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Autosomal dominant polycystic kidney disease (ADPKD), a single-gene-inherited kidney disease, is a common cause of end-stage kidney disease (ESKD). The PKD1 gene mutation is the most common cause of ADPKD, accounting for approximately 78% of cases. ADPKD is characterized by the scattered distribution of multiple cysts in the renal parenchyma, ultimately leading to ESKD. We collected peripheral blood mononuclear cells from a patient with ADPKD who carries a heterozygous PKD1 mutation and reprogrammed them into induced pluripotent stem cells (iPSCs). iPSC lines can provide a valuable resource for studying the pathogenesis of ADPKD and for drug screening purposes.

The COL7A1 gene mutation causes type VII collagen dysfunction, which subsequently leads to recessive dystrophic epidermolysis bullosa (RDEB). Patients who suffer from RDEB experience severe blisters and chronic trauma, which can eventually result in serious infection and the development of fatal squamous cell carcinoma. In our study, peripheral blood mononuclear cells (PBMCs) from an RDEB patient with the COL7A1 compound heterozygous mutation were collected and then reprogrammed into induced pluripotent stem cells (iPSC). The RDEB iPSC line can provide a cellular resource for the study of pathogenesis and drug screening.

As a critical indictor in the Battery Management System (BMS), State of Charge (SOC) is closely related to the reliable and safe operation of lithium-ion (Li-ion) batteries. Model-based methods are an effective solution for accurate and robust SOC estimation, the performance of which heavily relies on the battery model. This paper mainly focuses on battery modeling methods, which have the potential to be used in a model-based SOC estimation structure. Battery modeling methods are classified into four categories on the basis of their theoretical foundations, and their expressions and features are detailed. Furthermore, the four battery modeling methods are compared in terms of their pros and cons. Future research directions are also presented. In addition, after optimizing the parameters of the battery models by a Genetic Algorithm (GA), four typical battery models including a combined model, two RC Equivalent Circuit Model (ECM), a Single Particle Model (SPM), and a Support Vector Machine (SVM) battery model are compared in terms of their accuracy and execution time.