OBJECTIVE: This study aimed to determine the potential of ascorbic acid alone in inducing differentially expressed osteoblast-related proteins in dental stem cells via the liquid chromatography-mass spectrometry/ mass spectrometry (LC-MS/MS) approach.
METHODS: The cells were isolated from deciduous (SHED) and permanent teeth (DPSC) and induced with 10 μg/mL of ascorbic acid. Bone mineralisation and osteoblast gene expression were determined using von Kossa staining and reverse transcriptase-polymerase chain reaction. The label-free protein samples were harvested on days 7 and 21, followed by protein identification and quantification using LC-MS/MS. Based on the similar protein expressed throughout treatment and controls for SHED and DPSC, overall biological processes followed by osteoblast-related protein abundance were determined using the PANTHER database. STRING database was performed to determine differentially expressed proteins as candidates for SHED and DPSC during osteoblast development.
RESULTS: Both cells indicated brownish mineral stain and expression of osteoblast-related genes on day 21. Overall, a total of 700 proteins were similar among all treatments on days 7 and 21, with 482 proteins appearing in the PANTHER database. Osteoblast-related protein abundance indicated 31 and 14 proteins related to SHED and DPSC, respectively. Further analysis by the STRING database identified only 22 and 11 proteins from the respective group. Differential expressed analysis of similar proteins from these two groups revealed ACTN4 and ACTN1 as proteins involved in both SHED and DPSC. In addition, three (PSMD11/RPN11, PLS3, and CLIC1) and one (SYNCRIP) protein were differentially expressed specifically for SHED and DPSC, respectively.
CONCLUSION: Proteome differential expression showed that ascorbic acid alone could induce osteoblastrelated proteins in SHED and DPSC and generate specific differentially expressed protein markers.
METHOD: This was an unmatched case-control study in which children with ASD were recruited from an autism early intervention center and typically developed (TD) children were recruited from government-run nurseries and preschools. Urine samples were collected at home, assembled temporarily at study locations, and transported to the laboratory within 24 h. The Al concentration in the children's urine samples was determined using inductively coupled plasma mass spectrometry (ICP-MS).
RESULT: A total of 155 preschool children; 81 ASD children and 74 TD children, aged 3 to 6 years, were enlisted in the study. This study demonstrated that ASD children had significantly higher urinary Al levels than TD children (median (interquartile range (IQR): 2.89 (6.77) µg/dL versus 0.96 (2.95) µg/dL) (p 1, p
OBJECTIVES: Here, we explored the phytochemical diversity of the seven varieties from Peninsular Malaysia using Nuclear Magnetic Resonance (NMR) and Liquid Chromatography-Mass Spectrometry (LC-MS) analyses and correlated it with the α-glucosidase inhibitory activity.
METHODOLOGY: The Nuclear Overhauser Effect Spectroscopy (NOESY) One-Dimensional (1D)-NMR and LC-MS data were processed, annotated, and correlated with in vitro α-glucosidase inhibitory using multivariate data analysis.
RESULTS: The α-glucosidase results demonstrated that different varieties have varying inhibitory effects, with the highest inhibition rate being F. deltoidea var. trengganuensis and var. kunstleri. Furthermore, diverse habitats and plant ages could also influence the inhibitory rate. The heat map from NMR and LC-MS profiles showed unique patterns according to varying levels of α-glucosidase inhibition rate. The Partial Least Squares (PLS) model constructed from both NMR and LC-MS further confirmed the correlation between the α-glucosidase inhibition rate of F. deltoidea varieties and its metabolite profiles. The Variable Influence on Projection (VIP) and correlation coefficient (p(corr)) values values were used to determine the highly relevant metabolites for explaining the anticipated inhibitory action.
CONCLUSION: NMR and LC-MS annotations allow the identification of flavan-3-ols and proanthocyanidins as the key bioactive factors. Our current results demonstrated the value of multivariate data analysis to predict the quality of herbal materials from both biological and chemical aspects.