End-stage renal disease is the final stage of chronic kidney disease, and research on palliative care for end-stage renal disease patients in China is still in its infancy. The research content of palliative care for end-stage renal disease at home and abroad mainly includes identification and management of symptoms, advance care planning, psychosocial and spiritual support, and ethical issues in dialysis decision-making. However, practical experience is still insufficient. By focusing on the overview, development status, patient needs, as well as implementation forms and models of palliative care for endstage renal disease patients, this paper summarized the research progress and application status of related research, with a view to providing references for future domestic research and clinical practice in this field.

Objective : To analyze the overall survival( OS) of sequential osimertinib treatment in patients with epidermal growth factor receptor(EGFR) exon 20 T790M mutant advanced non⁃small cell lung cancer(NSCLC) and risk factors of the efficacy of sequential osimertinib treatment. Methods : The data of 138 advanced NSCLC patients with acquired EGFR exon 20 T790M mutation who took sequential osimertinib as second⁃line treatment. KaplanMeier variable was used for survival analysis. The Log⁃rank method was used for univariate analysis. The COX risk regression model was used for multivariate analysis. The survival status and influencing factors of patients treated with sequential osimertinib were analyzed. Results : At the last follow⁃up , 99 of the 138 patients died. Median progression free survival (PFS1)of first⁃line of first⁃ or second⁃generation epidermal growth factor receptor tyrosine kinase inhibitors(EGFR⁃TKIs) was 11 months (95% CI: 10. 1 - 11. 9) ; median PFS2 of osimertinib was 10 months (95% CI: 8. 5 - 11. 5) ; The median PFS with sequential osimertinib treatment was 24 months(95% CI: 21. 7 -26. 3) , the median OS was 32 months(95% CI: 28. 9 - 35. 1) . In univariate and multivariate analysis , PFS1 was an independent prognostic factor for PFS and OS(P < 0. 001) . Conclusion Sequential osimertinib treatment for advanced NSCLC patients with acquired EGFR exon 20 T790M mutation achieved good PFS(24 months) and OS (32 months) .

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.