Objective: To investigate the distribution frequency and molecular mechanism of the rare blood type Lu(a-b-) in Shenzhen, and to compare the polymorphisms of the Lutheran blood group system encoding gene LU and the In (Lu) phenotype-related gene KLF1 among Han Chinese, Indian, and Uyghur populations in Xinjiang. Methods: Serological methods were used to screen the Lu(a-b-) phenotype of blood donors in Shenzhen. Third-generation sequencing was employed to sequence the full-length of the LU and KLF1 genes in Lu (a-b-) phenotype samples as well as the samples from the Han Chinese, Indians, and Uyghur population, followed by analysis of gene haplotypes frequencies. Results: Ten individuals with the Lu(a-b-) phenotype were screened out of 14 367 blood donors in Shenzhen, yielding a frequency of approximately 0.07%. Only 2 cases showed mutations in the coding region of the LU gene, while all individuals showed heterozygous mutations in the coding region of the KLF1 gene. The highest mutation frequencies of the LU and KLF1 genes were observed in the Uyghur population in Xinjiang and the Han Chinese in Shenzhen, respectively. Conclusion: All Lu(a-b-) phenotypes are of the In (Lu) type, and their formation mechanism is mainly related to KLF1 gene mutations. Both the LU and KLF1 genes exhibit significant polymorphism in the Han Chinese, Indians, and Uyghur populations.

OBJECTIVE: To explore the serological characteristics and bioinformatics analysis results of 4 blood donors with RHCE*cE(281C, 282T) variant allele. METHODS: A total of 4 non-related blood donors with RHCE*cE (281C, 282T) variant allele (donors 1-4) were selected as the study objects. They donated blood at Shenzhen Blood Center from January 2022 to June 2023. The 4 blood donors were all Han. And 5 mL elbow venous blood was collected from these 4 blood donors. Regular serological assaying with 4 kinds of monoclonal antibody reagents was used for determination of the RhCcEe type. The nucleotide sequences of all 10 exons and adjacent flanking intron regions of RHCE gene in these 4 donors were analyzed by Sanger sequencing, and the full-length haplotype analysis of RHCE gene was performed by using the single-molecule real-time sequencing (SMRT) third-generation technology. DeepTMHMM software was used to analyze the structure of protein transmembrane region of wild type and variant RhCcEe protein and predict the location of amino acid substitution. The effects of mutations on RhCcEe protein function were analyzed using PolyPhen-2, SIFT and Mutation Taster bioinformatics software. Robetta and Swiss-PdbViewer v4.1.0 were used for modeling the tertiary structures of RhCcEe to analyze the difference between wild type and variant RhCcEe protein. The mutation was rated according to the standards and guidelines for the classification of genetic variants of the American College of Medical Genetics and Genomics (ACMG). This study has been approved by the Medical Ethics Committee of Shenzhen Blood Center (Approval No. SZBCMEC-2022-024). RESULTS: The RhCcEe phenotypes of the 4 blood donors were CCEweake by serological assaying. The RhE antigen were weakly expressed form 0 to 3+. The analysis of RHCE gene sequence indicated that all the 4 donors with RHCE*cE (281C, 282T) allele. The mutation caused the substitution of a single amino acid in the RhCcEe protein (p.Leu94 Pro) and the amino acid substitution was located in the transmembrane α3 chain resulted in significant changes in the 3D structure of the extracellular region of RhCcEe protein. The substitution was predicted to be "Probably damaging", "Damaging" and "Polymorphism" by PolyPhen-2, SIFT and Mutation Taster bioinformatics software. According to the guidelines of ACMG, the variant was rated to be likely pathogenic. CONCLUSION The RHCE*cE (281C, 282T) variant allele was first found in the Han Chinese population. The serological data of this allele were enriched. It provides an important guarantee for the safety of blood transfusion. Bioinformatics analysis provided evidences for further study of the structure and functions of RhCcEe protein.
Humans ; Blood Donors ; Computational Biology/methods* ; Alleles ; Rh-Hr Blood-Group System/genetics* ; Male ; Female ; Adult ; Exons

Objective To explore the value of deep learning(DL)model based on grayscale ultrasound for predicting asymptomatic advanced chronic liver disease(cACLD).Methods Totally 258 patients with asymptomatic compensatory chronic liver diseases were retrospectively included,among them 117 with F3 or F4 stage liver fibrosis were classified into cACLD group,while 141 with F1 or F2 stage liver fibrosis were taken as non-cACLD group.The patients were divided into training set(n=180,including 82 cases of cACLD and 98 cases of non-cACLD)and validation set(n=78,including 35 cases of cACLD and 43 cases of non-cACLD)at the ratio of 7∶3.Univariate and multivariate logistic regression were used to screen independent clinical predictors of cACLD and construct a clinical model.Based on liver grayscale ultrasound,optimal DL features were extracted and screened,and Resnet50 network was adopted as framework,na?ve Bayes classifier was used to construct DL model,and a combined model was constructed based on clinical model and DL model.The efficacy and clinical value of each model for predicting asymptomatic cACLD were evaluated.Results Age,gamma-glutamyl transferase and platelet count were all independent clinical predictors of cACLD,and a clinical model was constructed.Totally 38 optimal DL features were screened to build a DL model.The AUC of combined model in training set and validation set was 0.950 and 0.740,of DL model was 0.944 and 0.737,respectively,being not significantly different(both P＞0.05)but all higher than that of clinical model(0.667 and 0.573,all P＜0.05).Taken 0.59-0.90 as the threshold,the net benefits of combined model in both training and validation sets were higher than that of other models.Conclusion DL model based on grayscale ultrasound could be used to effectively predict asymptomatic cACLD.Combining with clinical characteristics might improve clinical net benefit of this model.

Objective:To explore the serological characteristics and bioinformatics analysis results of 4 blood donors with RHCE*cE( 281C, 282T) variant allele. Methods:A total of 4 non-related blood donors with RHCE*cE ( 281C, 282T) variant allele (donors 1-4) were selected as the study objects. They donated blood at Shenzhen Blood Center from January 2022 to June 2023. The 4 blood donors were all Han. And 5 mL elbow venous blood was collected from these 4 blood donors. Regular serological assaying with 4 kinds of monoclonal antibody reagents was used for determination of the RhCcEe type. The nucleotide sequences of all 10 exons and adjacent flanking intron regions of RHCE gene in these 4 donors were analyzed by Sanger sequencing, and the full-length haplotype analysis of RHCE gene was performed by using the single-molecule real-time sequencing (SMRT) third-generation technology. DeepTMHMM software was used to analyze the structure of protein transmembrane region of wild type and variant RhCcEe protein and predict the location of amino acid substitution. The effects of mutations on RhCcEe protein function were analyzed using PolyPhen-2, SIFT and Mutation Taster bioinformatics software. Robetta and Swiss-PdbViewer v4.1.0 were used for modeling the tertiary structures of RhCcEe to analyze the difference between wild type and variant RhCcEe protein. The mutation was rated according to the standards and guidelines for the classification of genetic variants of the American College of Medical Genetics and Genomics (ACMG). This study has been approved by the Medical Ethics Committee of Shenzhen Blood Center (Ethics No. SZBCMEC-2022-024). Results:The RhCcEe phenotypes of the 4 blood donors were CCE weake by serological assaying. The RhE antigen were weakly expressed form 0 to 3+. The analysis of RHCE gene sequence indicated that all the 4 donors with RHCE*cE ( 281C, 282T) allele. The mutation caused the substitution of a single amino acid in the RhCcEe protein (p. Leu94 Pro) and the amino acid substitution was located in the transmembrane α3 chain resulted in significant changes in the 3D structure of the extracellular region of RhCcEe protein. The substitution was predicted to be "Probably damaging", "Damaging" and "Polymorphism" by PolyPhen-2, SIFT and Mutation Taster bioinformatics software. According to the guidelines of ACMG, the variant was rated to be likely pathogenic. Conclusion:The RHCE*cE ( 281C, 282T) variant allele was first found in the Han Chinese population. The serological data of this allele were enriched. It provides an important guarantee for the safety of blood transfusion. Bioinformatics analysis provided evidences for further study of the structure and functions of RhCcEe protein.

This study investigates the pharmacokinetics and metabolic characteristics of three marine-derived piericidins as potential drug leads for kidney disease: piericidin A (PA) and its two glycosides (GPAs), glucopiericidin A (GPA) and 13-hydroxyglucopiericidin A (13-OH-GPA). The research aims to facilitate lead selection and optimization for developing a viable preclinical candidate. Rapid absorption of PA and GPAs in mice was observed, characterized by short half-lives and low bioavailability. Glycosides and hydroxyl groups significantly enhanced the absorption rate (13-OH-GPA > GPA > PA). PA and GPAs exhibited metabolic instability in liver microsomes due to Cytochrome P450 enzymes (CYPs) and uridine diphosphoglucuronosyl transferases (UGTs). Glucuronidation emerged as the primary metabolic pathway, with UGT1A7, UGT1A8, UGT1A9, and UGT1A10 demonstrating high elimination rates (30%-70%) for PA and GPAs. This rapid glucuronidation may contribute to the low bioavailability of GPAs. Despite its low bioavailability (2.69%), 13-OH-GPA showed higher kidney distribution (19.8%) compared to PA (10.0%) and GPA (7.3%), suggesting enhanced biological efficacy in kidney diseases. Modifying the C-13 hydroxyl group appears to be a promising approach to improve bioavailability. In conclusion, this study provides valuable metabolic insights for the development and optimization of marine-derived piericidins as potential drug leads for kidney disease.
Animals ; Male ; Mice ; Aquatic Organisms/chemistry* ; Biological Availability ; Cytochrome P-450 Enzyme System/metabolism* ; Glucuronosyltransferase/metabolism* ; Microsomes, Liver/metabolism* ; Molecular Structure ; Biological Products/pharmacokinetics* ; Pyridines/pharmacokinetics*

This review systematically summarizes the unique metabolic mechanisms of breast cancer,their interac-tions with the tumor microenvironment(TME),and the latest advances in targeted therapies.The interplay between metabolic reprogramming and the TME underpins malignant progression and therapeutic resistance.Breast cancer cells reshape energy supply through the Warburg effect,aberrant fatty acid synthesis,and amino acid metabolism,while immune cells,fibroblasts,and the acidic milieu within the TME promote immune evasion and drug resistance via metabolic coupling.Although traditional strategies targeting key metabolic enzymes remain valuable,they are often insufficient to overcome metabolic adaptability.In recent years,combined metabolic and immunotherapeutic approaches have emerged as promising strategies:by reducing lactate accumulation,restoring T-cell function,and reprogramming tumor-associated macrophages and cancer-associated fibroblasts,these therapies can remodel the immunosuppressive microenvironment and enhance immunotherapy efficacy.The application of metabolomics and single-cell sequencing further elucidates breast cancer heterogeneity,providing a basis for individualized precision treatment.Future challenges include deciphering resistance mechanisms,developing highly selective metabolic in-hibitors,and designing integrated multi-omics-based therapeutic regimens.

The paper reports a 32-year-old female acute myeloid leukemia patient who developed graft-versus-host disease after paternal hematopoietic stem cell transplantation, which subsequently led to renal thrombotic microangiopathy. She subsequently required a kidney transplant from the same donor 5 years later due to renal failure. Considering that both the bone marrow and kidney were from the same donor and the recovery of renal function was favorable, immunosuppressive therapy was discontinued after a short course of anti-rejection treatment, with maintained stable kidney function. This case suggests that under the condition of high chimerism, allogeneic hematopoietic stem cell transplantation and kidney transplantation from the same donor can achieve immune tolerance, potentially improving solid organ transplantation success rate. The findings provide a novel therapeutic approach for solid organ transplantation following allogeneic hematopoietic stem cell transplantation.

Purpose To develop an integrate model combining deep learning features from contrast-enhanced lung ultrasonography with clinical characteristics for predicting epidermal growth factor receptor mutation status in peripheral non-small cell lung cancer.Materials and Methods This retrospective study included 117 patients with pathologically confirmed non-small cell lung cancer from the First Affiliated Hospital of Guangxi Medical University(July 2021 to February 2024).Patients were randomly divided into training(n=93)and test(n=24)sets at an 8∶2 ratio.Regions of interest were delineated at the peak enhancement phase of contrast-enhanced lung ultrasonography.Various deep learning convolutional neural networks were pretrained,with ResNet18 selected as optimal for feature extraction.Deep learning,clinical,and integrated models were constructed using naive Bayesian algorithm.Performance was evaluated via receiver operating characteristic and calibration curves,while class activation mapping and Shapley additive explanation values provided model interpretability.Results In the training set,the deep learning,clinical and integrated models achieved area under the curve of 0.93(95%CI 0.88-0.98),0.86(95%CI 0.68-1.00),and 0.91(95%CI 0.85-0.97),respectively.Corresponding test set area under the curve were 0.81(95%CI 0.72-0.90),0.56(95%CI 0.33-0.80),and 0.87(95%CI 0.72-1.00).Both deep learning and integrated models significantly outperformed the clinical model in training(Z=2.380,P=0.017;Z=2.597,P=0.009)and test sets(Z=2.034,P=0.042;Z=2.577,P=0.010).The integrated model demonstrated excellent calibration and predictive performance.Conclusion The integrated model combining deep learning features from contrast-enhanced lung ultrasonography with clinical characteristics effectively predicts epidermal growth factor receptor mutation status in peripheral non-small cell lung cancer.

Objective To explore the application effect of introducing mind mapping into the dual-track mode of Prob-lem-Based Learning(PBL)and Case-Based Learning(CBL)in standardized training teaching in the cardiology department.Methods A total of 82 trainees in the cardiology department from March 2023 to September 2023 were selected and divided into two groups according to different teaching methods.The traditional group(n=41)received conventional teaching,while the du-al-track group(n=41)was taught using the mind mapping-introduced PBL and CBL dual-track mode.The assessment scores,teaching effectiveness,and teaching satisfaction were compared between the two groups.Results The dual-track group had high-er scores in theoretical knowledge,clinical thinking,and medical record writing than the traditional group(all P＜0.001).The proportion of trainees in the dual-track group who believed that the method could stimulate learning interest,enhance logical thinking,improve collaboration skills,and strengthen problem-solving abilities was higher than that in the traditional group(all P＜0.05).The overall teaching satisfaction rate in the dual-track group was 95.12％,significantly higher than 80.49％in the traditional group(x2=4.100,P=0.043).Conclusion Introducing mind mapping into the PBL and CBL dual-track mode in standardized training teaching in the cardiology department not only enhances trainees'cognitive and comprehension abilities re-garding complex cardiovascular diseases,improves their clinical decision-making and medical record analysis skills,but also pro-motes self-directed learning and teamwork abilities,forming a more systematic and structured knowledge system.This approach is worthy of promotion.

Purpose To develop an integrate model combining deep learning features from contrast-enhanced lung ultrasonography with clinical characteristics for predicting epidermal growth factor receptor mutation status in peripheral non-small cell lung cancer.Materials and Methods This retrospective study included 117 patients with pathologically confirmed non-small cell lung cancer from the First Affiliated Hospital of Guangxi Medical University(July 2021 to February 2024).Patients were randomly divided into training(n=93)and test(n=24)sets at an 8∶2 ratio.Regions of interest were delineated at the peak enhancement phase of contrast-enhanced lung ultrasonography.Various deep learning convolutional neural networks were pretrained,with ResNet18 selected as optimal for feature extraction.Deep learning,clinical,and integrated models were constructed using naive Bayesian algorithm.Performance was evaluated via receiver operating characteristic and calibration curves,while class activation mapping and Shapley additive explanation values provided model interpretability.Results In the training set,the deep learning,clinical and integrated models achieved area under the curve of 0.93(95%CI 0.88-0.98),0.86(95%CI 0.68-1.00),and 0.91(95%CI 0.85-0.97),respectively.Corresponding test set area under the curve were 0.81(95%CI 0.72-0.90),0.56(95%CI 0.33-0.80),and 0.87(95%CI 0.72-1.00).Both deep learning and integrated models significantly outperformed the clinical model in training(Z=2.380,P=0.017;Z=2.597,P=0.009)and test sets(Z=2.034,P=0.042;Z=2.577,P=0.010).The integrated model demonstrated excellent calibration and predictive performance.Conclusion The integrated model combining deep learning features from contrast-enhanced lung ultrasonography with clinical characteristics effectively predicts epidermal growth factor receptor mutation status in peripheral non-small cell lung cancer.