Background/Aims: Transplantation of bone-marrow-derived mesenchymal stem cells (MSCs) has been used to treat spinal cord injury (SCI) to enhance tissue repair and neural cell regeneration. Glial cell line derived neurotrophic factor (GDNF) is an identified neural growth and survival factor. Here, we examined whether modification of GDNF levels in MSCs may further increase the potential of MSCs in promoting neural cell regeneration and subsequently the therapeutic outcome. Methods: We examined the mRNA and protein levels of GDNF in human MSCs by RT-qPCR and Western blot, respectively. Bioinformatics analyses were done to predict microRNAs (miRNAs) that target GDNF in MSCs. The functional binding of miRNAs to GDNF mRNA was examined by a dual luciferase reporter assay. MSCs were transduced with adeno-associated virus (AAV) carrying null or antisense for miR-383 (as-miR-383), which were transplanted into nude rats that underwent SCI. The intact tissue, cavity volume, and recovery of locomotor activity were assessed. Results: MSCs expressed very low GDNF protein, but surprisingly high levels of GDNF mRNA. Bioinformatics analyses showed that miR-383 inhibited protein translation of GDNF, through binding to the 3’-UTR of the GDNF mRNA. MSCs transduced with AAV-as-miR-383 further increased the intact tissue percentage, decreased cavity volume, and enhanced the recovery of locomotor activity in nude rats that underwent SCI, compared to MSCs. Conclusions: Suppression of miR-383 may increase the therapeutic potential of human bone-marrow-derived MSCs in treating SCI via augmentation of GDNF protein levels.
Residues of harmful chemicals in fruit and vegetables pose risks to human health. Ordinary laser-induced breakdown spectroscopy (LIBS) techniques are unsatisfactory for detecting harmful chemicals in fruit and vegetables. In this study, we applied metal nanoparticles to fruit and vegetables samples to improve the ability of LIBS to detect trace pesticide and heavy metal residues in the samples. The nanoparticle-enhanced LIBS technique gave pesticide residue detection limits for fruit and vegetables two orders of magnitude lower than achieved using standard LIBS and heavy metal detection limits markedly better than achieved using standard LIBS. We used the nanoparticle-enhanced LIBS technique to study the distributions of harmful chemicals in vegetable leaves. We found that heavy metals are distributed unevenly in edible plant leaves, the heavy metal concentrations being higher in the veins than in the mesophyll.
The volatile compounds from fruits vary based on the spoilage stage. We used FTIR spectroscopy to analyze and to attempt to identify the spoilage process of strawberries. To enhance the sensitivity of the measuring system, we increased the optical pathlength by using multi-reflecting mirrors. The volatile compounds that were vaporized from strawberries in different spoilage stages were tested. We analyzed the spectra and found that the concentrations of esters, alcohols, ethylene, and similar compounds changed with deterioration. The change patterns of the infrared spectra for the volatiles were further examined using 2D correlation spectroscopy. We analyzed the spectral data using PCA and were able to distinguish the fresh, slightly spoiled strawberries from the seriously spoiled strawberries. This study demonstrates that FTIR is an effective tool for monitoring strawberry spoilage and for providing status alerts.
Ankylosing spondylitis (AS) is a common chronic inflammatory rheumatic disease. Early and accurate detection is essential for effective disease treatment. Recently, research has focused on genomics and proteomics. However, the associated metabolic variations, especially fatty acid profiles, have been poorly discussed. In this study, the gas chromatography-mass spectrometry (GC-MS) approach and multivariate statistical analysis were used to investigate the metabolic profiles of serum free fatty acids (FFAs) and esterified fatty acids (EFAs) in AS patients. The results showed that significant differences in most of the FFA (C12:0, C16:0, C16:1, C18:3, C20:4, C20:5, C22:5 and C22:6) and EFA (C12:0, C16:1, C18:0, C18:1, C18:2, C18:3, C20:4 and C22:6) concentrations were found between the AS patients and healthy controls (p < 0.05). Principal component analysis and partial least squares discriminant analysis were performed to classify the AS patients and controls. Additionally, FFAs C20:4, C12:0, C18:3 and EFAs C22:6, C12:0 were confirmed as potential biomarkers to identify AS patients and healthy controls. The present study highlights that differences in the serum FFA and EFA profiles of AS patients reflect the metabolic disorder. Moreover, FFA and EFA biomarkers appear to have clinical applications for the screening and diagnosis of AS.
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