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erythropoietin, Sygen, Rho Protein, and Riluzole were inconclusive, primarily due to insufficient sample size or outdated evidence.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.