Efficacy of cannabis-based medicine in the treatment of Tourette syndrome: a systematic review and meta-analysis.

BACKGROUND: Tourette syndrome (TS) is a neurodevelopmental disorder characterized by motor and phonic tics. It is a condition that affects between 0.3% and 0.7% of children, and its pathophysiology remains largely elusive. TS is associated with structural and functional alterations in corticostriatal circuits and neurochemical imbalances. Even though TS is currently incurable, there are established treatment options available, including behavioral therapy and neuroleptics. The use of cannabis-based medicine for tic management is an emerging therapeutic strategy, although its efficacy is still under investigation. It is hypothesized to interact with the endogenous cannabinoid system, but further research is required to ascertain its safety and effectiveness in TS. AIM: In our systematic review and meta-analysis, we aim to assess the effectiveness of cannabis-based medicine in the treatment of TS. METHODS: We searched PubMed, Cochrane, Scopus, and Web of Sciences until February 2024. We included clinical trials and cohort studies investigating the efficacy of cannabis-based medicine in the treatment of TS. Data extraction focused on baseline characteristics of the included studies and efficacy outcomes, including scores on the Yale Global Tic Severity Scale (YGTSS), Premonitory Urge for Tics Scale (PUTS), and Yale-Brown Obsessive Compulsive Scale (Y-BOCS). We conducted the meta-analysis using Review Manager version 5.4. software. We compared the measurements before and after drug intake using mean difference (MD) and 95% confidence interval (CI). RESULTS: In total, 357 articles were identified for screening, with nine studies included in the systematic review and 3 in the meta-analysis. These studies involved 401 adult patients with TS treated with cannabis. YGTSS revealed a significant reduction in total scores (MD = -23.71, 95% CI [-43.86 to -3.55], P = 0.02), PUTS revealed a significant decrease in scores (MD = -5.36, 95% CI [-8.46 to -2.27], P = 0.0007), and Y-BOCS revealed no significant difference in score reduction (MD = -6.22, 95% CI [-12.68 to 0.23], P = 0.06). CONCLUSION: The current study indicates promising and potentially effective outcomes with the use of cannabis-based medicine in mitigating the severity of tics and premonitory urges. However, there is a need for larger, placebo-controlled studies with more representative samples to validate these findings.

An updated systematic review of neuroprotective agents in the treatment of spinal cord injury.

This systematic review aims to summarize the findings from all clinical randomized trials assessing the efficacy of potential neuroprotective agents in influencing the outcomes of acute spinal cord injuries (SCI). Following the PRISMA guidelines, we conducted comprehensive searches in four electronic databases (PubMed, Scopus, Cochrane Library, and Web of Science) up to September 5th, 2023. Our analysis included a total of 30 studies. We examined the effects of 15 substances/drugs: methylprednisolone, tirilazad mesylate, erythropoietin, nimodipine, naloxone, Sygen, Rho protein antagonist, granulocyte colony-stimulating factor, autologous macrophages, autologous bone marrow cells, vitamin D, progesterone, riluzole, minocycline, and blood alcohol concentration. Notable improvements in neurological outcomes were observed with progesterone plus vitamin D and granulocyte colony-stimulating factor. In contrast, results for methylprednisolone, erythropoietin, Sygen, Rho Protein, and Riluzole were inconclusive, primarily due to insufficient sample size or outdated evidence. No significant differences were found in the remaining evaluated drugs. Progesterone plus vitamin D, granulocyte colony-stimulating factor, methylprednisolone, Sygen, Rho Protein, and Riluzole may enhance neurological outcomes in acute SCI cases. It is worth noting that different endpoints or additional subgroup analyses may potentially alter the conclusions of individual trials. Therefore, certain SCI grades may benefit more from these treatments than others, while the overall results may remain inconclusive.

Using artificial intelligence to improve body iron quantification: A scoping review.

This scoping review explores the potential of artificial intelligence (AI) in enhancing the screening, diagnosis, and monitoring of disorders related to body iron levels. A systematic search was performed to identify studies that utilize machine learning in iron-related disorders. The search revealed a wide range of machine learning algorithms used by different studies. Notably, most studies used a single data type. The studies varied in terms of sample sizes, participant ages, and geographical locations. AI's role in quantifying iron concentration is still in its early stages, yet its potential is significant. The question is whether AI-based diagnostic biomarkers can offer innovative approaches for screening, diagnosing, and monitoring of iron overload and anemia.