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The protective effect of seed-priming was explained by increasing the antioxidant defense markers including the antioxidant metabolites (i.e., total antioxidant capacity, carotenoids, phenolics, flavonoids, ASC, GSH, tocopherols) and enzymes (CAT, POX, GPX, SOD, GR, APX, MDHAR, DHAR), particularly in infected tomato seedlings. Additionally, cluster analysis indicated the differential impact of IAA- and SA-seed-priming, whereas lower oxidative damage and higher antioxidant enzymes’ activities in tomato root were particularly reported for IAA treatment. The principal component analysis (PCA) also proclaimed an organ specificity depending on their response to Orobanche infection. Collectively, here and for the first time, we shed the light on the potential of seed-priming with either IAA or SA to mitigate the adverse effect of O. ramosa stress in tomato plants, especially at oxidative stress levels.Background & objective The study of eating behavior has made significant progress towards understanding the association of specific eating behavioral patterns with medical problems, such as obesity and eating disorders. Smartphones have shown promise in monitoring and modifying unhealthy eating behavior patterns, often with the help of sensors for behavior data recording. However, when it comes to semi-controlled deployment settings, smartphone apps that facilitate eating behavior data collection are missing. To fill this gap, the present work introduces ASApp, one of the first smartphone apps to support researchers in the collection of heterogeneous objective (sensor-acquired) and subjective (self-reported) eating behavior data in an integrated manner from large-scale, naturalistic human subject research (HSR) studies. Methods This work presents the overarching and deployment-specific requirements that have driven the design of ASApp, followed by the heterogeneous eating behavior dataset that is collected ancribed and the promising results from the evaluation of the app with respect to attractiveness, usability, and technical soundness are discussed. Access details for ASApp are also provided. Conclusions This work presents the requirement elucidation, design, implementation and evaluation of a novel smartphone application that supports researchers in the integrated collection of a concise yet rich set of heterogeneous eating behavior data for semi-controlled HSR.The new severe acute respiratory syndrome- coronavirus 2 is reported to affect the nervous system. Dapagliflozin order Among the reports of the various neurological manifestations, there are a few documented specific processes to explain the neurological signs. We report a para-infectious encephalitis patient with clinical, laboratory, and imaging findings during evolution and convalescence phase of coronavirus infection. This comprehensive overview can illuminate the natural history of similar cases. As the two previously reported cases of encephalitis associated with this virus were not widely discussed regarding the treatment, we share our successful approach and add some recommendations about this new and scarce entity.Background SARS-CoV-2 viral infection causes COVID-19 that can result in severe acute respiratory distress syndrome (ARDS), which can cause significant mortality, leading to concern that immunosuppressive treatments for multiple sclerosis and other disorders have significant risks for both infection and ARDS. Objective To examine the biology that potentially underpins immunity to the SARS-Cov-2 virus and the immunity-induced pathology related to COVID-19 and determine how this impinges on the use of current disease modifying treatments in multiple sclerosis. Observations Although information about the mechanisms of immunity are scant, it appears that monocyte/macrophages and then CD8 T cells are important in eliminating the SARS-CoV-2 virus. This may be facilitated via anti-viral antibody responses that may prevent re-infection. However, viral escape and infection of leucocytes to promote lymphopenia, apparent CD8 T cell exhaustion coupled with a cytokine storm and vascular pathology appears to contribute to the damage in ARDS. Implications In contrast to ablative haematopoietic stem cell therapy, most multiple-sclerosis-related disease modifying therapies do not particularly target the innate immune system and few have any major long-term impact on CD8 T cells to limit protection against COVID-19. In addition, few block the formation of immature B cells within lymphoid tissue that will provide antibody-mediated protection from (re)infection. However, adjustments to dosing schedules may help de-risk the chance of infection further and reduce the concerns of people with MS being treated during the COVID-19 pandemic.Purpose To develop and validate a radiomics-based model for preoperative prediction of lymph node metastasis (LNM) in gastric cancer (GC). Method A total of 768 GC patients were enrolled in this retrospective study. Radiomics features were extracted from portal venous phase computed tomography (CT) scans. A radiomics signature was built with highly reproducible features using the least absolute shrinkage and selection operator (LASSO) method in the training cohort (n = 486). The signature was further validated in internal validation (n = 240) and external testing cohorts (n = 42). Multivariate logistic regression analysis was conducted to build a model that combined radiomics signature, serum biomarkers, and lymph node status according to CT. Performance of the model was determined by its discrimination, calibration, and clinical usefulness. The predictive value of the model was also evaluated in early stage GC (EGC) subgroup. Results The radiomics signature comprised 7 robust features showed favorable prediction efficacy in all cohorts. A radiomics-based model that incorporated radiomics signature, serum CA72-4, and CT-reported lymph node status had good calibration and discrimination in training cohort [AUC, 0.92; 95% confidence interval (CI), 0.89-0.95] and validation cohort (AUC 0.86; 95% CI, 0.81-0.91). The model also showed a favorable predictive performance for EGC patients with an AUC of 0.85 (95% CI, 0.76-0.94). Decision curve analysis confirmed the clinical utility of this model. Conclusions The radiomics-based model showed favorable accuracy for prediction of LNM in GC. The model may also serve as a noninvasive tool for preoperative evaluation of LNM in EGC.