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The organ-specificity of ferritin in human and horse liver and spleen.

ABSTRACT: 1. Ferritin was isolated from human and horse spleen and liver, and apoferritin prepared therefrom. 2. The electrophoretic mobilities of the four apoferritins were determined on polyacrylamide gels and on cellulose acetate strips, and all found to be equal. 3. Homologous ferritins share reactions of identity in immunodiffusion experiments, whereas heterologous ferritins show only partial identity. 4. The subunit molecular weight of each of the apoferritins was determined by polyacrylamide-gel electrophoresis in sodium dodecyl sulphate and by chromatography on agarose columns in 6m-guanidine-HCl. A value of approx. 18500 was found in all cases. The proteins all had sedimentation coefficients of 17-18S. It thus seems that they have identical quaternary structures. 5. The amino acid compositions of the proteins revealed distinct differences both between organs and between species. This was confirmed by analysis of the tryptic peptide patterns, where it was found that about one-third of the peptides were common to the four proteins and the other two-thirds varied from protein to protein. 6. It is concluded that the apoferritins present in the liver and spleen of human and horse are both organ- and species-specific. 7. The apoferritin isolated from the liver of a patient with idiopathic haemochromatosis was identical with normal human liver apoferritin by the criteria described above.

SUBMITTER: Crichton RR

PROVIDER: S-EPMC1177438 | biostudies-other | 1973 Jan

REPOSITORIES: biostudies-other

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Project description:Organ volume measurements are a key metric for managing ADPKD (the most common inherited renal disease). However, measuring organ volumes is tedious and involves manually contouring organ outlines on multiple cross-sectional MRI or CT images. The automation of kidney contouring using deep learning has been proposed, as it has small errors compared to manual contouring. Here, a deployed open-source deep learning ADPKD kidney segmentation pipeline is extended to also measure liver and spleen volumes, which are also important. This 2D U-net deep learning approach was developed with radiologist labeled T2-weighted images from 215 ADPKD subjects (70% training = 151, 30% validation = 64). Additional ADPKD subjects were utilized for prospective (n = 30) and external (n = 30) validations for a total of 275 subjects. Image cropping previously optimized for kidneys was included in training but removed for the validation and inference to accommodate the liver which is closer to the image border. An effective algorithm was developed to adjudicate overlap voxels that are labeled as more than one organ. Left kidney, right kidney, liver and spleen labels had average errors of 3%, 7%, 3%, and 1%, respectively, on external validation and 5%, 6%, 5%, and 1% on prospective validation. Dice scores also showed that the deep learning model was close to the radiologist contouring, measuring 0.98, 0.96, 0.97 and 0.96 on external validation and 0.96, 0.96, 0.96 and 0.95 on prospective validation for left kidney, right kidney, liver and spleen, respectively. The time required for manual correction of deep learning segmentation errors was only 19:17 min compared to 33:04 min for manual segmentations, a 42% time saving (p = 0.004). Standard deviation of model assisted segmentations was reduced to 7, 5, 11, 5 mL for right kidney, left kidney, liver and spleen respectively from 14, 10, 55 and 14 mL for manual segmentations. Thus, deep learning reduces the radiologist time required to perform multiorgan segmentations in ADPKD and reduces measurement variability.

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The organ-specificity of ferritin in human and horse liver and spleen.

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