OBJECTIVE: To explore the regulatory mechanisms underlying the weak expression of ABO blood group antigens due to variants in the non-coding regions of the ABO gene. METHODS: From June 2014 to October 2023, a total of 29 samples from the Taiyuan Blood Center and local hospitals, which were serologically identified as having weak ABO antigen expression without detectable coding region mutations, were selected for this study. Full-length ABO gene sequencing was performed using third-generation long-read sequencing technology (Pacific Biosciences) to obtain complete haplotype sequences of the ABO gene. Variants in the non-coding regions were compared and identified to infer their regulatory effects on weak antigen expression. The procedures followed in this study were in accordance with the ethical standards of the World Medical Association's Declaration of Helsinki (2013 revision). The Medical Ethics Committee of Taiyuan Blood Center has granted an exemption from ethical review. RESULTS: 18 bp deletions in the -35 to -18 region of the promoter were identified in 7 samples. Variants in intron 1 (+5.8 kb) were detected in 7 samples, including ABO*A (28+5792_5793delCT (1 case) and ABO*B (28+5793T>C) located in the GATA binding region; ABO*B (28+5808C>T) (1 case) in the E-box region; and ABO*B (28+5875C>T) (4 cases) in the RUNX1 binding region. Nucleotide variants at splice sites were detected in 2 samples, namely ABO*B (C.98+1G>A) and ABO*B (C.204-2A>C). CONCLUSION Variants in the non-coding regulatory sequences of the ABO gene are a significant factor contributing to weak ABO antigen expression. In clinical ABO sequencing, it is essential to screen not only the conventional coding regions but also the flanking sequences, introns, and splice sites of the ABO gene to facilitate precise blood transfusion.
ABO Blood-Group System/genetics* ; Humans ; Alleles ; Promoter Regions, Genetic ; Haplotypes ; Introns

Objective:To explore the regulatory mechanisms underlying the weak expression of ABO blood group antigens due to variants in the non-coding regions of the ABO gene. Methods:From June 2014 to October 2023, a total of 29 samples from the Taiyuan Blood Center and local hospitals, which were serologically identified as having weak ABO antigen expression without detectable coding region mutations, were selected for this study. Full-length ABO gene sequencing was performed using third-generation long-read sequencing technology (Pacific Biosciences) to obtain complete haplotype sequences of the ABO gene. Variants in the non-coding regions were compared and identified to infer their regulatory effects on weak antigen expression. The procedures followed in this study were in accordance with the ethical standards of the World Medical Association′s Declaration of Helsinki (2013 revision). The Medical Ethics Committee of Taiyuan Blood Center has granted an exemption from ethical review. Results:18 bp deletions in the -35 to -18 region of the promoter were identified in 7 samples. Variants in intron 1 (+ 5.8 kb) were detected in 7 samples, including ABO* A (28+ 5792_5793delCT (1 case) and ABO* B (28+ 5793T>C) located in the GATA binding region; ABO* B (28+ 5808C>T) (1 case) in the E-box region; and ABO* B (28+ 5875C>T) (4 cases) in the RUNX1 binding region. Nucleotide variants at splice sites were detected in 2 samples, namely ABO* B (C.98+ 1G>A) and ABO* B (C.204-2A>C). Conclusion:Variants in the non-coding regulatory sequences of the ABO gene are a significant factor contributing to weak ABO antigen expression. In clinical ABO sequencing, it is essential to screen not only the conventional coding regions but also the flanking sequences, introns, and splice sites of the ABO gene to facilitate precise blood transfusion.

The activation proteins released by fibroblasts in the tumor microenvironment regulate tumor growth, migration, and treatment response, thereby influencing tumor progression and therapeutic outcomes. Owing to the proliferation and metastasis of tumors, fibroblast activation protein (FAP) is typically highly expressed in the tumor stroma, whereas it is nearly absent in adult normal tissues and benign lesions, making it an attractive target for precision medicine. Radiolabeled agents targeting FAP have the potential for targeted cancer diagnosis and therapy. This comprehensive review aims to describe the evolution of FAPI-based radiopharmaceuticals and their structural optimization. Within its scope, this review summarizes the advances in the use of radiolabeled small molecule inhibitors for tumor imaging and therapy as well as the modification strategies for FAPIs, combined with insights from structure-activity relationships and clinical studies, providing a valuable perspective for radiopharmaceutical clinical development and application.

Mutations in ion channel genes have long been implicated in a spectrum of epilepsy syndromes. However, therapeutic decision-making is relatively complex for epilepsies associated with channelopathy. Therefore, in the present study, we used a patient-derived organoid model with a novel SCN2A mutation (p.E512K) to investigate the potential of utilizing such a model as a platform for preclinical testing of anti-seizure compounds. The electrophysiological properties of the variant Nav1.2 exhibited gain-of-function effects with increased current amplitude and premature activation. Immunofluorescence staining of patient-derived cortical organoids (COs) displayed normal neurodevelopment. Multielectrode array (MEA) recordings of patient-derived COs showed hyperexcitability with increased spiking and remarkable network bursts. Moreover, the application of patient-derived COs for preclinical drug testing using the MEA showed that they exhibit differential responses to various anti-seizure drugs and respond well to carbamazepine. Our results demonstrate that the individualized organoids have the potential to serve as a platform for preclinical pharmacological assessment.
Organoids/physiology* ; NAV1.2 Voltage-Gated Sodium Channel/genetics* ; Humans ; Anticonvulsants/pharmacology* ; Epilepsy/drug therapy* ; Mutation ; Cerebral Cortex/drug effects* ; Action Potentials/drug effects* ; Carbamazepine/pharmacology*

Objective:To investigate the application value of risk prediction model for acute kidney injury (AKI) after donation of cardiac death (DCD) liver transplantation based on machine learning algorithm.Methods:The retrospective cohort study was conducted. The clinicopathological data of 1 001 pairs of DCD liver transplant donors and recipients at five hospitals, including The First Affiliated Hospital of Zhejiang University School of Medicine et al, in the Chinese Liver Transplan-tation Registry from January 2015 to December 2023 were collected. Of the donors, there were 825 males and 176 females. Of the recipients, there were 806 males and 195 females, aged 52 (range, 18-75)years. There were 281 recipients included using oversampling technique, and all 1 282 recipients were divided to the training set of 897 recipients and the validation set of 385 recipients by a ratio of 7∶3 using computer-generated random numbers. Seven prediction models, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbors (KNN), and Categorical Boosting (CatBoost), were constructed for AKI after liver transplantation based on machine learning algorithm. Observation indicators: (1) comparison of clinicopathological characteristics between recipients with and without AKI and donors; (2) follow-up and survival of recipients with and without AKI; (3) construction and validation of nomogram prediction model of AKI after liver transplantation; (4) construction and validation of machine learning prediction model of AKI after liver transplantation. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test, and comparison among groups was conducted using the Kruskal-Wallis H test. Comparison of count data between groups was conducted using the chi-square test or corrected chi-square test. Kaplan-Meier method was used to calculate survival rates and plot survival curves. Logistic regression model was performed for univariate and multivariate analyses. The receiver operating characteristic (ROC) curve was plotted to calculate area under curve (AUC) and 95% confidence interval ( CI). The performance of prediction model was evaluated using DeLong test, accuracy, sensitivity, specificity. The calibration curve was plotted to evaluate the performance of predicted probability and actual probability. The interpretability analysis of machine learning algorithm and SHapley Additive exPlanations was used to explain the model decision separately. Results:(1) Comparison of clinicopathological characteristics between recipients with and without AKI and donors. Of 1 001 recipients, there were 360 cases with AKI and 641 cases without AKI after liver transplantation. There were significant differences in body mass index (BMI), hepatic encepha-lopathy, hepatitis B surfact antigen (HBsAg), hepatorenal syndrome (HRS) and donor diabetes, donor blood urea nitrogen, donor alanine aminotransferase, donor aspartate aminotransferase, mass of graft, volume of blood loss during liver transplantation, warm ischema time of donor liver, and operation time between recipients with and without AKI ( Z=-4.337, χ2=9.751, 9.088, H=11.142, χ2=5.286, Z=-3.360, -2.539, -3.084, -1.730, -3.497, -1.996, -2.644, P<0.05). (2) Follow-up and survival of recipients with and without AKI. All the 1 001 recipients received follow-up. The recipients with AKI after liver transplantation were followed up for 18.6(range, 0-102.3)months, and recipients without AKI after liver transplantation were followed up for 31.9(range, 0.1-105.5)months. The 1-, 3-, and 5-year overall survival rates were 72.1%, 63.5%, and 59.3% of recipients with AKI, versus 86.7%, 76.7%, and 72.5% of recipients without AKI, respectively, showing a significant difference in overall survival between them ( χ2=26.028, P<0.05). (3) Construction and validation of nomogram predic-tion model of AKI after liver transplantation. Results of multivariate analysis showed that recipient BMI, recipient creatinine, recipient HBsAg, recipient HRS, donor blood urea nitrogen, donor crea-tinine, anhepatic phase and volume of blood loss during liver transplantation were independent risk factors for AKI of recipients after liver transplantation ( odds ratio=1.113, 0.998, 0.605, 1.580, 1.047, 0.998, 1.006, 1.157, 95% CI as 1.070-1.157, 0.996-1.000, 0.450-0.812, 1.021-2.070, 1.021-1.074, 0.996-0.999, 1.000-1.012, 1.045-1.281, P<0.05). The nomogram prediction model of AKI after liver transplantation was constructed based on the results of multivariate analysis. Results of ROC curve showed that the AUC of 0.666 (95% CI as 0.637-0.696). (4) Construction and validation of machine learning prediction model of AKI after liver transplantation. Based on the Lasso regression analysis, seven machine learning algorithm prediction models, including RF, XGBoost, SVM, LR, DT, KNN, and CatBoost, were constructed, with ROC curves of the validation set plotted. The AUC of above models were 0.863, 0.841, 0.721, 0.637, 0.620, 0.708, 0.731, accuracies were 0.764, 0.782, 0.701, 0.592, 0.605, 0.605, 0.681, sensitivities were 0.764, 0.789, 0.719, 0.588, 0.694, 0.694, 0.704, specificities were 0.763, 0.774, 0.683, 0.597, 0.511, 0.511, 0.656, respectively. Delong test showed that the RF model with the highest AUC of 0.863(95% CI as 0.828-0.899). Calibration curve analysis showed the predicted probability closest to the actual probability of RF model, indicating the model with a good validation value. Further sorting of SHAP of different clinical factors based on RF model showed that recipient BMI, donor blood urea nitrogen, volume of blood loss during liver transplantation, donor age had large effects on the output outcomes. Conclusion:The nomogram prediction model and seven machine learning algorithm prediction models for AKI after DCD liver transplantation are constructed, and the RF model based on machine learning has a better predictive performance.

Objective:To investigate the application value of risk prediction model for acute kidney injury (AKI) after donation of cardiac death (DCD) liver transplantation based on machine learning algorithm.Methods:The retrospective cohort study was conducted. The clinicopathological data of 1 001 pairs of DCD liver transplant donors and recipients at five hospitals, including The First Affiliated Hospital of Zhejiang University School of Medicine et al, in the Chinese Liver Transplan-tation Registry from January 2015 to December 2023 were collected. Of the donors, there were 825 males and 176 females. Of the recipients, there were 806 males and 195 females, aged 52 (range, 18-75)years. There were 281 recipients included using oversampling technique, and all 1 282 recipients were divided to the training set of 897 recipients and the validation set of 385 recipients by a ratio of 7∶3 using computer-generated random numbers. Seven prediction models, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbors (KNN), and Categorical Boosting (CatBoost), were constructed for AKI after liver transplantation based on machine learning algorithm. Observation indicators: (1) comparison of clinicopathological characteristics between recipients with and without AKI and donors; (2) follow-up and survival of recipients with and without AKI; (3) construction and validation of nomogram prediction model of AKI after liver transplantation; (4) construction and validation of machine learning prediction model of AKI after liver transplantation. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test, and comparison among groups was conducted using the Kruskal-Wallis H test. Comparison of count data between groups was conducted using the chi-square test or corrected chi-square test. Kaplan-Meier method was used to calculate survival rates and plot survival curves. Logistic regression model was performed for univariate and multivariate analyses. The receiver operating characteristic (ROC) curve was plotted to calculate area under curve (AUC) and 95% confidence interval ( CI). The performance of prediction model was evaluated using DeLong test, accuracy, sensitivity, specificity. The calibration curve was plotted to evaluate the performance of predicted probability and actual probability. The interpretability analysis of machine learning algorithm and SHapley Additive exPlanations was used to explain the model decision separately. Results:(1) Comparison of clinicopathological characteristics between recipients with and without AKI and donors. Of 1 001 recipients, there were 360 cases with AKI and 641 cases without AKI after liver transplantation. There were significant differences in body mass index (BMI), hepatic encepha-lopathy, hepatitis B surfact antigen (HBsAg), hepatorenal syndrome (HRS) and donor diabetes, donor blood urea nitrogen, donor alanine aminotransferase, donor aspartate aminotransferase, mass of graft, volume of blood loss during liver transplantation, warm ischema time of donor liver, and operation time between recipients with and without AKI ( Z=-4.337, χ2=9.751, 9.088, H=11.142, χ2=5.286, Z=-3.360, -2.539, -3.084, -1.730, -3.497, -1.996, -2.644, P<0.05). (2) Follow-up and survival of recipients with and without AKI. All the 1 001 recipients received follow-up. The recipients with AKI after liver transplantation were followed up for 18.6(range, 0-102.3)months, and recipients without AKI after liver transplantation were followed up for 31.9(range, 0.1-105.5)months. The 1-, 3-, and 5-year overall survival rates were 72.1%, 63.5%, and 59.3% of recipients with AKI, versus 86.7%, 76.7%, and 72.5% of recipients without AKI, respectively, showing a significant difference in overall survival between them ( χ2=26.028, P<0.05). (3) Construction and validation of nomogram predic-tion model of AKI after liver transplantation. Results of multivariate analysis showed that recipient BMI, recipient creatinine, recipient HBsAg, recipient HRS, donor blood urea nitrogen, donor crea-tinine, anhepatic phase and volume of blood loss during liver transplantation were independent risk factors for AKI of recipients after liver transplantation ( odds ratio=1.113, 0.998, 0.605, 1.580, 1.047, 0.998, 1.006, 1.157, 95% CI as 1.070-1.157, 0.996-1.000, 0.450-0.812, 1.021-2.070, 1.021-1.074, 0.996-0.999, 1.000-1.012, 1.045-1.281, P<0.05). The nomogram prediction model of AKI after liver transplantation was constructed based on the results of multivariate analysis. Results of ROC curve showed that the AUC of 0.666 (95% CI as 0.637-0.696). (4) Construction and validation of machine learning prediction model of AKI after liver transplantation. Based on the Lasso regression analysis, seven machine learning algorithm prediction models, including RF, XGBoost, SVM, LR, DT, KNN, and CatBoost, were constructed, with ROC curves of the validation set plotted. The AUC of above models were 0.863, 0.841, 0.721, 0.637, 0.620, 0.708, 0.731, accuracies were 0.764, 0.782, 0.701, 0.592, 0.605, 0.605, 0.681, sensitivities were 0.764, 0.789, 0.719, 0.588, 0.694, 0.694, 0.704, specificities were 0.763, 0.774, 0.683, 0.597, 0.511, 0.511, 0.656, respectively. Delong test showed that the RF model with the highest AUC of 0.863(95% CI as 0.828-0.899). Calibration curve analysis showed the predicted probability closest to the actual probability of RF model, indicating the model with a good validation value. Further sorting of SHAP of different clinical factors based on RF model showed that recipient BMI, donor blood urea nitrogen, volume of blood loss during liver transplantation, donor age had large effects on the output outcomes. Conclusion:The nomogram prediction model and seven machine learning algorithm prediction models for AKI after DCD liver transplantation are constructed, and the RF model based on machine learning has a better predictive performance.

Objective:To explore the regulatory mechanisms underlying the weak expression of ABO blood group antigens due to variants in the non-coding regions of the ABO gene. Methods:From June 2014 to October 2023, a total of 29 samples from the Taiyuan Blood Center and local hospitals, which were serologically identified as having weak ABO antigen expression without detectable coding region mutations, were selected for this study. Full-length ABO gene sequencing was performed using third-generation long-read sequencing technology (Pacific Biosciences) to obtain complete haplotype sequences of the ABO gene. Variants in the non-coding regions were compared and identified to infer their regulatory effects on weak antigen expression. The procedures followed in this study were in accordance with the ethical standards of the World Medical Association′s Declaration of Helsinki (2013 revision). The Medical Ethics Committee of Taiyuan Blood Center has granted an exemption from ethical review. Results:18 bp deletions in the -35 to -18 region of the promoter were identified in 7 samples. Variants in intron 1 (+ 5.8 kb) were detected in 7 samples, including ABO* A (28+ 5792_5793delCT (1 case) and ABO* B (28+ 5793T>C) located in the GATA binding region; ABO* B (28+ 5808C>T) (1 case) in the E-box region; and ABO* B (28+ 5875C>T) (4 cases) in the RUNX1 binding region. Nucleotide variants at splice sites were detected in 2 samples, namely ABO* B (C.98+ 1G>A) and ABO* B (C.204-2A>C). Conclusion:Variants in the non-coding regulatory sequences of the ABO gene are a significant factor contributing to weak ABO antigen expression. In clinical ABO sequencing, it is essential to screen not only the conventional coding regions but also the flanking sequences, introns, and splice sites of the ABO gene to facilitate precise blood transfusion.

Objective:To investigate the clinical efficacy of neuroendoscopic hematoma removal versus soft channel drainage in the treatment of chronic subdural hematoma (CSDH) and their effects on neurological function and quality of life. Methods:The clinical data of 97 patients with CSDH who received treatment between February 2018 and December 2019 were retrospectively analyzed. These patients were divided into group A ( n = 48, soft channel drainage) and group B ( n = 49, neuroendoscopic hematoma removal) according to different surgical methods. Clinical indicators, neurological function, quality of life, and incidence of complications were compared between groups A and B. Results:Operative time, length of hospital stay, and latency to hematoma disappearance in group B were (31.3 ± 2.18) minutes, (8.16 ± 1.32) days, (7.45 ± 1.49) days, which were significantly shorter than those in group A [(35.15 ± 4.32) minutes, (13.18 ± 1.56) days, (11.32 ± 1.88) days, t = 5.53, 17.12, 11.25, all P < 0.001]. At 3 months after surgery, the score of each dimension of SF-36 in each group was increased. The scores of physiological functioning, bodily pain, mental health, general health perceptions, social role functioning, vitality, role limitations due to emotional health, role limitations due to physical health in group B were (84.94 ± 7.25) points, (84.02 ± 6.29) points, (82.85 ± 8.16) points, (84.36 ± 9.15) points, (83.51 ± 10.39) points, (82.68 ± 8.36) points, (84.93 ± 10.15) points, (86.12 ± 9.13) points, which were significantly higher than those in group A [(62.68 ± 5.47) points, (71.39 ± 7.42) points, (69.51 ± 6.39) points, (72.68 ± 7.36) points, (72.81 ± 8.15) points, (73.12 ± 10.13) points, (77.91 ± 9.52) points, (75.32 ± 7.51) points, t = 19.82, 18.34, 19.75, 16.71, 17.94, 20.57, 18.22, 16.44, all P < 0.001]. At 7 days after surgery, neurotrophic factor, neuron specific enolase, hydrogen sulfide and S100B protein levels in group B were (42.53 ± 6.09) μg/L, (6.52 ± 2.79) μg/L, (203.17 ± 15.03) μmol/L, (0.25 ± 0.05) μg/L, respectively, which were significantly lower than those in group A [(67.38 ± 7.42) μg/L, (9.18 ± 2.27) μg/L, (242.79 ± 14.08) μmol/L, (0.36 ± 0.07) μg/L, t = 17.94, 5.12, 13.33, 8.86, all P < 0.001]. There was no significant difference in the incidence of complications between group B and group A [8.16% (4/49) vs. 18.75% (9/48), χ2 = 2.22, P = 0.136]. Conclusion:Compared with soft channel drainage, neuroendoscopic hematoma removal can better improve clinical indicators, neurological function, and quality of life in patients with CSDH, and is highly safe Neuroendoscopic hematoma removal is of certain clinical application value and innovation.