Developing and Validating a Multimodal Dataset for Neonatal Pain Assessment to Improve AI Algorithms With Clinical Data.

Background: Using Artificial Intelligence (AI) for neonatal pain assessment has great potential, but its effectiveness depends on accurate data labeling. Therefore, precise and reliable neonatal pain datasets are essential for managing neonatal pain.

Purpose: To develop and validate a comprehensive multimodal dataset with accurately labeled clinical data, enhancing AI algorithms for neonatal pain assessment.

A high performance 3D cluster-based test of unsmoothed fMRI data.

Cluster-size tests (CST) based on random field theory have been widely adopted in fMRI data analysis to detect brain activation. However, most existing approaches can be used appropriately only when the image is highly smoothed in the spatial domain. Unfortunately, spatial smoothing degrades spatial specificity.

[Application of real-time fluorescence quantitative polymerase chain reaction techniques for prenatal diagnosis of Down syndrome].

Objective: To investigate the value of real-time fluorescence quantitative PCR in diagnosis of Down syndrome with uncultured amniotic cells.

Methods: The uncultured amniocytes of 80 fetuses who were confirmed disomy 21 by chromosome analysis and 5 fetuses detected trisomy 21 and peripheral blood samples of 7 children diagnosed as Down syndrome were collected to extract gDNA and the real-time fluorescence quantitative PCR method was used to detect the original copies of Down syndrome critical region gene(3) (DSCR(3)) and GAPDH gene and then the ratio of DSCR(3)/GAPDH was calculated.

Results: The PCR product ratios of DSCR(3) to GAPDH in trisomy 21 from amniocytes and peripheral blood were ranged from 1.