Self-reported depression and its risk factors among hypertensive patients, Morocco: a cross-sectional study.

Hypertensive patients are at an elevated risk of developing mental diseases such as depression, which can impair their quality of life. The purpose of this study is to measure the prevalence of self-reported depression among hypertensive patients treated at primary health care facilities in Marrakech. Between May 2021 and December 2022, a cross-sectional study of 1053 hypertensive patients attending primary health care facilities in Marrakech was conducted. A face-to-face questionnaire was used to collect socio-demographic, behavioral, and clinical data, as well as hypertension treatment characteristics and the care-patient-physician triad. The Patient Health Questionnaire-9 was used to assess self-reported depression. To identify self-reported depression risk factors, multivariate logistic regression was used. Depressive symptoms were reported by 56.1% of hypertensive patients. The patients' average age was 63.2 ± 9.5 years, and 508 (85.9%) were female. Female sex, stress, a low-salt diet, pain and physical discomfort, an urban living environment, a lack of self-monitoring of hypertension, an unsatisfactory relationship with the healthcare system, a family history of hypertension, and the perception of adverse effects of the antihypertensive drug were all associated with self-reported depression. Self-reported depression is prevalent among hypertensive patients in Marrakech. The mental health component should be emphasized while addressing hypertensive patients in primary health care facilities.

A survey on deep learning-based identification of plant and crop diseases from UAV-based aerial images.

The agricultural crop productivity can be affected and reduced due to many factors such as weeds, pests, and diseases. Traditional methods that are based on terrestrial engines, devices, and farmers' naked eyes are facing many limitations in terms of accuracy and the required time to cover large fields. Currently, precision agriculture that is based on the use of deep learning algorithms and Unmanned Aerial Vehicles (UAVs) provides an effective solution to achieve agriculture applications, including plant disease identification and treatment. In the last few years, plant disease monitoring using UAV platforms is one of the most important agriculture applications that have gained increasing interest by researchers. Accurate detection and treatment of plant diseases at early stages is crucial to improving agricultural production. To this end, in this review, we analyze the recent advances in the use of computer vision techniques that are based on deep learning algorithms and UAV technologies to identify and treat crop diseases.

Deep learning techniques to classify agricultural crops through UAV imagery: a review.

During the last few years, Unmanned Aerial Vehicles (UAVs) technologies are widely used to improve agriculture productivity while reducing drudgery, inspection time, and crop management cost. Moreover, they are able to cover large areas in a matter of a few minutes. Due to the impressive technological advancement, UAV-based remote sensing technologies are increasingly used to collect valuable data that could be used to achieve many precision agriculture applications, including crop/plant classification. In order to process these data accurately, we need powerful tools and algorithms such as Deep Learning approaches. Recently, Convolutional Neural Network (CNN) has emerged as a powerful tool for image processing tasks achieving remarkable results making it the state-of-the-art technique for vision applications. In the present study, we reviewed the recent CNN-based methods applied to the UAV-based remote sensing image analysis for crop/plant classification to help researchers and farmers to decide what algorithms they should use accordingly to their studied crops and the used hardware. Fusing different UAV-based data and deep learning approaches have emerged as a powerful tool to classify different crop types accurately. The readers of the present review could acquire the most challenging issues facing researchers to classify different crop types from UAV imagery and their potential solutions to improve the performance of deep learning-based algorithms.

Vehicle Detection From UAV Imagery With Deep Learning: A Review.

Vehicle detection from unmanned aerial vehicle (UAV) imagery is one of the most important tasks in a large number of computer vision-based applications. This crucial task needed to be done with high accuracy and speed. However, it is a very challenging task due to many characteristics related to the aerial images and the used hardware, such as different vehicle sizes, orientations, types, density, limited datasets, and inference speed. In recent years, many classical and deep-learning-based methods have been proposed in the literature to address these problems. Handed engineering- and shallow learning-based techniques suffer from poor accuracy and generalization to other complex cases. Deep-learning-based vehicle detection algorithms achieved better results due to their powerful learning ability. In this article, we provide a review on vehicle detection from UAV imagery using deep learning techniques. We start by presenting the different types of deep learning architectures, such as convolutional neural networks, recurrent neural networks, autoencoders, generative adversarial networks, and their contribution to improve the vehicle detection task. Then, we focus on investigating the different vehicle detection methods, datasets, and the encountered challenges all along with the suggested solutions. Finally, we summarize and compare the techniques used to improve vehicle detection from UAV-based images, which could be a useful aid to researchers and developers to select the most adequate method for their needs.

Acute Disseminated Encephalomyelitis: A rare form of COVID-19's neurotropism.

INTRODUCTION: the COVID-19 pandemic still accounts for thousands of cases every day. It's neurological involvement has been well documented most likely due to auto-immune mechanisms than the virus itself. CASE REPORT: we report the case of a 38 years old women who developed an Acute Disseminated Encephalomyelitis following a COVID-19 infection, with a favorable outcome after immunosuppressive therapy. DISCUSSION: In this chapter, we discuss ADEM's pathogenesis as well as its clinical and radiological features before detailing its relationship with infectious and vaccination episodes. We also discuss how our patient disease evolved. CONCLUSION: Acute Disseminated Encephalomyelitis is an immune-mediated disorder in which the widespread inflammation of the brain and spinal cord is responsible for a variety of symptoms. The novel COVID-19 virus and its vaccine are both a newly incriminated etiologies of this demyelinating disorder.

Stimulation Pattern Efficiency in Percutaneous Auricular Vagus Nerve Stimulation: Experimental Versus Numerical Data.

OBJECTIVE: Percutaneous electrical stimulation of the auricular vagus nerve (pVNS) is an electroceutical technology. The selection of stimulation patterns is empirical, which may lead to under-stimulation or over-stimulation. The objective is to assess the efficiency of different stimulation patterns with respect to individual perception and to compare it with numerical data based on in-silico ear models. METHODS: Monophasic (MS), biphasic (BS) and triphasic stimulation (TS) patterns were tested in volunteers. Different clinically-relevant perception levels were assessed. In-silico models of the human ear were created with embedded fibers and vessels to assess different excitation levels. RESULTS: TS indicates experimental superiority over BS which is superior to MS while reaching different perception levels. TS requires about 57% and 35% of BS and MS magnitude, respectively, to reach the comfortable perception. Experimental thresholds decrease from non-bursted to bursted stimulation. Numerical results indicate a slight superiority of BS and TS over MS while reaching different excitation levels, whereas the burst length has no influence. TS yields the highest number of asynchronous action impulses per stimulation symbol for the used tripolar electrode set-up. CONCLUSION: The comparison of experimental and numerical data favors the novel TS pattern. The analysis separates excitatory pVNS effects in the auricular periphery, as accounted by in-silico data, from the combination of peripheral and central pVNS effects in the brain, as accounted by experimental data. SIGNIFICANCE: The proposed approach moves from an empirical selection of stimulation patterns towards efficient and optimized pVNS settings.

Current Directions in the Auricular Vagus Nerve Stimulation I - A Physiological Perspective.

Electrical stimulation of the auricular vagus nerve (aVNS) is an emerging technology in the field of bioelectronic medicine with applications in therapy. Modulation of the afferent vagus nerve affects a large number of physiological processes and bodily states associated with information transfer between the brain and body. These include disease mitigating effects and sustainable therapeutic applications ranging from chronic pain diseases, neurodegenerative and metabolic ailments to inflammatory and cardiovascular diseases. Given the current evidence from experimental research in animal and clinical studies we discuss basic aVNS mechanisms and their potential clinical effects. Collectively, we provide a focused review on the physiological role of the vagus nerve and formulate a biology-driven rationale for aVNS. For the first time, two international workshops on aVNS have been held in Warsaw and Vienna in 2017 within the framework of EU COST Action "European network for innovative uses of EMFs in biomedical applications (BM1309)." Both workshops focused critically on the driving physiological mechanisms of aVNS, its experimental and clinical studies in animals and humans, in silico aVNS studies, technological advancements, and regulatory barriers. The results of the workshops are covered in two reviews, covering physiological and engineering aspects. The present review summarizes on physiological aspects - a discussion of engineering aspects is provided by our accompanying article (Kaniusas et al., 2019). Both reviews build a reasonable bridge from the rationale of aVNS as a therapeutic tool to current research lines, all of them being highly relevant for the promising aVNS technology to reach the patient.

Current Directions in the Auricular Vagus Nerve Stimulation II - An Engineering Perspective.

Electrical stimulation of the auricular vagus nerve (aVNS) is an emerging electroceutical technology in the field of bioelectronic medicine with applications in therapy. Artificial modulation of the afferent vagus nerve - a powerful entrance to the brain - affects a large number of physiological processes implicating interactions between the brain and body. Engineering aspects of aVNS determine its efficiency in application. The relevant safety and regulatory issues need to be appropriately addressed. In particular, in silico modeling acts as a tool for aVNS optimization. The evolution of personalized electroceuticals using novel architectures of the closed-loop aVNS paradigms with biofeedback can be expected to optimally meet therapy needs. For the first time, two international workshops on aVNS have been held in Warsaw and Vienna in 2017 within the scope of EU COST Action "European network for innovative uses of EMFs in biomedical applications (BM1309)." Both workshops focused critically on the driving physiological mechanisms of aVNS, its experimental and clinical studies in animals and humans, in silico aVNS studies, technological advancements, and regulatory barriers. The results of the workshops are covered in two reviews, covering physiological and engineering aspects. The present review summarizes on engineering aspects - a discussion of physiological aspects is provided by our accompanying article (Kaniusas et al., 2019). Both reviews build a reasonable bridge from the rationale of aVNS as a therapeutic tool to current research lines, all of them being highly relevant for the promising aVNS technology to reach the patient.