A unified deep-learning framework for enhanced patient-specific quality assurance of intensity-modulated radiation therapy plans.

Background: Modern radiation therapy techniques, such as intensity-modulated radiation therapy (IMRT) and volumetric-modulated arc therapy (VMAT), use complex fluence modulation strategies to achieve optimal patient dose distribution. Ensuring their accuracy necessitates rigorous patient-specific quality assurance (PSQA), traditionally done through pretreatment measurements with detector arrays. While effective, these methods are labor-intensive and time-consuming.

The dithiol mechanism of class I glutaredoxins promotes specificity for glutathione as a reducing agent.

Class I glutaredoxins reversibly reduce glutathione- and nonglutathione disulfides with the help of reduced glutathione (GSH) using either a monothiol mechanism or a dithiol mechanism. The monothiol mechanism exclusively involves a single glutathionylated active-site cysteinyl residue, whereas the dithiol mechanism requires the additional formation of an intramolecular disulfide bond between the active-site cysteinyl residue and a resolving cysteinyl residue. While the oxidation of glutaredoxins by glutathione disulfide substrates has been extensively characterized, the enzyme-substrate interactions for the reduction of S-glutathionylated glutaredoxins or intramolecular glutaredoxin disulfides are still poorly characterized.