It is known that there is an increase in the frequency of psychiatric disturbances in the acute and post‐illness phase of coronavirus disease (COVID‐19). Comorbid psychiatric symptoms complicate the management of patients and negatively affect the prognosis, but there is no clear evidence of their progress. We aimed to determine psychiatric comorbidity in inpatients and outpatients with COVID‐19 and recognize the factors that predict psychiatric comorbidity. For this purpose, we evaluated patients on the first admission and after 4 weeks. We investigated psychiatric symptoms in outpatients (n = 106) and inpatients (n = 128) diagnosed with COVID‐19. In the first 7 days after diagnosis (first phase), sociodemographic and clinic data were collected, a symptom checklist was constructed, and the Hospital Anxiety and Depression Scale (HADS) and the Severity of Acute Stress Symptoms Scale (SASSS) were applied. After 30–35 days following the diagnosis, the SASSS and the HADS were repeated. In the first phase, the frequency of depression and anxiety were 55% and 20% in inpatients, and 39% and 18% in outpatients, respectively. In the second phase, depression scores are significantly decreased in both groups whereas anxiety scores were decreased only in inpatients. The frequencies of patients reporting sleep and attention problems, irritability, and suicide ideas decreased after 1 month. Patients with loss of smell and taste exhibit higher anxiety and depression scores in both stages. Our results revealed that the rate of psychiatric symptoms in COVID‐19 patients improves within 1 month. Inpatients have a more significant decrease in both depression and anxiety frequency than do outpatients. The main factor affecting anxiety and depression was the treatment modality. Considering that all patients who were hospitalized were discharged at the end of the first month, this difference may be due to the elimination of the stress caused by hospitalization.
Background: The Broadman Area 17 (V1) has a good representation of retinotopic map. Similarity between visual input and the representation of it in V1 would be affected from both an intrinsic noise and the saccadic eye movements. GABA’s role in increasing signal to noise ratio is known but, how GABAergic activity helps to control noise, based on input and saccades, has not been modelled.
Methods: A computational model of V1 was designed by using the MATLAB 2021a platform, and different six images, each containing a circle, triangle, and square, were used to test the model. The developed V1 was constituted of six different orientation columns (OCs). Each OC contains GABAergic and glutamatergic connections. Thus, OCs were activated not only based on afferent image inputs but also on the interaction among fired columns via the sum of glutamate and GABAergic neuron weights. V1 representation states for twelve, twenty and thirty saccades were summed and visualized. Finally, the original and representational forms of the image were compared. In the model, GABA activity levels have been tuned and the results of each level analysed.
Results: It has been shown that level of GABA activity in the orientation columns during saccades is a critical factor for an ideal image representation. Decreased levels of GABA activity can be associated with inadequacy of noise elimination which could impair correct contour perception.
Conclusion: Orientation columns can be conceptualized as microprocessors of V1. In this region, images are represented with high similarity. This similarity seems to need efficient GABAergic activity.
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