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 construct an evaluation index system of"Internet+Traditional Chinese Medicine nursing"service capability,in order to provide references for the standardized and effective evaluation of"Internet+Traditional Chinese Medicine nursing"service capability.Methods Literature analysis and semi-structured interview method were adopted,and three-dimensional quality structure model was used as the theoretical framework to initially construct the"Internet+Traditional Chinese Medicine nursing"service capability evaluation index item pool.From July to 0ctober 2022,the Delphi method was used to conduct 2 rounds of consultation with 16 experts from Guangdong Province,to evaluate the enthusiasm,authority,degree of opinion concentration and degree of opinion coordination of the experts in the correspondence consultation,and the weight of the index system was determined with the combination of chromatography analysis.Results 2 rounds of expert letter consultation were conducted.The questionnaire recovery rates were 100％,and the authority coefficients were 0.844 and 0.834,respectively.Kendall coordination coefficients were 0.161 and 0.110,respectively(P＜0.001).The first level indexes of the index system are structure evaluation,process evaluation and outcome evaluation.There were 3 first-level evaluation indicators,14 second-level evaluation indicators and 57 third-level evaluation indicators.Conclusion The evaluation index is scientific and practical,and it is carried out around the Internet+Traditional Chinese Medicine nursing capability,which provides a certain reference for the effective evaluation of the service capability of"Internet+Traditional Chinese Medicine nursing".

ObjectiveTo discover and analyze single or several correlative key amino acid sites that influence the host tropism during the influenza A virus （IAV） infection based on complete internal protein gene segments of IAV strains， and to provide evidence for the study of human host-adaptive mutations of IAV. MethodsThe full-length nucleotide sequences of 43 671 IAV strains containing 6 complete internal gene segments were downloaded from the GISAID EpiFlu^TM database， and 698 human-tropic （HU） and 1 266 avian-tropic （AV） representative strains were included. The consensus coding sequences of the representative strains from the amphitropic category were compared by R script， and the differential amino acid sites and their polymorphisms were then obtained. The multi-site combination analysis of differential sites was conducted with R script. ResultsA total of 49 and 57 conserved differential sites were obtained from the consensus sequence comparison between AV and H1N1 （subtype from HU）， and comparison between AV and H3N2 （another subtype from HU）， separately. 79 and 65 multi-site combinations were found between HU and AV strains through 3 and 4 sites combination analysis， respectively， and a total of 11 conserved sites were involved： site 271 and 684 in PB2； site 336， 486， 581 and 621 in PB1； site 204 and 356 in PA； site 33， 305 and 357 in NP. No eligible differential sites were found in M1 and NS1. ConclusionSeveral conserved amino acid differential sites， between HU and AV strains of IAV， are found in PB2， PB1， PA and NP proteins. Instead of working as single units， these sites may have interactions， forming specific amino acid combinations that determine the host tropism of IAV collectively.

OBJECTIVE To estab lish the pre paration method of clopidogrel active thiol metabolite （CATM），and to provide reference for the synthesis of cis-CATM. METHODS CATM was prepared ，separated and purified with isolated rat liver perfusion and ChromCore 120 C18 preparative column ，using（S）-2-oxo-clopidogrel as substrate. The target compounds were identified by mass spectrometry and nuclear magnetic resonance spectroscopy. The retention time of the active configuration of CATM in the human body （cis-CATM）were compared to confirm the proportion of active configuration in the target product. RESULTS The conversion rate of the target product was 11.71％. The target products were identified as CATM by MS and 1H-NMR. Peak 2-peak 5 of CATM were four stereoisomers. The retention time of them were 21.3，22.3，26.5，27.3 min. The peak area ratios of them were 7.13％，7.23％，63.52％，14.97％，respectively. Based on that retention time of the active configuration of CATM in human body was 26.3 min，the active cis-stereoisomer in the target product CATM accounted for 63.52％. CONCLUSIONS This method is low-cost ，simple，and can prepare CATM with higher active configuration.

Under the diagnosis-related groups(DRG) prospective payment system, innovative health technologies with high costs and risks may be limited to some extent. How to balance the increase of health care cost and the development of innovative health technology is a difficult problem to be solved in the current reform. By studying the relatively mature payment systems of innovative health technologies in the world, the authors found that countries generally adopted additional payment or compensation to encourage the development of new technologies. But at the same time, a relatively perfect health technology assessment and payment management mechanism had been established to ensure the standardized operation of payment plan. These international advanced experience and practice could provide references for China′s innovative health technology payment strategy under the DRG payment system. It is suggested to establish a scientific and reasonable assessment mechanism of innovative health technology, create a special access channel for innovative health technology with limited short-term evidence, and gradually form a long-term incentive mechanism of innovative health technology in DRG payment system.

Objective:To investigate the importance and clinical significance of breast reconstruction’s procedure classification and coding.Methods:By retrieving the medical record information system, the breast reconstruction cases with a diagnosis code (ICD-10) of C50 or Z85.3 and a procedure code (ICD-9-CM-3) of 85.33, 85.35, 85.53, 85.54, 85.55, 85.7, 85.95, or 85.96 were collected from Wuhan Union Hospital from Jan. 2016 to Dec. 2019. The reconstruction techniques and timing of the cases were counted according to the clinical procedure names in the operation notes and to the ICD codes verified by the content from operation notes and progress notes, respectively. The results were compared and analyzed by chi-square test with P<0.05 indicating statistically significant difference. Results:A total of 108 cases were included in the study. The difference between clinical procedure names and ICD codes regarding the reconstruction techniques is statistically significant ( P<0.05) with 51 clinical procedure naming ambiguities (47.2%) i. e., the names do not precisely indicate the reconstruction techniques. Similarly, the difference between clinical procedure names and ICD codes regarding the reconstruction timing is statistically significant ( P<0.05) with 29 clinical procedure name errors (26.9%). i. e., the reconstruction timing in the name does not correspond to its counterpart in reality. Conclusions:The clinical procedure names cannot accurately tell the reconstruction techniques or the timing of the procedure, affecting the correctness of the procedure coding and the diagnosis-related groups (DRGs) result. We suggest the reconstruction surgeons to learn some procedure classification and coding knowledge in a timely manner in order to enhance the correctness of the procedure names and coding and to get adapt to the medical insurance payment reform based on CHS-DRG.

Objective:To investigate the importance and clinical significance of breast reconstruction’s procedure classification and coding.Methods:By retrieving the medical record information system, the breast reconstruction cases with a diagnosis code (ICD-10) of C50 or Z85.3 and a procedure code (ICD-9-CM-3) of 85.33, 85.35, 85.53, 85.54, 85.55, 85.7, 85.95, or 85.96 were collected from Wuhan Union Hospital from Jan. 2016 to Dec. 2019. The reconstruction techniques and timing of the cases were counted according to the clinical procedure names in the operation notes and to the ICD codes verified by the content from operation notes and progress notes, respectively. The results were compared and analyzed by chi-square test with P<0.05 indicating statistically significant difference. Results:A total of 108 cases were included in the study. The difference between clinical procedure names and ICD codes regarding the reconstruction techniques is statistically significant ( P<0.05) with 51 clinical procedure naming ambiguities (47.2%) i. e., the names do not precisely indicate the reconstruction techniques. Similarly, the difference between clinical procedure names and ICD codes regarding the reconstruction timing is statistically significant ( P<0.05) with 29 clinical procedure name errors (26.9%). i. e., the reconstruction timing in the name does not correspond to its counterpart in reality. Conclusions:The clinical procedure names cannot accurately tell the reconstruction techniques or the timing of the procedure, affecting the correctness of the procedure coding and the diagnosis-related groups (DRGs) result. We suggest the reconstruction surgeons to learn some procedure classification and coding knowledge in a timely manner in order to enhance the correctness of the procedure names and coding and to get adapt to the medical insurance payment reform based on CHS-DRG.

Objective:To investigate the application value of artificial neural network in laparoscopic surgery training.Methods:The prospective cohort study was conducted. A total of 158 trainees from the First Hospital Affiliated to Army Medical University between Semptember and November, 2019 who had no experience in laparoscopic technology were selected for laparoscopic surgery training, including 52 graduate students of surgery from grade 2019, 2018 and 2017, 58 surgeons receiving standardized residency training, 12 interns and 36 refresher physicians. The 158 trainees were divided into two groups using the random number table. Trainees trained by artificial neural network laparoscopic simulator were allocated into artificial neural network group, and trainees trained by box laparoscopic simulator were allocated into general laparoscopic simulator group. Trainees in both groups were trained using the laparoscopic simulator for 10 hours (5-day continuous training, 2 hours per day) on fundamentals of laparoscopic surgery. Observation indicators: (1) comparison of operation grades on laparoscopic simulator before and after training in the two groups; (2) comparison of improvement of the operation grades on laparoscopic simulator after training between the two groups. Measurement data with normal distribution were represented as Mean± SD, comparison within groups was analyzed using the paired t test and comparison between groups was analyzed using the independent sample t test. Measurement data with skewed distribution were represented as M (range). Results:A total of 158 trainees were selected for eligibility, including 140 males and 18 females, aged from 23 to 34 years, with a median age of 27 years. Of the 158 trainees, 79 were in the artificial neural network group and 79 were in the general laparoscopic simulator group. (1) Comparison of operation grades on laparoscopic simulator before and after training in the two groups: operation grades of the nails transferring, pattern cutting, ligation, sewing knots in vivo and sewing knots in vitro for the artificial neural network group before training were 51.2±4.9, 45.6±3.7, 43.0±3.6, 42.1±3.1, and 39.6±3.1, respectively. The above indicators for the artificial neural network group after training were 78.6±3.0, 76.4±3.9, 79.9±2.5, 78.3±3.5, and 84.1±3.8, respectively. There were significant differences in the above indicators for the artificial neural network group before and after training ( t=-42.490, -56.256, -80.373, -70.802, -79.742, P<0.05). The above indicators for the general laparoscopic simulator group before training were 50.1±2.9, 45.4±3.9, 42.7±3.0, 42.3±3.4, and 39.2±4.7, respectively. The above indicators for the general laparoscopic simulator group after training were 70.4±5.0, 69.8±4.0, 72.3±3.3, 72.3±3.5, and 72.8±3.2, respectively. There were significant differences in the above indicators for the general laparoscopic simulator group before and after training ( t=-28.942, -42.436, -58.357, -52.322, -53.098, P<0.05). (2) Comparison of improvement of the operation grades on laparoscopic simulator after training between the two groups: improvement of the operation grades in the nails transferring, pattern cutting, ligation, sewing knots in vivo and sewing knots in vitro for the artificial neural network group after training were 27.4±5.7, 30.8±5.0, 36.9±4.1, 36.2±4.5 and 39.5±5.4, respectively. The above indicators for the general laparoscopic simulator group after training were 20.3±6.2, 24.4±5.1, 29.6±4.5, 29.9±5.1 and 33.5±5.6, respectively. There were significant differences in the above indicators between the two groups ( t=7.597, 7.946, 10.638, 8.200, 6.969, P<0.05). Conclusion:The introduction of artificial neural network in laparoscopic surgery training can improve the training effects.