METHODS: The hMSCs derived from human Wharton's jelly umbilical cord (hWJMSCs; n = 6) were treated with RECA at different concentrations; 400, 800, 1200, 1600, 2000 and 2400 μg/ml. The cytotoxicity of RECA was evaluated via the MTT (3-(4, 5-dimethylthiazolyl-2)-2, 5-diphenyltetrazolium bromide) and cell proliferation assays. The hWJMSCs were then induced to neural lineage for 9 days either with RECA alone or RECA in combination with neurotrophic factors (NF). Cell morphological changes were observed under an inverted microscope, while the expression of the neural markers S100β, p75 NGFR, MBP, GFAP and MOG was analyzed by quantitative polymerase chain reaction and immunocytochemistry. The cell cycle profile of differentiated and undifferentiated hWJMSCs was investigated through cell cycle analysis.
RESULTS: RECA exerted effects on both proliferation and neural differentiation of hWJMSCs in a dose-dependent manner. RECA reduced the proliferation of hWJMSCs and was cytotoxic to cells above 1600 μg/ml, with IC50 value, 1875 ± 55.67 μg/ml. In parallel with the reduction in cell viability, cell enlargement was also observed at the end of the induction. Cells treated with RECA alone had more obvious protein expression of the neural markers compared to the other groups. Meanwhile, gene expression of the aforementioned markers was detected at low levels across the experimental groups. The supplementation of hWJMSCs with RECA did not change the normal life cycle of the cells.
CONCLUSIONS: Although RECA reduced the proliferation of hWJMSCs, a low dose of RECA (400 μg/ml), alone or in combination of neurotrophic factors (NF + RECA 400 μg/ml), has the potential to differentiate hWJMSCs into Schwann cells and other neural lineage cells.
MATERIALS AND METHODS: A literature search was carried out to gather eligible studies from the following widely sourced electronic databases such as Scopus, PubMed and Google Scholar using the combination of the following keywords: AD, MRS, brain metabolites, deep learning (DL), machine learning (ML) and artificial intelligence (AI); having the aim of taking the readers through the advancements in the usage of MRS analysis and related AI applications for the detection of AD.
RESULTS: We elaborate on the MRS data acquisition, processing, analysis, and interpretation techniques. Recommendation is made for MRS parameters that can obtain the best quality spectrum for fingerprinting the brain metabolomics composition in AD. Furthermore, we summarise ML and DL techniques that have been utilised to estimate the uncertainty in the machine-predicted metabolite content, as well as streamline the process of displaying results of metabolites derangement that occurs as part of ageing.
CONCLUSION: MRS has a role as a non-invasive tool for the detection of brain metabolite biomarkers that indicate brain metabolic health, which can be integral in the management of AD.
Materials and Methods: This was a cross-sectional study involving patients aged between 18 and 65 years diagnosed with T2DM with IHD (n = 150). Ultrasonography of the abdomen to determine NAFLD severity category and CIMT measurements was performed by two independent radiologists. NAFLD was graded according to the severity of steatosis (NAFLD-3, NAFLD-2, NAFLD-1, and NAFLD-0). Comparison between different stages of NAFLD (NAFLD-3, NAFLD-2, NAFLD-1, and NAFLD-0) was analyzed using Chi-square and analysis of variance tests for categorical and continuous variables, respectively.
Results: The prevalence of NAFLD was 71% (n = 107). NAFLD-1 was detected in 39% of the patients, 32% had NAFLD-2, no patients with NAFLD-3, and 29% had non-NAFLD. There were no patients with NAFLD-2 having higher systolic and diastolic blood pressure, weight, body mass index, waist circumference, total cholesterol, triglycerides, high-density lipoprotein cholesterol, and low-density lipoprotein cholesterol. Glycated hemoglobin (HbA1c) concentration was highest within the NAFLD-2. NAFLD-2 showed higher mean CIMT. Every 1% rise in HbA1c for patients with NAFLD significantly increases the CIMT by 0.03 mm (95% CI: 0.009, 0.052, P = 0.006).
Conclusion: These findings suggest additional atherosclerotic risks within the NAFLD-2 group with significantly higher HbA1c and CIMT compared to the NAFLD-1 and NAFLD-0 groups. It is, therefore, vital to incorporate stricter glycemic control among patients with T2DM and IHD with moderate NAFLD as part of atherosclerotic risk management strategy.