Purpose: The genomic characteristics of uterine sarcomas have not been fully elucidated. This study aimed to explore the genomic landscape of the uterine sarcomas (USs). Materials and Methods: Comprehensive genomic analysis through RNA-sequencing was conducted. Gene fusion, differentially expressed genes (DEGs), signaling pathway enrichment, immune cell infiltration, and prognosis were analyzed. A deep learning model was constructed to predict the survival of US patients. Results: A total of 71 US samples were examined, including 47 endometrial stromal sarcomas (ESS), 18 uterine leiomyosarcomas (uLMS), three adenosarcomas, two carcinosarcomas, and one uterine tumor resembling an ovarian sex-cord tumor. ESS (including high-grade ESS [HGESS] and low-grade ESS [LGESS]) and uLMS showed distinct gene fusion signatures; a novel gene fusion site, MRPS18A–PDC-AS1 could be a potential diagnostic marker for the pathology differential diagnosis of uLMS and ESS; 797 and 477 uterine sarcoma DEGs (uDEGs) were identified in the ESS vs. uLMS and HGESS vs. LGESS groups, respectively. The uDEGs were enriched in multiple pathways. Fifteen genes including LAMB4 were confirmed with prognostic value in USs; immune infiltration analysis revealed the prognositic value of myeloid dendritic cells, plasmacytoid dendritic cells, natural killer cells, macrophage M1, monocytes and hematopoietic stem cells in USs; the deep learning model named Max-Mean Non-Local multi-instance learning (MMN-MIL) showed satisfactory performance in predicting the survival of US patients, with the area under the receiver operating curve curve reached 0.909 and accuracy achieved 0.804. Conclusion USs harbored distinct gene fusion characteristics and gene expression features between HGESS, LGESS, and uLMS. The MMN-MIL model could effectively predict the survival of US patients.

Purpose: The genomic characteristics of uterine sarcomas have not been fully elucidated. This study aimed to explore the genomic landscape of the uterine sarcomas (USs). Materials and Methods: Comprehensive genomic analysis through RNA-sequencing was conducted. Gene fusion, differentially expressed genes (DEGs), signaling pathway enrichment, immune cell infiltration, and prognosis were analyzed. A deep learning model was constructed to predict the survival of US patients. Results: A total of 71 US samples were examined, including 47 endometrial stromal sarcomas (ESS), 18 uterine leiomyosarcomas (uLMS), three adenosarcomas, two carcinosarcomas, and one uterine tumor resembling an ovarian sex-cord tumor. ESS (including high-grade ESS [HGESS] and low-grade ESS [LGESS]) and uLMS showed distinct gene fusion signatures; a novel gene fusion site, MRPS18A–PDC-AS1 could be a potential diagnostic marker for the pathology differential diagnosis of uLMS and ESS; 797 and 477 uterine sarcoma DEGs (uDEGs) were identified in the ESS vs. uLMS and HGESS vs. LGESS groups, respectively. The uDEGs were enriched in multiple pathways. Fifteen genes including LAMB4 were confirmed with prognostic value in USs; immune infiltration analysis revealed the prognositic value of myeloid dendritic cells, plasmacytoid dendritic cells, natural killer cells, macrophage M1, monocytes and hematopoietic stem cells in USs; the deep learning model named Max-Mean Non-Local multi-instance learning (MMN-MIL) showed satisfactory performance in predicting the survival of US patients, with the area under the receiver operating curve curve reached 0.909 and accuracy achieved 0.804. Conclusion USs harbored distinct gene fusion characteristics and gene expression features between HGESS, LGESS, and uLMS. The MMN-MIL model could effectively predict the survival of US patients.

Purpose: The genomic characteristics of uterine sarcomas have not been fully elucidated. This study aimed to explore the genomic landscape of the uterine sarcomas (USs). Materials and Methods: Comprehensive genomic analysis through RNA-sequencing was conducted. Gene fusion, differentially expressed genes (DEGs), signaling pathway enrichment, immune cell infiltration, and prognosis were analyzed. A deep learning model was constructed to predict the survival of US patients. Results: A total of 71 US samples were examined, including 47 endometrial stromal sarcomas (ESS), 18 uterine leiomyosarcomas (uLMS), three adenosarcomas, two carcinosarcomas, and one uterine tumor resembling an ovarian sex-cord tumor. ESS (including high-grade ESS [HGESS] and low-grade ESS [LGESS]) and uLMS showed distinct gene fusion signatures; a novel gene fusion site, MRPS18A–PDC-AS1 could be a potential diagnostic marker for the pathology differential diagnosis of uLMS and ESS; 797 and 477 uterine sarcoma DEGs (uDEGs) were identified in the ESS vs. uLMS and HGESS vs. LGESS groups, respectively. The uDEGs were enriched in multiple pathways. Fifteen genes including LAMB4 were confirmed with prognostic value in USs; immune infiltration analysis revealed the prognositic value of myeloid dendritic cells, plasmacytoid dendritic cells, natural killer cells, macrophage M1, monocytes and hematopoietic stem cells in USs; the deep learning model named Max-Mean Non-Local multi-instance learning (MMN-MIL) showed satisfactory performance in predicting the survival of US patients, with the area under the receiver operating curve curve reached 0.909 and accuracy achieved 0.804. Conclusion USs harbored distinct gene fusion characteristics and gene expression features between HGESS, LGESS, and uLMS. The MMN-MIL model could effectively predict the survival of US patients.

OBJECTIVE: To investigate the molecular mechanism of weak D phenotype in a Chinese family. METHODS: Routine Rh typing tests were performed first, and RHD exons 1-10 of the proband and his family members were sequenced by first-generation sequencing. RHD zygosity was also determined. Third-generation sequencing was used to analyze the haplotypes of the RHD gene. RESULTS: The proband showed a weak D serological phenotype. First-generation sequencing revealed a c.787G>A point mutation in exon 5. The family pedigree investigation showed that the proband and his younger sister had the same serological phenotype and molecular mechanism. His father carried this gene mutation, while his mother and younger brother were normal. Hybrid box was not detected, suggesting that all the family members did not have a haplotype with a complete deletion of the RHD gene. The results of third-generation sequencing showed that the proband and his sister inherited the weak D allele from their father and the non-functional allele RHD -CE(3-9)-D from their mother, respectively. CONCLUSION Third-generation sequencing technology enables haplotype analysis of the RHD gene and can detect complex genotypes such as genetic exchanges between RHD and RHCE combined with other mutations.
Female ; Humans ; Male ; Alleles ; Exons ; Genotype ; Haplotypes ; High-Throughput Nucleotide Sequencing ; Pedigree ; Phenotype ; Rh-Hr Blood-Group System/genetics* ; East Asian People/genetics*

The purpose of this study was to establish a luciferase immunosorbent assay(LISA)using the Crimean-Congo hemorrhagic fever virus(CCHFV)glycoprotein C(Gc),a specific antigen,for the detection of CCHFV IgG antibodies.Three antigenic fragments based on CCHFV glycoprotein C were designed,and three recombinant plasmids were constructed by liga-tion with the NanoLuc luciferase(NLuc)expression vector pNLF1-N through molecular cloning.The accuracy of the sequences in the recombinant plasmids was confirmed through sequencing.The recombinant plasmids were transfected into eukaryotic cells to obtain fusion proteins containing specific antigens and luciferase,and the expression of the fusion proteins was verified by western blotting,thereby facilitating the establishment of the CCHFV-LISA detection technique.The assay's sensitivity,specificity,and stability were evaluated and compared with those of a commercial CCHFV IgG antibody test kit.Three recom-binant antigen fragments of CCHFV Gc—NLuc-Gc-Full,NLuc-Gc-C1,and NLuc-Gc-C2—were expressed,with molecular weights of 80.1 kDa,62.8 kDa,and 53.9 kDa,respectively.The optimal fragment for CCHFV detection was NLuc-Gc-C2.The sensitivity of the CCHFV-LISA was 90.9％,and the specificity was 100％;the findings were highly concordant with those for the commercial CCHFV enzyme-linked immunosorbent assay kit.Repeatability tests indicated no statistically significant differ-ences in inter-and intra-assay variability within the same batch.The LISA was highly specific,sensitive,and user-friendly in detecting IgG antibodies against the CCHFV.Therefore,this method may facilitate serological diagnosis and epidemiological studies in endemic regions,and provide essential technical support for disease surveillance and early warning.

Objective:To evaluate the efficacy and feasibility of a domestically developed robotic surgical system based on fiber-optic dedicated line communication in cross-regional urological telesurgery.Methods:This was multicenter，single-arm，retrospective case series study. The data of patients who underwent urological telesurgeries using the telesurgical system between January 2023 and December 2024 were analyzed. The cohort included 59 patients from seven hospitals across China. Among the patients，47 were male（79.7%）and 12 were female（20.3%），with a median age of 63.0（56.0，68.0）years and a body mass index of（24.7 ± 3.0）kg/m 2. Surgical procedures included 32 radical prostatectomies，24 partial nephrectomies，one radical nephrectomy，one adrenalectomy，and one ureteral reconstruction. The perioperative indicators，pathological results and postoperative complications were analyzed. The network monitoring data were collected，and the perioperative data of patients，remote system monitoring data and costs were compared between the two communication modes of optical transport network（OTN）and cloud-connect network（CCN）. Results:All 59 remote surgeries were successfully completed，with a mean operative time of（138.0 ± 54.0）minutes，median intraoperative blood loss of 50.0（30.0，100.0）ml and a postoperative hospital stay of 5.0（4.0，6.0）days. No cases required reoperation，Clavien-Dindo grade ≥3 complications，or readmission. The geographical distance between the primary and remote surgical sites ranged from 450 to 2 800 km. Network monitoring revealed increased bidirectional latency with distance increasing：the shortest latency time（Hefei-Hangzhou，450 km）was（16.59 ± 0.80）ms，while the longest（Harbin-Hangzhou，2 200 km）latency time was（53.31 ± 0.31）ms. Average frame loss per procedure was 0?1.27 frames. The results of subgroup analysis comparing OTN and CCN communication modes showed no significant differences in operative time［（130.7 ± 70.5）minutes vs.（142.1 ± 42.9）minutes， P = 0.442］，postoperative hospitalization［6.0（4.0，8.0）d vs. 5.0（4.0，6.0）d， P = 0.581］，or readmission rates（0 vs. 0）. However，CCN demonstrated significant cost advantages with 500 RMB per operation vs. 3 000 RMB per operation for OTN. Conclusions:Urological telesurgery using fiber-optic communication is feasible. The CCN mode，with its cost-effectiveness，excellent usability，and multi-point interconnection flexibility，is currently the preferred communication model for telesurgical applications.

Diabetic kidney disease(DKD)is a microvascular complication of diabetes mellitus with a complex pathogenesis.Recent studies have revealed that autophagy and ferroptosis,as two forms of programmed cell death,exhibit dynamic interactions in DKD:autophagy maintains homeostasis by eliminating damaged organelles,while ferroptosis is driven by iron-dependent lipid peroxidation.Imbalance between the two exacerbates renal injury.This review systematically summarizes the signaling pathways and key regulatory factors related to autophagy and ferroptosis,as well as their interaction mechanisms[such as nuclear receptor coactivator 4(NCOA4)-mediated ferritinophagy,clock autophagy,and lipid autophagy].It further elaborates the molecular network by which these processes synergistically regulate DKD progression.Additionally,the potential of modern pharmaceuticals and active components of traditional Chinese medicine to improve kidney injury by targeting autophagy and ferroptosis is discussed,proposing that targeting their cross-talk pathways may provide novel therapeutic strategies for DKD,aiming to lay a theoretical foundation for the development of targeted intervention strategies and precision therapeutic regimens.

Objective To design a VR-based air evacuation training system for simulating the on-board medical treatment process during air evacuation.Methods A VR-based air evacuation training system was developed which used 3D modeling technology to construct models of the medical aircraft cabin,medical devices and virtual characters to achieve scene interaction.The hardware part of the system included server computers,training terminal computers,VR equipment,3D fusion projection equipment,motion capture equipment,etc.The software of the system was developed using C++,UE4 Blueprint and C# programming languages,including two modules for medical treatment unit and medical treatment training process evaluation.The efficacy of the system was verified by the trials in air evacuation.Results The system developed successfully simulated the scenarios of tracheal tube dislodgement and increased intracranial pressure in the scenario model of open severe craniocerebral injury.The expert evaluation showed that the system gained advantages in training efficiency,low cost,safety,sense of immersion and recorded the operation data in real time to optimize the follow-up training.Conclusion The system developed delivers a virtual training environment with high-fidelity replication of real-mission conditions,enabling whole-course and immersive air evacuation drills.[Chinese Medical Equipment Journal,2025,46(3):15-20]

Objective To design a VR-based air evacuation training system for simulating the on-board medical treatment process during air evacuation.Methods A VR-based air evacuation training system was developed which used 3D modeling technology to construct models of the medical aircraft cabin,medical devices and virtual characters to achieve scene interaction.The hardware part of the system included server computers,training terminal computers,VR equipment,3D fusion projection equipment,motion capture equipment,etc.The software of the system was developed using C++,UE4 Blueprint and C# programming languages,including two modules for medical treatment unit and medical treatment training process evaluation.The efficacy of the system was verified by the trials in air evacuation.Results The system developed successfully simulated the scenarios of tracheal tube dislodgement and increased intracranial pressure in the scenario model of open severe craniocerebral injury.The expert evaluation showed that the system gained advantages in training efficiency,low cost,safety,sense of immersion and recorded the operation data in real time to optimize the follow-up training.Conclusion The system developed delivers a virtual training environment with high-fidelity replication of real-mission conditions,enabling whole-course and immersive air evacuation drills.[Chinese Medical Equipment Journal,2025,46(3):15-20]

The purpose of this study was to establish a luciferase immunosorbent assay(LISA)using the Crimean-Congo hemorrhagic fever virus(CCHFV)glycoprotein C(Gc),a specific antigen,for the detection of CCHFV IgG antibodies.Three antigenic fragments based on CCHFV glycoprotein C were designed,and three recombinant plasmids were constructed by liga-tion with the NanoLuc luciferase(NLuc)expression vector pNLF1-N through molecular cloning.The accuracy of the sequences in the recombinant plasmids was confirmed through sequencing.The recombinant plasmids were transfected into eukaryotic cells to obtain fusion proteins containing specific antigens and luciferase,and the expression of the fusion proteins was verified by western blotting,thereby facilitating the establishment of the CCHFV-LISA detection technique.The assay's sensitivity,specificity,and stability were evaluated and compared with those of a commercial CCHFV IgG antibody test kit.Three recom-binant antigen fragments of CCHFV Gc—NLuc-Gc-Full,NLuc-Gc-C1,and NLuc-Gc-C2—were expressed,with molecular weights of 80.1 kDa,62.8 kDa,and 53.9 kDa,respectively.The optimal fragment for CCHFV detection was NLuc-Gc-C2.The sensitivity of the CCHFV-LISA was 90.9％,and the specificity was 100％;the findings were highly concordant with those for the commercial CCHFV enzyme-linked immunosorbent assay kit.Repeatability tests indicated no statistically significant differ-ences in inter-and intra-assay variability within the same batch.The LISA was highly specific,sensitive,and user-friendly in detecting IgG antibodies against the CCHFV.Therefore,this method may facilitate serological diagnosis and epidemiological studies in endemic regions,and provide essential technical support for disease surveillance and early warning.