Kidney Health and Care: Current Status, Challenges, and Developments.

The concept of chronic kidney disease (CKD) originated in the 2000s, and an estimated 850 million patients are currently suffering from health threats from different degrees of CKD. However, it is unclear whether the existing CKD care systems are optimal for improving patient prognosis and outcomes, so this review summarizes the burden, existing care models, effectiveness, challenges, and developments of CKD care. Even under the general care principles, there are still significant gaps in our understanding of the causes of CKD, prevention or care resources, and care burdens between countries worldwide. Receiving care from multidisciplinary teams rather than only a nephrologist shows potential profits in comprehensive and preferable outcomes. In addition, we propose a novel CKD care structure that combines modern technologies, biosensors, longitudinal data visualization, machine learning algorithms, and mobile care. The novel care structure could simultaneously change the care process, significantly reduce human contact, and make the vulnerable population less likely to be exposed to infectious diseases such as COVID-19. The information offered should be beneficial, allowing us to rethink future CKD care models and applications to reach the goals of health equality and sustainability.

A competing risk analysis of sequential complication development in Asian type 2 diabetes mellitus patients.

This retrospective cohort study investigated the progression risk of sequential complication in Asian type 2 diabetes (T2D) patients using the Taiwan Pay-for-Performance Diabetes Registry and claim data from November 2003 to February 2009. 226,310 adult T2D patients without complication were followed from diagnosis to complications, including myocardial infarction (MI), other ischemic heart disease (IHD), congestive heart failure (CHF), stroke, chronic kidney disease (CKD), retinopathy, amputation, death or to the end of study. Cumulative incidences (CIs) of first and second complications were analyzed in 30 and 4 years using the cumulative incidence competing risk method. IHD (29.8%), CKD (24.5%) and stroke (16.0%) are the most common first complications. The further development of T2D complications depends on a patient's existing complication profiles. Patients who initially developed cardiovascular complications had a higher risk (9.2% to 24.4%) of developing IHD or CKD, respectively. All-cause mortality was the most likely consequence for patients with a prior MI (12.0%), so as stroke in patients with a prior MI (10.8%) or IHD (8.9%). Patients with CKD had higher risk of developing IHD (16.3%), stroke (8.9%) and all-cause mortality (8.7%) than end-stage renal disease (4.0%). Following an amputation, patients had a considerable risk of all-cause mortality (42.1%).

Optimal policies of non-cross-resistant chemotherapy on Goldie and Coldman's cancer model.

Mathematical models can be used to study the chemotherapy on tumor cells. Especially, in 1979, Goldie and Coldman proposed the first mathematical model to relate the drug sensitivity of tumors to their mutation rates. Many scientists have since referred to this pioneering work because of its simplicity and elegance. Its original idea has also been extended and further investigated in massive follow-up studies of cancer modeling and optimal treatment. Goldie and Coldman, together with Guaduskas, later used their model to explain why an alternating non-cross-resistant chemotherapy is optimal with a simulation approach. Subsequently in 1983, Goldie and Coldman proposed an extended stochastic based model and provided a rigorous mathematical proof to their earlier simulation work when the extended model is approximated by its quasi-approximation. However, Goldie and Coldman's analytic study of optimal treatments majorly focused on a process with symmetrical parameter settings, and presented few theoretical results for asymmetrical settings. In this paper, we recast and restate Goldie, Coldman, and Guaduskas' model as a multi-stage optimization problem. Under an asymmetrical assumption, the conditions under which a treatment policy can be optimal are derived. The proposed framework enables us to consider some optimal policies on the model analytically. In addition, Goldie, Coldman and Guaduskas' work with symmetrical settings can be treated as a special case of our framework. Based on the derived conditions, this study provides an alternative proof to Goldie and Coldman's work. In addition to the theoretical derivation, numerical results are included to justify the correctness of our work.