins in the endoplasmic reticulum (ER). In plants, consolidated evidence supports the role of UPR in the tolerance to abiotic stress, whereas much less information is available concerning the induction of ER stress by pathogen infection and consequent UPR elicitation as part of the defense response. In this study, the infection process of D. gigantea in Arabidopsis thaliana wild type and UPR-defective bzip28 bzip60 double mutant plants was comparatively investigated, with the aim to address the role of UPR in the expression of resistance to the fungal pathogen. The results of confocal microscopy, as well as of qRT-PCR transcript level analysis of UPR genes, proteomics, microRNAs expression profile and HPLC-based hormone analyses demonstrated that ophiobolin produced by the fungus during infection compromised ER integrity and that impairment of the IRE1 /bZIP60 pathway of UPR hampered the full expression of resistance, thereby enhancing plant susceptibility to the pathogen

Automated assessment of COVID-19 reporting and data system and chest CT severity scores in patients suspected of having COVID-19 using artificial intelligence

Background: The coronavirus disease 2019 (COVID-19) pandemic has spread across the globe with alarming speed, morbidity, and mortality. Immediate triage of patients with chest infections suspected to be caused by COVID-19 using chest CT may be of assistance when results from definitive viral testing are delayed.Purpose: To develop and validate an artificial intelligence (AI) system to score the likelihood and extent of pulmonary COVID-19 on chest CT scans using the COVID-19 Reporting and Data System (CO-RADS) and CT severity scoring systems.Materials and Methods: The CO-RADS AI system consists of three deep-learning algorithms that automatically segment the five pulmonary lobes, assign a CO-RADS score for the suspicion of COVID-19, and assign a CT severity score for the degree of parenchymal involvement per lobe. This study retrospectively included patients who underwent a nonenhanced chest CT examination because of clinical suspicion of COVID-19 at two medical centers. The system was trained, validated, and tested with data from one of the centers. Data from the second center served as an external test set. Diagnostic performance and agreement with scores assigned by eight independent observers were measured using receiver operating characteristic analysis, linearly weighted kappa values, and classification accuracy.Results: A total of 105 patients (mean age, 62 years +/- 16 [standard deviation]; 61 men) and 262 patients (mean age, 64 years +/- 16; 154 men) were evaluated in the internal and external test sets, respectively. The system discriminated between patients with COVID-19 and those without COVID-19, with areas under the receiver operating characteristic curve of 0.95 (95% CI: 0.91, 0.98) and 0.88 (95% CI: 0.84, 0.93), for the internal and external test sets, respectively. Agreement with the eight human observers was moderate to substantial, with mean linearly weighted k values of 0.60 +/- 0.01 for CO-RADS scores and 0.54 +/- 0.01 for CT severity scores.Conclusion: With high diagnostic performance, the CO-RADS AI system correctly identified patients with COVID-19 using chest CT scans and assigned standardized CO-RADS and CT severity scores that demonstrated good agreement with findings from eight independent observers and generalized well to external data. (C) RSNA, 2020Cardiovascular Aspects of Radiolog

Comparative Analysis of the Response to Polyethylene Glycol-Simulated Drought Stress in Roots from Seedlings of “Modern” and “Ancient” Wheat Varieties

: Durum wheat is widely cultivated in the Mediterranean, where it is the basis for the production of high added-value food derivatives such as pasta. In the next few years, the detrimental effects of global climate change will represent a serious challenge to crop yields. For durum wheat, the threat of climate change is worsened by the fact that cultivation relies on a few genetically uniform, elite varieties, better suited to intensive cultivation than "traditional" ones but less resistant to environmental stress. Hence, the renewed interest in "ancient" traditional varieties are expected to be more tolerant to environmental stress as a source of genetic resources to be exploited for the selection of useful agronomic traits such as drought tolerance. The aim of this study was to perform a comparative analysis of the effect and response of roots from the seedlings of two durum wheat cultivars: Svevo, a widely cultivated elite variety, and Saragolla, a traditional variety appreciated for its organoleptic characteristics, to Polyethylene glycol-simulated drought stress. The effect of water stress on root growth was analyzed and related to biochemical data such as hydrogen peroxide production, electrolyte leakage, membrane lipid peroxidation, proline synthesis, as well as to molecular data such as qRT-PCR analysis of drought responsive genes and proteomic analysis of changes in the protein repertoire of roots from the two cultivars

Automated Assessment of COVID-19 Reporting and Data System and Chest CT Severity Scores in Patients Suspected of Having COVID-19 Using Artificial Intelligence

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