BACKGROUND: The establishment of amniotic fluid derived stem cells (AFS) can provide an individual reserve for cell therapy in nerve degenerative diseases.OBJECTIVE: To observe the effects of Noggin and basic fibroblast growth factor (bFGF) on AFS differentiation into neural cells.METHODS: Samples of amniotic fluid were obtained through amniocentesis by ultrasound from gestational age of 16-22 weeks for routine prenatal diagnosis. AFS were obtained from the 2~(nd) trimester amniotic fluid samples by immunomagnetic beads selection using CD117 antibody, and identified the surface antigen expression by flow cytometry after amplification. The 3~(rd) generation of AFS with good growth state were induced to differentiate into nerve cells, which were divided into the blank control,based-induced, Noggin-induced and bFGF-induced groups. The induced cell morphology was observed under inverted phase contrast microscopy, and the expression of nestin, β-Ⅲ tubulin and neurofilament in the induced cells was measured by using cell immunofluorescence detection.RESULTS AND CONCLUSION: Flow cytometry analysis indicated that most of AFS cells expressed CD44 and HLA-ABC, but negative for CD45 and HLA-DR. At 2 weeks after induction, the cell morphology exhibited significant changes with increased Nestin,β-Ⅲ tubulin and NF-positive rates in the bFGF-induced group. However, it had no significant difference in the Noggin-induced group and the based-induced group. It revealed that bFGF plays a vital role in the AFS differentiated into nerve cells.

Objective:To investigate the impact of the deep learning reconstruction algorithm TrueFidelity TM for Gemstone Spectral Imaging (TF-GSI) and the adaptive statistical iterative reconstruction algorithm (ASiR-V, hereinafter referred to as ASiR-V) based on phantom and animal models on the image quality of dual-energy CT images. Methods:GE Revolution Apex CT was used to scan the ACR 464 phantom and a mouse model of gastric cancer with lymph node metastasis ( n=16). TF-GSI and ASiR-V were separately used to reconstruct middle and high-grade images (TF-GSI-M, TF-GSI-H, ASiR-V-50%, and ASiR-V-100%) on the phantom and mouse based on virtual monoenergetic images at 70 keV. The task transfer function (TTF) of bone and acrylic, image noise power spectrum (NPS), and detectability index (d′) of the phantom images were evaluated. One-way ANOVA analysis was used to compare the image noise, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) for brain and liver on images of mice. The consistency of the two reconstruction-algorithm images (TF-GSI-H and ASiR-V100%) in the detection of small lesions by two radiologists (A and B) was evaluated using kappa test. Results:In terms of the phantom, the TF-GSI-H group had the best performance in TTF, NPS, and d′. Compared to ASiR-V-100%, the TTF50% of bone and acrylic in the TF-GSI-H group increased by 2.4% and 8.9%, respectively; the NPS peak decreased by 54.1%, compared to ASiR-V-100%; the d′ of bone and acrylic in the TF-GSI-H group relative to ASiR-V-100% increased by 52.7% and 59.5%, respectively. The TF-GSI group had reduced image noise compared to the ASiR-V group, and both SNR and CNR of the two tissues increased, but the differences between the groups were not statistically significant (all P>0.05). The two reconstruction-algorithm images showed good consistency in image evaluation by the two radiologists (A, Kappa=0.875, P<0.001; B, Kappa=0.625, P=0.012). In terms of the detection of micro-metastases in mice, the TF-GSI group outperformed the ASiR-V group (average accuracy: 83.5% vs 71.9%; average sensitivity: 77.8% vs 61.2%; average specificity: 85.7% vs 85.7%). Conclusion:Compared with iterative reconstruction algorithm, the DLIR algorithm showed improved spatial resolution, reduced image noise, and enabled detectability of micro-lesion for images from dual-energy CT.

In the present research, molecular simulation and quantum chemistry calculations were combined to investigate the thermal stability of three kinds of insulin aggregations and the effect of Zn (II) ion coordination on these aggregations. The results of molecular simulation indicated that the three insulin dimers in the same sphere closed hexamer had synergistic stability. It is the synergistic stability that enhances the structural and thermal stability of insulin, preserves its bioactivity during production, storage, and delivery of insulin formulations, and prolongs its halflife in human bodies. According to the results of quantum chemistry calculations, each Zn (II)-N (Im-insulin) bond energy can reach 73.610 kJ/mol for insulin hexamer and 79.907 kJ/mol for insulin tetramer. However, the results of Gibbs free energy changes still indicats that the coordination of zinc (II) ions is unfavorable for the formation of insulin hexamer, because the standard Gibbs free energy change of the coordinate reaction of zinc (II) ions associated with the formatting insulin hexamer is positive and increased.
Insulin ; chemistry ; metabolism ; Molecular Dynamics Simulation ; Protein Stability ; Zinc ; chemistry