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 investigate the application value of single nucleotide polymorphism(SNP)linkage analysis based on next-generation sequencing(NGS)technology in preimplantation genetic testing(PGT)of families with autosomal recessive polycystic kidney disease(ARPKD).Methods A family with ARPKD was selected,where the female member had a pregnancy ultrasound revealing polycystic kidney in the fetus.Genetic testing showed compound heterozygous mutations of the polycystic kidney/polycystic liver disease 1 gene(PKHD1),c.10444C>T(paternal)and c.4303del(maternal),with the c.4303del mutation being reported for the first time.Targeting the coding region of the PKHD1 gene,335 high-density tightly linked SNP sites were selected in the upstream and downstream 2M regions using multiplex polymerase chain reaction(PCR)and NGS.The couple′s SNP risk haplotypes carrying gene mutations were constructed.After in vitro fertilization,blastocyst culture was performed.Trophoblastic cells obtained from the biopsy were subjected to whole-genome amplification,and NGS was used for linkage analysis and low-depth chromosomal aneuploidy screening of the embryos.Sanger sequencing was used to verify the results of embryo linkage analysis.Results Among the 6 biopsied embryos,4 were mutation-free and euploid,1 exhibited heterozygous for the mutation and mosaic while another unstable sequencing data,making it impossible to judge.One of the mutation-free and developmentally healthy euploid embryos was implanted into the maternal uterus,resulting in the full-term delivery of a healthy baby.Conclusion Application of NGS-based SNP linkage analysis in PGT can effectively blocking the vertical transmission of ARPKD within families,while avoiding abortion issues caused by aneuploid embryos.This study is also the first PGT report target-ing the PKHD1 gene c.4303del mutation.

A teaching clinic is an outpatient clinic specialized for teaching, where trainees are responsible for medical activities such as medical history taking, physical examination, and diagnosis and treatment, under the assistance and guidance of teachers. Only a few hospitals in China have built up teaching clinics for standardized training of pediatric residents. This paper summarizes the experience in the operation management of the standardized residency training teaching clinic in Children's Hospital, Zhejiang University School of Medicine. The teaching clinic takes teaching as its core task, adheres to humanistic care, and follows the principle of hierarchical progression. Its operation involves organizational approval, preliminary arrangements, outpatient appointments, the implementation of teaching activities, and other processes, which are carried out under organizational management and quality management. We have explored a preliminary strategy for evaluating the teaching effects of teaching clinics, and proposed some suggestions for the future development of pediatric residency training teaching clinics such as increasing objective evaluation methods and increasing pediatric subspecialty teaching clinics.

Objective To observe the value of intratumoral and peritumoral radiomics based on diffusion weighted imaging(DWI)for predicting histological grade of breast cancer.Methods Preoperative DWI data of 700 patients with single breast cancer diagnosed by pathology were retrospectively analyzed.The patients were divided into training set(n= 560,including 381 of grade Ⅰ+Ⅱ and 179 of grade Ⅲ)and test set(n=140,including 95 of grade Ⅰ+Ⅱ and 45 of grade Ⅲ)at the ratio of 8∶2.Intratumoral ROI(ROIintra)was manually delineated on DWI,which was automatically expanded by 3 mm and 5 mm to decline peritumoral ROI(ROIperi,including ROI3 mm and ROI5 mm),then intratumoral-peritumoral ROI(ROIintra+3 mm,ROIintra+5 mm)were obtained.The optimal radiomics features were extracted and screened,and the radiomics model(RM)for predicting the histological grade of breast cancer were constructed.Receiver operating characteristic curves were drawn,and the areas under the curve(AUC)were calculated to evaluate the predictive efficacy of each model.Calibration curve method was used to evaluate the calibration degree,while decision curve analysis(DCA)was performed to explore the clinical practicability of each model.Results AUC of RMintra,RM+3 mm,RM+5mm,RMintra+3 mm and RMintra+5 mm was 0.750,0.724,0.749,0.833 and 0.807 in training set,while was 0.723,0.718,0.736,0.759 and 0.782 in test set,respectively.In training set,significant differences of AUC was found(all P＜0.01),while in test set,no significant difference of AUC was found among models(all P＞0.05).The calibrations of models were all high.DCA showed that taken 0.02-0.88 as the threshold,the clinical net benefit of RMintra+per were greater in training set,while taken 0.40-0.72 as the threshold,the clinical net benefit of RMintra+per was greater in test set.Conclusion Both DWI intratumoral and peritumoral radiomics could effectively predict histological grade of breast cancer.Combination of intratumoral and peritumoral radiomics was more effective.

Objective:To investigate the clinicopathological features and differential diagnosis of eosinophilic vacuolated tumor (EVT).Methods:Seven cases of EVT with characteristic morphology and unequivocal diagnosis from the Affiliated Hospital of Qingdao University (6 cases), Qingdao, China and the 971 Hospital of PLA Navy (1 case), Qingdao, China between January 2010 and December 2021 were subject to morphological and immunohistochemical analyses. Additionally, whole exome sequencing (WES) was performed in two cases. Twenty-two cases of renal oncocytoma (RO) and 17 cases of eosinophilic chromophobe renal cell carcinoma (eChRCC) diagnosed at the same time were used as controls.Results:Four males and three females with a mean age of 42 years (range: 29-61 years) were included in the study. The tumors were nodular and well-circumscribed, with sizes ranging from 1.5 to 4.5 cm. On cross-section, they appeared gray-red or gray-white, solid, and soft. Tumor cells were arranged in nests, solid sheets, and acinar or small vesicular structures. These cells exhibited eosinophilic cytoplasm with large, prominent clear vacuoles and round nuclei with prominent nucleoli. Perinuclear halos were focally present in four cases, while small tumor cells with sparse cytoplasm and hyperchromatic nuclei were seen in one case. No necrosis or mitosis was noted. Edematous stroma was detected in three cases. All tumors were positive for CD117 and Cathepsin K, but negative for vimentin and CK7. CK20 was positive in scattered individual cells, and Ki-67 positivity ranged from 1% to 4%. Point mutations in MTOR were identified in both patients who were subject to the molecular analysis. Statistical differences in the expression of Cathepsin K, CD10, S-100A1, and Cyclin D1 between EVT and RO ( P<0.05) were significant, so were the differences in the expression of Cathepsin K, CD10, CK7 and claudin 7 between EVT and eChRCC ( P<0.001). Seven patients were followed up for 4 to 96 months (mean, 50 months), with no recurrences or metastases. Conclusions:EVT is a rare renal tumor that shares morphological and immunophenotypic features with RO and eChRCC, and it is closely linked to the TSC/MTOR pathway. The presence of large prominent transparent vacuoles in eosinophilic cytoplasm along with conspicuous nucleoli is its key morphological characteristics. The use of combined immunohistochemical stains greatly aids in its diagnosis. Typically, the tumor exhibits indolent biological behaviors with a favorable prognosis.

The development of longitudinal cohorts has made the identification and surveillance of multiple biological markers and behavioral factors which influence disease course or health status become possible. However, traditional statistical methods typically use univariate longitudinal data for research, failing to fully exploit the information from multivariate longitudinal data. The group-based multi-trajectory model (GBMTM) emerged as a method to study the developmental trajectory of multivariate data in recent years. GBMTM has distinct advantages in analyzing multivariate longitudinal data by identifying potential subgroups of populations following similar trajectories by multiple indicators that influence the outcome of interest. In this study, we introduced the application of GBMTM by explaining the fundamental principles and using the data from a health management study in the elderly by using smart wearing equipment to investigate the relationship between multiple life-related variables and hypertension to promote the wider use of GBMTM in longitudinal cohort studies.

Objective To investigate the effects of Anmian Dan on neurotransmitters in the brain of model rats,which were sleep deprived by multi-platform water environment.Methods Fifty SD rats were randomly and evenly divided into 5 groups with 10 rats in each group,which were blank control group(Control group),Model group(Model group),Estazolam group(Estazolam group),low dose group(AMD-L group)and high dose group(AMD-H group).The rats were subjected to sleep deprivation in a multi-platform water environment for 20 hours per day for 21 days.The movement distance and movement time of rats at different time points were recorded by autonomous activity analyzer to evaluate the changes of autonomous activity.The contents of glutamic acid(Glu)and gamma-aminobutyric acid(GABA)were detected by ELISA,and the mRNA expression levels of NDRG2,GLT-1,GAD65 and GAD67 were detected by Real-time PCR.Western blot was used to detect the expressions of NDRG2,p-PI3K,p-Akt,GLT-1,GAD65 and GAD67.Results The Model group was more active than the Control group,and the concentration of GABA in the cortex of the Model group was decreased and the concentration of Glu was increased.The mrna and protein expression levels of NDRG2 in Model group were higher than those in Control group(P<0.01),but the mrna and protein expression levels of GLT-1,GAD65 and GAD67 in model group were lower than those in Control group(P<0.01).The protein expression levels of P-PI3K and P-AKT in the cortex of model group were significantly decreased(P<0.01).Compared with Model group,Anmeidan could reduce the autonomic activity of sleep deprived rats,increase the concentration of GABA,decrease the concentration of Glu in cortex(P<0.05),and increase the mrna relative expression levels and protein expression levels of GLT-1,GAD65 and GAD67(P<0.05).The expression levels of P-PI3K and P-Akt were increased(P<0.01),and mrna and protein expression levels of NDRG2 were decreased(P<0.01).Conclusion Anmian Dan may regulate the activity of astrocytes and affect the levels of neurotransmitters GABA and GLU in the brain through the PI3K/AKT signaling pathway,thus playing a role in improving the circadian rhythm disturbance in sleep-deprived rats.

Objective To investigate the correlation between the expression of hsa_circ_0001785 in triple negative breast cancer(TNBC)and the efficacy of neoadjuvant chemotherapy(NAC).Methods A total of 129 patients with triple negative breast cancer who were admitted to our hospital from October 2021 to February 2023 were regarded as the study group,and 125 patients with benign breast lesions who underwent surgery in our hospital were served as the control group.The influencing factors of NAC efficacy in TNBC patients were analyzed by multivariate logistic regression model;receiver operating characteristic(ROC)curve was applied to analyze the predictive value of hsa_circ_0001785 level for NAC efficacy in triple negative breast cancer patients.Results Compared with the control group(1.05±0.16),the expression level of hsa_circ_0001785 in the study group(2.47±0.39)increased(P＜0.05);compared with the effective group(2.34±0.35),the expression level of hsa_circ_0001785 in the ineffective group(3.48±0.56)increased(P＜0.05);hsa_circ_0001785 was highly expressed in triple negative breast cancer patients with tumor diameter＞2 cm,lymph node metastasis and high tissue grade(P＜0.05);high expression of hsa_circ_0001785,tumor diameter＞2 cm,occurrence of lymph node metastasis and high histological grade were risk factors for NAC efficacy in triple negative breast cancer patients(P＜0.05).Hsa_circ_0001785 level has certain predictive value for NAC efficacy in TNBC patients.Conclusion Hsa_circ_0001785 is highly expressed in triple negative breast cancer,and the level of hsa_circ_0001785 has a certain predictive value for the efficacy of NAC in patients.