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Pembrolizumab is currently a standard first-line treatment option in stage IV non-small cell lung cancer (NSCLC) patients without targetable mutations. Discrepancies between pembrolizumab registration and its application in daily practice exist due to lack of clinical data in specific patient populations. Pharmacokinetic monitoring of pembrolizumab concentrations might provide more insight in the impact of certain conditions on immunotherapy exposure. In this report, we describe a 55-year-old male with metastasized programmed death ligand 1 (PD-L1)-high NSCLC and clinically diagnosed nephrotic syndrome with widespread edema, proteinuria, and severe hypoalbuminemia. The patient was treated with first-line 200 mg pembrolizumab every 3 weeks. Nephrotic syndrome is reported to be a potential source of therapeutic antibody loss during immunotherapy, due to increased protein turnover and excretion. Serum and urine pembrolizumab levels were measured to assess pembrolizumab exposure. Despite severe hypoalbuminemia and proteinuria, treatment was successful. The patient achieved therapeutic exposure and had an impressive clinical response with a vast improvement of edema and hypoalbuminemia, a radiological partial tumor response and ECOG performance status improving from 2 to 0.

Attempts to reconstruct the spatial location, size, and form of buried heat sources from the measured pattern of thermograms are, in general, prohibited by the lack of a priori information about the thermal (flow) model and the source structure. In this paper, a method is introduced based on geometric reconstruction of a buried heat source configuration. This configuration must contain point sources and/or sharp edges and be confined to a plane region parallel to the surface. The medium, in which the heat source is embedded, is assumed to be homogeneous, isotropic, and of large size compared to the size of the source and to its depth below the surface. The heat flux from the surface to the ambient is assumed to follow the Newtonian cooling law. The spatial density distribution of the flux can be described by a Green function with coefficients determined by the depth of the source plane. It is possible to approximate a corresponding inverse mapping algorithm (reconstruction filter) for each source plane depth, with only one (depth) scaling parameter. The density distribution of the source structure is optimally deblurred when the reconstruction filter's scaling parameter matches the actual depth of the source plane below the surface. In the reconstruction procedure, this reconstruction filter is consecutively applied for several values of the scaling parameter. The so-called ``deblurring quality'' of the point or edge information is utilized to decide which scaling parameter achieves the sharpest image. This procedure resembles the focusing of a lens.