Abstract: A honeycomb core with S-shaped reinforced structures includes multiple cylindrical unit cells arranged in a honeycomb shape and with a polygonal cross section, the cylindrical unit cells are divided into plain unit cells and S-shaped reinforced unit cells, an inner cavity of the plain unit cell is hollow, and the inner cavity of the S-shaped reinforced unit cell is equipped with an S-shaped reinforced structure, a cross section of the S-shaped reinforced structure is S-shaped and the inner cavity of the S-shaped reinforced unit cell is divided into two halves on average. This structure guides the load-transfer path effectively by controlling the rotation of S-shaped reinforced cells, it changes the position of plastic collapse in the structure, and finally changes the peak load of the core. Under different working conditions, the design of the arrangement of S-shaped reinforced structures can make the core produce different peak loads.

Abstract: Exemplary systems and methods for estimating progression of chronic kidney disease in a patient and applying clinical interventions may include determining historic values of at least one patient parameter that varies as a function of the progression of the chronic kidney disease over time, and computationally estimating a trend corresponding to the historic values. Based on the trend, at least one marker may be automatically provided that identifies a clinical intervention and a time in the future when the clinical intervention is expected to be needed. Based on the at least one marker, clinical preparations may be executed at a time prior to the administration of the intervention in order to improve at least one of a) timeliness of the execution of the intervention and b) effectiveness of the intervention.

Abstract: Exemplary systems and methods for estimating progression of chronic kidney disease in a patient and applying clinical interventions may include determining historic values of at least one patient parameter that varies as a function of the progression of the chronic kidney disease over time, and computationally estimating a trend corresponding to the historic values. Based on the trend, at least one marker may be automatically provided that identifies a clinical intervention and a time in the future when the clinical intervention is expected to be needed. Based on the at least one marker, clinical preparations may be executed at a time prior to the administration of the intervention in order to improve at least one of a) timeliness of the execution of the intervention and b) effectiveness of the intervention.

Abstract: Exemplary systems and methods for estimating progression of chronic kidney disease in a patient and applying clinical interventions may include determining historic values of at least one patient parameter that varies as a function of the progression of the chronic kidney disease over time, and computationally estimating a trend corresponding to the historic values. Based on the trend, at least one marker may be automatically provided that identifies a clinical intervention and a time in the future when the clinical intervention is expected to be needed. Based on the at least one marker, clinical preparations may be executed at a time prior to the administration of the intervention in order to improve at least one of a) timeliness of the execution of the intervention and b) effectiveness of the intervention.