Photobiomodulation's potential as a non-invasive therapy for alzheimer's disease and minimal cognitive impairment: A 12-week investigation.

BACKGROUND: Alzheimer's Disease (AD), undergoing a faster increase in occurrence than any other type of dementia, lacks a curative remedy despite advanced discoveries. To explore the realm of non-pharmacologic therapies, our study evaluates the 12-week impact of non-invasive Photobiomodulation (PBM) on cognitive and psychological aspects in individuals with AD and minimal cognitive impairment (MCI). The urgency of exploring innovative interventions is underscored by the rising occurrence of AD, particularly in regions with aging populations like Iran. METHOD: 13 patients (6 case patients and 7 control patients) participated in the study. Sham treatment was administered to seven individuals, while another six received PBM treatment over 12 weeks, with daily at-home LED (810 nm wavelength) device usage lasting 20 min. Initially, the patient and their caregiver participated in two hospital sessions to acquaint them with the device's operation. RESULTS: The mean reduction of Hamilton's anxiety questionnaire score was 3.33±6.08 in the intervention group and 2.00±3.46 in the control group (p-value=0.836). The mean score reduction of the Hamilton depression questionnaire was 3.16±3.86 in the intervention group and 4.85±6.20 in the control group (p-value=0.836). The mean score of the DAD questionnaire in the intervention group before the study was 25.50±13.13 and after the intervention was 29.83±12.12 (p-value=0.084) and in the control group it was 29.71±8.19 and after the study was 29±0.972 (p-value = 0.526). The mean changes in the DAD questionnaire score in the intervention group increased by 4.33±4.92 and decreased by 0.71±2.81 in the control group (p-value=0.041). CONCLUSION: In general, PBM appears to hold promise as a potentially safe method for enhancing the cognitive, functional, and psychological status of individuals with Alzheimer's disease, though further research with larger sample size and cautious interpretation are warranted.

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.