Sixty relapsing remitting multiple sclerosis (MS) patients were selected on the basis of their score on the Fatigue Severity Scale (FSS) and formed two groups: 40 patients (fatigued MS; MSf) scored above the 75th percentile of a previously assessed representative MS sample (100 patients), and 20 age- and sex-matched patients (nonfatigued MS patients; MSnf) scored below the 25th percentile. The patients underwent clinical evaluation (Expanded Disability Status Scale (EDSS)), further assessment of fatigue (Fatigue Impact Scale), scales evaluating depression (Hamilton Depression Rating Scale (HDRS) and Beck's Depression Inventory (BDI)) and neuropsychological tests. All patients were evaluated for muscle fatigability and central activation by means of a biomechanical test of sustained contraction; they also underwent somatosensory evoked potentials (SSEPs) and transcranial magnetic stimulation (TMS). The patients of the MSf subgroup were then randomized to one of the following two treatments: 4-aminopyridine (4-AP) 24 mg/day and fluoxetine (FLX) 20 mg/day. After a one-week titration this treatment proceeded for 8 weeks. At the end of the treatment, EDSS, fatigue and depression scores were further evaluated. At baseline, fatigue test scores consistently correlated with depression and cognitive test scores, but not with the fatigability test. Fatigue scores decreased in both treatment groups in a similar way. Due to the design of the study, this cannot be disjoined from a placebo effect. The changes of fatigue scores could not be predicted in the FLX group, whereas in the 4-AP group higher basal fatigability test scores were associated with greater reduction in fatigue scores.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1191/1352458504ms1051oa | DOI Listing |
Curr Eye Res
January 2025
Department of Ophthalmology, Edward S. Harkness Eye Institute, Columbia University, Vagelos College of Physicians and Surgeons, New York, NY, USA.
Purpose: This study aimed to initially test whether machine learning approaches could categorically predict two simple biological features, mouse age and mouse species, using the retinal segmentation metrics.
Methods: The retinal layer thickness data obtained from C57BL/6 and DBA/2J mice were processed for machine learning after segmenting mouse retinal SD-OCT scans. Twenty-two models were trained to predict the mouse groups.
J Neurol
January 2025
Vienna Cognitive Science Hub, University of Vienna, Vienna, Austria.
Background: Conventional medical management, while essential, cannot address all multifaceted consequences of Parkinson's disease (PD). This pilot study explores the potential of a co-designed creative arts therapy on health-related quality of life, well-being, and pertinent non-motor symptoms.
Methods: We conducted an exploratory pilot study with a pre-post design using validated questionnaires.
Sci Rep
January 2025
Department of Respiratory and Critical Care Medicine, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, People's Republic of China.
The traditional Chinese medicine compound preparation known as Jinbei Oral Liquid (JBOL) consists of 12 herbs, including Astragalus membranaceus (Fisch.) Bge, Codonopsis pilosula (Franch.) Nannf, et al.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Public Health, Sirjan School of Medical Sciences, Sirjan, Iran.
Academic procrastination is one of the major factors that can be a serious obstacle for students to achieve academic progress and success. This research aimed to investigate and predict academic procrastination based on academic self-efficacy and emotional regulation difficulties of students of one of the medical sciences universities in southern Iran in 2024. This descriptive-analytical cross-sectional study was conducted on 290 students of different fields in the south of Iran between January and April 2024.
View Article and Find Full Text PDFSci Rep
January 2025
National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, Sichuan Province, China.
This study aimed to develop a real-time, noninvasive hyperkalemia monitoring system for dialysis patients with chronic kidney disease. Hyperkalemia, common in dialysis patients, can lead to life-threatening arrhythmias or sudden death if untreated. Therefore, real-time monitoring of hyperkalemia in this population is crucial.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!