Objective To research for a new method of arthroscopic total meniscectomy with simple instru- ments. Methods One hundred and sixty nine cases were followed up for short period that had been operated on with a forward knife, a back cutting knife, a punch and a 30?scope from Nov. 1992. to Aug. 1998. Results The av- erage time of operation was 78 minutes. The operations had no injury to cartilage and ligament. It is necessary for only 7 .9 percent of patients to take analgesic intramuscularly after operation. Eight patients had a transient tourniquet paral- ysis. All of them could begin to work and exercise after postoperative 2 weeks. Conclusion As compared with the method introduced by David Sisk ,this method has the advantages of simple instruments, fewer ports and complications and minimal trauma Either lateral meniscus or medial meniscus can be cut off by the same method. This method is a safety and a better way to arthroscopic meniscectomy.

Objective To study the outcomes of autologous osteo-periosteal cylinder graft transplantation for Hepple V osteochondral lesions of the talus (OLT) with large subchondral cyst.Methods The data of 27 consecutive patients of OLT with subchondral cyst was retrospectively analyzed who were treated by autologous osteo-periosteal cylinder graft transplantation from October 2007 to September 2011.There were 26 males and 1 female with an average age of 35.8 years (range,22-53 years).Visual analogue score (VAS) for pain during daily activities,the American Orthopaedic Foot and Ankle Society (AOFAS) ankle and hindfoot score,and subjective satisfaction were investigated.The plain radiographs,magnetic resonance imaging (MRI) of the ankle,and second look arthroscopy were analyzed.Results All the 26 patients were followed up for 22.4 months.At the last follow-up,the VAS score decreased from 5.4±1.0 points preoperatively to 0.8±0.8 points postoperatively,and the mean (50％) AOFAS score improved from 73.9±3.1 points preoperatively to 93.0±6.5 points postoperatively.In 26 cases,the radiolucent area of cysts disappeared on plain radiographs.The mean magnetic resonance observation of cartilage repair tissue (MOCART) score was 57.2,though small subchondral bone cyst was still found in 3 cases on postoperative MRI.The mean (50％) ICRS arthroscopic score of cartilage repair was 9.2 points according to second look arthroscopy of 18 cases.There were 16 cases receiving excellent effect,8 good and 2 fair.The excellent and good rate was 92.3％ (24/26).There were no major complications.Conclusion Autologous osteo-periosteal cylinder graft transplantation could repair the osteochondral defects.It yields satisfactory results,and is suitable for treating OLT with large subchondral cyst.

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.