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. We subsequently conducted a comprehensive study utilizing a single dataset to evaluate the clinical utility of GMF for CKD prediction. The GMF-based identification model performed strongly, achieving an AUC between 0.80 and 0.82. In prediction, the GMF generated with complete parameters attained high performance with an AUC of 0.86, while with incomplete parameters, it achieved an AUC of 0.75. The GMF-based prediction model utilizing complete inputs is the standard implementation of our algorithm: HealthVector Diabetes®. We have established the GMF digital twin-based model as a robust clinical tool capable of predicting and stratifying the risk of future CKD within a 3-year time horizon. We report the correlation of GMF with basic input parameters, their ability to differentiate between future health states and medication status at baseline, and their capability to quantify CKD progression rates. This holistic methodology provides insights into patients' health states and CKD progression rates based on GMF metabolic profile differences, enabling personalized care plans.

[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. RESULTS: The area concentration of acid-resistant silicic particles for the drowning water sample was 4.4 mm2/mL. For the lungs of drowning group, the post-mortem immersion group and the control group, the determined average values were (25.30 +/- 10.95) mm2/g, (1.68 +/- 0.63) mm2/g and (1.65 +/- 0.85) mm2/g, respectively, with a statistically significant difference between the drowning group and the other two groups. CONCLUSION: The area concentration of acid-resistant silicic particles in lungs may be used as an indicator of drowning. The method is highly sensitive and rapid. It provides a potential application in drowning diagnosis.

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. Transition metal ions: Cu2+, Ni2+, Zn2+, Co2+ and Fe3+, typically employed in the immobilised metal affinity chromatography (IMAC), showed very different binding behaviour depending on the type of metal ions and their existing state, i.e. immobilised or free. In the alkaline cell lysate, pDNA showed no binding to any of the IMAC chemistries tested whereas RNA interacted significantly with Cu2+-iminodiacetic acid (IDA) and Ni2+-IDA but showed no substantial binding to the rest of the IMAC chemistries. pDNA and RNA, however, interacted to varying degrees with free metal ions in the solution. The greatest selectivity in terms of pDNA and RNA separation was achieved with Zn2+ which enabled almost full precipitation of RNA while keeping pDNA soluble. For both immobilised and free metal ions, ionic strength of solution affected the metal ion-nucleic acid interaction significantly. Endotoxin, being more flexible, was able to interact better with the immobilised metal ions than the nucleic acids and showed binding to all the IMAC chemistries. The specific interactions of immobilised and/or free metal ions with pDNA, RNA and endotoxin showed a good potential, by selectively removing RNA and endotoxin at high efficiency, to develop a simplified pDNA purification process with improved process economics.