BACKGROUND:A short-peptide small molecule hydrogel (SMH) developed in the previous study has more obvious advantages than other hydrogels to improve local microenvironment, carry bioactive substances and interfere with stem cell signal transduction pathways. OBJECTIVE:To explore the effect of SMHs on bone marrow mesenchymal stem cells (BMSCs) proliferation, apoptosis and differentiation into myocardial cells. METHODS: (1) Passage 9 rat BMSCs in vitro were divided into control group and experimental group, followed by routine culture and culture in SMHs, respectively. At 7 days of culture, cell proliferation and apoptosis were detected. Cells in the two groups were exposed to anaerobic environment for 12 hours, and expression levels of Bcl-2, Bax and Caspase-3 in BMSCs were detected. (2) Passage 9 BMSCs were divided into four groups and then cultured in 5-azacytidine, SMHs, SMHs+5-azacytidine, and L-DMEM (normal control), respectively. After 4 weeks of induction, expression of CTnT, desmin and Cx-43 proteins was detected and expression levels of early cardiac transcription factors, NKX2.5 and GATA-4, were also measured. RESULTS AND CONCLUSION: (1) Compared with the control group, better proliferation and lower apoptosis of BMSCs were found in the experimental group. Under anaerobic conditions, the number of survival cells was reduced in both groups, but less apoptosis or necrosis was found in the experimental group than the control group (P < 0.05). Moreover, the level of Bcl-2 was higher in the experimental group than the control group (P < 0.01), while the levels of Bax and Caspases-3 protiens were lower in the experimental group than the control group (P < 0.01). (2) NKx2.5 and GATA-4 mRNA expression was found in both 5-azacytidine and SMHs+5-azacytidine groups, and moreover, the mRNA levels of early cardiac transcription factors were significantly higher in the SMHs+5-azacytidine group than in the 5-azacytidine group (P < 0.05). In the normal control group, cTnT expressed negatively, and desmin and Cx-43 expressed weakly. The expression of cTnT, desmin and Cx-43 proteins was higher in the SMHs+5-azacytidine group than in the 5-azacytidine and SMHs groups, while there was no significant difference between the latter two groups. To conclude, SMHs as a culture medium is conducive to the proliferation of BMSCs, reduces cell apoptosis, and promotes myocardial differentiation of BMSCs.

Objective:To explore the change characteristics of amino acid levels in neonates, so as to provide theoretical basis for accurate clinical interpretation.Methods:By preliminary screening and diagnosis from 32 855 newborns, 32 843 samples were collected using tandem mass spectrometry to inherited metabolic disease (IMD) scree-ning in Sichuan Province from January to December 2018.Afterwards, according to gestational age (1 363 premature infants, 31 468 full-term infants and 12 overdue infants), blood collection time (3-7 days old, 3 095 cases, 8-28 days old, 1 637 cases, and more than 28 days old, 248 cases) and season (7 737 cases in the first quarter, 11 428 cases in the second quarter, 5 482 cases in the third quarter, and 8 196 cases in the fourth quarter), neonates were divided into different study groups.The difference of amino acid level in each group was compared, and the correlation between various influencing factors and metabolic index was analyzed.Results:(1) The distribution of 11 amino acids [alanine(ALA), arginine(ARG), citrulline(CIT), glycine(GLY), leucine+ isoleucine+ hydroxyproline (LEU+ ILE+ PRO-OH), methionine(MET), ornithine(ORN), phenylalanine(PHE), proline(PRO), tyrosine(TYR), and valine(VAL)] in neonates showed non-normally distribution.(2)The distribution of 11 amino acids in different gestational age were tested by nonparametric test, except for PHE( H=0.61, P>0.05)and TYR( H=2.02, P>0.05), and other indicators were significantly different [ALA( H=187.11, P<0.05), ARG( H=23.60, P<0.05), CIT( H=22.90, P<0.05), GLY( H=85.18, P<0.05), LEU( H=56.42, P<0.05), MET( H=18.74, P<0.05), ORN( H=129.27, P<0.05), PRO( H=344.40, P<0.05), and VAL( H=272.92, P<0.05)]. (3) The distribution of 11 amino acids in different blood collection time were significantly different [ALA( H=65.19, P<0.05), ARG( H=404.48, P<0.05), CIT( H=502.13, P<0.05), GLY( H=1 719.44, P<0.05), LEU( H=396.41, P<0.05), MET( H=199.39, P<0.05), ORN( H=31.26, P<0.05), PHE( H=325.49, P<0.05), PRO( H=70.09, P<0.05), TYR( H=159.29, P<0.05), and VAL( H=102.52, P<0.05)]. (4) The distribution of 11 amino acids in different birth seasons were significantly different [ALA( H=401.37, P<0.05), ARG( H=3 229.03, P<0.05), CIT( H=65.45, P<0.05), GLY( H=597.82, P<0.05), LEU( H=1 120.42, P<0.05), MET( H=10 515.18, P<0.05), ORN( H=1 275.23, P<0.05), PHE( H=2 260.17, P<0.05), PRO( H=319.57, P<0.05), TYR( H=884.37, P<0.05), and VAL( H=1 824.49, P<0.05)]. Conclusion:According to different gestational age, season and blood collection time, the metabolism of amino acids in neonates was different.When using tandem mass spectrometry for detection, appropriate interpretation criteria should be selected based on different conditions.

Follow-up is a crucial step for the screening of neonatal genetic and metabolic diseases, which can directly influence the detection, diagnosis, efficacy of treatment, as well as the quality of neonatal screening. In view of the lack of follow-up, full understanding, and inconsistent requirement between various agencies and personnel in China, there is an urgent need for standardization. The Committee for Proficiency Testing of the Neonatal Genetic Metabolic Disease Screening Center of the National Health Committee of China has organized the writing of expert consensus for follow-up of neonatal genetic and metabolic disease screening after thorough discussion, so as to guide the follow-up work and improve its quality.
China ; Consensus ; Follow-Up Studies ; Genetic Diseases, Inborn ; diagnosis ; Humans ; Infant, Newborn ; Metabolic Diseases ; diagnosis ; genetics ; Neonatal Screening

Objective:To detective the cut-off values of amino acid levels in premature infants in Sichuan.Methods:Data of newborns screening for inherited metabolic diseases (IMD) by tandem mass spectrometry in Sichuan Province from January 2018 to December 2019 were retrospectively analyzed.They were divided into premature infant group ( n=2 264, 1 312 males and 952 females) and full-term infant group ( n=53 275, 28 269 males and 25 006 females). The cut-off values of amino acids in dry blood spots were expressed as percentage ( P0.5 - P99.5), and rank sum test was used for comparison between preterm and full-term infants. Results:(1) The distribution of 11 amino acids [alanine (ALA), arginine (ARG), citrulline (CIT), glycine(GLY), leucine (LEU), methionine (MET), ornithine (ORN), phenylalanine (PHE), proline (PRO), tyrosine (TYR) and valine (VAL)] in premature infants were abnormal.(2) The cut-off values of amino acids in premature infants were as follows: ALA: 135.20-552.33 μmol/L, ARG: 1.34-47.04 μmol/L, CIT: 5.66-32.02 μmol/L, GLY: 181.48-909.93 μmol/L, LEU : 71.10-283.29 μmol/L, MET: 4.21-34.51 μmol/L, ORN: 40.58-293.76 μmol/L, PHE: 23.60-106.30 μmol/L, PRO: 77.76-358.24 μmol/L, TYR: 27.52-352.91 μmol/L, VAL: 53.74-228.37 μmol/L.(3) The cut-off values of amino acid in full-term infants were as follows: ALA: 135.20-552.33 μmol/L, ARG: 1.30-42.73 μmol/L, CIT: 5.92-30.35 μmol/L, GLY: 208.17-980.09 μmol/L, LEU: 72.91-287.49 μmol/L, MET: 4.27-33.90 μmol/L, ORN: 48.40-305.59 μmol/L, PHE: 27.63-92.27 μmol/L, PRO: 97.38-372.75 μmol/L, TYR: 40.19-276.54 μmol/L, VAL: 65.75-237.92 μmol/L.(4) Except for PHE ( Z=-0.58, P>0.05), the other indicators were significantly different between 2 groups [ALA ( Z=-15.32, P<0.05), ARG ( Z=-5.62, P<0.05), CIT ( Z=-5.86, P<0.05), GLY ( Z=-14.52, P<0.05), LEU ( Z=-5.62, P<0.05), MET ( Z=-5.22, P<0.05), ORN ( Z=-13.01, P<0.05), PRO ( Z=-22.09, P<0.05), TRY ( Z=-2.09, P<0.05), VAL ( Z=-17.82, P<0.05)]. Conclusions:The establishment of the cut-off values of amino acids in premature infants in Sichuan provides a theoretical basis for laboratory diagnosis of IMD screening, which enhances the accuracy of diagnosis and avoids excessive medical treatment.

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.