Background With China's socio-economic development, the dietary structure of Chinese residents has gradually shifted from a traditional Eastern pattern characterized by high carbohydrate intake to a relatively high-fat Western dietary model, alongside a growing burden of chronic diseases. However, dietary changes may vary across different occupational groups. Objective To analyze the long-term trends in dietary energy and three major macronutrient intake among various occupational groups aged 18-59 years in nine provinces of China from 1989 to 2018, providing a scientific basis for developing occupation-specific dietary intervention strategies. Methods Based on 11 waves of data (1989–2018) from the China Health and Nutrition Survey (CHNS), 57458 employed individuals aged 18-59 years were selected as observation subjects after applying inclusion and exclusion criteria. Joinpoint regression was used to analyze temporal trends by calculating the annual percentage change (APC) and average annual percentage change (AAPC) with 95% confidence intervals. The analysis covered the energy supply ratio from the three major macronutrients and the proportion of the participants within the acceptable macronutrient distribution ranges (AMDR) for the total occupational population and specific occupational group. Results From 1989 to 2018, the dietary structure of the occupational population in the nine provinces gradually Westernized. The carbohydrate energy supply ratio showed a decreasing trend (AAPC=−0.77%, 95%CI: −1.00%, −0.55%, P<0.05), while the fat energy supply ratio increased (AAPC=1.42%, 95%CI: 1.07%, 1.77%, P<0.05). The protein energy supply ratio remained largely unchanged (AAPC=−0.19%, 95%CI: −0.08%, 0.46%, P>0.05), and the total energy intake declined (AAPC=−0.62%, 95%CI: −0.94%, −0.30%, P<0.05). Among the occupational groups studied, white-collar workers were the first to show characteristics of dietary Westernization. Using the AMDR as the criterion, their average fat energy supply ratio first exceeded the AMDR upper limit (30%) in 1993, and their average carbohydrate energy supply ratio first fell below the AMDR lower limit (50%) in 2006, both time points being earlier than other occupational groups. Agricultural workers lagged in this transition (their average fat energy supply ratio first reached ≥30% in 2011), but their rate of increase in the fat energy supply ratio was the fastest among all occupational groups (AAPC=1.59%, 95%CI: 0.90%, 2.30%, P<0.05; P for trend comparison between groups<0.05). The other three occupational groups (blue-collar workers, service workers, and others) generally fell between these two extremes. Taking the fat energy supply ratio as an example, their AAPCs of increase were 1.02% (95%CI: 0.73%, 1.32%, P<0.05) for blue-collar workers, 0.85% (95%CI: 0.61%, 1.09%, P<0.05) for service workers, and 0.88% (95%CI: 0.58%, 1.17%, P<0.05) for others. Conclusion From 1989 to 2018, the macronutrient structure of the occupational population aged 18-59 years in nine China provinces is gradually Westernized. The energy supply ratios of carbohydrates and proteins primarily face issues of potential inadequacy, while the fat energy supply ratio is excessively high. Furthermore, the patterns of change vary among different occupational groups. It is recommended to propose personalized dietary advice tailored to specific occupations.

Objective To develop a deep learning model based on fundus retinal images to improve the detection rate of mild cognitive impairment(MCI)in patients with coronary heart disease,achieve early intervention and improve prognosis.Methods The study was a single-center cross-sectional study that retrospectively included patients diagnosed with coronary heart disease(CHD)by coronary angiography(≥50％ stenosis of at least one coronary vessel)from Beijing Anzhen Hospital between November 2021 and December 2022.The whole data set was randomly divided into the training set and the testing set according to the ratio of 8∶2 for model development.After that,the patient data of the same center from January 2023 to April 2023 were included in the time verification method to verify the model.The diagnostic criteria for MCI were MMSE＜27 or MoCA＜26.Four kinds of convolutional neural network(CNN)architectures were used to train fundus images,and a comprehensive vision model of MCI detection was established through model integration.The area under the curve(AUC),sensitivity and specificity of the receiver operating curve(ROC)were used to evaluate the performance of the AI model.Results We collected 5 880 eligible fundus images from 3 368 CHD patients.Based on the results of the MMSE scale,the algorithm was labeled,including 2 898 males and 527 MCI patients.The AUC of the deep learning model in the test group is 0.733(95％CI 0.688-0.778),and the sensitivity of the algorithm in the test group is 0.577(95％CI 0.528-0.625)by using the operating point with the maximum sum of sensitivity and specificity.With a specificity of 0.758(95％CI 0.714-0.802),corresponding to a validated AUC of 0.710(95％CI 0.601-0.818).Based on the results of the MoCA scale,the algorithm labels 2 437 males and 1 626 MCI patients.The AUC of the deep learning model in the test group was 0.702(95％CI 0.671-0.733).The operating point with the maximum sum of sensitivity and specificity was selected,and the sensitivity of the algorithm was 0.749(95％CI 0.719-0.778)and the specificity was 0.561(95％CI 0.527-0.595),corresponding to the AUC value of the verification group was 0.674(95％CI 0.622-0.726).Conclusions The deep learning algorithm model based on fundus images has good diagnostic performance,and may be used as a new non-invasive,convenient and rapid screening method for MCI in CHD population.

Objective:To explore the diagnostic value of blood flow parameters of carotid ultrasound combined with serum Sortilin and paraoxonase-1 (PON-1) for stability of carotid atherosclerotic plaque (CAP). Methods:A total of 100 CAP patients who admitted to Rugao Hospital of Traditional Chinese Medicine from January 2021 to December 2023 were selected,and they were divided into a stable plaque group (n=54) and a unstable plaque group (n=46). Enzyme linked immunosorbent assay (ELISA) was adopted to determine the expression levels of serum Sortilin and PON-1 of patients in two groups. Multi factor logistic regression was implemented to analyze the influencing factors on CAP stability. The receiver operating characteristic (ROC) curves were drawn to analyze the diagnostic efficiency of blood flow parameters of carotid ultrasound combined with Sortilin and PON-1 on CAP stability. Results:Compared with the end diastolic blood flow velocity (EDV) and intima-media thickness (IMT) of stable plaque group,unstable plaque group showed increased EDV and IMT,and decreased PSV,and the differences were significant (t=7.536,4.680,5.487,P<0.05),respectively. Compared with stable plaque group,the serum Sortilin level in unstable plaque group significantly increased,while the PON-1 level significantly decreased,and the differences of them were significant (t=8.362,5.581,P<0.05),respectively. Multivariate logistic analysis found that Sortilin was a risk factor that affected CAP stability (OR=3.165,P<0.05),while PON-1 was a protective factor that affected CAP stability (OR=0.765,P<0.05). ROC curve analysis indicated that the area under curve (AUC) values of single Sortilin,PON-1,EDV,IMT,PSV,and the combination of them were respectively 0.788 (95％CI:0.695-0.864),0.814 (95％CI:0.724-0.885),0.847 (95％ CI:0.761-0.911),0.767 (95％ CI:0.672-0.846),0.707 (95％ CI:0.607-0.793) and 0.963 (95％ CI:0.839-0.907). The AUC value of the combination of above 5 indicators was highest,which was better than that of each single indicator,and its sensitivity and specificity were respectively 95.65％ and 83.33％. Conclusion:The EDV,IMT and PSV of blood flow parameters of ultrasound can provide CAP information of morphology and hemodynamics,and serum Sortilin level and PON-1 level can reflect the biological processes of formation and development of CAP. The combined detection of the five indicators can better diagnose CAP stability.

Objective To understand the nursing practice of prone position ventilation for patients with moderate to severe acute respiratory distress syndrome(ARDS)in intensive care unit(ICU)in Shandong province,so as to provide basis for standardizing the nursing practice process of prone position ventilation and carrying out training for hospitals.Methods A self-made questionnaire was used,and convenience sampling was adopted.From September 15th to November 5th,2023,ICU nurses were selected from various hospital levels in Shandong province to investigate the obstructive factors of prone ventilation implementation,the weak links in nursing practice and status,and the occurrence of complications.Results A total of 1 188 questionnaires were collected,of which 991 were valid.92.8%(920/991)of nurses had performed prone position ventilation.The biggest obstacle to the implementation of prone position ventilation was the complexity of patient treatments and multiple devices involved[74.6%(686/920)].Regarding the status of training,90.5%(897/991)of nurses had received training on prone position ventilation and 77.0%(763/991)of nurses felt that training was needed.As for pre-operation assessment,more than 80.0%of nurses evaluated patients'vital signs,airway and secretions and so on,among which the evaluation awareness of analgesia was the worst[81.6%(751/920)].As for the main points of implementation,only 14.0%(129/920)of nurses chose the opposite side of the most important pipeline as the turning direction;48.6%(447/920)of nurses chose the anti-Trendelenburg position;36.3%(334/920)of nurses chose to ventilate≥12 hours.Facial edema[81.7%(752/920)],skin pressure injury[78.9%(726/920)]and eye complication[75.8%(697/920)]were the top 3 most frequent complications.Conclusions ICU nurses'prone position ventilation practices were generally line with the nursing team standard for prone position of adult mechanically ventilated patients and the best evidence recommendation,and needs to be further standardized in aspects of turning direction,position management,ventilation duration,and enteral nutrition management.It is recommended that nursing managers at all levels of hospitals further improve the quality of nursing practice of prone position ventilation according to relevant evidence-based evidence and the actual situation of hospitals.

Objective To develop a deep learning model based on fundus retinal images to improve the detection rate of mild cognitive impairment(MCI)in patients with coronary heart disease,achieve early intervention and improve prognosis.Methods The study was a single-center cross-sectional study that retrospectively included patients diagnosed with coronary heart disease(CHD)by coronary angiography(≥50％ stenosis of at least one coronary vessel)from Beijing Anzhen Hospital between November 2021 and December 2022.The whole data set was randomly divided into the training set and the testing set according to the ratio of 8∶2 for model development.After that,the patient data of the same center from January 2023 to April 2023 were included in the time verification method to verify the model.The diagnostic criteria for MCI were MMSE＜27 or MoCA＜26.Four kinds of convolutional neural network(CNN)architectures were used to train fundus images,and a comprehensive vision model of MCI detection was established through model integration.The area under the curve(AUC),sensitivity and specificity of the receiver operating curve(ROC)were used to evaluate the performance of the AI model.Results We collected 5 880 eligible fundus images from 3 368 CHD patients.Based on the results of the MMSE scale,the algorithm was labeled,including 2 898 males and 527 MCI patients.The AUC of the deep learning model in the test group is 0.733(95％CI 0.688-0.778),and the sensitivity of the algorithm in the test group is 0.577(95％CI 0.528-0.625)by using the operating point with the maximum sum of sensitivity and specificity.With a specificity of 0.758(95％CI 0.714-0.802),corresponding to a validated AUC of 0.710(95％CI 0.601-0.818).Based on the results of the MoCA scale,the algorithm labels 2 437 males and 1 626 MCI patients.The AUC of the deep learning model in the test group was 0.702(95％CI 0.671-0.733).The operating point with the maximum sum of sensitivity and specificity was selected,and the sensitivity of the algorithm was 0.749(95％CI 0.719-0.778)and the specificity was 0.561(95％CI 0.527-0.595),corresponding to the AUC value of the verification group was 0.674(95％CI 0.622-0.726).Conclusions The deep learning algorithm model based on fundus images has good diagnostic performance,and may be used as a new non-invasive,convenient and rapid screening method for MCI in CHD population.

Prostate cancer is one of the most common male urological malignancies,in which bone metastasis of desmo-plasia-resistant prostate cancer is an important stage in the progression of the disease,which seriously affects the quality of life and survival of patients.With the development of nuclide therapy technology in recent years,223-Ra,as a new type of alpha-targeted therapy,has shown good efficacy in the treatment of desmoplasia-resistant prostate cancer bone metastasis.The purpose of this pa-per is to review the characteristics,mechanism of action,treatment,and the main research results of its treatment of desmoplasia-resistant prostate cancer bone metastasis,and provide a comprehensive review of the clinical application of 223-Ra in the treatment of desmoplasia-resistant prostate cancer bone metastasis for the clinical application of 223-Ra in prostate cancer bone metastasis.

African swine fever(ASF)is an acute,febrile,and highly fatal disease caused by African swine fever virus(ASFV)in pigs.Given the current lack of commercial vaccines and the continu-ous evolution of ASFV in recent years,the emergence of moderately virulent genotype Ⅱ strains and the introduction of genotype Ⅰ attenuated strains have led to persistent and chronic infections in pigs.Therefore,the detection of specific antibodies against ASFV has become imperative.In this study,we established a competitive chemiluminescence immunoassay(p30-cCLIA)for detecting ASFV p30 antibodies using p30 monoclonal antibodies.By detecting sera with clear negative and positive backgrounds,we determined that the Cut-off value of this method was 50％,with both di-agnostic sensitivity(Dsn)and diagnostic specificity(Dsp)reaching 100％.Under optimal reaction conditions,we screened out an enzyme-labeled stabilizer suitable for p30 monoclonal antibody 16-5E7E8-HRP.Furthermore,the sensitivity of the established p30-cCLIA method was higher than that of the commercial blocking ELISA kit(1∶2 048 vs 1∶512)and exhibited good repeatability.Detection of sera positive for other porcine virus infections showed no cross-reactivity.The estab-lishment of this method provides a powerful tool for early diagnosis of ASF.

Objective To introduce how to import and analyze data using the Research Electronic Data Capture(REDCap)system,taking a multi-center randomized controlled clinical research of total knee arthroplasty as an example.Methods Various tools within the REDCap system,including data import tools,data export functions,reports and statistics,project dashboards,and coding manuals,were used to systematically process and analyze the multi-center randomized controlled clinical trial data for total knee arthroplasty.Initially,electronically collected clinical data were adjusted and standardized,then uploaded in bulk to the system using the REDCap data import tool.Subsequently,the data were organized through REDCap's data export feature,and basic descriptive statistical analysis was performed using its reporting and statistical functions to ensure data quality and completeness.Results An electronic data collection and management platform for clinical research on knee osteoarthritis wase successfully created by the REDCap system.The platform enabled real-time data collection from multiple centers,and ensured data accuracy and consistency through built-in data management and quality control mechanisms.With the statistical analysis features of REDCap,the research team could monitor the progress of data in real time,conduct effective quality assessments,and perform dynamic analysis for further in-depth statistical evaluations.Conclusion The REDCap system can be used not only to build a new clinical research project,but also to import and analyze data that has been previously digitized of ongoing clinical researches into the system,which improved the scientificity of data management and research efficiency.

Objective:To explore the diagnostic value of blood flow parameters of carotid ultrasound combined with serum Sortilin and paraoxonase-1 (PON-1) for stability of carotid atherosclerotic plaque (CAP). Methods:A total of 100 CAP patients who admitted to Rugao Hospital of Traditional Chinese Medicine from January 2021 to December 2023 were selected,and they were divided into a stable plaque group (n=54) and a unstable plaque group (n=46). Enzyme linked immunosorbent assay (ELISA) was adopted to determine the expression levels of serum Sortilin and PON-1 of patients in two groups. Multi factor logistic regression was implemented to analyze the influencing factors on CAP stability. The receiver operating characteristic (ROC) curves were drawn to analyze the diagnostic efficiency of blood flow parameters of carotid ultrasound combined with Sortilin and PON-1 on CAP stability. Results:Compared with the end diastolic blood flow velocity (EDV) and intima-media thickness (IMT) of stable plaque group,unstable plaque group showed increased EDV and IMT,and decreased PSV,and the differences were significant (t=7.536,4.680,5.487,P<0.05),respectively. Compared with stable plaque group,the serum Sortilin level in unstable plaque group significantly increased,while the PON-1 level significantly decreased,and the differences of them were significant (t=8.362,5.581,P<0.05),respectively. Multivariate logistic analysis found that Sortilin was a risk factor that affected CAP stability (OR=3.165,P<0.05),while PON-1 was a protective factor that affected CAP stability (OR=0.765,P<0.05). ROC curve analysis indicated that the area under curve (AUC) values of single Sortilin,PON-1,EDV,IMT,PSV,and the combination of them were respectively 0.788 (95％CI:0.695-0.864),0.814 (95％CI:0.724-0.885),0.847 (95％ CI:0.761-0.911),0.767 (95％ CI:0.672-0.846),0.707 (95％ CI:0.607-0.793) and 0.963 (95％ CI:0.839-0.907). The AUC value of the combination of above 5 indicators was highest,which was better than that of each single indicator,and its sensitivity and specificity were respectively 95.65％ and 83.33％. Conclusion:The EDV,IMT and PSV of blood flow parameters of ultrasound can provide CAP information of morphology and hemodynamics,and serum Sortilin level and PON-1 level can reflect the biological processes of formation and development of CAP. The combined detection of the five indicators can better diagnose CAP stability.

To analyze the standard establishment approach and compilation rationale for metallic pharmaceutical packaging standard development in the 2025 edition of the Pharmacopeia of the People's Republic of China.This article systematically explained the background and process of establishing the guiding principles for metallic materials and containers used in pharmaceutical packaging in the Chinese Pharmacopoeia through basic information,relevant domestic and international standards,the establishment of key quality attributes of metallic pharmaceutical packaging materials,and the construction of metallic pharmaceutical packaging material standards.The newly established guidelines,the Pharmacopeia of the People's Republic of China 9625,prioritized product critical quality attributes(CQAs)and real-world applicability.This dual emphasis on rigidity and adaptability enhances drug safety,meets the regulatory requirements,and promotes the globalization and scientific advancement of China's pharmaceutical packaging industry.