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 compare the prognostic value of two predictive models based on C-reactive protein(CRP)and albumin(ALB),namely the CRP to ALB ratio(CAR)and the Glasgow prognostic score(GPS),in newly diagnosed patients with diffuse large B-cell lymphoma(DLBCL).Methods:The data of newly diagnosed DLBCL patients admitted to our center from May 2014 to January 2022 were reviewed.A total of 111 patients who completed at least 4 cycles of R-CHOP or R-CHOP-like chemotherapy with detailed clinical,laboratory data and follow-up information were included.The receiver operating characteristic(ROC)curve was performed to evaluate the predictive value of pre-treatment CAR on disease progression and survival.Furthermore,the association between CAR and baseline clinical,laboratory characteristics of patients was evaluated,and progression-free survival(PFS)and overall survival(OS)were compared between different CAR and GPS subgroups.Finally,the univariate and multivariate COX propor-tional hazard regression models were used to analyze the factors affecting disease outcomes.Results:ROC curve showed that the area under the curve(AUC)of CAR predicting PFS and OS in DLBCL patients was 0.687(P=0.002)and 0.695(P=0.005),respectively,with the optimal cut-off value of 0.11 for both predicting PFS and OS.Compared with the lower CAR(＜0.11)group,the higher CAR(≥0.11)group had more clinical risk factors,including age＞60 years(P=0.025),ECOG score ≥2(P=0.004),Lugano stage Ⅲ-Ⅳ(P＜0.001),non-germinal center B-cell-like(non-GCB)subtype(P=0.035),elevated lactate dehydrogenase(LDH)(P＜0.001),extranodal involved site＞1(P=0.004)and IPI score＞2(P＜0.001).The interim response evaluation of patients showed that the overall response rate(ORR)and complete response rate(CRR)in the lower CAR group were both significantly better than those in the higher CAR group(ORR:96.9％vs 80.0％,P=0.035;CRR:63.6％vs 32.5％,P=0.008).With a median follow-up of 24 months,patients with lower CAR had significantly longer median PFS and OS than those with higher CAR(median PFS:not reached vs 67 months,P=0.0026;median OS:not reached vs 67 months,P=0.002),while there was no statistical difference in PFS(P=0.11)and OS(P=0.11)in patients with GPS of 0,1,and 2.Multivariate Cox regression analysis indicated that only sex(male)and IPI score＞2 were independent risk factors for both PFS and OS.Conclusion:CAR is significantly correlated with disease progression and survival in DLBCL patients;And compared with GPS,CAR has more advantages in predicting disease outcomes in DLBCL patients.

Objective:To compare the efficacy and safety of flumatinib (FM)and imatinib (IM)as first-line treatment in newly-diagnosed patients with chronic myeloid leukemia in chronic phase (CML-CP ) in real world. Methods:A total of 84 newly-diagnosed CP-CML patients in our center from December 2019 to December 2022 were retrospectively analyzed.Among them,32 cases received FM as first-line treatment,and 52 cases received IM. Molecular response (MR),disease progression,survival and incidence of adverse events (AEs)were compared between the two groups.Results:At 3 months of treatment,the incidences of early molecular response (EMR ),MR2.0 and MR3.0 were 96.7％,70.0％ and 20.0％ in FM group,respectively,which were significantly higher than 77.1％,29. 2％ and 0 in IM group (all P<0.05 ).At 6,9 and 12 months of treatment,the incidences of major molecular response (MMR)in FM group were 68.2％,85.7％ and 90.0％,respectively,which were significantly higher than 22.9％,34.0％ and 51.1％ in IM group (all P<0.01).The median time to achieve MMR in FM group was 6(6-9)months,which was significantly shorter than 18(12-22)months in IM group (P<0.001 ).The 3-year progression-free survival rate and 3-year event-free survival rate in FM group were 100％ and 68.8％,respectively,while in IM group were 98.1％ and 55.8％.There were no significant differences between the two groups (P>0.05). The incidence of grade 3-4 hematologic AEs in FM group was 21 .9％,which was slightly lower than 25.0％ in IM group,but the difference was not significant (P>0.05 ).Conclusion:In real clinical practice,FM as first-line treatment achieves MMR earlier than IM,and exhibits good safety profile in newly-diagnosed CML-CP patients,which potentially leads to improved long-term survival and treatment-free remission.

Objective To explore effect of nerve growth factor(NGF)antibody on knee osteoarthritis(KOA)pain model was evaluated by in vitro model.Methods Thirty male SPF rats aged 28-week-old were divided into blank group(10 rats with anesthesia only).The other 20 rats were with monoiodoacetate(MIA)on the right knee joint to establish pain model of OA,and were randomly divided into control group(injected intraperitoneal injection of normal saline)and treatment group(injected anti-NGF)intraperitoneal after successful modeling,and 10 rats in each group.All rats were received retrograde injection of fluorogold(FG)into the right knee joint.Gait was assessed using catwalk gait analysis system before treatment,1 and 2 weeks after treatment.Three weeks after treatment,right dorsal root ganglia(DRG)were excised on L4-L6 level,immunostained for calcitonin gene-related peptide(CGRP),and the number of DRGS was counted.Results In terms of gait analysis using cat track system,duty cycle,swing speed and print area ratio in control and treatment group were significantly reduced compared with blank group(P＜0.05).Compared with control group,duty cycle and swing speed of treatment group were significantly im-proved(P＜0.05),and there was no significant difference in print area ratio between treatment group and blank group(P＞0.05).The number of FG-labeled DRG neurons in control group was significantly higher than that in treatment group and blank group(P＜0.05).The expression of CGRP in control group was up-regulated,and differences were statistically significant compared with treatment group(P＜0.05).Conclusion Intraperitoneal injection of anti-NGF antibody inhibited gait injury and upregulation of CGRP in DRG neurons.The results suggest that anti-nerve growth factor therapy may be of value in treating knee pain.NGF may be an important target for the treatment of knee OA pain.

Objective To summarize the epidemiological and clinical features of infectious diseases of the central nervous system(CNS)by a single-center analysis.Methods A retrospective analysis was conducted on the data of 1247 cases of CNS infectious diseases diagnosed and treated in the First Medical Center of PLA General Hospital from 2001 to 2020.Results The data for this group of CNS infectious diseases by disease type in descending order of number of cases were viruses 743(59.6％),Mycobacterium tuberculosis 249(20.0％),other bacteria 150(12.0％),fungi 68(5.5％),parasites 18(1.4％),Treponema pallidum 18(1.4％)and rickettsia 1(0.1％).The number of cases increased by 177 cases(33.1％)in the latter 10 years compared to the previous 10 years(P<0.05).No significant difference in seasonal distribution pattern of data between disease types(P>0.05).Male to female ratio is 1.87︰1,mostly under 60 years of age.Viruses are more likely to infect students,most often at university/college level and above,farmers are overrepresented among bacteria and Mycobacterium tuberculosis,and more infections of Treponema pallidum in workers.CNS infectious diseases are characterized by fever,headache and signs of meningeal irritation,with the adductor nerve being the more commonly involved cranial nerve.Matagenomic next-generation sequencing improves clinical diagnostic capabilities.The median hospital days for CNS infectious diseases are 18.00(11.00,27.00)and median hospital costs are ￥29,500(￥16,000,￥59,200).The mortality rate from CNS infectious diseases is 1.6％.Conclusions The incidence of CNS infectious diseases is increasing last ten years,with complex clinical presentation,severe symptoms and poor prognosis.Early and accurate diagnosis and standardized clinical treatment can significantly reduce the morbidity and mortality rate and ease the burden of disease.

OBJECTIVE: To investigate the causes of ineffectiveness of platelet transfusion with monoclonal antibody solid phase platelet antibody test (MASPAT) matching in patients with allogeneic hematopoietic stem cell transplantation and explore the strategies of platelet transfusion. METHODS: A case of donor-specific HLA antibodies (DSA) induced by transfusion which ultimately resulted in transplantation failure and ineffective platelet transfusion with MASPAT matching was selected, and the causes of ineffective platelet transfusion and platelet transfusion strategy were retrospectively analyzed. RESULTS: The 32-year-old female patient was diagnosed as acute myeloid leukemia (high risk) in another hospital with the main symptoms of fever and leukopenia, who should be admitted for hematopoietic stem cell transplantation after remission by chemotherapy. In the course of chemotherapy, DSA was generated due to platelet transfusion, and had HLA gene loci incompatible with the donor of the first transplant, leading to the failure of the first transplant. The patient received platelet transfusion for several times before and after transplantation, and the results showed that the effective rate of MASPAT matched platelet transfusion was only 35.3%. Further analysis showed that the reason for the ineffective platelet transfusion was due to the missed detection of antibodies by MASPAT method. During the second hematopoietic stem cell transplantation, the DSA-negative donor was selected, and the matching platelets but ineffective transfusion during the primary transplantation were avoided. Finally, the patient was successfully transplanted and discharged from hospital. CONCLUSIONS DSA can cause graft failure or render the graft ineffective. For the platelet transfusion of patients with DSA, the platelet transfusion strategy with matching type only using MASPAT method will miss the detection of antibodies, resulting in invalid platelet transfusion.
Female ; Humans ; Adult ; Platelet Transfusion ; Antibodies, Monoclonal ; Retrospective Studies ; HLA Antigens ; Hematopoietic Stem Cell Transplantation

OBJECTIVE: To explore the value of interim METHODS: Twenty-one patients with ENKTL who were pathologically diagnosed at Shanghai General Hospital of Nanjing Medical University (Shanghai General Hospital) from January 2015 to December 2018 were retrospectively collected, and RESULTS: After treatment, 11 patients had complete remission (CR), 3 had partial remission (PR), 1 had stable disease (SD), and 6 had disease progression (PD). The CR patients' △SUVmax was significantly higher than non-CR patients ［(66.07±22.33)% vs (36.87±23.28)%, t=2.927, P=0.009］. Calculated from the receiver operating curve (ROC), the optimal cut-off point of ΔSUVmax was 51.45%. The median follow-up time was 32 months. Kaplan-Meier survival analysis showed that KPI, DS and ΔSUVmax had significance in predicting PFS and OS (P<0.05). COX regression analysis showed that DS was an independent risk factor affecting PFS (P<0.05), and KPI and ΔSUVmax were independent risk factors affecting OS (P<0.05). CONCLUSION Interim
China ; Disease-Free Survival ; Fluorodeoxyglucose F18 ; Humans ; Lymphoma, Extranodal NK-T-Cell/diagnostic imaging* ; Positron Emission Tomography Computed Tomography ; Prognosis ; Retrospective Studies

BACKGROUND: Prenatal evaluation of fetal lung maturity (FLM) is a challenge, and an effective non-invasive method for prenatal assessment of FLM is needed. The study aimed to establish a normal fetal lung gestational age (GA) grading model based on deep learning (DL) algorithms, validate the effectiveness of the model, and explore the potential value of DL algorithms in assessing FLM. METHODS: A total of 7013 ultrasound images obtained from 1023 normal pregnancies between 20 and 41 + 6 weeks were analyzed in this study. There were no pregnancy-related complications that affected fetal lung development, and all infants were born without neonatal respiratory diseases. The images were divided into three classes based on the gestational week: class I: 20 to 29 + 6 weeks, class II: 30 to 36 + 6 weeks, and class III: 37 to 41 + 6 weeks. There were 3323, 2142, and 1548 images in each class, respectively. First, we performed a pre-processing algorithm to remove irrelevant information from each image. Then, a convolutional neural network was designed to identify different categories of fetal lung ultrasound images. Finally, we used ten-fold cross-validation to validate the performance of our model. This new machine learning algorithm automatically extracted and classified lung ultrasound image information related to GA. This was used to establish a grading model. The performance of the grading model was assessed using accuracy, sensitivity, specificity, and receiver operating characteristic curves. RESULTS: A normal fetal lung GA grading model was established and validated. The sensitivity of each class in the independent test set was 91.7%, 69.8%, and 86.4%, respectively. The specificity of each class in the independent test set was 76.8%, 90.0%, and 83.1%, respectively. The total accuracy was 83.8%. The area under the curve (AUC) of each class was 0.982, 0.907, and 0.960, respectively. The micro-average AUC was 0.957, and the macro-average AUC was 0.949. CONCLUSIONS The normal fetal lung GA grading model could accurately identify ultrasound images of the fetal lung at different GAs, which can be used to identify cases of abnormal lung development due to gestational diseases and evaluate lung maturity after antenatal corticosteroid therapy. The results indicate that DL algorithms can be used as a non-invasive method to predict FLM.
Algorithms ; Deep Learning ; Female ; Gestational Age ; Humans ; Infant ; Infant, Newborn ; Lung/diagnostic imaging* ; Neural Networks, Computer ; Pregnancy