A randomized open-label clinical trial on the effect of Amantadine on post Covid 19 fatigue.

Many COVID-19 survivors experience lingering post-COVID-19 symptoms, notably chronic fatigue persisting for months after the acute phase. Despite its prevalence, limited research has explored effective treatments for post-COVID-19 fatigue. This randomized controlled clinical trial assessed the impact of Amantadine on patients with post-COVID-19 fatigue. The intervention group received Amantadine for two weeks, while the control group received no treatment. Fatigue levels were assessed using the Visual Analog Fatigue Scale (VAFS) and Fatigue Severity Scale (FSS) questionnaires before and after the trial. At the study's onset, VAFS mean scores were 7.90 ± 0.60 in the intervention group and 7.34 ± 0.58 in the control group (P-value = 0.087). After two weeks, intervention group scores dropped to 3.37 ± 0.44, significantly lower than the control group's 5.97 ± 0.29 (P-value < 0.001). Similarly, FSS mean scores at the trial's commencement were 53.10 ± 5.96 in the intervention group and 50.38 ± 4.88 in the control group (P-value = 0.053). At the trial's end, intervention group scores decreased to 28.40 ± 2.42, markedly lower than the control group's 42.59 ± 1.50 (P-value < 0.001). In this study, we report the safety, tolerability, and substantial fatigue-relieving effects of Amantadine in post-COVID-19 fatigue. The intervention demonstrates a statistically significant reduction in fatigue levels, suggesting Amantadine's potential as an effective treatment for this persistent condition.

Cerebral hemodynamic response to generalized anxiety disorder.

BACKGROUND: Generalized anxiety disorder (GAD) is the least studied among anxiety disorders. Therefore, we aimed to compare the cervical blood flow velocities using doppler ultrasonography in untreated chronic GAD patients and healthy individuals. MATERIAL AND METHODS: In this study, thirty-eight GAD patients were enrolled. And thirty-eight healthy volunteers were recruited as control participants. The common carotid artery (CCA), internal carotid artery (ICA), and vertebral artery (VA) of both sides were explored. Also, we trained machine learning models based on cervical arteries characteristics to diagnose GAD patients. RESULTS: Patients with chronic untreated GAD showed a significant increase in peak systolic velocity (PSV) bilaterally in the CCA and the ICA (P value < 0.05). In GAD patients, the end-diastolic velocity (EDV) of bilateral CCA, VA, and left ICA was significantly decreased. The Resistive Index (RI) showed a significant increase in all patients with GAD. Moreover, the Support Vector Machine (SVM) model showed the best accuracy in identifying anxiety disorder. CONCLUSION: GAD is associated with hemodynamic alterations of extracranial cervical arteries. With a larger sample size and more generalized data, it is possible to make a robust machine learning-based model for GAD diagnosis.