No abstract

We previously reported an unexpected augmentation of mycophenolic acid (MPA) levels (trough and AUC0-12) in patients receiving mycophenolate mofetil (MMF) in combination with tacrolimus versus patients receiving the same dose of MMF in combination with cyclosporin A (CsA). This finding was accompanied by a corresponding reduction of the inactive glucuronide metabolite of MPA (MPAG) in patients, suggesting that tacrolimus may effect the conversion of MPA to MPAG by the enzyme UDP-glucuronosyltransferase (UDPGT). To investigate this possibility directly, UDPGT was extracted from human liver and kidney tissue and its activity was characterized using MPA as a substrate in vitro, assessing the conversion of MPA to MPAG using analysis by high-performance liquid chromatography. With crude microsomal preparations, amounts of UDPGT at least 100 times higher in specific activity (i.e., units to milligrams of protein) could be extracted per gram of tissue from kidney as opposed to liver. This result did not appear to be related to the coextraction of a liver-specific UDPGT inhibitor because initial enzyme kinetic values (Vmax and km) were identical for kidney and liver extracts, and further purification of the liver enzyme did not enhance activity (as is seen when inhibitors are removed during purification). With further UDPGT purification (approximately 200-fold) from kidney extracts using a combination of ammonium sulfate precipitation, followed by anion exchange, hydroxyapatite, and size exclusion chromatography, the enzyme was more than 80% pure when assessed by sodium dodecyl sulfate-polyacrylamide gel electrophoresis. Initial enzyme kinetic analysis of this purified product showed a km value for MPA of 35.4+/-5.7 microg/mL and a Vmax of 2.87+/-0.31 MPAG produced per hour (n = 7). The addition of clinically relevant concentrations of CsA (200-1,000 ng/mL) or tacrolimus (10-25 ng/mL) resulted in a dose-dependent inhibition of the UDPGT enzyme by both agents with tacrolimus, which was approximately 60-fold more efficient as an inhibitor. The calculated inhibition constants (KI) of tacrolimus and CsA for the purified UDPGT were 27.3+/-5.6 ng/ml and 2,518+/-1473 ng/ml. respectively. Both agents displayed an inhibition profile characteristic of a competitive inhibitor (substrate) that could be demonstrated in a reciprocal experiment with CsA as a substrate, but not with tacrolimus. This finding suggested that the significantly more efficient inhibition of UDPGT by tacrolimus may occur by a more complicated mechanism that is yet to be determined.

No abstract

Thrombocytopenia in the immediate posttransplant period is correlated with low preoperative PLT, massive platelet transfusions, and re-transplantation. These factors reflect a poor preoperative condition. There is also a correlation with allograft dysfunction, rejection, and poorer patient and graft survival. A rise in the mean PLT after the 2nd postoperative week reflects proper graft function.

The development of artificial intelligence (AI) algorithms has permeated the medical field with great success. The widespread use of AI technology in diagnosing and treating several types of cancer, especially colorectal cancer (CRC), is now attracting substantial attention. CRC, which represents the third most commonly diagnosed malignancy in both men and women, is considered a leading cause of cancer-related deaths globally. Our review herein aims to provide in-depth knowledge and analysis of the AI applications in CRC screening, diagnosis, and treatment based on current literature. We also explore the role of recent advances in AI systems regarding medical diagnosis and therapy, with several promising results. CRC is a highly preventable disease, and AI-assisted techniques in routine screening represent a pivotal step in declining incidence rates of this malignancy. So far, computer-aided detection and characterization systems have been developed to increase the detection rate of adenomas. Furthermore, CRC treatment enters a new era with robotic surgery and novel computer-assisted drug delivery techniques. At the same time, healthcare is rapidly moving toward precision or personalized medicine. Machine learning models have the potential to contribute to individual-based cancer care and transform the future of medicine.