Adult classic Bartter syndrome: a case report with 5-year follow-up and literature review.

Bartter syndrome (BS) is a rare, inherited salt-losing renal tubular disorder characterized by secondary hyperaldosteronism, hypokalemia, hypochloremia, metabolic alkalosis, and low-to-normal blood pressure. Classic BS, or BS Type 3, the most common subtype in the Asian population, is caused by a molecular defect in ClC-Kb, a voltage-gated chloride channel in renal tubules, due to CLCNKB gene mutation. Because the onset of BS is more common in children than in adults, the diagnosis, treatment outcomes, genotype/phenotype association, and follow-up of adult-onset BS Type 3 are limited. This case report describes the findings in a 20-year-old man who was admitted with hypokalemic paralysis, with clinical manifestations were similar to those of Gitelman syndrome (GS); however, the patient was later diagnosed to have BS Type 3 through genetic testing (NM_000085.4 (CLCNKB): c.1052G>T). A literature review showed that no homozygous mutations have been reported to date. After 5 years of treatment and follow-up, we found that this genotype requires high levels of potassium and is prone to urinary protein and metabolic syndrome. Distinguishing adult-onset BS from GS is challenging in clinical practice. However, genetic diagnosis can help solve this problem effectively, and genotypes play a guiding role in treatment planning.

Numerical simulation of deformed red blood cell by utilizing neural network approach and finite element analysis.

In order to have research on the deformation characteristics and mechanical properties of human red blood cells (RBCs), finite element models of RBC optical tweezers stretching and atomic force microscope (AFM) indentation were established. Non-linear elasticity of cell membrane was determined by using the neo-Hookean hyperelastic material model, and the deformation of RBC during stretching and indentation had been researched in ABAQUS, respectively. Considering the application of machine learning (ML) in material parameters identification, ML algorithm was combined with finite element (FE) method to identify the constitutive parameters. The material parameters were estimated according to the deformation characteristics of RBC obtained from the change of cell diameter with stretching force when RBC was stretched. The non-linear relationship between material parameter and RBC deformation was established by building a FE-model. The FE simulation of RBC stretching was used to construct the training set and the neural network trained by a large number of samples was used to predict the material parameter. With the predicted parameter, FE simulation of RBC under AFM indentation to explore the local deformation mechanism was completed.