Background: The detection of internal defects in seeds via non-destructive imaging techniques is a topic of high interest to optimize the quality of seed lots. In this context, X-ray imaging is especially suited. Recent studies have shown the feasibility of defect detection via deep learning models in 3D tomography images. We demonstrate the possibility of performing such deep learning-based analysis on 2D X-ray radiography for a faster yet robust method via the X-Robustifier pipeline proposed in this article. Results: 2D X-ray images of both defective and defect-free seeds were acquired. A deep learning model based on state-of-the-art object detection neural networks is proposed. Specific data augmentation techniques are introduced to compensate for the low ratio of defects and increase the robustness to variation of the physical parameters of the X-ray imaging systems. The seed defects were accurately detected (F1-score >90%), surpassing human performance in computation time and error rates. The robustness of these models against the principal distortions commonly found in actual agro-industrial conditions is demonstrated, in particular, the robustness to physical noise, dimensionality reduction and the presence of seed coating. Conclusion: This work provides a full pipeline to automatically detect common defects in seeds via 2D X-ray imaging. The method is illustrated on sugar beet and faba bean and could be efficiently extended to other species via the proposed generic X-ray data processing approach (X-Robustifier). Beyond a simple proof of feasibility, this constitutes important results toward the effective use in the routine of deep learning-based automatic detection of seed defects.

Background: The COVID-19 pandemic has reached over 276 million people globally with 5.3 million deaths as of 22nd December 2021. COVID-19-associated acute and long-term neurological manifestations are well recognized. The exact profile and the timing of neurological events in relation to the onset of infection are worth exploring. The aim of the current body of work was to determine the frequency, pattern, and temporal profile of neurological manifestations in a cohort of Egyptian patients with confirmed COVID-19 infection. Methods: This was a prospective study conducted on 582 hospitalized COVID-19 patients within the first two weeks of the diagnosis of COVID-19 to detect any specific or non-specific neurological events. Results: The patients’ mean (SD) age was 46.74 (17.26) years, and 340 (58.42%) patients were females. The most commonly encountered COVID-19 symptoms were fever (90.72%), cough (82.99%), and fatigue (76.98%). Neurological events (NE) detected in 283 patients (48.63%) and were significantly associated with a severe COVID-19 at the onset (OR: 3.13; 95% CI: 2.18–4.51; p < 0.0001) and with a higher mortality (OR: 2.56; 95% CI: 1.48–5.46; p = 0.019). The most frequently reported NEs were headaches (n = 167) and myalgias (n = 126). Neurological syndromes included stroke (n = 14), encephalitis (n = 12), encephalopathy (n = 11), transverse myelitis (n = 6) and Guillain-Barré syndrome (n = 4). Conclusions: Neurological involvement is common (48.63%) in COVID-19 patients within the first two weeks of the illness. This includes neurological symptoms such as anosmia, headaches, as well as a constellation of neurological syndromes such as stroke, encephalitis, transverse myelitis, and Guillain-Barré syndrome. Severity of acute COVID-19 illness and older age are the main risk factors.

: This study aimed to report the severity of COVID-19 in a cohort of Egyptian patients with multiple sclerosis (MS) with particular attention on the impact of disease modifying drugs (DMDs). : We included 119 MS patients recruited from two centers, Ain-Shams university and Cairo university with confirmed or suspected COVID-19 during the period from May to September 2020 as a part of the MuSC-19 project. Univariate logistic regression was fitted to assess risk factors for severe COVID-19 (at least one outcome among hospitalization, ICU admission and death). : Females were 77%, mean age was 34 years, mean duration of MS was 5.28 years, median EDSS was 3, most of the patients (83%) had RRMS, while 15% and 2% had respectively SPMS and PPMS. Only eleven patients (9% of study population) had a severe outcome and 3 patients (3%) died. Headache was the only symptom significantly associated with the severity of COVID-19 (OR=10.85, P = 0.001). There was no association between any of the DMDs and severe COVID-19 outcome. : This study showed an acceptable safety profile of DMDs in Egyptian MS patients who developed COVID-19, as 91% of the cohort had a favorable outcome. Headache as a symptom associated with severe outcome in Egyptian patients’ needs further validation.