Objective To investigate the value of combination of echocardiographic(ECG)parameters and geriatric nutritional risk index(GNRI)in predicting the prognosis of elderly chronic heart failure(CHF).Methods A retrospective study was conducted on 102 elderly CHF patients admitted to our hospital from February 2023 to July 2024.According to their clinical outcomes after 6 months of follow-up,they were divided into a good prognosis group(67 cases)and a poor prognosis group(35 cases).The clinical data,such as left ventricular ejection fraction(LVEF),left ventricular end-diastolic inner diameter(LVEDD)and left ventricular end-systolic inner diameter(LVESD),and GNRI were compared between two groups.Logistic regression analysis was adopted to identify the influencing factors of prognosis of elderly CHF patients,and ROC curve was plotted to analyze the predictive efficiency of ECG parameters and GNRI for the prognosis.Results The poor prognosis group exhibited significantly advanced age,higher LVEDD and LVESD,longer course of CHF,and lower LVEF and GNRI in comparison with the good prognosis group(P＜0.01).Multivariate logistic regression analysis indicated that LVEF,LVEDD,LVESD and GNRI were identified as influencing factors for elderly CHF prognosis(P＜0.05,P＜0.01).In evaluating the poor prognosis,LVEF,LVEDD,LVESD,GNRI and their combination presented an AUC value of 0.854,0.773,0.785,0.850 and 0.902,respectively.The evaluation efficiency of combined detection was superior to that of single detection in assessing the poor prognosis in elderly CHF patients(P＜0.01).Conclusion ECG parameters and GNRI are related to the prognosis in elderly CHF patients,and combined detection has good evaluation efficiency for the poor prognosis.

Objective: To evaluate the suitability of the existing hemolysis rate standards for locally processed red blood cell components by retrospectively analyzing 5-year hemolysis rate data at the end of storage. Methods: A total of 720 blood samples of three types of red blood cell components from our blood station from January 2019 to December 2023 were collected. Parameters included hemoglobin concentration (Hb), hematocrit (Hct), and free hemoglobin concentration (fHb). Hemolysis rate were taken as the control standard of 0.8% in accordance with the national standard. The hemolysis rates were compared against the national standard threshold of 0.8% (GB18469-2012), and annual trends of the detection parameters were observed. Results: The hemolysis rates (x-+s,%) of leukocyte-depleted whole blood at the end of storage were (0.038±0.023 8) in 2019, (0.049±0.039 5) in 2020, (0.043±0.040 7) in 2021, (0.049±0.030 7) in 2022, and (0.058±0.054 8) in 2023, respectively; The hemolysis rates (x-+s" />,%) of leukocyte-depleted suspended red blood cells at the end of storage were (0.093±0.050 2) in 2019, (0.086±0.049 5) in 2020, (0.123±0.072 3) in 2021, (0.122±0.052 1) in 2022, and (0.106±0.058 6) in 2023, respectively; The hemolysis rates (x-+s,%) of washed red blood cells at the end of storage were (0.127±0.038 2) in 2019, (0.150±0.066 5) in 2020, (0.121±0.052 2) in 2021, (0.124±0.038 9) in 2022, and (0.128±0.044 3) in 2023, respectively. Conclusion: Hemolysis rates at the end of blood storage of three red blood cell components were significantly lower than the limits specified in Quality Requirements for Whole Blood and Components (GB18469-2012), as well as standards from the EU, AABB and the United States. The results demonstrate excellent product quality control. A regional internal control standard of <0.2% is proposed for hemolysis rates at the end of storage.

Objective:To assess the current level of acceptance of generative artificial intelligence (GAI) among maternal and infant care providers and to explore the factors influencing their acceptance.Methods:A convenience sampling method was used to survey 164 maternal and infant care providers from Beijing Hospital and China-Japan Friendship Hospital between March and May 2024. The survey instruments included a general information questionnaire, GAI Acceptance among Maternal and Infant Care Providers Scale, and Self-assessment Scales for Digital Technology Awareness and Self-assessment Scales for Digital Competence. Multiple linear regression analysis was conducted to identify influencing factors.Results:The total acceptance score of GAI among 164 participants was [181 (158, 200) ]. Multiple linear regression analysis showed that interest in GAI, digital technology awareness, and digital competence were significant influencing factors for GAI acceptance, with statistically significant differences ( P<0.05) . Conclusions:The level of GAI acceptance among maternal and infant care providers was relatively high. It is recommended to strengthen the promotion and training on the application and precautions of GAI, improve their digital competence and professional literacy, while fostering critical and independent thinking, to enhance the integration of GAI into maternal and infant care services.

Objective:To assess the current level of acceptance of generative artificial intelligence (GAI) among maternal and infant care providers and to explore the factors influencing their acceptance.Methods:A convenience sampling method was used to survey 164 maternal and infant care providers from Beijing Hospital and China-Japan Friendship Hospital between March and May 2024. The survey instruments included a general information questionnaire, GAI Acceptance among Maternal and Infant Care Providers Scale, and Self-assessment Scales for Digital Technology Awareness and Self-assessment Scales for Digital Competence. Multiple linear regression analysis was conducted to identify influencing factors.Results:The total acceptance score of GAI among 164 participants was [181 (158, 200) ]. Multiple linear regression analysis showed that interest in GAI, digital technology awareness, and digital competence were significant influencing factors for GAI acceptance, with statistically significant differences ( P<0.05) . Conclusions:The level of GAI acceptance among maternal and infant care providers was relatively high. It is recommended to strengthen the promotion and training on the application and precautions of GAI, improve their digital competence and professional literacy, while fostering critical and independent thinking, to enhance the integration of GAI into maternal and infant care services.

Objective To investigate the value of combination of echocardiographic(ECG)parameters and geriatric nutritional risk index(GNRI)in predicting the prognosis of elderly chronic heart failure(CHF).Methods A retrospective study was conducted on 102 elderly CHF patients admitted to our hospital from February 2023 to July 2024.According to their clinical outcomes after 6 months of follow-up,they were divided into a good prognosis group(67 cases)and a poor prognosis group(35 cases).The clinical data,such as left ventricular ejection fraction(LVEF),left ventricular end-diastolic inner diameter(LVEDD)and left ventricular end-systolic inner diameter(LVESD),and GNRI were compared between two groups.Logistic regression analysis was adopted to identify the influencing factors of prognosis of elderly CHF patients,and ROC curve was plotted to analyze the predictive efficiency of ECG parameters and GNRI for the prognosis.Results The poor prognosis group exhibited significantly advanced age,higher LVEDD and LVESD,longer course of CHF,and lower LVEF and GNRI in comparison with the good prognosis group(P＜0.01).Multivariate logistic regression analysis indicated that LVEF,LVEDD,LVESD and GNRI were identified as influencing factors for elderly CHF prognosis(P＜0.05,P＜0.01).In evaluating the poor prognosis,LVEF,LVEDD,LVESD,GNRI and their combination presented an AUC value of 0.854,0.773,0.785,0.850 and 0.902,respectively.The evaluation efficiency of combined detection was superior to that of single detection in assessing the poor prognosis in elderly CHF patients(P＜0.01).Conclusion ECG parameters and GNRI are related to the prognosis in elderly CHF patients,and combined detection has good evaluation efficiency for the poor prognosis.

Objective: To investigate the relationship among depression, anxiety, stress and addictive substance use behavior in secondary vocational students. Methods: Cluster sampling method and the Adolescent Health-related Behaviors Questionnaire were used to collect demographic characteristics, psychological symptoms, and addictive substance usage among 5 935 students in nine vocational schools in Chongqing, Zhaoqing, Ningbo, and Taiyuan. Multivariate logistic regression analysis was used to analyze the relationship between the addictive substance use behavior and psychological factors. Results: The detection rates of depression, anxiety and stress were 46.5% (n=2 762), 58.7% (n=3 483), and 29.8% (n= 1 770), respectively. The prevalence of addictive substances was 74.8% (n=4 440), traditional drugs was 0.8% (n=50), new drugs was 2.8% (n=166), other addictive drugs was 4.1% (n=241). Multivariate logistic regression analysis showed that compared with the normal psychological states of secondary vocational students, the OR value of mild depression tendency alcohol and tobacco use behavior of secondary vocational students was 1.45; the OR values of mild anxiety, moderate anxiety, severe anxiety and very serious anxiety were 1.46, 1.46, 1.71, and 1.83, respectively; the traditional drugs use behaviors were 5.51, and 2.61, respectively, for the severe anxiety and very serious anxiety. Compared with the normal psychological state of secondary vocational students, the OR values of the severe anxiety and very severe anxiety were 2.56, and 2.66, respectively, for severe anxiety and very serious anxiety. Compared with normal psychological status of secondary vocational students, the OR values of mild, moderate, severe, and very severe anxiety were 2.14, 2.47, 2.39, and 3.45, respectively; all P values <0.05. Conclusion Anxiety and mild depression were risk factors of tobacco and alcohol use in secondary vocational students; severe and above anxiety were the risk factors of drug use in secondary vocational students; anxiety was the risk factor for other addictive drug use in secondary vocational students.

Objective To evaluate the method, clinical efficacy and safety of one phase treat-ment of renal calculi associated with pyonephrosis by percutaneous nephrolithotripsy(PCNL) by pneu-matic combined with ultrasonic lithotriptor. Methods Sixty-six cases of renal calculi accompanied with pyonephrosis were treated with PCNL. The renal calyx was punctured under ultrasound gui-dance, then the tract was dilated from F8 to F16 by peel-away vascular access sheathes. After the in-sertion of the flexible sheath, metallic dilator was inserted and the flexible sheath was pulled out. The tract was dilated by metallic sheath to F21 and the operation sheath and nephroseope were placed into working tract. EMS III LithoClast Master was used. Ultrasonic powered lithotriptor probe with suc-tion was used to clear the liquor puris and calculus fragments with low-pressure or no-pressure. The combined pneumatic and ultrasonic powered lithotriptor was used to break and clear the calculi. Re-salts Of the 66 cases, there was no bacteremia or pyaemia intraoperatively and postoperatively. And there was no other severe complication occurred intraoperatively. One phase PCNL was successfully completed in 60 cases. Other 4 cases had residual calculi less than 1.5 em in diameter and received ESWL to break the calculi, 2 cases had bigger residual calculi and accepted second PCNL 1 week after the first intervention. In the follow-up period, the 3 month post-operative serum Cr was 56-203 μmol/L with an average decrease of 40 μmol/L, GFR was 5.0-56.2 ml/min with an average increase of 23.6 ml/min compared with the pre-operative data. At 6 months postoperative serum Cr was 56-158 μmol/L with average decrease of 31 μmol/L, GFR was 5.0-79.2 ml/min with an average in-crease of 30.2 ml/min. Conclusion Application of PCNL in the treatment of patients with renal cal-culi accompanied with pyonephrosis is safe, cost-effective and clinically efficient by pneumatic com-bined with ultrasonic lithotriptor.