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Aim: Limitations in the measurement of glycated hemoglobin (HbA1c) in patients with type 2 diabetes (T2D) and chronic kidney disease (CKD) result in uncertainty about the best predictor of mortality among these patients. Our study aimed to determine the association between the mean and average real variability (ARV) of HbA1c, as well as HbA1c-hemoglobin (HH) ratio with mortality among patients with T2D and CKD. Materials and methods: We identified 16,868 T2D patients with stage 3 or above CKD from outpatient visits during 2003–2018. We ascertained all-cause and cardiovascular mortality through linkage to Taiwan's National Death Registry. Mortality rates were estimated using the Poisson distribution, and we conducted Cox proportional hazards regressions to assess relative risks of mortality corresponding to the mean HbA1c, ARV of HbA1c and HH ratio. Results: Compared to patients with a mean HbA1c of 7.0–7.9%, a mean HbA1c < 7.0% was persistently associated with highest risk of all-cause but not cardiovascular mortality after adjusting for confounders. On the contrary, patients with HbA1c-ARV in the second to fourth quartiles and HH ratios in the higher quartiles showed increased risk of all-cause and cardiovascular mortality compared to those in the first quartiles. Conclusions: HbA1c-ARV was more effective than mean HbA1c or HH ratio in predicting mortality in T2D patients with CKD. Apart from optimal glucose control, multidisciplinary care focusing on glycemic variability is essential for reducing mortality in these patients. [ABSTRACT FROM AUTHOR]

A microscopic finite element model validated by the quasi-static mechanical test on lithium ion battery cell is presented in this study. The investigated condition involves three-point bending and cell discharge test, which include the mechanical response and thermal response of lithium ion battery cell. The cell is investigated through computational simulation. Results show great correlation between the experiment and finite element simulation. A microscopic model requires a greater effort during simulation. Investigating the fracture on steel casing and cell core is possible by considering the fracture mechanism in steel shell and jelly roll. The classic von-Mises criterion is applied to predict the failure of carbon steel shell, and the maximum von-Mises strain criterion is used to investigate the jelly roll region. On the basis of the verified battery cell model, battery package with microscopic model, including cell core (cathode, anode, and separator) is constructed to analyze the mechanical response of battery pack. The von-Mises criterion is used to predict the fracture point location of the battery pack with various packing modes and packing densities. [ABSTRACT FROM AUTHOR]