Background::Breast cancer is ranked among the most prevalent malignancies in the Chinese female population. However, comprehensive reports detailing the latest epidemiological data and attributable disease burden have not been extensively documented.Methods::In 2018, high-quality cancer surveillance data were recorded in 700 population-based cancer registries in China. We extracted data on female breast cancers (International Classification of Diseases, Tenth Revision [ICD-10]: C50) and estimated the incidence and mortality in 2022 according to the baseline data and corresponding trends from 2010 to 2018. Pathological types were classified according to the ICD for Oncology, 3rd Edition codes. Disability-adjusted life years (DALYs) were calculated as the sum of the years of life lost (YLLs) and years lived with disability (YLDs).Results::In 2022, approximately 357,200 new female breast cancer cases and 75,000 deaths occurred in China, accounting for 15.59% and 7.94% of total new cancer cases and deaths, respectively. The age-standardized incidence rate (ASIR) was 33.04 per 100,000. When analyzed by pathological type, the ASIRs for papillary neoplasms, invasive breast carcinoma, rare and salivary gland-type tumors, and other types were 1.13, 29.79, 0.24, and 1.88 per 100,000, respectively. The age-standardized mortality rate (ASMR) was 6.10 per 100,000. A total of 2,628,000 DALYs were found to be attributable to female breast cancer in China, comprising 2,278,300 YLLs and 349,700 YLDs. The ASIR, ASMR, and age-standardized rate (ASR) for DALYs in urban areas were consistently higher than those in rural areas. We observed a four-fold increase in the ASIR and ASR for DALYs and an eight-fold increase in the ASMR among females over 55 years compared with those aged under 55 years.Conclusion::These data provide invaluable insights into the latest epidemiology of female breast cancer in China and highlight the urgency for disease prevention and control strategy formulation.

Aim To explore the effect and mechanism of the artesunate(ART)on hepatocellular carcinoma(HCC).Methods The cell lines MHCC-97H and HCC-LM3 were used to be detected.MTT and clone formation were used to determine the cell proliferation;Wound healing was used to detect the cell migration;Transwell was used to test the cell invasion.Flow-cy-tometry was used to detect cell apoptosis and cell cy-cle.RNA-seq and qRT-PCR was used to detect the genes expression.Results The proliferation,migra-tion and invasion of treated cells were obviously inhibi-ted(P＜0.01).Moreover,the apoptosis rate in-creased significantly,so did the proportion of G2/M cells.Transcriptomic analysis identified GADD45A as a potential target of ART through RNA-sequencing da-ta,and suggested that ART might induce apoptosis and cell cycle arrest through regulating the expression of GADD45A.In addition,the results of mechanism studies and signaling analysis suggested that GADD45A had interaction with its upstream gene NACC1(nucle-us accumbens associated 1).Moreover,after ART treatment,the expressions of GADD45A and NACC1 were changed significantly.Conclusion ART may be a potential drug to resist HCC by affecting the expres-sion of GADD45A and its upstream gene NACC1,which provides a new drug,a new direction and a new method for the clinical treatment of HCC.

Objective:To explore the application effect of nursing intervention based on the mindfulness-attention-acceptance theory in children with bronchopneumonia.Methods:Totally 127 children with bronchopneumonia admitted to the First Affiliated Hospital of Xinjiang Medical University from January to July 2023 were selected by convenience sampling. They were randomly divided into a control group and an observation group according to a random number table. The control group (63 cases) received routine nursing intervention, while the observation group (64 cases) received nursing intervention based on the mindfulness-attention-acceptance theory in addition to the routine nursing intervention. The improvement in psychological resilience, coping styles, and emotional behaviors of the two groups were compared before and after the intervention.Results:After the intervention, the scores of all dimensions of the Psychological Resilience Scale in both groups were higher than that before the intervention, with the observation group scoring higher than the control group ( P<0.05). The observation group scored higher on all dimensions of positive coping and lower on all dimensions of negative coping compared to the control group ( P<0.05). The prosocial behavior dimension score of the observation group was higher than that of the control group, while the scores of emotional symptoms, conduct problems, and hyperactivity-inattention dimensions were lower than those of the control group ( P<0.05) . Conclusions:The application of nursing intervention based on the mindfulness-attention-acceptance theory in children with bronchopneumonia can effectively improve their psychological resilience, coping styles, and emotional behaviors.

Objective:To detect the pharmacokinetic(PK)parameters of coagulation factor Ⅷ(FⅧ)in adult patients with severe hemophilia A,identify the potential factors influencing FⅧ PK,and optimize the use of FⅧ in individual prophylaxis regimens.Methods:PK characteristics of FⅧ were studied in a total of 23 severe hemophilia A adults.The correlation of patients'characteristics including age,von Willebrand factor antigen(vWF:Ag),blood group,weight,body mass index(BMI)and FⅧ genotype,with FⅧ PK were evaluated.Individual prophylaxis regimens were given based on FⅧ PK parameters.Results:The mean terminal half-life(t1/2)of FⅧ was 20.6±9.3 h,ranged from 11.47 h to 30.12 h.The age(r=0.580)and vWF:Ag(r=0.814)were significantly positively correlated with t1/2 of FⅧ.The mean area under the plasma concentration curve(AUC)of FⅧ was 913±399(328-1 878)IU h/dl,and the AUC of FⅧ was positively correlated with age(r=0.557)and vWF:Ag(r=0.784).The mean residence time(MRT)of FⅧ was 24.7±12.4(13.2-62.2)h,and the MRT of FⅧ was positively correlated with age(r=0.664)and vWF:Ag(r=0.868).The mean in vivo recovery(IVR)of FⅧ was 2.59±0.888(1.5-4.29)IU/dl per IU/kg,the mean clearance(CL)of FⅧ was 3±1.58(0.97-7.18)ml/(kg·h),and there was no significant correlation of IVR and CL with age and vWF:Ag.According to the individual PK parameters,ultra low-dose,low-dose and moderate-dose FⅧ were applied to 15,6,2 adults patients with severe hemophilia A for prophylaxis,respectively.Conclusion:There are significant individual differences in the FⅧ half-life of adult patients with severe hemophilia A.The older the patient,the higher the vWF:Ag level,and the longer the FⅧ half-life.Individual administration is required based on the FⅧ PK parameters to optimize prophylaxis treatment.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective To develop and validate a fatigue risk nomogram model in Chronic Obstructive Pulmonary Disease(COPD)patients.Methods A prospective study design was adopted,and 430 COPD patients recruited from a tertiary A hospital in Huzhou City from January to December 2022 were conveniently selected for model construction,and 129 patients were recruited from the same hospital from January to June 2023 for external validation of the model.The general information questionnaire,Pittsburgh Sleep Quality Index,2-item Generalized Anxiety Disorder Scale,2-item Patient Health Questionnaire,modified British Medical Research Council Dyspnea Index,International Physical Activity Questionnaire,and Fatigue Severity Scale were used for questionnaire survey.The risk prediction model and nomograms model were constructed using Logistic regression analysis and R 4.3.2 software,and the area under the receiver operating characteristic(ROC)curve was used to test the prediction effect of the model.Results Univariate and binary logistic regression analysis results showed that age(OR=1.095),gender(OR=2.077),dyspnea(OR=3.309),sleep quality(OR=1.979),anemia(OR=3.289),the number of acute exacerbation(OR=2.991)were independent influencing factors for fatigue in COPD patients.The internal evaluation and external validation results of the model showed that the areas under the curve are 0.912 and 0.844 respectively,and the Hosmer-Lemeshow goodness of fit test P values were 0.806 and 0.526 respectively.The average absolute errors were 0.013 and 0.019 respectively.Conclusion The COPD fatigue risk prediction model constructed in this study has good prediction effect.The visual nomogram is intuitive,convenient and easy to operate.It can provide a tool for early screening of fatigue in COPD patients.

AIM To evaluate the quality of Yanyangke Mixture.METHODS The HPLC fingerprints were established,after which cluster analysis,principal component analysis and partial least squares discriminant analysis were performed.The contents of liquiritin,rosmarinic acid,sheganoside,irisgenin,honokiol,monoammonium glycyrrhizinate,irisflorentin,isoliquiritin and magnolol were determined,the analysis was performed on a 35 ℃ thermostatic Agilent ZORBAX SB-C18 column(5 μm,250 mmx4.6 mm),with the mobile phase comprising of 0.1％phosphoric acid-acetonitrile flowing at 1 mL/min in a gradient elution manner,and multi-wavelength detection was adopted.RESULTS There were ten common peaks in the fingerprints for twelve batches of samples with the similarities of more than 0.9.Various batches of samples were clustered into three types,three principal components displayed the acumulative variance contribution rate of 87.448％,peaks 5、14(honokiol),3(liquiritin),11(monoammonium glycyrrhizinate)and 15(asarinin)were quality markers.Nine constituents showed good linear relationships within their own ranges(r＞0.999 0),whose average recoveries were 98.5％-103.6％with the RSDs of 0.92％-1.7％.CONCLUSION This stable and reliable method can provide a basis for the quality control of Yanyangke Mixture.

Objective To clarify the understanding of types of laboratories and manufacturers for GB 9706.1-2020 Medical electrical equipment-Part 1:General requirements for basic safety and essential performance by laboratory proficiency testing for creepage distance and electrical clearance test.Methods An operation guide was formed according to the testing program in GB 9706.1-2020,and the homogeneity and stability of the samples were evaluated according to CNAS-GL003:2018 Guidance on Evaluating the Homogenneity and Stability of Samples Used for Proficiency Testing.Robust statistic methods were used to assess the quantitative parameters of the test results of the participating laboratories according to the requirements in GB/T 28043-2019 Statistical methods for use in proficiency testing by interlaboratory comparison;the results reported by the expert laboratories were used as the specified values of the qualitative parameters.SPSS 25.0 statistical software was used for data analysis.Results All the results of the crreepage distance and electrical clearance tests met the requirements for homogeneity and stability.Of the 46 laboratories involved in,37 ones did have comprehensive satisfactory determinations while the remained 9 ones not.Conclusion Some laboratories don't behave well in understanding the standard,which have to be reformed accordingly to enhance their proficiencies.[Chinese Medical Equipment Journal,2024,45(10):54-59]

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.