Objectives:To develop a risk prediction model for sudden cardiac arrest(CA)in children with congenital heart disease(CHD)after surgery and validate its predictive efficacy,providing a reference for the prevention of CA and risk stratification.Methods:Medical records were retrospectively analyzed from 5 029 children who were hospitalized in Fuwai Hospital,Chinese Academy of Medical Sciences from January 1,2020 to May 31,2022 and underwent CHD surgery.The patients were divided into two groups:those who experienced CA after surgery(n=33)and those who did not(n=4 996).A random forest model for predicting the risk of postoperative CA was established on the training dataset using R software,and the predictive effect of the model was evaluated on the validation dataset using indicators of predictive accuracy,sensitivity,specificity,positive predictive value,negative predictive value.Results:The incidence of CA in this center was 0.66%,survival rate is 72.73%.Using the random forest algorithm,the importance of risk factors for sudden CA after CHD surgery was ranked by variable importance scoring,with the following top 6 important predictive variables:blood pressure,lactate levels,heart rate,cardiac rhythm,arterial oxygen partial pressure,and blood oxygen saturation on the first day after surgery.The model established by the random forest algorithm on the training set was validated on the test set,yielding a predictive accuracy of 99.8%,specificity of 87.5%,sensitivity of 99.9%,kappa coefficient of 0.8225,positive predictive value of 99.9%,and negative predictive value of 77.8%.Conclusions:The established prediction model of sudden CA in children with CHD after surgery had good performance.It might help medical staffon decision making of early intervention,preventing the occurrence of CA,and improving the outcomes of children with high risk of CA post surgery.

Objective To sort out the management system of clinical trial drugs in our hospital,and analyze the construction and practical experience of the information system in the whole process management of clinical trial drugs,and to further improve the efficiency and quality of clinical trials.Methods Based on the original management of clinical trial drugs,an information system in line with the situation of our hospital was applied to manage the whole process of clinical trial drugs.Results Compared with the traditional management,the information system can automatically record the data of inbound and outbound storage,distribution,retrieval and return of trial drugs in real time,pre-review the prescriptions and randomized documents of clinical trial drugs,and realise paperless office.Conclusions The information system can ensure the safety,accuracy and traceability of the data of drugs used in clinical trial.The system can also save resources,improve the efficiency and quality of clinical trial management.

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.

Objectives:To develop a risk prediction model for sudden cardiac arrest(CA)in children with congenital heart disease(CHD)after surgery and validate its predictive efficacy,providing a reference for the prevention of CA and risk stratification.Methods:Medical records were retrospectively analyzed from 5 029 children who were hospitalized in Fuwai Hospital,Chinese Academy of Medical Sciences from January 1,2020 to May 31,2022 and underwent CHD surgery.The patients were divided into two groups:those who experienced CA after surgery(n=33)and those who did not(n=4 996).A random forest model for predicting the risk of postoperative CA was established on the training dataset using R software,and the predictive effect of the model was evaluated on the validation dataset using indicators of predictive accuracy,sensitivity,specificity,positive predictive value,negative predictive value.Results:The incidence of CA in this center was 0.66%,survival rate is 72.73%.Using the random forest algorithm,the importance of risk factors for sudden CA after CHD surgery was ranked by variable importance scoring,with the following top 6 important predictive variables:blood pressure,lactate levels,heart rate,cardiac rhythm,arterial oxygen partial pressure,and blood oxygen saturation on the first day after surgery.The model established by the random forest algorithm on the training set was validated on the test set,yielding a predictive accuracy of 99.8%,specificity of 87.5%,sensitivity of 99.9%,kappa coefficient of 0.8225,positive predictive value of 99.9%,and negative predictive value of 77.8%.Conclusions:The established prediction model of sudden CA in children with CHD after surgery had good performance.It might help medical staffon decision making of early intervention,preventing the occurrence of CA,and improving the outcomes of children with high risk of CA post surgery.

Objective To sort out the management system of clinical trial drugs in our hospital,and analyze the construction and practical experience of the information system in the whole process management of clinical trial drugs,and to further improve the efficiency and quality of clinical trials.Methods Based on the original management of clinical trial drugs,an information system in line with the situation of our hospital was applied to manage the whole process of clinical trial drugs.Results Compared with the traditional management,the information system can automatically record the data of inbound and outbound storage,distribution,retrieval and return of trial drugs in real time,pre-review the prescriptions and randomized documents of clinical trial drugs,and realise paperless office.Conclusions The information system can ensure the safety,accuracy and traceability of the data of drugs used in clinical trial.The system can also save resources,improve the efficiency and quality of clinical trial management.

【Objective】 To investigate the confirmatory status of HIV-1 antibody detection and Western blot (WB) test among voluntary blood donors in Wuhu, and to explore the strategies and methods to further ensure blood quality and safety. 【Methods】 Blood samples were preliminarily screened by ELISA and NAT, and the reactive samples were sent to Wuhu CDC for further WB test of HIV-1 antibody. The confirmation results of HIV-1 antibodies of voluntary blood donors in Wuhu in the past 10 years were retrospectively collected. The characteristics of WB bands of positive samples were analyzed, and the demographic characteristics of HIV-infected voluntary blood donors were sorted out. 【Results】 A total of 354 864 blood samples from voluntary blood donors in Wuhu during January 2011 to May 2021 were investigated, among which 42 were confirmed HIV positive (HIV-1 antibody positive in 41, and solo HIV-RNA reactive in 1), with a total HIV positive rate of 11.8/100 000(42/354 864). Statistical differences were found in gender [males 97.6% (41/42) vs females 2.4% (1/42)], marital status [unmarried 17.3/100 000 vs married 8.0/100 000] and occupation [staff/workers 37.5/100 000 vs students11.4/100 000 vs others 7.7/100 000]. Among the positive samples, the yield rate of WB bands gp160 was 100% (41/41), both gp41 and p24 were 97.6% (40/41),, and p55 was the lowest 46.3% (19/41). P51 and P66 presented the highest yield consistency (Kappa=1.000, P<0.05). Four samples were solo HIV-RNA reactive, and one of them was>5 000 cps/mL by viral load (VL) testing, indicating HIV window period infection. 【Conclusion】 HIV infection statistically affected male donors more than females in Wuhu area, and most were early infection that revealed by WB band analysis. NAT plays an important role in the detection and confirmation of HIV infection during the window period, and is essential for blood safety.

Objective To investigate the clinical efficacy of SOX and XELOX in neoadjuvant chemo-therapy for advanced gastric carcinoma .Methods Seventy-five cases with advanced gastric carcinoma were se-lected from our hospital from Feb 1,2011 to Oct 1,2015.SOX and XELOX were used to these patients treatment . The relationship between SOX and XELOX with tumor stage ,clinical curative effect evaluation ,operation ,postop-erative pathology ,adverse reaction ,survival analysis with advanced gastric carcinoma had been analyzed retrospec -tively.Results The efficacy of SOX and XELOX were 47.22%,41.03%.Compared with the two groups ,the CT curative effect evaluation,effective rate,disease control rate had no significant differences (P>0.05).The median survival time of DFS and OS were significant between the two groups (P<0.05).Conclusion The SOX and XELOX for neoadjuvant chemotherapy of advanced gastric carcinoma treatment is safety and effective , and the XELOX is better than SOX in terms of long -term effect,prognosis and clinical benefits .

Objective To investigate the clinical application value of peripheral blood lymphocyte subsets expression levels in autoimmune disease(AID) among the patients with antinuclear antibody(ANA) positive .Methods 200 patients with ANA positive and 196 patients with ANA negative were selected as the experimental group and control group respectively .The experimental group adopted indirect immunofluorescence assay (IFA) and immunoblotting assay(LIA) for detecting ANA ,moreover divided into the IFA group and LIA group according to the detection results .Meanwhile the flow cytometry was adopted to detect peripheral blood T lymphocytes ,helper T lymphocytes ,cytotoxic T lymphocyte ,NK lymphocytes and B lymphocytes absolute values of each group .The detection results were statistically analyzed .Results Helper T lymphocytes ,NK lymphocytes and B lymphocytes absolute values in the experimental group were significantly lower than those in the control group ,the differences were statistically significant(P＜0 .01);in the experimental group ,helper T lymphocytes and NK lymphocytes absolute values in the IIF group were significantly lower than those in the LIA group ,the differences were statistically significant (P＜0 .01) .Conclusion Peripheral blood lymphocytes subsets can serve as the important detection indicators during the diagnosis and treatment process of AID .