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Twig-like or unfused middle cerebral artery (MCA) is a rare congenital vascular anomaly defined by the absence of the M1 segment. It can be found incidentally or can be revealed by cerebral ischemia or hemorrhage. Although rare, neuroradiologists should be familiar with such findings in order to differentiate them from differential diagnoses such as Moyamoya disease and steno-occlusive disorders of the MCA.We report a case of a twig-like MCA revealed by intracranial bleeding in an 84-year-old woman.

Emphysematous hepatitis (EH) is a recently described gas forming liver infection, often associated with diabetes mellitus. It is often associated with poor prognosis, with rapidly fatal course in most reported cases. Its diagnosis is based on CT findings when an extensive gas replacing the liver parenchyma is present. We report the case of a 77-year-old woman with history of diabetes mellitus who presented in the emergency department with acute onset of cutaneomucous jaundice and fever, and in whom an abdominopelvic CT scan showed findings consistent with emphysematous hepatitis. EH is a rare condition with high mortality rate, however early diagnosis can be crucial in order to decrease mortality.

Ganglioneuromas are rare benign neurogenic tumors of the sympathetic nervous system. Its presence in the neck is relatively rare, characterized by a slow growing mass, usually asymptomatic. Its imaging findings are nonspecific and its diagnosis usually requires surgical biopsy. The treatment of choice is surgical removal.Here, we report the case of a cervical GN in a 16-years-old boy with slowly growing right cervical mass.

Introduction: Malnutrition is the most common nutritional disorder in developing countries and it remains one of the most common causes of morbidity and mortality among children worldwide. Therefore, the current study aimed to assess the prevalence of wasting and its associated factors among under-five children in Marrakesh province in Morocco. Method: A community-based cross-sectional study design was used to determine the prevalence of wasting and its associated factors among under-5 years old children. A structured questionnaire was used to collect data from 430 children paired with their mothers. Anthropometric measurements and determinant factors were collected. SPSS version 19.0 statistical software was used for analysis. Multivariate logistic regression analysis was conducted to identify factors associated to the wasting of the children. Statistical association was declared significant if p value was less than0.05. Results: In this study, the prevalence of wasting was 9.3%. The proportions of severe and moderate wasting children were 6.5% and 2.8%, respectively. Higher odds of wasting children among illiterate mothers were 17 times more likely compared with mothers with high levels (ORa = 17.17; 95% CI: 1.01–13.70). In line with this, the odds of wasting children among lower household incomes were 13 times more likely when compared to those with higher household incomes (ORa = 13.114; 95% CI: 1.13–7.60). Similarly, the odds of wasting children among mothers primiparous were 12 times more likely compared with multiparous (ORa = 12.88; 95% CI: 1.04–4.08). The odds of wasting children were more observed among children who did not benefit from exclusive breastfeeding (ORa = 12.297; 95% CI: 2.47–13.03, 1.03–5.34). Conclusions: The current study showed that the prevalence of wasting among under-five children in Marrakesh province was relatively common. Mother’s education level, household income, parity, and exclusive breastfeeding were significantly associated with wasting. Therefore, nutritional educational intervention programs in Marrakesh province should focus on these factors.

Ortner's syndrome or cardiovocal syndrome is a rare condition referring to laryngeal recurrent nerve paralysis due to cardiovascular conditions. We report the case of a 66-year-old man, with a medical history of active smoking, who complained of hoarseness of voice secondary to vocal cord palsy. A neck and thoracic CT scan revealed severe enlargement of pulmonary main artery, which caused compression of the left recurrent laryngeal nerve in the aortopulmonary window. The prolonged course of the left laryngeal nerve makes it susceptible to injury from cardiovascular structures in the mediastinum. Consequently, cardiovascular conditions such as dilated pulmonary artery or an aneurysm of the aortic arch can compress the nerve, resulting in Ortner's syndrome.

In this study, we report a facile hydrothermal synthesis of strontium-doped SnS nanoflowers that were used as a catalyst for the degradation of antibiotic molecules in water. The prepared sample was characterized using X-ray diffraction (XRD), scanning electron microscopy (SEM), and ultraviolet–visible absorption spectroscopy (UV–Vis). The photocatalytic ability of the strontium-doped SnS nanoflowers was evaluated by studying the degradation of metronidazole in an aqueous solution under photocatalytic conditions. The degradation study was conducted for a reaction period of 300 min at neutral pH, and it was found that the degradation of metronidazole reached 91%, indicating the excellent photocatalytic performance of the catalyst. The influence of experimental parameters such as catalyst dosage, initial metronidazole concentration, initial reaction pH, and light source nature was optimized with respect to metronidazole degradation over time. The reusability of the strontium-doped SnS nanoflowers catalyst was investigated, and its photocatalytic efficiency remained unchanged even after four cycles of use. HIGHLIGHTS Sr-doped SnS is synthesized by the hydrothermal process.; Sr-doped SnS nanoflowers were selected to remove dyes from water.; The removal percentage of Sr-doped SnS could reach 91%.; The reusability of our catalyst Sr-doped SnS was also explored.;

A common consequence of diabetes mellitus called diabetic retinopathy (DR) results in lesions on the retina that impair vision. It can cause blindness if not detected in time. Unfortunately, DR cannot be reversed, and treatment simply keeps eyesight intact. The risk of vision loss can be considerably decreased with early detection and treatment of DR. Ophtalmologists must manually diagnose DR retinal fundus images, which takes time, effort, and is cost-consuming. It is also more prone to error than computer-aided diagnosis methods. Deep learning has recently become one of the methods used most frequently to improve performance in a variety of fields, including medical image analysis and classification. In this paper, we develop a federated learning approach to detect diabetic retinopathy using four distributed institutions in order to build a robust model. Our federated learning approach is based on Vision Transformer architecture to classify DR and Normal cases. Several performance measures were used such as accuracy, area under the curve (AUC), sensitivity and specificity. The results show an improvement of up to 3% in terms of accuracy with the proposed federated learning technique. The technique also resolving crucial issues like data security, data access rights, and data protection.

Introduction: Breakfast skipping is an unhealthy eating behavior reportedly common among adolescents. The aim of the present study was to determine the prevalence and factors associated with breakfast skipping among schoolchildren aged 12–19 years in Beni Mellal city, Morocco, and to assist in the design of interventions to improve breakfast consumption habits of school-aged children in this region. Methods: A school-based cross-sectional study was carried out. A total of 550 children were included in the study. Multivariable logistic regression analysis was used to identify factors associated with skipping breakfast consumption. The association between dependent and independent variables was assessed using an odds ratio (OR) with a 95% confidence interval and p value

Viewed as a significant natural disaster, wildfires present a serious threat to human communities, wildlife, and forest ecosystems. The frequency of wildfire occurrences has increased recently, with the impacts of global warming and human interaction with the environment playing pivotal roles. Addressing this challenge necessitates the ability of firefighters to promptly identify fires based on early signs of smoke, allowing them to intervene and prevent further spread. In this work, we adapted and optimized recent deep learning object detection, namely YOLOv8 and YOLOv7 models, for the detection of smoke and fire. Our approach involved utilizing a dataset comprising over 11,000 images for smoke and fires. The YOLOv8 models successfully identified fire and smoke, achieving a mAP:50 of 92.6%, a precision score of 83.7%, and a recall of 95.2%. The results were compared with a YOLOv6 with large model, Faster-RCNN, and DEtection TRansformer. The obtained scores confirm the potential of the proposed models for wide application and promotion in the fire safety industry.

IntroductionHuman induced pluripotent stem cells (iPSCs), with their ability to generate human neural cells (astrocytes and neurons) from patients, hold great promise for understanding the pathophysiology of major neuropsychiatric diseases such as schizophrenia and bipolar disorders, which includes alterations in cerebral development. Indeed, the in vitro neurodifferentiation of iPSCs, while recapitulating certain major stages of neurodevelopment in vivo, makes it possible to obtain networks of living human neurons. The culture model presented is particularly attractive within this framework since it involves iPSC-derived neural cells, which more specifically differentiate into cortical neurons of diverse types (in particular glutamatergic and GABAergic) and astrocytes. However, these in vitro neuronal networks, which may be heterogeneous in their degree of differentiation, remain challenging to bring to an appropriate level of maturation. It is therefore necessary to develop tools capable of analyzing a large number of cells to assess this maturation process. Calcium (Ca2+) imaging, which has been extensively developed, undoubtedly offers an incredibly good approach, particularly in its versions using genetically encoded calcium indicators. However, in the context of these iPSC-derived neural cell cultures, there is a lack of studies that propose Ca2+ imaging methods that can finely characterize the evolution of neuronal maturation during the neurodifferentiation process.MethodsIn this study, we propose a robust and reliable method for specifically measuring neuronal activity at two different time points of the neurodifferentiation process in such human neural cultures. To this end, we have developed a specific Ca2+ signal analysis procedure and tested a series of different AAV serotypes to obtain expression levels of GCaMP6f under the control of the neuron-specific human synapsin1 (hSyn) promoter.ResultsThe retro serotype has been found to be the most efficient in driving the expression of the GCaMP6f and is compatible with multi-time point neuronal Ca2+ imaging in our human iPSC-derived neural cultures. An AAV2/retro carrying GCaMP6f under the hSyn promoter (AAV2/retro-hSyn-GCaMP6f) is an efficient vector that we have identified. To establish the method, calcium measurements were carried out at two time points in the neurodifferentiation process with both hSyn and CAG promoters, the latter being known to provide high transient gene expression across various cell types.DiscussionOur results stress that this methodology involving AAV2/retro-hSyn-GCaMP6f is suitable for specifically measuring neuronal calcium activities over multiple time points and is compatible with the neurodifferentiation process in our mixed human neural cultures.

Abstract Threatened species are frequently patchily distributed across small wild populations, ex situ populations managed with varying levels of intensity and reintroduced populations. Best practice advocates for integrated management across in situ and ex situ populations. Wild addax (Addax nasomaculatus) now number fewer than 100 individuals, yet 1000 of addax remain in ex situ populations, which can provide addax for reintroductions, as has been the case in Tunisia since the mid‐1980s. However, integrated management requires genetic data to ascertain the relationships between wild and ex situ populations that have incomplete knowledge of founder origins, management histories, and pedigrees. We undertook a global assessment of genetic diversity across wild, ex situ and reintroduced populations in Tunisia to assist conservation planning for this Critically Endangered species. We show that the remnant wild populations retain more mitochondrial haplotypes that are more diverse than the entirety of the ex situ populations across Europe, North America and the United Arab Emirates, and the reintroduced Tunisian population. Additionally, 1704 SNPs revealed that whilst population structure within the ex situ population is minimal, each population carries unique diversity. Finally, we show that careful selection of founders and subsequent genetic management is vital to ensure genetic diversity is provided to, and minimize drift and inbreeding within reintroductions. Our results highlight a vital need to conserve the last remaining wild addax population, and we provide a genetic foundation for determining integrated conservation strategies to prevent extinction and optimize future reintroductions.

Introduction: The widespread lockdown due to the COVID-19 pandemic was insured by Moroccan authorities in early 2020 to preserve the health of citizens. The lockdown and the pandemic imposed psychological effects on the population including anxiety. Celiac disease (CeD), a chronic disease among the most common inflammatory intestinal disorders, has been linked to adult emotional disturbances. Hence, CeD patients may suffer from anxiety or increase this condition due to the concomitant situation. The objective of this study was to assess COVID-19-related anxiety in a sample of adults with CeD in comparison with a matched healthy group. Method: CeD patients (103) and matched healthy group (101) were investigated using a web-based COVID-19 and related lockdown anxiety survey. Anxiety was assessed with the State and Trait Anxiety Inventory (STAI test: Y1 for state anxiety and Y2 for trait anxiety). Results: State anxiety was present among 65.3% of CeD and 41.6% of the comparison group, and the difference was statistically significant. CeD women suffered more from state anxiety than their compeers in the comparison group (t = 3.23; p = 0.002), and a significant correlation between good compliance to GFD and less state anxiety was found among CeD patients (r = 0.31; p = 0.002). 61.8% of CeD participants thought they were at higher risk of contamination by COVID-19, and they were mostly women (χ2 = 7.66, p < 0.006) and had significantly higher state anxiety mean scores than their compatriots who did not express these thoughts (t = 2.93; p = 0.004). Additionally, 41.5% of CeD participants had anxiety as a trait against 26.7% in the comparison group and the difference between the two groups was statistically significant. Conclusion: Results of this survey allow a better understanding of the health-related pandemic effects on Moroccan CeD patients. It demonstrates that COVID-19 and related lockdowns had a serious impact on the psychological balance of these patients by increasing their anxiety. The survey results underlined the need to improve the psychological care of CeD patients notably by considering remote medical visits during this ongoing pandemic to provide mental health support.

In this article, we prepared europium ions (Eu3+) doped lithium lutetium double tungstate material using a classical solid-state reaction. X-ray diffraction (XRD), optical absorption and photoluminescence techniques were used to characterize the samples. XRD analysis and structural refinement measurements revealed that LiLu(WO4)2 has a monoclinic structure with a P2/n space group with a formula unit (Z = 2). Additionally, we measured, analysed, and discussed the room-temperature photoluminescence emission and excitation spectra of Eu3+-doped LiLu(WO4)2. The emission spectra show the intra-configurational red emission characteristic of Eu3+ ions, namely 5D0 → 7Fj, J = 0–4. The quenching emission by temperature as well as the 5D0 emission decay curves were measured and discussed. It was found that the quenching of luminescence is obtained for 1.5 at% Eu3+ concentration. Judd–Ofelt intensity parameters (Ω2 = 5.26 × 10−20 cm2 and Ω4 = 1.04 × 10−20 cm2) and the 'Commission Internationale de l'Eclairage' (CIE) chromaticity coordinates were calculated from the emission spectrum of LiLu1−xEux(WO4)2 for x = 0.015. The results indicate that Eu3+-doped LiLu(WO4)2 could be a suitable material for solid-state lighting applications. [ABSTRACT FROM AUTHOR]

COVID-19,which is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is one of the worst pandemics in recent history. The identification of patients suspected to be infected with COVID-19 is becoming crucial to reduce its spread. We aimed to validate and test a deep learning model to detect COVID-19 based on chest X-rays. The recent deep convolutional neural network (CNN) RegNetX032 was adapted for detecting COVID-19 from chest X-ray (CXR) images using polymerase chain reaction (RT-PCR) as a reference. The model was customized and trained on five datasets containing more than 15,000 CXR images (including 4148COVID-19-positive cases) and then tested on 321 images (150 COVID-19-positive) from Montfort Hospital. Twenty percent of the data from the five datasets were used as validation data for hyperparameter optimization. Each CXR image was processed by the model to detect COVID-19. Multi-binary classifications were proposed, such as: COVID-19 vs. normal, COVID-19 + pneumonia vs. normal, and pneumonia vs. normal. The performance results were based on the area under the curve (AUC), sensitivity, and specificity. In addition, an explainability model was developed that demonstrated the high performance and high generalization degree of the proposed model in detecting and highlighting the signs of the disease. The fine-tuned RegNetX032 model achieved an overall accuracy score of 96.0%, with an AUC score of 99.1%. The model showed a superior sensitivity of 98.0% in detecting signs from CXR images of COVID-19 patients, and a specificity of 93.0% in detecting healthy CXR images. A second scenario compared COVID-19 + pneumonia vs. normal (healthy X-ray) patients. The model achieved an overall score of 99.1% (AUC) with a sensitivity of 96.0% and specificity of 93.0% on the Montfort dataset. For the validation set, the model achieved an average accuracy of 98.6%, an AUC score of 98.0%, a sensitivity of 98.0%, and a specificity of 96.0% for detection (COVID-19 patients vs. healthy patients). The second scenario compared COVID-19 + pneumonia vs. normal patients. The model achieved an overall score of 98.8% (AUC) with a sensitivity of 97.0% and a specificity of 96.0%. This robust deep learning model demonstrated excellent performance in detecting COVID-19 from chest X-rays. This model could be used to automate the detection of COVID-19 and improve decision making for patient triage and isolation in hospital settings. This could also be used as a complementary aid for radiologists or clinicians when differentiating to make smart decisions.

Introduction Acute and chronic exposure to cannabis have been associated with neurocognitive deficits in executive function, including inhibitory control processes. Objectives To research memory deficiency in the young consumers of cannabis in Tunisia. Methods this is a transversal descriptive study conducted during two months (January and February 2020). The research involved about 137 participants in the emergency department at the university hospital of Mahdia Results In our study population, there was a noticeable male predominance of 71%. Hence, the age structure ranged between 18 years old and 35 years old. Among the latters, 65.9% were single, and 29.7% experienced school failure. In this sample, 23.2% had a psychiatric history. The average age of the first use of cannabis was between 18 and 25 years old in 70% of cases. Besides, a high percentage of association of other substances was found among cannabis users as follows: use of tobacco 74.6%, alcohol 72.5% ecstasy 41.3%, and cocaine 25.4%. The use of cannabis was considered as a means of indulgence for 66.7% of the study population, as an anxiolytic for 26.8%, and as a sedative for 23.9%. Additionally, the effect of cannabis use on working memory deficiency according to the functional impact assessment scale was: no deficiency in 19% of cannabis users, minimal in 34%, mild in 32%, moderate in 9%, fairly severe in 4%, very severe in 1%, and extreme in1% of cases. Conclusions The assumption of the effect of cannabis on memory and cognitive deficiency remains controversial and leads us to suggest further in-depth study of this subject. Disclosure No significant relationships.

The simultaneous advances in deep learning and the Internet of Things (IoT) have benefited distributed deep learning paradigms. Federated learning is one of the most promising frameworks, where a server works with local learners to train a global model. The intrinsic heterogeneity of IoT devices, or non-independent and identically distributed (Non-I.I.D.) data, combined with the unstable communication network environment, causes a bottleneck that slows convergence and degrades learning efficiency. Additionally, the majority of weight averaging-based model aggregation approaches raise questions about learning fairness. In this paper, we propose a peer-to-peer federated learning (P2PFL) framework based on Vision Transformers (ViT) models to help solve some of the above issues and classify COVID-19 vs. normal cases on Chest-X-Ray (CXR) images. Particularly, clients jointly iterate and aggregate the models in order to build a robust model. The experimental results demonstrate that the proposed approach is capable of significantly improving the performance of the model with an Area Under Curve (AUC) of 0.92 and 0.99 for hospital-1 and hospital-2, respectively.