Quantitative determination of microbial materials activity based on infrared extinction properties.

Microbial bacteria play an irreplaceable role in natural and human production and life; thus, determining their activities is an important issue. This study proposed a method to quantitatively determine the activity of microbial materials through extinction property calculation using infrared spectroscopy. Complex refractive indices of different active biomaterials were calculated based on their infrared spectra, and their extinction properties were determined using the discrete dipole approximation method. Using partial least squares (PLS), support vector regression (SVR), and extreme learning machine (ELM) regression, quantitative determination models of microbial materials based on infrared extinction properties were established to predict their activity. The results demonstrated that the model of least angle regression (LAR) combined with PLS exhibited better potential for the determination of biomaterial activity. The coefficient of determination (R2) values acquired by the optimal model for the three biomaterials were 0.9699, 0.9744, and 0.9621, respectively. These findings suggested that a rapid and accurate quantitative determination of microbial activity can be achieved based on extinction property.

Denture use and risk for cardiometabolic disease: observational and Mendelian randomization analyses.

AIMS: Denture use may potentially increase the risk of cardiometabolic diseases (CMDs), but the casual relevance and strength of the associations are currently unknown. METHODS AND RESULTS: A total of 495 938 participants from the UK Biobank were included in the observational analyses. Linkage disequilibrium score (LDSC) regression and Mendelian randomization analyses were employed to estimate genetic correlation and the associations between the genetic liability for denture use with coronary artery disease, myocardial infarction, heart failure (HF), any stroke (AS), ischaemic stroke, haemorrhagic stroke, type 2 diabetes (T2D), and related clinical risk factors. In observational analysis, denture use was associated with 14-25% higher risks of various CMDs. The LDSC analysis found that denture use showed a positive genetic correlation with CMDs (rg 0.21-0.38). Genetic liability for denture use was associated with an elevated risk of HF [odds ratio: 1.49 (1.20-1.83)] and T2D [1.11 (1.01-1.24)]. By integrating genetic summary data of denture use with the sum of decayed, missing, and filled tooth surfaces (DMFS), a clinical measure of dental caries obtained from an independent source, genetically determined denture use/DMFS was also associated with an elevated risk of AS [1.21 (1.04-1.40)]. Furthermore, genetically predicted denture use/DMFS was significantly associated with established cardiometabolic risk factors, including HDL cholesterol, triglycerides, waist circumference, waist-to-hip ratio, and height. CONCLUSION: Our study supported potential causal associations between the genetic liability for denture use and risks for HF, AS, T2D, and related clinical risk factors. These findings may inform prevention and intervention strategies targeting dental diseases and CMDs.

This study examined the association of denture use with cardiometabolic diseases (CMDs) and related clinical risk factors through Mendelian randomization analyses using data from UK Biobank and published consortia. Genetic liability for denture use was associated with an 11­49% higher risk of heart failure, stroke, and type 2 diabetes.The potential causal relationship between denture use and CMDs was further strengthened by the associations of denture use with HDL cholesterol, triglycerides, waist circumference, waist-to-hip ratio, and height, which are among the major risk factors of CMDs.