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 expression of peroxiredoxin 4 (PRDX4) in oral squamous cell carcinoma (OSCC) and its effect on the proliferation, migration, and invasion of OSCC cells. Methods: The Cancer Genome Atlas(TCGA) database was used to analyze the expression of PRDX4 in OSCC. Quantitative reverse transcription polymerase chain reaction (qRT-PCR) and Western Blot (WB) were used to detect the mRNA and protein expression of PRDX4 in OSCC cell lines and normal oral mucosal epithelial cells. PRDX4 was knocked down in CAL-27 cells and divided into two groups: the si-PRDX4 group and si-NC group. SCC-9 cells overexpressing PRDX4 were divided into two groups: the PRDX4 overexpression group (transfected with pcDNA3.1-PRDX4 plasmid) and the vector group (the control group; transfected with pcDNA3.1-NC plasmid). A cell counting kit-8 (CCK-8) and plate colony formation assay were used to detect cell proliferation. Transwell assay and cell scratch test were used to detect cell invasion and migration ability. WB was used to detect the effects of knockdown or overexpression of PRDX4, p38MAPK agonist or inhibitor on the expression of p38MAPK-related signaling pathway proteins, and epithelial mesenchymal transition proteins in OSCC cells. Results: PRDX4 was highly expressed in OSCC tissues and cell lines. The results of qRT-PCR and WB showed that PRDX4 was highly expressed in OSCC cell lines compared with normal oral mucosal epithelial cells. The CCK-8 assay showed that the si-PRDX4 group had significantly lower OD values than the si-NC group at 24, 48, and 72 h (P<0.05). The PRDX4 overexpression group had a significantly higher OD value than the vector group at 24, 48, and 72 h (P<0.05). The plate colony formation assay showed that the si-PRDX4 group had a significantly lower number of colonies than the si-NC group (P<0.05). The number of colonies formed in the PRDX4 overexpression group was significantly higher than that in the vector group (P<0.05). The cell scratch test showed that the wound healing area of the si-PRDX4 group was less than that of the si-NC group (P<0.05). The scratch healing area of the PRDX4 overexpression group was significantly higher than that of the vector group (P<0.05). The Transwell invasion assay showed that the number of transmembrane cells in the si-PRDX4 group was lower than that in the si-NC group (P<0.05). The number of transmembrane cells in the PRDX4 overexpression group was significantly higher than that in the vector group (P<0.05). The WB results showed that knockdown and overexpression of PRDX4 could downregulate and upregulate the expression of the p38MAPK signaling pathway and epithelial-mesenchymal transition related proteins, respectively, and the addition of p38MAPK agonist and inhibitor could significantly reverse the expression of related proteins. Conclusion PRDX4 is highly expressed in OSCC. Knocking down the expression of PRDX4 in OSCC cells can downregulate the expression of p38 MAPK signal axis and EMT-related signal proteins, thereby inhibiting the proliferation, migration, invasion, and epithelial-mesenchymal transition of cells.

Objective To understand the status of quality control in occupational medical examination (OME) institutions in Guizhou Province. Methods A total of 124 registered OME institutions actively conducting OME in Guizhou Province were selected as the study subjects using the judgment sampling method. The evaluation was conducted by on-site document reviews, practical skill assessments, and investigation of OME practices for quality evaluation and analyzing their quality control performance. Results The public institutions accounted for 71.0% with a 41.5% of OME workload, while private institutions accounted for 29.0% with a 58.5% of OME workload among these 124 OME institutions. The overall pass rate for quality evaluation of OME institutions was 16.9% (21/124), with a total of 1 296 items failed to pass the quality evaluation. Among the unqualified items, organizational structure, quality control management systems, OME quality control, and information reporting accounted for 15.2%, 21.7%, 52.8%, and 10.3%, respectively. The unqualified rate of quality assessment items of OME institutions was 24.5% (1 296/5 288), and the unqualified rate was lower in public institutions compared with private institutions (22.4% vs 29.3%, P<0.01). The rates of the three key unqualified items, including chest radiography conclusion evaluation, audiogram calculation and conclusion evaluation, and blood lead comparison were 9.8%, 74.8% and 71.4%, respectively. The rates of unqualified audiometry operation test and chief physician theory test were 74.8% and 9.7%, respectively. Conclusion The quality of OME institutions in Guizhou Province requires continuous improvement, particularly in enhancing the abilities of audiometry operation, calculating audiogram results and conducing right conclusion, and blood lead inter-laboratory comparision.

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.

ObjectiveTo analyze the characteristics and influencing factors of elderly hospitalized patients with carbapenem-resistant gram-negative bacillus (CRO) infection in the intensive care unit (ICU) of a gradeⅡ level A general hospital in Yangpu District of Shanghai, and to provide scientific basis for the prevention and control of hospital-acquired CRO infection in such hospitals. MethodsThe clinical data of elderly ICU patients (age ≥60 years) from January 2019 to December 2023 were retrospectively collected. A total of 122 cases with hospital-acquired CRO infection were used as the case group, and a total of 68 cases with carbapenem-sensitive gram-negative (CSO) infection were used as the control group. The clinical characteristics of the two groups were analyzed, and univariate analysis and logistic regression analysis were performed for screening for possible influencing factors on hospital-acquired CRO infection. ResultsThe main pathogens of CRO infection were carbapenem-resistant Acinetobacter baumannii (CRAB) (53 cases, 43.44%) and carbapenem-resistant Klebsiella pneumoniae (CRKP) (46 cases, 37.70%), and 17 patients (13.93%) had more than two types of CRO infection. Among the CRO infection, the main sites were lower respiratory tract infection (58 cases, 47.54%), ventilator-associated pneumonia (21 cases, 17.21%), and catheter-associated urinary tract infections (16 cases, 13.11%). The incidence rate of poor prognosis was higher in the CRO infection group (54.10%) than that in the CSO infection group (36.76%) (P=0.021). The results of univariate analysis showed that male, history of hospitalization within three months, chronic respiratory disease, hypoproteinemia, anemia, and history of invasive procedures prior to infection, including indwelling central venous catheter, invasive mechanical ventilation, urinary catheter, gastric tube placement and parenteral nutrition, in addition, heparin anticoagulation, the use of broad-spectrum penicillin, third-generation cephalosporins, fluoroquinolones, carbapenems, carbapenems combined with fluoroquinolones, carbapenems combined with glycopeptides, use of ≥3 antibiotics and long time of antibiotic use prior to infection were all associated with the CRO infection (P<0.05). The results of logistic regression analysis showed that use of carbapenems (OR=7.739, 95%CI: 2.226‒26.911), ≥3 types of antibiotics (OR=6.307, 95%CI: 1.674‒23.754), invasive mechanical ventilation (OR=4.082, 95%CI: 1.795‒9.281), urinary catheter (OR=3.554, 95%CI: 1.074‒11.758), and comorbid hypoproteinemia (OR=4.741, 95%CI: 2.039‒11.022) and diabetes (OR=3.245, 95%CI: 1.344‒7.839) were positively correlated with the risk of CRO infection. ConclusionConcurrent use of carbapenems with multiple other antibiotics, as well as the use of invasive mechanical ventilation, urinary catheter, and comorbid hypoproteinemia and diabetes, may be associated with an increased influencing of CRO infection. More attention should be paid to the prevention and control of infection in elderly patients with the above-mentioned risk factors, and active screening of drug-resistant bacteria should be strengthened. Besides, the rational use of broad-spectrum antibiotics such as carbapenems, avoiding unnecessary invasive operations, and paying attention to patient nutrition and blood glucose control all can reduce the incidence of CRO infection and help to improve clinical outcomes.

Objective:To explore the central venous catheter maintenance compliance among nurses in Class Ⅱ and Ⅲ hospitals in Hunan Province.Methods:From January to March 2022, 297 nurses from 22 Class Ⅱ and Ⅲ hospitals in Hunan Province were selected as the research subject by convenience sampling. Nurses were surveyed using the self-made Central Venous Catheter Maintenance Compliance Questionnaire. Multiple linear regression was used to analyze the influencing factors of nurse compliance with central venous catheter maintenance, the items of the Central Venous Catheter Maintenance Compliance Questionnaire were analyzed.Results:A total of 297 questionnaires were distributed, and 268 valid questionnaires were collected, with an effective response rate of 90.24%. The hospital level, specialized training in intravenous therapy, and age were the influencing factors on the compliance of nurses with central venous catheter maintenance. Analyzing specific items, only 34.3% (92/268) of nurses correctly executed the item "alcohol cotton pads were recommended", and only 66.4% (178/268) of nurses correctly performed the item "sterile gauze dressings should be replaced at least every two days".Conclusions:There is a certain gap in the central venous catheter maintenance compliance among nurses at all levels of hospitals, and there is a need to improve compliance in disinfection of infusion joints and replacement of sterile gauze dressings. We should strengthen the training of specialist nurses, especially the training of nurses in ClassⅡ hospitals on specialized skills related to intravenous therapy.

Standardized patient (SP) has been widely used for medical teaching and assessment in medical colleges at home and abroad. Pediatric consultations are mostly directed toward parents, so in pediatric education, SP is usually referred to as standardized family (Sfam), which is trained to portray the patient's family members. At present, the development of Sfam in pediatric teaching in China is relatively slow. Based on the characteristics of pediatric teaching, the paper summarizes the necessity of Sfam, the application of different types of Sfam, the integration of Sfam with other clinical teaching methods, and the value of Sfam in pediatric teaching, and also discusses the future direction and prospects of Sfam combined with artificial intelligence in pediatric teaching. After years of development, Sfam has been proved to be an effective teaching model. We hope this paper can help more pediatric clinical educators gain a deeper understanding of the Sfam teaching method, and promote the application of Sfam in pediatric teaching to maximize its role in advancing the development of pediatric education.

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.

Objective To evaluate the biochemical indicators,nutritional status,and immune levels of pa-tients with pulmonary aspergillosis(PA)and other pulmonary diseases,and to construct a predictive model for PA so as to improve the diagnostic efficacy of clinical PA.Methods A total of 40 PA patients and 39 pa-tients with other pulmonary diseases who were hospitalized in the hospital from January 2020 to August 2022 were retrospectively analyzed.The expression trends and differences of serum 1,3-β-D Glucan(G test),galac-tomannan test(GM test),biochemical indexes,blood routine indexes and immune cell subsets were analyzed and compared.The receiver operating characteristic(ROC)curve and binary Logistic regression analysis were used to construct the predictive model for PA by the combination of clinical indicators.Results Serum GM test,G test,albumin,hemoglobin,hematocrit,lymphocytes,B lymphocytes,CD44 T lymphocytes and CD4/CD8 ratio displayed significant differences between PA patients and patients with other lung disease(P＜0.05).The levels of GM test in alveolar lavage fluid of PA patients were significantly higher than that in the serum,and the differences were statistically significant(P＜0.05).The ROC curve analysis showed that the GM test,as an independent predictor of PA,had good predictive accuracy[0.85＜area under the curve(AUC)＜0.95].Besides,albumin,natural killev cells,CD4+T lymphocytes and CD4/CD8 ratio had general predictive efficacy(0.70＜AUC＜0.85).The prediction efficacy of G test and B lymphocytes was poor(AUC＜0.70).The Logistic regression analysis showed that the combination of GM test and serum albumin could construct the optimal prediction model,and the prediction formula of the combined model was as fol-lows:Logit(P)=17.781× GM-0.131×albumin+1.394.The prediction accuracy of the combined model was 0.924(95％CI:0.865-0.982),the sensitivity was 87.5％,the specificity was 81.2％,and the cut off value was 17.781×GM-0.131×albumin-1.735.Conclusion This study retrospectively analyzed the differences in various clinical indicators between patients with PA and patients with other pulmonary diseases,and then screen the key clinical indicators as candidate predictors which displayed significantly different ex-pression between the two groups.The optimal prediction model for the diagnosis of PA is constructed by the combination of GM test and serum albumin through ROC curve and Logistic regression analysis.This model may significantly improve the diagnostic efficiency of PA in clinical,and provide the reference for the early di-agnosis and effective treatment of PA patients.