AIM:To investigate the relationship between the level of monocyte chemoattractant protein-1(MCP-1)in the aqueous humor and macular microcirculation in patients with macular edema secondary to retinal vein occlusion(RVO-ME).METHODS:A total of 327 patients(327 eyes)with RVO-ME treated in our hospital from July 2022 to July 2024 were selected as the research objects. According to the recurrence or not, they were divided into non-recurrence group(291 cases, 291 eyes)and recurrence group(36 cases, 36 eyes). The clinical data, macular microcirculation index and MCP-1 level were collected. Unordered multinomial Logistic regression was used to analyze the effect of MCP-1 on RVO-ME recurrence after excluding the influence of other factors. Cubic spline was used to analyze the dose-response relationship between macular microcirculation indexes and MCP-1 expression level and RVO-ME recurrence. Multiple linear regression was used to analyze the relationship between macular microcirculation parameters and MCP-1 expression level, and the difference of MCP-1 expression level in recurrence under different macular microcirculation parameters was analyzed. Bootstrap method was used to test the mediating effect of macular microcirculation indexes on MCP-1 expression level and RVO-ME recurrence.RESULTS: The course of ME, the incidence of vitreomacular traction(VMT), the incidence of fibrous membrane epiretinal membrane(ERM), best corrected visual acuity(BCVA), hyperreflective foci in the inner and outer retinal layers(HRF), the frequency and dose of anti-vascular endothelial growth factor(VEGF)injection in the recurrence group were significantly higher than those in the non-recurrence group(all P<0.05). The external limiting membrane(ELM)integrity and ellipsoid zone(EZ)integrity in the recurrence group were significantly worse than those in the non-recurrence group(all P<0.05). The central foveal thickness(CFT), central macular thickness(CMT), superficial capillary plexus(SCP)vascular density, deep capillary plexus(DCP)vascular density and MCP-1 in the recurrence group were higher than those in the non-recurrence group(all P<0.05), and the foveal avascular zone(FAZ)area was lower than that in the non-recurrence group(P<0.05). Logistic analysis showed that MCP-1 was a risk factor for RVO-ME recurrence before and after adjusting for confounding factors. There was a significant non-linear dose-response relationship between macular microcirculation indexes and MCP-1 expression and the risk of RVO-ME recurrence(non-linear test, all P<0.001). The vascular density of CFT, CMT, SCP and DCP was positively correlated with the expression level of MCP-1(all P<0.05), while the FAZ was negatively correlated with MCP-1 expression level(P<0.05). With the increase of vessel density in CFT, CMT, SCP and DCP, and the decrease of FAZ, the expression level of MCP-1 increased, and the risk of RVO-ME recurrence showed an upward trend. The proportion of MCP-1 in Q3(>28.47 pg/mL)group was the highest(P<0.05). Macular microcirculation parameters play a mediating effect between MCP-1 expression level and RVO-ME recurrence.CONCLUSION: The level of MCP-1 in aqueous humor is positively correlated with RVO-ME recurrence, and it is closely related to macular microcirculation. Macular microcirculation has a mediating effect between MCP-1 level and RVO-ME recurrence.

Objective:To establish a disease risk prediction model for the newborn screening system of inherited metabolic diseases by artificial intelligence technology.Methods:This was a retrospectively study. Newborn screening data ( n=5 907 547) from February 2010 to May 2019 from 31 hospitals in China and verified data ( n=3 028) from 34 hospitals of the same period were collected to establish the artificial intelligence model for the prediction of inherited metabolic diseases in neonates. The validity of the artificial intelligence disease risk prediction model was verified by 360 814 newborns ' screening data from January 2018 to September 2018 through a single-blind experiment. The effectiveness of the artificial intelligence disease risk prediction model was verified by comparing the detection rate of clinically confirmed cases, the positive rate of initial screening and the positive predictive value between the clinicians and the artificial intelligence prediction model of inherited metabolic diseases. Results:A total of 3 665 697 newborns ' screening data were collected including 3 019 cases ' positive data to establish the 16 artificial intelligence models for 32 inherited metabolic diseases. The single-blind experiment ( n=360 814) showed that 45 clinically diagnosed infants were detected by both artificial intelligence model and clinicians. A total of 2 684 cases were positive in tandem mass spectrometry screening and 1 694 cases were with high risk in artificial intelligence prediction model of inherited metabolic diseases, with the positive rates of tandem 0.74% (2 684/360 814)and 0.46% (1 694/360 814), respectively. Compared to clinicians, the positive rate of newborns was reduced by 36.89% (990/2 684) after the application of the artificial intelligence model, and the positive predictive values of clinicians and artificial intelligence prediction model of inherited metabolic diseases were 1.68% (45/2 684) and 2.66% (45/1 694) respectively. Conclusion:An accurate, fast, and the lower false positive rate auxiliary diagnosis system for neonatal inherited metabolic diseases by artificial intelligence technology has been established, which may have an important clinical value.