Coronavirus Disease-2019 in Older People with Cognitive Impairment

Rolland, Yves; Baziard, Marion; Mauléon, Adélaïde De; Dubus, Estelle; Saidlitz, P.; Soto, María

doi:10.1016/j.cger.2022.03.002

Cited by 3 publications

(1 citation statement)

References 108 publications

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“…Elderly patients are among the most vulnerable populations facing COVID-19 infection. Cognitive complaints are frequent in elderly patients after COVID-19 [ 51 , 52 ] and verse versa, patients suffering from dementia are at higher risk of COVID-19 infection and higher risk of severe forms and hospitalization [ 53 ]. The meta-analysis on the risks associated with COVID-19 infection showed that suffering from dementia increases by 2 to 3 the risk of contracting COVID-19 compared with patients of comparable age [ 54 ].…”

Section: Predictors Of Post-covid Neurological Complicationsmentioning

confidence: 99%

Role of Demyelination in the Persistence of Neurological and Mental Impairments after COVID-19

Khodanovich

Parshukova

Naumova

2022

IJMS

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Long-term neurological and mental complications of COVID-19, the so-called post-COVID syndrome or long COVID, affect the quality of life. The most persistent manifestations of long COVID include fatigue, anosmia/hyposmia, insomnia, depression/anxiety, and memory/attention deficits. The physiological basis of neurological and psychiatric disorders is still poorly understood. This review summarizes the current knowledge of neurological sequelae in post-COVID patients and discusses brain demyelination as a possible mechanism of these complications with a focus on neuroimaging findings. Numerous reviews, experimental and theoretical studies consider brain demyelination as one of the mechanisms of the central neural system impairment. Several factors might cause demyelination, such as inflammation, direct effect of the virus on oligodendrocytes, and cerebrovascular disorders, inducing myelin damage. There is a contradiction between the solid fundamental basis underlying demyelination as the mechanism of the neurological injuries and relatively little published clinical evidence related to demyelination in COVID-19 patients. The reason for this probably lies in the fact that most clinical studies used conventional MRI techniques, which can detect only large, clearly visible demyelinating lesions. A very limited number of studies use specific methods for myelin quantification detected changes in the white matter tracts 3 and 10 months after the acute phase of COVID-19. Future research applying quantitative MRI assessment of myelin in combination with neurological and psychological studies will help in understanding the mechanisms of post-COVID complications associated with demyelination.

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Section: Predictors Of Post-covid Neurological Complicationsmentioning

confidence: 99%

Role of Demyelination in the Persistence of Neurological and Mental Impairments after COVID-19

Khodanovich

Parshukova

Naumova

2022

IJMS

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Cognitive Aspects of COVID-19

Gonzalez-Fernandez,

Huang

2023

Curr Neurol Neurosci Rep

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Multi Deep Learning Based Approaches for COVID-19 Diagnosis Using Class Resampling on Chest X-ray Images

ALAKUŞ,

BAYKARA

2023

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi

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Nowadays, current medical imaging techniques provide means of diagnosing disorders like the recent COVID-19 and pneumonia due to technological advancements in medicine. However, the lack of sufficient medical experts, particularly amidst the breakout of the epidemic, poses severe challenges in early diagnoses and treatments, resulting in complications and unexpected fatalities. In this study, a convolutional neural network (CNN) model, VGG16 + XGBoost and VGG16 + SVM hybrid models, were used for three-class image classification on a generated dataset named Dataset-A with 6,432 chest X-ray (CXR) images (containing Normal, Covid-19, and Pneumonia classes). Then, pre-trained ResNet50, Xception, and DenseNet201 models were employed for binary classification on Dataset-B with 7,000 images (consisting of Normal and Covid-19). The suggested CNN model achieved a test accuracy of 98.91 %. Then the hybrid models (VGG16 + XGBoost and VGG16 + SVM) gained accuracies of 98.44 % and 95.60 %, respectively. The fine-tuned ResNet50, Xception, and DenseNet201 models achieved accuracies of 98.90 %, 99.14 %, and 99.00 %, respectively. Finally, the models were further evaluated and tested, yielding impressive results. These outcomes demonstrate that the models can aid radiologists with robust tools for early lungs related disease diagnoses and treatment.

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Coronavirus Disease-2019 in Older People with Cognitive Impairment

Cited by 3 publications

References 108 publications

Role of Demyelination in the Persistence of Neurological and Mental Impairments after COVID-19

Role of Demyelination in the Persistence of Neurological and Mental Impairments after COVID-19

Cognitive Aspects of COVID-19

Multi Deep Learning Based Approaches for COVID-19 Diagnosis Using Class Resampling on Chest X-ray Images

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