Abstract: Disclosed embodiments pertain to determining and communicating purchasing power through a visualization. A trained machine learning model can be invoked on a credit card without a preset limit to predict a dynamic spending limit based on credit card data or credit score data. Purchasing power can be computed as the difference between the dynamic spending limit and a current balance, and a purchase power class can be determined based on the purchasing power. Further, a graphic representation of a purchasing power class can be determined. Subsequently, presentation of the graphic representation can be triggered in a manner that overlays the graphic representation on or around the physical card.

Abstract: The present invention provides a method of detecting the presence of the calicheamicin component of a calicheamicin-carrier conjugate in a fluid sample, wherein the calicheamicin is covalently bound to the carrier. A bond between the calicheamicin and carrier is disrupted, the calicheamicin portion is released from the calicheamicin-carrier conjugate and detected.