PubMed · 13109016
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H N ANTONIADES. 1953. A new liver function turbidity test.. https://doi.org/10.1136/jcp.6.4.290
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On abdominal MRI, since priority is given to time shortening during scanning breath-hold imaging, the signal-to-noise ratio (SNR) and resolution fall victim. We examined the RF power of the 90-degree pulse becomes the maximum signal of fat in T(1)-weighted imaging, and liver with a long T(1) value was noted not to have reached the maximum signal compared with fat. That is, without sacrificing scanning time and resolution by adjusting RF output so that liver would become the maximum signal, it was considered that the SNR of liver substance could be improved. In the phantom experiment, the difference in RF power from which fat and liver became the maximum signal was 1 dB at TR 400 msec. Although a decrease of 10% or less in the fat signal was produced by adding this difference to the RF power obtained by automatic pre-scan, the SNR of liver substance was improved.
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Liver clearance models serve to improve our understanding of the relationships between the physiological determinants and hepatic clearance and predict changes in the disposition of substrates when homeostasis of the organ is perturbed. Their ability to describe metabolism was presently extended to the sequential formation and elimination of primary (M1), secondary (M2), and tertiary (M3) metabolites during a single passage of drug (P) across the liver, under steady state and first-order conditions. The well-stirred model is distinct from other models in that metabolite formation and elimination is independent of enzymic distributions, the number of steps involved in metabolite formation, and the intrinsic clearances of the precursors. This model predicts that the extraction ratio of a formed primary metabolite derived from drug (E[M1, P]) is identical to that for the preformed primary metabolite (E[M1]), and that the extraction ratios of a secondary metabolite derived from drug (E[M2, P]) and primary metabolite (E[M2, M1]) or preformed secondary metabolite (E[M2]) are identical. For the more physiologically acceptable, parallel-tube and dispersion models, metabolite sequential elimination is highly influenced by the intrinsic clearances of the precursors and the enzymic distributions that mediate removal of precursor species and the metabolites. Furthermore, the extent of sequential metabolism recedes as the number of steps involved for metabolite formation increases. These models predict that E[M1, P] less than E[M1], and E[M2, P] less than E[M2, M1] less than E[M2], with the magnitude of the changes being less for the dispersion model than for the parallel-tube model. Competing pathways that divert substrate from entering the sequential pathway were found to exert only minimal influence on the sequential pathway.