Introduction: Restless legs syndrome (RLS) is a common neurological disorder that might impair nocturnal rest causing decreased alertness, depressed mood, reduced job performance, and poor quality of life. In patients affected by severe RLS, a pharmacological treatment is mandatory.
Areas Covered: The present review is based on a search using PubMed from 1994 to 2012. It is focused on the Absorption, Distribution, Metabolism, Elimination and Toxicology (ADMET) characteristics of the most used medications for RLS. In particular, the ADMET characteristics of dopaminergic agents, anticonvulsants able to improve neuropathic pain, and iron were discussed.
Expert Opinion: Clinical trials have showed that non-ergolic dopamine agonists are efficacious and safe for patients affected by moderate to severe idiopathic RLS. However, no head-to-head study has compared the long-term effects of the three dopamine agonists approved by the FDA for RLS (ropinirole, pramipexole, and rotigotine). Moreover, further studies should investigate the extended-release formulation of ropinirole and pramipexole in RLS patients affected by all day long distressing symptoms. A standardized treatment for symptomatic forms of RLS is lacking. Randomized, placebo-controlled trials should be performed at least in RLS patients with peripheral neuropathic and chronic kidney disease. Concerning RLS due to iron deficiency, a head-to-head study comparing efficacy, safety and compliance of oral iron versus intravenous one seems to be needed.
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http://dx.doi.org/10.1517/17425255.2012.708023 | DOI Listing |
Random lasers (RLs) with a simple structure and low-cost properties have been recognized as an ideal analytical platform and are still challenging for liquid detecting, remaining beset for low sensitivity, complicated operation, and large analyte consumption. Here, inspired by a microfluidic sensor, a microtubule structured random laser for multifunctional sensing is demonstrated. The random laser is achieved resorting to a curly PMMA film with gain and scatterers embedded in it.
View Article and Find Full Text PDFPhotosynthetica
January 2025
University of Reims Champagne-Ardenne, INRAE, RIBP, USC 1488, 51100 Reims, France.
High temperatures severely affect plant growth and development leading to major yield losses. These temperatures are expected to increase further due to global warming, with longer and more frequent heat waves. Rhamnolipids (RLs) are known to protect several plants against various pathogens.
View Article and Find Full Text PDFSleep Biol Rhythms
January 2025
Bahcesehir University Medical Faculty, Neurology, Istanbul, Turkey.
Restless legs syndrome (RLS) is characterized by an uncomfortable urge to move the legs, worsened in the evening, occurring at rest, and relieved temporarily by movement. Although its pathophysiology remains incompletely understood, oxidative stress has been suggested. Uric acid (UA) is a marker associated with oxidative stress, and its reduced levels pose a risk for certain neurodegenerative diseases.
View Article and Find Full Text PDFSleep Biol Rhythms
January 2025
Department of Neurology, Dokkyo Medical University, 880 Kitakobayashi, Mibu, Shimotsuga, Tochigi 321-0293 Japan.
Although many studies have indicated a significant association between migraine and restless legs syndrome (RLS), few long-term longitudinal studies have examined RLS in patients with migraine. We conducted a single-center, 12-year, longitudinal study of migraine patients and assessed whether RLS was present in 2010, 2017, or 2022 to evaluate its associations with clinical factors. Headache-related disability was assessed using the Migraine Disability Assessment (MIDAS).
View Article and Find Full Text PDFSensors (Basel)
January 2025
Key Laboratory of Automotive Power Train and Electronics, Hubei University of Automotive Technology, Shiyan 442002, China.
Autonomous driving has demonstrated impressive driving capabilities, with behavior decision-making playing a crucial role as a bridge between perception and control. Imitation Learning (IL) and Reinforcement Learning (RL) have introduced innovative approaches to behavior decision-making in autonomous driving, but challenges remain. On one hand, RL's policy networks often lack sufficient reasoning ability to make optimal decisions in highly complex and stochastic environments.
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