Results of a Pilot External Quality Assessment Scheme for Genetic Testing of Newborns with Spinal Muscular Atrophy.

BACKGROUND: This study aims to evaluate the ability of laboratories to perform spinal muscular atrophy (SMA) genetic testing in newborns based on dried blood spot (DBS) samples, and to provide reference data and advance preparation for establishing the pilot external quality assessment (EQA) scheme for SMA genetic testing of newborns in China. METHODS: The pilot EQA scheme contents and evaluation principles of this project were designed by National Center for Clinical Laboratories (NCCL), National Health Commission. Two surveys were carried out in 2022, and 5 batches of blood spots were submitted to the participating laboratory each time. All participating laboratories conducted testing upon receiving samples, and test results were submitted to NCCL within the specified date. RESULTS: The return rates were 75.0% (21/28) and 95.2% (20/21) in the first and second surveys, respectively. The total return rate of the two examinations was 83.7% (41/49). Nineteen laboratories (19/21, 90.5%) had a full score passing on the first survey, while in the second survey twenty laboratories (20/20, 100%) scored full. CONCLUSIONS: This pilot EQA survey provides a preliminary understanding of the capability of SMA genetic testing for newborns across laboratories in China. A few laboratories had technical or operational problems in testing. It is, therefore, of importance to strengthen laboratory management and to improve testing capacity for the establishment of a national EQA scheme for newborn SMA genetic testing.

Research on remote sensing ecological environmental assessment method optimized by regional scale.

As the global ecosystem has been severely disturbed by an increasing number of human activities at different scales, remote sensing technology, as an effective quantitative measure of environmental quality, has been widely used. The remote sensing ecological index (RSEI) is one of the most popular and comprehensive ecological quality assessment indices based on the remote sensing data. However, the RSEI model exhibits that the ecological environment under natural conditions is not limited by the spatial scales. In addition, the model has major shortcomings in index selection and eigenvector, which greatly limit the application of RSEI. In this paper, the RSEI model is improved and a remote sensing ecological index optimized by the regional scale (RO-RSEI) is proposed. The result of the study, conducted in Shuangyang District, Changchun City, Jilin Province, shows that the RO-RSEI model has regional ecological significance after the introduction of the scale theory of landscape ecology; the index is preferred to solve problems like the RSEI model applied mechanization and baseless index selection. Meanwhile, due to the optimization of the eigenvector contribution of the optimal index, it solves the problems like non-unique model calculation result caused by principal component analysis or even antipodal calculation result. Compared with the RSEI model, the monitoring result of RO-RSEI model can better reflect the regional ecological changes. The improved model offers the possibility of monitoring ecological environment quality with remote sensing big data and provides a scientific basis for future scholars' batch computing.