[Objective] To analyse the distribution of antigen phenotypes in the Rh, MNS and Kidd blood group systems of Han and Tujia blood donors in Chongqing, and to provide data support for the establishment of an expanded blood group antigen phenotype database and the development of expanded blood group coordinated transfusion in blood donors. [Methods] The antigens of Rh, MNS and Kidd blood group systems in Han and Tujia blood donors in Chongqing were detected by test-tube method, and the Hardy-Weinborg anastomosis of the three blood group systems was calculated. Pearson's chi-square test and Fisher's exact probability method were used to compare the differences in phenotypic distribution frequencies among different regions and ethnic groups. [Results] Han and Tujia blood donors accounted for the highest proportion of CCee in the antigenic phenotype of the Rh blood group system, followed by CcEe, and then Ccee and ccEE. Tujia blood donors accounted for 52.02% of CCee, which was higher than that of Han blood donors (47.24%), while Han blood donors accounted for 32.20% of CcEe, which was higher than that of Tujia blood donors (28.94%). In the antigenic phenotype of the MNS blood group system, the blood donors of Han nationality and Tujia were MN>MM>NN,. The antigen phenotype distribution frequency of the Kidd blood group system was highest for Jk(a+b+) among both Han and Tujia blood donors, and the blood donors of Han nationality were Jk(a+b+)>Jk(a+b+), while those of Tujia were Jk(a-b+)>Jk(a+b-). The antigens of the three blood groups of Han and Tujia blood donors were consistent with the Hardy-Weinberg balance(P>0.05). There was no statistically significant difference in the frequency of antigen phenotypes of the three blood group systems between Han and Tujia blood donors(P>0.05). There were statistically significant differences in the phenotypic distribution frequency of Rh antigens between Chongqing and Xi'an, Zhejiang, Shantou, Foshan, Nanning and Yangzhou(P<0.05), but not with Guang'an and Shenzhen(P>0.05). There were statistically significant differences in the phenotypic distribution frequency of Rh antigens between Han, Tujia, Zang, Mongolian, Korean and Hani ethnic groups in Chongqing(P<0.05). There were statistically significant differences in the phenotypic distribution frequency of MNS antigens between Han blood donors in Chongqing and Urumqi, Hainan and Yuncheng, but not with Xi'an and Wenzhou. There was a statistically significant difference in the phenotypic distribution frequency of MNS antigen between Tujia blood donors in Chongqing and Urumqi and Hainan(P<0.05), but there was no significant difference in the phenotypic distribution frequency of MNS antigen between Tujia blood donors in Chongqing, Urumqi and Hainan(P>0.05). There was a statistically significant difference in the phenotypic distribution frequency of Kidd antigens between blood donors in Chongqing and Harbin(P<0.05), but not in Huizhou, Wenzhou and Yichang(P>0.05). [Conclusion] The population in Chongqing has multi-ethnic characteristics, and the antigenic phenotypes of Rh, MNS and Kidd blood group systems exhibit diversity and regional differences. Establishing an expanded blood bank can provide more options for precision blood transfusion.

[Objective] To assess the data characteristics of quality monitoring indicators for red blood cell (RBC) preparations, so as to provide reference for continuous improvement of blood quality. [Methods] The quality inspection data of 6 types of RBC preparations from Chongqing blood center from 2019 to 2023 were summarized. For the same indicators, the numerical range of quality indicators was monitored by comparing different types of preparations with the national standard GB18469. The loss and/or damage to RBCs caused by different preparation process were compared, and the impact of different preparation processes on the quality of RBCs was discussed. [Results] The appearance and sterility test compliance rates of the six types of RBC preparations were both 100%, while the compliance rates of other items were all ≥75%. The compliance rate of hematocrit for suspended RBCs was the lowest at 75%, with a median of 0.52, which was close to the lower limit of GB18469, while the medians of hematocrit for the other types were all at the midline level of GB18469. The Hb content for different types of RBCs was significantly higher than the corresponding requirements of GB18469 (P<0.05). The hemolysis rate at the end of storage for different types of RBCs was significantly lower than the requirements of GB18469 (P<0.05). The 1 U leukoreduction process resulted in a hemoglobin content loss of about 5% and had a significant impact on the hemolysis rate at the end of storage (P<0.05). The washing process resulted in a hemoglobin content loss of <3% and had no significant impact on the hemolysis rate at the end of storage (P>0.05). The concentration process resulted in a hemoglobin content loss of <3% and had a significant impact on the hemolysis rate at the end of storage (P<0.05). [Conclusion] The impact of different processes on RBC preparations is within a controllable range and meets the requirements of GB18469. The quality monitoring data can provide a reference for clinical blood selection, effectiveness evaluation and revision of related standards.

This review summarizes recent advances in neoadjuvant immunotherapy for advanced gastric cancer.Through literature search in PubMed,Web of Science,and CNKI databases from 2020 to 2023,we systematically analyzed the mechanisms,clinical applications,and bio-marker research.Programmed death-1(PD-1)inhibitors combined with chemotherapy significantly improve patient outcomes,while mi-crosatellite instability(MSI),programmed death-ligand 1(PD-L1)expression,and tumor mutational burden(TMB)have been identified as important predictive biomarkers.Multi-omics analysis shows great potential in identifying optimal responders,with pyroptosis-related gene scoring system(PRS)positively correlating with anti-tumor immune infiltration.Metabolic reprogramming and epigenetic regulation in the tumor microenvironment play key roles in immune evasion,while emerging targets such as Claudin 18.2 and combination targeting strategies further enhance therapeutic efficacy.Despite significant progress,precise patient selection and overcoming resistance mechan-isms remain major challenges.Future research should focus on biomarker validation,personalized treatment strategy development,tumor microenvironment dynamic analysis,and novel combination therapy exploration to improve clinical outcomes.

Objective: To investigate the clinical characteristics, the types of unexpected antibodies, and their impacts on immunological risks among patients with different frequencies of cross-matching incompatibility, so as to propose corresponding solutions. Methods: Data of cross-matching incompatibility samples from 92 medical institutions during 2022 to 2024 were collected and divided into three groups based on the frequency of cross-matching. Statistical analysis was performed on disease types, distribution of hematologic diseases, alloantibody detection rates, and proportions of alloantibody types. Results: The 858 patients were divided into three groups based on the frequency of blood cross-matching incompatibility: ≥5 times (8.28%, 71/858), 2 to 4 times (28.21%, 242/858); 1 time (63.52%, 545/858). There was a clustered distribution of disease types in the ≥5 cross-matchings group, with 71.83% (51/71) of patients having tumors or hematologic and hematopoietic diseases. In contrast, the disease types in the 2 to 4 cross-matchings and 1 cross-matching groups were more diverse. An analysis of 249 patients with hematologic diseases found that multiple myeloma was the most common disease in all three groups, accounting for 31.43% (11/35), 35.37% (29/82), and 37.88% (50/132) respectively. In the ≥5 cross-matchings group, myelodysplastic syndrome (14.29%, 5/35) and thalassemia (14.29%, 5/35) were the second most common diseases. In contrast, in the 2 to 4 cross-matchings group and 1 cross-matching group, autoimmune hemolytic anemia was the second most common disease, with prevalence rates of 20.73% (17/82) and 24.24% (32/132), respectively. Alloantibodies were detected in 54.66% of the patients, with antibodies against Rh blood group being most frequent (>50%) in all three groups. The detection rates of alloantibodies/alloantibodies with coexisting autoantibodies decreased across groups: the ≥5 cross-matchings group (70.42%, 50/71) > the 2 to 4 cross-matchings group (54.96%, 133/242) > the 1 cross-matching group (52.48%, 286/545). Conclusion: The risk of alloantibody production increases in patients with multiple cross-matching incompatibilities, especially in those with tumors or hematologic diseases. For handling of cross-matching incompatibility cases, it is recommended to optimize the cross-matching process, implement individualized transfusion plans, and enhance the technical capabilities of clinical transfusion departments and blood group reference laboratories to ensure the safety and effectiveness of transfusions.

This review summarizes recent advances in neoadjuvant immunotherapy for advanced gastric cancer.Through literature search in PubMed,Web of Science,and CNKI databases from 2020 to 2023,we systematically analyzed the mechanisms,clinical applications,and bio-marker research.Programmed death-1(PD-1)inhibitors combined with chemotherapy significantly improve patient outcomes,while mi-crosatellite instability(MSI),programmed death-ligand 1(PD-L1)expression,and tumor mutational burden(TMB)have been identified as important predictive biomarkers.Multi-omics analysis shows great potential in identifying optimal responders,with pyroptosis-related gene scoring system(PRS)positively correlating with anti-tumor immune infiltration.Metabolic reprogramming and epigenetic regulation in the tumor microenvironment play key roles in immune evasion,while emerging targets such as Claudin 18.2 and combination targeting strategies further enhance therapeutic efficacy.Despite significant progress,precise patient selection and overcoming resistance mechan-isms remain major challenges.Future research should focus on biomarker validation,personalized treatment strategy development,tumor microenvironment dynamic analysis,and novel combination therapy exploration to improve clinical outcomes.

Objective:To construct a general deep learning dose prediction model applicable to radiotherapy for head and neck tumors, establish design methods for artificial intelligence (AI)-assisted radiotherapy plan and evaluate the accuracy of prediction.Methods:Radiotherapy plans of 818 patients who received radiotherapy for head and neck cancers from January 2018 to June 2021 in Cancer Hospital of Chinese Academy of Medical Sciences were enrolled. Patients involved 17 types of common head and neck cancers, and the prescribed dose covered 5 kinds of dose gradients ranging from 54 Gy to 73.92 Gy. And 1-2 cases per each cancer type (31 cases in total) were randomly selected as the validation set, and the remaining 787 cases were used as the training set to build a deep learning head and neck radiotherapy generalized dose prediction model. Then based on the dose prediction results of this model, a program was written to automatically generate inverse optimization condition scripts, which were sent back to the treatment planning system to achieve AI-assisted radiotherapy plan design. Among the patients who received radiotherapy in our hospital from June 2021 to January 2022, 1 patient for each disease type (17 cases in total) was selected to evaluate the AI-assisted plan design program and evaluate its clinical feasibility using paired t-test. Results:Dose prediction model accuracy evaluation revealed that in the 31-case validation set, there was no statistical difference in the evaluation metrics of clinical concern for organs at risks, except for the D 1 cm3 prediction for spinal cord planning risk volume, which was statistically different compared with the clinical reference plan. The AI-assisted plan design program had higher plan quality metric scores (37.88±6.42) than manual plans (35.00±7.63) in 17 test cases ( t=-1.00, P=0.166). The number of manual adjustments to the inverse optimization conditions was reduced from (5.47±2.97) times to (2.76±1.00) times for the AI-assisted plan compared to the manual-only plan ( t=4.12, P<0.001). And the number of outlined dose shaping structures was reduced from 7.35±3.98 to 3.12±1.18 ( t=5.61, P<0.001). Conclusions:The unified universal model of dose prediction established for different head and neck cancers has high accuracy in dose prediction for all types of head and neck tumor plans. The AI-assisted planning method established in this pattern can reduce the clinical workload of physicists and improve the efficiency of their work.

Artificial intelligence (AI) technologies are being widely applied in radiotherapy. However, the integration of AI into clinical workflows of radiotherapy faces a series of challenges, such as poor model interpretability, domain shifts between clinical application and training data, and the inherent model uncertainties. Therefore, case-specific quality assurance (QA) is essential before deploying AI models in clinical practice. This paper reviews and summarizes QA methodologies for the application of AI models in radiotherapy across four key areas: image registration, image generation, region of interest segmentation, and treatment planning.

Objective:To construct a general deep learning dose prediction model applicable to radiotherapy for head and neck tumors, establish design methods for artificial intelligence (AI)-assisted radiotherapy plan and evaluate the accuracy of prediction.Methods:Radiotherapy plans of 818 patients who received radiotherapy for head and neck cancers from January 2018 to June 2021 in Cancer Hospital of Chinese Academy of Medical Sciences were enrolled. Patients involved 17 types of common head and neck cancers, and the prescribed dose covered 5 kinds of dose gradients ranging from 54 Gy to 73.92 Gy. And 1-2 cases per each cancer type (31 cases in total) were randomly selected as the validation set, and the remaining 787 cases were used as the training set to build a deep learning head and neck radiotherapy generalized dose prediction model. Then based on the dose prediction results of this model, a program was written to automatically generate inverse optimization condition scripts, which were sent back to the treatment planning system to achieve AI-assisted radiotherapy plan design. Among the patients who received radiotherapy in our hospital from June 2021 to January 2022, 1 patient for each disease type (17 cases in total) was selected to evaluate the AI-assisted plan design program and evaluate its clinical feasibility using paired t-test. Results:Dose prediction model accuracy evaluation revealed that in the 31-case validation set, there was no statistical difference in the evaluation metrics of clinical concern for organs at risks, except for the D 1 cm3 prediction for spinal cord planning risk volume, which was statistically different compared with the clinical reference plan. The AI-assisted plan design program had higher plan quality metric scores (37.88±6.42) than manual plans (35.00±7.63) in 17 test cases ( t=-1.00, P=0.166). The number of manual adjustments to the inverse optimization conditions was reduced from (5.47±2.97) times to (2.76±1.00) times for the AI-assisted plan compared to the manual-only plan ( t=4.12, P<0.001). And the number of outlined dose shaping structures was reduced from 7.35±3.98 to 3.12±1.18 ( t=5.61, P<0.001). Conclusions:The unified universal model of dose prediction established for different head and neck cancers has high accuracy in dose prediction for all types of head and neck tumor plans. The AI-assisted planning method established in this pattern can reduce the clinical workload of physicists and improve the efficiency of their work.

Artificial intelligence (AI) technologies are being widely applied in radiotherapy. However, the integration of AI into clinical workflows of radiotherapy faces a series of challenges, such as poor model interpretability, domain shifts between clinical application and training data, and the inherent model uncertainties. Therefore, case-specific quality assurance (QA) is essential before deploying AI models in clinical practice. This paper reviews and summarizes QA methodologies for the application of AI models in radiotherapy across four key areas: image registration, image generation, region of interest segmentation, and treatment planning.

【Objective】 This study endeavors to introduce the statistical process control (SPC) method to analyze the quality control index concerning red blood cells in additive solution with leukocytes reduced, with the aspiration to advance the effective utilization of blood quality control data, thereby providing empirical foundations for the continual enhancement of blood quality. 【Methods】 Between 2020 and 2022, test data pertaining to the quality control index of red blood cells in additive solution with leukocytes reduced were amassed from six blood stations in Chongqing area. Utilizing Minitab software, the SPC analysis was carried out, p-control charts were delineated, the non-conformance rates of each quality control index along with their 95% confidence intervals were computed, as well as the Process Capability Index (Z value). 【Results】 In accordance with the Whole Blood and Blood Components Quality Requirements, the appraisal of the quality control indexes for red blood cells in additive solution with leukocytes reduced manifested a conformity rate of 100% for appearance, end-of-storage hemolysis rate and sterility test. Nonetheless, the conformity rates for volume, hemoglobin, hematocrit and residual leukocytes did not attain 100%, albeit all were ≥75%. Through the employment of binomial distribution-based p-control charts, the controlled state of the production process was discerned. Although the overarching conformity rate satisfied the national standard stipulations, it was discerned that there were out-of-control points concerning volume, hemoglobin, hematocrit, and residual leukocytes across different institutions, exhibiting palpable trends. The non-conformance rates of all quality control indexes were less than 25%, yet at a 95% confidence level, the residual leukocyte counts from institutions B, C, E, and F did not adhere to the stipulations (exceeding 25%). By architecting the ability evaluation index Z value for count data process capability analysis, it was unveiled that the volume of institution E, the hematocrit of institutions B, C, and F, and the residual leukocytes Z values of all six blood collection and supply institutions were below 2, hinting at avenues for amelioration. 【Conclusion】 The SPC method anchored in binomial distribution exhibits substantial application merit in blood component quality management, facilitating real-time surveillance of blood collection, preparation, and storage procedures.