METHODS: We evaluated 88 breast cancer risk variants that were identified previously by GWAS in 11,760 cases and 11,612 controls of Asian ancestry. SNPs confirmed to be associated with breast cancer risk in Asian women were used to construct a polygenic risk score (PRS). The relative and absolute risks of breast cancer by the PRS percentiles were estimated based on the PRS distribution, and were used to stratify women into different levels of breast cancer risk.
RESULTS: We confirmed significant associations with breast cancer risk for SNPs in 44 of the 78 previously reported loci at P
METHODS: We conducted a fine-mapping analysis in 55,540 breast cancer cases and 51,168 controls from the Breast Cancer Association Consortium.
RESULTS: Conditional analyses identified two independent association signals among women of European ancestry, represented by rs9790517 [conditional P = 2.51 × 10(-4); OR, 1.04; 95% confidence interval (CI), 1.02-1.07] and rs77928427 (P = 1.86 × 10(-4); OR, 1.04; 95% CI, 1.02-1.07). Functional annotation using data from the Encyclopedia of DNA Elements (ENCODE) project revealed two putative functional variants, rs62331150 and rs73838678 in linkage disequilibrium (LD) with rs9790517 (r(2) ≥ 0.90) residing in the active promoter or enhancer, respectively, of the nearest gene, TET2. Both variants are located in DNase I hypersensitivity and transcription factor-binding sites. Using data from both The Cancer Genome Atlas (TCGA) and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC), we showed that rs62331150 was associated with level of expression of TET2 in breast normal and tumor tissue.
CONCLUSION: Our study identified two independent association signals at 4q24 in relation to breast cancer risk and suggested that observed association in this locus may be mediated through the regulation of TET2.
IMPACT: Fine-mapping study with large sample size warranted for identification of independent loci for breast cancer risk.
OBJECTIVE: The aim of this proof-of-concept study was to evaluate whether combining population pharmacokinetic and machine learning approaches could provide a more accurate prediction of the clearance of renally eliminated drugs in individual neonates.
METHODS: Six drugs that are primarily eliminated by the kidneys were selected (vancomycin, latamoxef, cefepime, azlocillin, ceftazidime, and amoxicillin) as 'proof of concept' compounds. Individual estimates of clearance obtained from population pharmacokinetic models were used as reference clearances, and diverse machine learning methods and nested cross-validation were adopted and evaluated against these reference clearances. The predictive performance of these combined methods was compared with the performance of two other predictive methods: a covariate-based maturation model and a postmenstrual age and body weight scaling model. Relative error was used to evaluate the different methods.
RESULTS: The extra tree regressor was selected as the best-fit machine learning method. Using the combined method, more than 95% of predictions for all six drugs had a relative error of < 50% and the mean relative error was reduced by an average of 44.3% and 71.3% compared with the other two predictive methods.
CONCLUSION: A combined population pharmacokinetic and machine learning approach provided improved predictions of individual clearances of renally cleared drugs in neonates. For a new patient treated in clinical practice, individual clearance can be predicted a priori using our model code combined with demographic data.
METHODS: C0 were retrieved from a large neonatal vancomycin dataset. Individual estimates of AUC0-24 were obtained from Bayesian post hoc estimation. Various ML algorithms were used for model building to C0 and AUC0-24. An external dataset was used for predictive performance evaluation.
RESULTS: Before starting treatment, C0 can be predicted a priori using the Catboost-based C0-ML model combined with dosing regimen and nine covariates. External validation results showed a 42.5% improvement in prediction accuracy by using the ML model compared with the population pharmacokinetic model. The virtual trial showed that using the ML optimized dose; 80.3% of the virtual neonates achieved the pharmacodynamic target (C0 in the range of 10-20 mg/L), much higher than the international standard dose (37.7-61.5%). Once therapeutic drug monitoring (TDM) measurements (C0) in patients have been obtained, AUC0-24 can be further predicted using the Catboost-based AUC-ML model combined with C0 and nine covariates. External validation results showed that the AUC-ML model can achieve an prediction accuracy of 80.3%.
CONCLUSION: C0-based and AUC0-24-based ML models were developed accurately and precisely. These can be used for individual dose recommendations of vancomycin in neonates before treatment and dose revision after the first TDM result is obtained, respectively.
METHODS: We obtained viral hepatitis mortality data from the WHO Mortality Database for six East and Southeast Asian countries between 1987 and 2015. We produced choropleth maps of viral hepatitis mortality rates in 1987 and 2015 in East and Southeast Asia to illustrate geographic variations. We made predictions of mortality rates for each included country until the year 2030 using a series of joinpoint models.
RESULTS: Viral hepatitis mortality rates declined in China (the average annual percent change (AAPC) = -5.1%, 95% CI: -7.5, -2.6), Singapore (AAPC = -5.4%, 95% CI: -7.5, -3.2), and the Philippines (AAPC = -3.4%, 95% CI: -4.9, -1.8). In contrast, Japan, the Republic of Korea, and Malaysia have experienced increasing trends in mortality rates, followed by decreasing trends. Our predictions indicate that all countries will experience slight to moderate downward trends until 2030.
CONCLUSION: Favourable decreasing trends have been noted in East and Southeast Asian countries, which may not only inform the control and management of viral hepatitis in this region but also guide the prevention of viral hepatitis deaths in another region with a similar viral hepatitis epidemic.
SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at (10.1007/s13197-021-05039-y).
RESULTS: Heat and UHP treatments induced the unfolding of DLp to varied degrees, as revealed by fluorescence spectroscopy, ultraviolet-visible absorption, circular dichroism spectra and surface hydrophobicity measurements. Two types of heating-denatured states with varied unfolding degrees were obtained, while UHP at both levels of 100/500 MPa caused partial unfolding of DLp and the presence of a molten-globule state, which significantly enhanced the binding affinity between DLp and (E,E)-2,4-heptadienal. In particular, significantly modified secondary structures of DLp were observed in heating-denatured samples. Excessive denaturing and unfolding degrees resulted in no significant changes in the absorption behavior of the volatile ligand, as characterized by observations of fluorescence quenching and analysis of headspace concentrations.
CONCLUSION: Defining process-induced conformational transition behavior of matrix proteins could be a promising strategy to regulate food flavor attributes and, particularly, to produce DLp coextracted with limited off-flavor components by modifying their interaction during extraction processes. © 2023 Society of Chemical Industry.