A digital twin model incorporating generalized metabolic fluxes to identify and predict chronic kidney disease in type 2 diabetes mellitus.

We have developed a digital twin-based CKD identification and prediction model that leverages generalized metabolic fluxes (GMF) for patients with Type 2 Diabetes Mellitus (T2DM). GMF digital twins utilized basic clinical and physiological biomarkers as inputs for identification and prediction of CKD. We employed four diverse multi-ethnic cohorts (n = 7072): a Singaporean cohort (EVAS, n = 289) and a North American cohort (NHANES, n = 1044) for baseline CKD identification, and two multi-center Singaporean cohorts (CDMD, n = 2119 and SDR, n = 3627) for 3-year CKD prediction and risk stratification.

[Determination of acid-resistant silicic particles in lung by micro-XRF and its application in diagnosis of drowning].

Objective: Using microbeam X-ray fluorescence (Micro-XRF) analyzer for determination of acid-resistant silicic particles in lung, and to explore its potential application in diagnosis of drowning.

Methods: Thirty two white rabbits were divided randomly into drowning group (n=12), post-mortem immersion group (n=10) and control group (n=10). Lungs and water sample were collected for determination of area concentration of acid-resistant silicic particles using Micro-XRF method.

Differential interactions of plasmid DNA, RNA and endotoxin with immobilised and free metal ions.

Separation of negatively charged molecules, such as plasmid DNA (pDNA), RNA and endotoxin forms a bottleneck for the development of pDNA vaccine production process. The use of affinity interactions of transition metal ions with these molecules may provide an ideal separation methodology. In this study, the binding behaviour of pDNA, RNA and endotoxin to transition metal ions, either in immobilised or free form, was investigated.