MATERIALS AND METHODS: A literature review was performed following the PRISMA guidelines. Systematic searches were performed in PubMed, Scopus, Cochrane and Embase databases from the earliest record up to September 2022. Related studies on deep learning models for radiotherapy toxicity prediction were selected based on predefined PICOS criteria.
RESULTS: Fourteen studies of radiotherapy-treated patients on different types of cancer [prostate (n=2), HNC (n=4), liver (n=2), lung (n=4), cervical (n=1), and oesophagus (n=1)] were eligible for inclusion in the systematic review. Information regarding patient characteristics and model development was summarized. Several approaches, such as ensemble learning, data augmentation, and transfer learning, that were utilized by selected studies were discussed.
CONCLUSION: Deep learning techniques are able to produce a consistent performance for toxicity prediction. Future research using large and diverse datasets and standardization of the study methodologies are required to improve the consistency of the research output.
Methods: A cross-sectional study involving 503 drug naive subjects (163 males, aged 30-65 years old (mean age ± SD = 47.4 ± 8.3 years)) divided into MS, COB and NC groups. COB was defined as central obesity (waist circumference (WC) males ≥90 cm, females ≥80 cm) in the absence of MS according to the International Diabetes Federation 2006. Fasting blood levels of tPA and PAI-1were analyzed.
Results: MS and COB had significantly higher concentration of all biomarkers compared to NC. The MS group had significantly higher concentration of tPA and PAI-1 compared to COB. WC and HDL-c had significant correlation with all biomarkers (tPA p < 0.001, PAI-1 p < 0.001). Fasting plasma glucose and diastolic blood pressure were independent predictors after correcting for confounding factors.
Conclusion: Central obesity with or without MS both demonstrated enhanced prothrombogenesis. This suggests that simple obesity possibly increases the risk of coronary artery disease in part, via increased susceptibility to thrombogenesis.
OBJECTIVES: (1) To compare the concentrations of biomarkers of inflammation, endothelial activation and oxidative stress in subjects with low HDL-c compared to normal HDL-c; (2) To examine the association and correlation between HDL-c and these biomarkers and (3) To determine whether HDL-c is an independent predictor of these biomarkers.
METHODS: 422 subjects (mean age±SD = 43.2±11.9 years) of whom 207 had low HDL-c concentrations (HDL-c <1.0 mmol/L and <1.3 mmol/L for males and females respectively) and 215 normal controls (HDL-c ≥1.0 and ≥1.3 mmol/L for males and females respectively) were recruited in this study. The groups were matched for age, gender, ethnicity, smoking status, diabetes mellitus and hypertension. Fasting blood samples were collected for analysis of biomarkers of inflammation [high-sensitivity C-reactive protein (hsCRP) and Interleukin-6 (IL-6)], endothelial activation [soluble Vascular Cell Adhesion Molecule-1 (sVCAM-1), soluble Intercellular Adhesion Molecule-1 (sICAM-1) and E-selectin)] and oxidative stress [F2-Isoprostanes, oxidized Low Density Lipoprotein (ox-LDL) and Malondialdehyde (MDA)].
RESULTS: Subjects with low HDL-c had greater concentrations of inflammation, endothelial activation and oxidative stress biomarkers compared to controls. There were negative correlations between HDL-c concentration and biomarkers of inflammation (IL-6, p = 0.02), endothelial activation (sVCAM-1 and E-selectin, p = 0.029 and 0.002, respectively), and oxidative stress (MDA and F2-isoprostane, p = 0.036 and <0.0001, respectively). Multiple linear regression analysis showed HDL-c as an independent predictor of IL-6 (p = 0.02) and sVCAM-1 (p<0.03) after correcting for various confounding factors.
CONCLUSION: Low serum HDL-c concentration is strongly correlated with enhanced status of inflammation, endothelial activation and oxidative stress. It is also an independent predictor for enhanced inflammation and endothelial activation, which are pivotal in the pathogenesis of atherosclerosis and atherosclerosis-related complications.
RESULT: By combining SNP information from literatures, GWAS study and NCBI ClinVar, 18 unique SNPs were selected for further analysis. From these 18 SNPs, 10 SNPs came from previous study of Helicobacter pylori infection among Malay patients, 6 SNPs were from NCBI ClinVar and 2 SNPs from GWAS studies. The analysis reveals that both Royal Kelantan Malay genomes shared all the 10 SNPs identified by Maran (Single Nucleotide Polymorphims (SNPs) genotypic profiling of Malay patients with and without Helicobacter pylori infection in Kelantan, 2011) and one SNP from GWAS study. In addition, the analysis also reveals that both Royal Kelantan Malay genomes shared 3 SNP markers; HBG1 (rs1061234), HBB (rs1609812) and BCL11A (rs766432) where all three markers were associated with beta-thalassemia.
CONCLUSIONS: Our findings suggest that the Royal Kelantan Malays carry the SNPs which are associated with protection to Helicobacter pylori infection. In addition they also carry SNPs which are associated with beta-thalassemia. These findings are in line with the findings by other researchers who conducted studies on thalassemia and Helicobacter pylori infection in the non-royal Malay population.