Objective: To explore the influencing factors of blood transfusion volume in obstetric patients based on the national health development strategy, and to provide evidence for clinical transfusion management and maternal and infant safety. Methods: A total of 1 203 obstetric patients who received blood transfusion in a tertiary hospital in Beijing from 2013 to 2023 were enrolled retrospectively. Seventeen indicators including age, body mass index (BMI), hemoglobin and prothrombin time were selected as research variables. Descriptive analysis was conducted firstly. Scatter plot matrix was used to analyze the correlation between continuous variables and transfusion volume, and box plots were used to analyze the relationship between categorical variables and transfusion volume. Linear regression, logistic regression and random forest models were established for empirical analysis, and ROC curves and AUC values were compared to evaluate model performance. Results: Descriptive analysis showed that the distribution of obstetric blood transfusion volume was right-skewed, and multiple indicators were significantly correlated with transfusion volume. Linear regression indicated that 9 variables such as age, BMI and hemoglobin had linear correlation with transfusion volume. Logistic regression confirmed that 7 variables including BMI and hemoglobin were statistically correlated with the probability of massive blood transfusion. Random forest model screened the core influencing factors, which were ranked by importance: hemoglobin, prothrombin time, hematocrit, BMI, activated partial thromboplastin time, platelet count and age. The AUC values of logistic regression and random forest were 0.78 and 0.81 respectively, and the latter had better predictive performance. Conclusion: Prenatal anemia, coagulation disorders, advanced age, obesity, gestational hypertension, placental abnormalities and organ damage can increase the risk of massive blood transfusion in obstetric patients. The random forest model has better analytical and predictive ability in this study. The results can provide reference for accurate blood transfusion and risk prediction in obstetrics.

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.

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.

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: miR-34c-3p is down-regulated in nasopharyngeal carcinoma (NPC). The biological role of miR-34c-3p in NPC and its underlying mechanisms are unknown and were explored in this study. METHODS: Flow cytometry and immunohistochemical staining were employed to detect cluster of differentiation 86 (CD86) and cluster of differentiation 206 (CD206) expression; quantitative real-time polymerase chain reaction (qRT-PCR) and western blotting were employed to examine mRNA expression and protein levels; cell counting kit-8 (CCK8) and transwell assays were employed to assess cell proliferation, migration, and invasion; and hematoxylin-eosin (HE) staining was employed to assess pathological changes in tumor tissues. RESULTS: Our results revealed that the miR-34c-3p mimic markedly inhibited M2 polarization of macrophages by targeting SLC7A11, and M2 macrophages transfected with the miR-34c-3p mimic inhibited the proliferation, migration, and invasion of NPC cells. The in vivo experiments further confirmed that miR-34c-3p mimics blocked tumor growth and reduced inflammatory infiltration in tumor tissues. CONCLUSION This study provides novel insights into the pathogenesis of NPC and a new treatment strategy.
MicroRNAs/metabolism* ; Nasopharyngeal Carcinoma/genetics* ; Humans ; Animals ; Nasopharyngeal Neoplasms/genetics* ; Macrophages/physiology* ; Cell Line, Tumor ; Mice ; Cell Proliferation ; Mice, Inbred BALB C ; Cell Movement ; Male ; Gene Expression Regulation, Neoplastic ; Mice, Nude ; Female