Objective: To accurately determine the ABO blood group of samples exhibiting forward/reverse grouping discrepancies by combining first-generation (Sanger) and third-generation (long-read) sequencing technologies. Methods: Five samples with ABO forward/reverse grouping discrepancies were selected. Serological testing was conducted using automated blood typing instruments and the tube method. Genotyping was conducted using both Sanger and long-read sequencing technologies. Results: Sanger sequencing identified specific genetic mutations in two samples, with genotypes of ABO BA. 04/ABO O.01.01 and ABO B3.05/ABO O.01.02. Further analysis with long-read sequencing revealed specific mutations in the +5.8kb region of intron 1 (c.28+5885C>T and c.28+5861T>G) in three samples where mutations were not detected by Sanger sequencing. These mutations affect the expression of the ABO antigens and are likely responsible for the ABO subgroup phenotypes. Conclusion: The integration of Sanger and long-read sequencing technologies effectively identifies genetic variations causing ABO subtypes, providing a scientific basis for enhancing clinical transfusion safety and ensuring accurate blood group determination.

Objective: To accurately determine the ABO blood group of samples exhibiting forward/reverse grouping discrepancies by combining first-generation (Sanger) and third-generation (long-read) sequencing technologies. Methods: Five samples with ABO forward/reverse grouping discrepancies were selected. Serological testing was conducted using automated blood typing instruments and the tube method. Genotyping was conducted using both Sanger and long-read sequencing technologies. Results: Sanger sequencing identified specific genetic mutations in two samples, with genotypes of ABO BA. 04/ABO O.01.01 and ABO B3.05/ABO O.01.02. Further analysis with long-read sequencing revealed specific mutations in the +5.8kb region of intron 1 (c.28+5885C>T and c.28+5861T>G) in three samples where mutations were not detected by Sanger sequencing. These mutations affect the expression of the ABO antigens and are likely responsible for the ABO subgroup phenotypes. Conclusion: The integration of Sanger and long-read sequencing technologies effectively identifies genetic variations causing ABO subtypes, providing a scientific basis for enhancing clinical transfusion safety and ensuring accurate blood group determination.

Access to the clinical application of medical technology is one of the core institutional contents of medical quality management， involving medical quality assurance， the achievement of patient safety goals， and medical service satisfaction. Medical technology is only permitted for clinical use after its safety and effectiveness have been verified through clinical research， as well as evaluated and reviewed by the medical technology clinical application management committee and ethics committee of this medical and health institution. Based on the relevant laws， regulations， and ethical principles， combined with the experience of ethical review in the clinical application of medical technology from some medical and health institutions， a thematic discussion was held to formulate ethical review guidelines for the clinical application of medical technology for references. These guidelines elaborated on the management system for access to the clinical application of medical technology in medical and health institutions， the system of ethics committees and the requirements of review norms， technical plans and their review points， key points for the implementation of informed consent， technical teams and conditions， and other aspects.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-associated death globally. Liver transplantation (LT) has emerged as a key treatment for patients with HCC, and the Milan criteria have been adopted as the cornerstone of the selection policy. To allow more patients to benefit from LT, a number of expanded criteria have been proposed, many of which use radiologic morphological characteristics with larger and more tumors as surrogates to predict outcomes. Other groups developed indices incorporating biological variables and dynamic markers of response to locoregional treatment. These expanded selection criteria achieved satisfactory results with limited liver supplies. In addition, a number of prognostic models have been developed using clinicopathological characteristics, imaging radiomics features, genetic data, and advanced techniques such as artificial intelligence. These models could improve prognostic estimation, establish surveillance strategies, and bolster long-term outcomes in patients with HCC. In this study, we reviewed the latest findings and achievements regarding the selection criteria and post-transplant prognostic models for LT in patients with HCC.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-associated death globally. Liver transplantation (LT) has emerged as a key treatment for patients with HCC, and the Milan criteria have been adopted as the cornerstone of the selection policy. To allow more patients to benefit from LT, a number of expanded criteria have been proposed, many of which use radiologic morphological characteristics with larger and more tumors as surrogates to predict outcomes. Other groups developed indices incorporating biological variables and dynamic markers of response to locoregional treatment. These expanded selection criteria achieved satisfactory results with limited liver supplies. In addition, a number of prognostic models have been developed using clinicopathological characteristics, imaging radiomics features, genetic data, and advanced techniques such as artificial intelligence. These models could improve prognostic estimation, establish surveillance strategies, and bolster long-term outcomes in patients with HCC. In this study, we reviewed the latest findings and achievements regarding the selection criteria and post-transplant prognostic models for LT in patients with HCC.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-associated death globally. Liver transplantation (LT) has emerged as a key treatment for patients with HCC, and the Milan criteria have been adopted as the cornerstone of the selection policy. To allow more patients to benefit from LT, a number of expanded criteria have been proposed, many of which use radiologic morphological characteristics with larger and more tumors as surrogates to predict outcomes. Other groups developed indices incorporating biological variables and dynamic markers of response to locoregional treatment. These expanded selection criteria achieved satisfactory results with limited liver supplies. In addition, a number of prognostic models have been developed using clinicopathological characteristics, imaging radiomics features, genetic data, and advanced techniques such as artificial intelligence. These models could improve prognostic estimation, establish surveillance strategies, and bolster long-term outcomes in patients with HCC. In this study, we reviewed the latest findings and achievements regarding the selection criteria and post-transplant prognostic models for LT in patients with HCC.