RESUMEN
OBJECTIVE To evaluate the efficacy and safety of belimumab in the treatment childhood-onset systemic lupus erythematosus （cSLE）， and to provide evidence-based references for clinical medication. METHODS Randomized controlled trials （RCTs） about belimumab or belimumab combined with hormone or belimumab combined with hormone and traditional drugs （test group） compared with placebo or hormone or traditional drugs or traditional drugs combined with hormone （control group） were collected by computer searching CNKI， Wanfang data， VIP， SinoMed， PubMed， Embase， Web of Science and the Cochrane Library； the search deadline was from the establishment of the databases to April 9th， 2023. After screening the literature and extracting the data， the quality of the included literature was evaluated by using the bias risk assessment tool recommended by Cochrane system evaluation manual 5.1.0； meta-analysis and sensitivity analysis were conducted by using RevMan 5.4 software. RESULTS A total of 510 children were included in 7 RCTs. Results of the meta-analysis showed that the clinically effective rate of test group was significantly better than the control group [OR＝6.16， 95%CI （2.23， 17.00）， P＝0.000 4]. There were no statistically significant differences in SLE disease activity index （SLEDAI） [MD＝－1.73， 95%CI （－3.50， 0.05）， P＝0.06]， the incidence of adverse drug reactions [OR＝0.72， 95%CI （0.43， 1.19）， P＝0.02]， complement C3 levels [MD＝0.12， 95%CI （－0.06， 0.30）， P＝0.18]， complement C4 levels [MD＝0.08，95%CI （－0.07，0.24）， P＝0.30] or the response rate of SLE responder index 4 [OR＝1.52， 95%CI （0.94，2.44）， P＝0.09] between 2 groups. The results of sensitivity analysis showed that when SLEDAI， the complement C3 levels and complement C4 levels were used as indicators， the results obtained in this study were robust. CONCLUSIONS The efficacy of belimumab in the treatment of cSLE is good， and its safety is comparable to the basic treatment.
RESUMEN
In recent years, artificial intelligence has been persistently developed and increasingly applied in the medical field, including risk prediction, diagnosis and treatment of various diseases, which enhances the diagnosis and management levels of diseases and shows a promising application prospect in the medical field. Artificial intelligence has been rapidly advanced in the field of kidney transplantation. Researchers have attempted to apply it in multiple scenarios, such as preoperative evaluation and prediction of postoperative complications of kidney transplantation, prompting that artificial intelligence has tremendous application prospect in the field of kidney transplantation. In this article, the application of artificial intelligence in donor-recipient matching, evaluation of renal allograft function, prediction of clinical outcomes, diagnosis of postoperative complications, monitoring and management of immunosuppressants were reviewed, research progress on the application of artificial intelligence in the field of kidney transplantation was summarized, and the limitations of artificial intelligence were discussed, aiming to provide reference for promoting the practical application and popularization of artificial intelligence in the field of kidney transplantation.