Background Unless a cutoff level of the parameters of newborn screening (NBS) is defined, a screening test\'s results would end in high recall rates and apprehensive parents. The study aimed to establish a cutoff level of the healthy term newborns. Materials and methods The study was a retrospective observational data analysis on a cohort of 1158 term newborns who underwent NBS in our institute. The percentile distribution of the NBS parameters was computed and the 99th percentile value was considered the new cutoff. For lower values, such as neonatal glucose 6-phosphate dehydrogenase (nG6PD) and neonatal biotinidase (nBIOT), low percentile values were regarded as new cutoff value. Results Neonatal thyroid stimulating hormone (nTSH), nG6PD, neonatal immunoreactive trypsinogen (nIRT), and nBIOT showed a wide variation in the distribution. Most newborns had neonatal galactose (nGAL), nIRT, and nBIOT values above the median. The 99th percentile value of nTSH was 14.5 mIU/L, and that of neonatal 17-hydroxyprogesterone (n17-OHP) was 43.7 nmol/L. The 1.0th percentile value for nG6PD was decreased to 2.18 IU/gHb. The new cutoff values for nBIOT, nIRT, neonatal phenylketonuria (nPKU) and nGAL were 48.59 U, 95.3 µg/L, 2.3 mg/dL and 15.9 mg/dL. The mean and median nTSH values did not significantly differ (p=0.99) in the first five days of birth. On the contrary, the study population depicted considerably raised levels of n17-OHP on day 3, followed by a sharp decrease (p=0.029). Similarly, nIRT displayed significant differences in the first five days (p=0.017). Conclusion Using the 99th percentile values of the NBS parameters as the new cutoff levels might be beneficial in terms of the recall rates and cost burden.

The incidence of inborn errors of metabolism (IEM) varies across countries and areas. Currently, there are no studies on IEM using newborn screening (NBS) in eastern coastal areas of China. We aimed to estimate the incidence and genetic variants of IEM and understand the spectrum of diseases caused by IEM and variants among them in this area.
The NBS performed by tandem mass spectrometry (MS/MS) from 2016 to 2021 was retrospectively reviewed. Heel blood was collected from all newborns 72 h after birth. Targeted massively parallel sequencing was performed for genetic analysis.
Among 245,194 newborns, 95 were diagnosed with IEM, the overall incidence observed was-IEM: 1/2581; amino acid metabolism disorder: 1/4715; organic acid metabolism disorder: 1/11676; and fatty acid metabolism disorder: 1/11145. The incidence of different IEM was in the range of 1/245194 to 1/6452. Phenylketonuria (PKU, 1/7211) was the most common IEM, followed by methylmalonic acidemia (MMA, 1/27244), short-chain acyl-CoA dehydrogenase deficiency (SCADD, 1/30649), and citrin deficiency (CD, 1/35028). For genetic variants, the common hotspot variants found were-PAH gene for PKU: c.728G > A, c.442-1G > A, c.611A > G, c.721C > T; PTS gene for non-classical PKU: c.259C > T; MMACHC gene for MMA: c.658_660delAAG, c.609G > A; MMUT gene for MMA: c.1663G > A; ACADS gene for SCADD: c.1031A > G and c.1130C > T; and SLC25A13 gene for CD: c.1638_1660dup, c.852_855del.
This study displayed the diseases and varied spectrum of IEM in eastern coastal areas of China. Implementing NBS for IEM by MS/MS combined with massively parallel sequencing can offer an improved plan for NBS to detect IEM.

De novo germline variants in several components of the SWI/SNF-like BAF complex can cause Coffin-Siris syndrome (CSS), Nicolaides-Baraitser syndrome (NCBRS), and nonsyndromic intellectual disability. We screened 63 patients with a clinical diagnosis of CSS for these genes (ARID1A, ARID1B, SMARCA2, SMARCA4, SMARCB1, and SMARCE1) and identified pathogenic variants in 45 (71%) patients. We found a high proportion of variants in ARID1B (68%). All four pathogenic variants in ARID1A appeared to be mosaic. By using all variants from the Exome Variant Server as test data, we were able to classify variants in ARID1A, ARID1B, and SMARCB1 reliably as being pathogenic or nonpathogenic. For SMARCA2, SMARCA4, and SMARCE1 several variants in the EVS remained unclassified, underlining the importance of parental testing. We have entered all variant and clinical information in LOVD-powered databases to facilitate further genotype-phenotype correlations, as these will become increasingly important because of the uptake of targeted and untargeted next generation sequencing in diagnostics. The emerging phenotype-genotype correlation is that SMARCB1 patients have the most marked physical phenotype and severe cognitive and growth delay. The variability in phenotype seems most marked in ARID1A and ARID1B patients. Distal limbs anomalies are most marked in ARID1A patients and least in SMARCB1 patients. Numbers are small however, and larger series are needed to confirm this correlation.