Antibiotic concentrations in ascitic fluid of patients with ascites and bacterial peritonitis

Thirty-six paired specimens of serum and ascitic fluid from 21 patients with peritonitis and ascites, most with sponetaneous bacterial peritonitis and alcoholic cirrhosis, were assayed for antibiotic content. Antibiotics assayed and number of determinations were gentamicin, 14; tobramycin, 7; ampicillin, 5; clindamycin, 3; penicillin G, 2; cephalothin, 2; chloramphenico, 2; and cefazolin, 1. In 31 pared specimens the ascitic fluid antibiotic concentration was about one half or more of the simultaneous serum level and in 17 assays exceeded 90% of the serum level. All antibiotics studied penetrated ascitic fluid equally well. Clinical response to antibiotic therapy was good in 12 of 16 patients with culture-proven bacterial peritonitis. Antibiotic levels in ascitic fluid exceeded the minimal inhibitory concentration of the infecting organisms in all but one patient who responded. Direct intraperitoneal instillation of antibiotics does not appear to be necessary routinely; however, there may be an initial lag of several hours before antibiotic concentrations is ascites achieve therapeutic levels.     

Image Morphing With Perceptual Constraints and STN Alignment

In image morphing, a sequence of plausible frames are synthesized and composited together to form a smooth transformation between given instances. Intermediates must remain faithful to the input, stand on their own as members of the set and maintain a well-paced visual transition from one to the next. In this paper, we propose a conditional generative adversarial network (GAN) morphing framework operating on a pair of input images. The network is trained to synthesize frames corresponding to temporal samples along the transformation, and learns a proper shape prior that enhances the plausibility of intermediate frames. While individual frame plausibility is boosted by the adversarial setup, a special training protocol producing sequences of frames, combined with a perceptual similarity loss, promote smooth transformation over time. Explicit stating of correspondences is replaced with a grid-based freeform deformation spatial transformer that predicts the geometric warp between the inputs, instituting the smooth geometric effect by bringing the shapes into an initial alignment. We provide comparisons to classic as well as latent space morphing techniques, and demonstrate that, given a set of images for self-supervision, our network learns to generate visually pleasing morphing effects featuring believable in-betweens, with robustness to changes in shape and texture, requiring no correspondence annotation.