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.

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 clinical effects and pregnancy outcomes of using luteal phase long protocol and GnRH antagonist protocol in patients with polycystic ovary syndrome(PCOS)who have failed their first GnRH antagonist protocol therapy.Methods The clinical data of 163 PCOS patients who underwent IVF/ICSI-ET were retrieved.After the failure of their first GnRH antagonist protocol treatment,they were divided into two groups in the second controlled ovarian hyperstimulation(COH)cycle:Luteal phase long protocol group(n=95)and Gn-RH antagonist protocol group(n=68).A retrospective analysis and comparison of basic clinical data,clinical and laboratory indicators,and pregnancy outcomes between two groups were conducted.Results ① There was no sta-tistically significant difference in basic clinical indicators between two group except LH.② Compared the first and second cycle treatments of patients in the luteal phase long protocol group,the initiation dose of gonadotropin(Gn),total number of Gn days,total Gn usage,estradiol(E2)on the day of hCG injection,number of retrieved eggs,oocyte maturation rate,2PN fertilization rate,2PN cleavage rate,blastocyst formation rate,high-quality blas-tocyst formation rate,and moderate to severe OHSS rate were significantly higher than those in the first GnRH an-tagonist cycle(P＜0.05).The GnRH antagonist protocol group also showed similar improvements.③ The com-parison of the second COH cycle between two groups showed that the total number of Gn days,total Gn usage,and total Gn cost in the luteal phase long protocol group were significantly higher(P＜0.05),while the E2 and LH on the day of hCG injection,and the maturation rate of eggs were significantly lower than those in the GnRH antagonist protocol group(P＜0.05).However,there was no statistically significant difference in the number of retrieved eggs,2PN fertilization,2PN cleavage,blastocyst formation rate,high-quality blastocyst formation rate,and OHSS rate between the two groups;④ The comparison of fresh transplantation cycles for the second COH cycle between the two groups showed that the luteal phase long protocol fresh transplantation rate,implantation rate,clinical preg-nancy rate,and live birth rate were slightly higher than those of the GnRH antagonist protocol group,but the differ-ence was not statistically significant.Comparing the outcomes of pregnancy following the initial frozen-thawed em-bryo transfer(FET)between two groups,the biochemical pregnancy rate and clinical pregnancy rate of the GnRH antagonist protocol group were higher than those of the luteal phase long protocol group(P＜0.05).However,no significant statistical variations were found in implantation rate,live birth rate,neonatal gestational age,and birth weight.Conclusion For PCOS patients who fail the first GnRH antagonist protocol,an appropriate increase in the initiating dose and usage of Gn can achieve satisfactory pregnancy outcomes with both protocols.Compared with change to a luteal phase long protocol,reusing the GnRH antagonist protocol still maintains its long-standing advan-tages,such as shorter total Gn days,lower costs,and better patient compliance.

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 establish a bacterial endotoxin test method for nafamostat mesilate for injection.Methods:The interference test of the bacterial endotoxin test method-kinetic chromogenic assay was carried out according to the Chinese Pharmacopoeia 2020 Volume Ⅳ general chapter 1143.Results:The interference effect of naphtholimus mesylate can be effectively eliminated by treating the test solution with an alkaline regulator for a certain period of time and diluting it.Conclusion:The bacteria endotoxin test-kinetic chromogenic assay for nafa-mostat mesilate for injection is applicable.

Background::Total human immunodeficiency virus (HIV) DNA and integrated HIV DNA are widely used markers of HIV persistence. Droplet digital polymerase chain reaction (ddPCR) can be used for absolute quantification without needing a standard curve. Here, we developed duplex ddPCR assays to detect and quantify total HIV DNA and integrated HIV DNA.Methods::The limit of detection, dynamic ranges, sensitivity, and reproducibility were evaluated by plasmid constructs containing both the HIV long terminal repeat (LTR) and human CD3 gene (for total HIV DNA) and ACH-2 cells (for integrated HIV DNA). Forty-two cases on stable suppressive antiretroviral therapy (ART) were assayed in total HIV DNA and integrated HIV DNA. Correlation coefficient analysis was performed on the data related to DNA copies and cluster of differentiation 4 positive (CD4 +) T-cell counts, CD8 + T-cell counts and CD4/CD8 T-cell ratio, respectively. The assay linear dynamic range and lower limit of detection (LLOD) were also assessed. Results::The assay could detect the presence of HIV-1 copies 100% at concentrations of 6.3 copies/reaction, and the estimated LLOD of the ddPCR assay was 4.4 HIV DNA copies/reaction (95% confidence intervals [CI]: 3.6-6.5 copies/reaction) with linearity over a 5-log 10-unit range in total HIV DNA assay. For the integrated HIV DNA assay, the LLOD was 8.0 copies/reaction (95% CI: 5.8-16.6 copies/reaction) with linearity over a 3-log 10-unit range. Total HIV DNA in CD4 + T cells was positively associated with integrated HIV DNA ( r = 0.76, P <0.0001). Meanwhile, both total HIV DNA and integrated HIV DNA in CD4 + T cells were inversely correlated with the ratio of CD4/CD8 but positively correlated with the CD8 + T-cell counts. Conclusions::This ddPCR assay can quantify total HIV DNA and integrated HIV DNA efficiently with robustness and sensitivity. It can be readily adapted for measuring HIV DNA with non-B clades, and it could be beneficial for testing in clinical trials.