Molecular Signatures of Normal Pressure Hydrocephalus: A Largescale Proteomic Analysis of Cerebrospinal Fluid.

Given the persistent challenge of differentiating idiopathic Normal Pressure Hydrocephalus (iNPH) from similar clinical entities, we conducted an in-depth proteomic study of cerebrospinal fluid (CSF) in 28 shunt-responsive iNPH patients, 38 Mild Cognitive Impairment (MCI) due to Alzheimer's disease, and 49 healthy controls. Utilizing the Olink Explore 3072 panel, we identified distinct proteomic profiles in iNPH that highlight significant downregulation of synaptic markers and cell-cell adhesion proteins. Alongside vimentin and inflammatory markers upregulation, these results suggest ependymal layer and transependymal flow dysfunction. Moreover, downregulation of multiple proteins associated with congenital hydrocephalus (e.g., L1CAM, PCDH9, ISLR2, ADAMTSL2, and B4GAT1) points to a possible shared molecular foundation between congenital hydrocephalus and iNPH. Through orthogonal partial least squares discriminant analysis (OPLS-DA), a panel comprising 13 proteins has been identified as potential diagnostic biomarkers of iNPH, pending external validation. These findings offer novel insights into the pathophysiology of iNPH, with implications for improved diagnosis.

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.

Generalizable machine learning approach for COVID-19 mortality risk prediction using on-admission clinical and laboratory features.

We aimed to propose a mortality risk prediction model using on-admission clinical and laboratory predictors. We used a dataset of confirmed COVID-19 patients admitted to three general hospitals in Tehran. Clinical and laboratory values were gathered on admission. Six different machine learning models and two feature selection methods were used to assess the risk of in-hospital mortality. The proposed model was selected using the area under the receiver operator curve (AUC). Furthermore, a dataset from an additional hospital was used for external validation. 5320 hospitalized COVID-19 patients were enrolled in the study, with a mortality rate of 17.24% (N = 917). Among 82 features, ten laboratories and 27 clinical features were selected by LASSO. All methods showed acceptable performance (AUC > 80%), except for K-nearest neighbor. Our proposed deep neural network on features selected by LASSO showed AUC scores of 83.4% and 82.8% in internal and external validation, respectively. Furthermore, our imputer worked efficiently when two out of ten laboratory parameters were missing (AUC = 81.8%). We worked intimately with healthcare professionals to provide a tool that can solve real-world needs. Our model confirmed the potential of machine learning methods for use in clinical practice as a decision-support system.

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.