Clinical outcome prediction from analysis of microelectrode recordings using deep learning in subthalamic deep brain stimulation for Parkinson`s disease

Machine learning in deep brain stimulation: A systematic review

Deep Brain Stimulation (DBS) is an increasingly common therapy for a large range of neurological disorders, such as abnormal movement disorders. The effectiveness of DBS in terms of controlling patient symptomatology has made this procedure increasingly used over the past few decades. Concurrently, the popularity of Machine Learning (ML), a subfield of artificial intelligence, has skyrocketed and its influence has more recently extended to medical domains such as neurosurgery. Despite its growing research interest, there has yet to be a literature review specifically on the use of ML in DBS. We have followed a fully systematic methodology to obtain a corpus of 73 papers. In each paper, we identified the clinical application, the type/amount of data used, the method employed, and the validation strategy, further decomposed into 12 different sub-categories. The papers overall illustrated some existing trends in how ML is used in the context of DBS, including the breath of the problem domain and evolving techniques, as well as common frameworks and limitations. This systematic review analyzes at a broad level how ML have been recently used to address clinical problems on DBS, giving insight into how these new computational methods are helping to push the state-of-the-art of functional neurosurgery. DBS clinical workflow is complex, involves many specialists, and raises several clinical issues which have partly been addressed with artificial intelligence. However, several areas remain and those that have been recently addressed with ML are by no means considered "solved" by the community nor are they closed to new and evolving methods.

Therapeutic Potential of Ursolic Acid in Cancer and Diabetic Neuropathy Diseases

Ursolic acid (UA) is a pentacyclic triterpenoid frequently found in medicinal herbs and plants, having numerous pharmacological effects. UA and its analogs treat multiple diseases, including cancer, diabetic neuropathy, and inflammatory diseases. UA inhibits cancer proliferation, metastasis, angiogenesis, and induced cell death, scavenging free radicals and triggering numerous anti- and pro-apoptotic proteins. The biochemistry of UA has been examined broadly based on the literature, with alterations frequently having been prepared on positions C-3 (hydroxyl), C12-C13 (double bonds), and C-28 (carboxylic acid), leading to several UA derivatives with increased potency, bioavailability and water solubility. UA could be used as a protective agent to counter neural dysfunction via anti-oxidant and anti-inflammatory effects. It is a potential therapeutic drug implicated in the treatment of cancer and diabetic complications diseases provide novel machinery to the anti-inflammatory properties of UA. The pharmacological efficiency of UA is exhibited by the therapeutic theory of one-drug → several targets → one/multiple diseases. Hence, UA shows promising therapeutic potential for cancer and diabetic neuropathy diseases. This review aims to discuss mechanistic insights into promising beneficial effects of UA. We further explained the pharmacological aspects, clinical trials, and potential limitations of UA for the management of cancer and diabetic neuropathy diseases.