Debates persist regarding the efficacy and safety of azvudine, particularly its real-world outcomes. This study involved patients aged ≥60 years who were admitted to 25 hospitals in mainland China with confirmed SARS-CoV-2 infection between December 1, 2022, and February 28, 2023. Efficacy outcomes were all-cause mortality during hospitalization, the proportion of patients discharged with recovery, time to nucleic acid-negative conversion (T _NANC), time to symptom improvement (T _SI), and time of hospital stay (T _HS). Safety was also assessed. Among the 5884 participants identified, 1999 received azvudine, and 1999 matched controls were included after exclusion and propensity score matching. Azvudine recipients exhibited lower all-cause mortality compared with controls in the overall population (13.3% vs. 17.1%, RR, 0.78; 95% CI, 0.67-0.90; P = 0.001) and in the severe subgroup (25.7% vs. 33.7%; RR, 0.76; 95% CI, 0.66-0.88; P < 0.001). A higher proportion of patients discharged with recovery, and a shorter T _NANC were associated with azvudine recipients, especially in the severe subgroup. The incidence of adverse events in azvudine recipients was comparable to that in the control group (2.3% vs. 1.7%, P = 0.170). In conclusion, azvudine showed efficacy and safety in older patients hospitalized with COVID-19 during the SARS-CoV-2 omicron wave in China.

Pregnancy with chronic kidney disease (CKD), particularly in stages 4-5, carries high risks of adverse outcomes including maternal renal failure, preeclampsia/eclampsia, fetal growth restriction, and preterm birth. This report described a 26-year-old woman with congenital solitary kidney, polycystic ovaries, and uterine septum due to PAX2 gene variant, complicated by CKD stage 4. Through multidisciplinary team precision management and individualized treatment strategies, including timely initiation of dialysis, the patient successfully maintained pregnancy until 34 +1 weeks and delivered a female infant via cesarean section. This case summarizes key management experiences for end-stage renal disease in pregnancy, highlighting early risk assessment, precise nutritional management, hemodialysis protocol optimization, and the crucial role of multidisciplinary collaboration, providing valuable references for managing CKD-complicated pregnancies.

ln the context of the high-quality development of public hospitals,research management is transitioning from a results-oriented approach to a full-process management model,necessitating the exploration of a governance system that integrates topic guidance,process support,and outcome transformation.Based on the Knowledge-to-Action and SECl model,it analyzes the construction logic and application effectiveness of the nursing research rounds mechanism at a teritary grade A hospital in Hainan Province.Through a"three-tier stratification+dual-track collaboration+full-process closed-loop"design,the mechanism bridges the chain from"clinical problem identification to research path optimization to outcome transformation",significantly enhancing research participation,research plan standardization,and research output.The findings validate the practical value of the research rounds mechanism in promoting knowledge transformation,capability enhancement,and organizational knowledge retention.

ln the context of the high-quality development of public hospitals,research management is transitioning from a results-oriented approach to a full-process management model,necessitating the exploration of a governance system that integrates topic guidance,process support,and outcome transformation.Based on the Knowledge-to-Action and SECl model,it analyzes the construction logic and application effectiveness of the nursing research rounds mechanism at a teritary grade A hospital in Hainan Province.Through a"three-tier stratification+dual-track collaboration+full-process closed-loop"design,the mechanism bridges the chain from"clinical problem identification to research path optimization to outcome transformation",significantly enhancing research participation,research plan standardization,and research output.The findings validate the practical value of the research rounds mechanism in promoting knowledge transformation,capability enhancement,and organizational knowledge retention.

Objective To develop a nomogram-based predictive model for assessing the risk of respiratory distress syndrome(RDS)in premature infants in the high-altitude region of Dali.The predictive performance and clinical applicability of the model will be systematically evaluated to provide evidence-based guidance for the early diagnosis and clinical management of respiratory distress in premature infants.Methods A total of 680 preterm infants admitted to the Dali Maternal and Child Health Hospital between January 2020 and December 2024 were enrolled in the study and randomly divided into a training set(n=476)and a validation set(n=204)at a ratio of 7∶3.Independent predictors were identified through univariate logistic regression and multivariate stepwise regression analyses,and a nomogram model was subsequently developed using R software.The performance of the model,including its discrimination,calibration,stability,and clinical applicability,was evaluated using the receiver operating characteristic curve(ROC),Hosmer-Lemeshow goodness-of-fit test,bootstrap resampling method,and decision curve analysis(DCA).Results The final model incorporated seven independent variables:gestational age,birth weight,Apgar score,blood oxygen saturation,gestational hyperglycemia,prenatal glucocor-ticoid therapy,and maternal history of infection.The areas under the curve(AUCs)for the training and validation sets were 0.88(95%CI:0.84～0.92)and 0.83(95%CI:0.76～0.89),respectively,with all Hosmer-Lemeshow test p-values exceeding 0.05.The bootstrap-corrected AUC was 0.85(95%CI:0.81～0.89).DCA indicated that the model achieved the highest net benefit at a risk threshold range of 10%to 35%.Conclusions This model integrates multiple risk factors associated with the occurrence of RDS in plateau environments,demonstrating robust predictive performance for RDS in preterm infants residing in high-altitude areas such as Dali.It can serve as a valuable tool for risk stratification and clinical decision-making,and may also provide a reference for future multicenter prospective studies.

Objective To develop a nomogram-based predictive model for assessing the risk of respiratory distress syndrome(RDS)in premature infants in the high-altitude region of Dali.The predictive performance and clinical applicability of the model will be systematically evaluated to provide evidence-based guidance for the early diagnosis and clinical management of respiratory distress in premature infants.Methods A total of 680 preterm infants admitted to the Dali Maternal and Child Health Hospital between January 2020 and December 2024 were enrolled in the study and randomly divided into a training set(n=476)and a validation set(n=204)at a ratio of 7∶3.Independent predictors were identified through univariate logistic regression and multivariate stepwise regression analyses,and a nomogram model was subsequently developed using R software.The performance of the model,including its discrimination,calibration,stability,and clinical applicability,was evaluated using the receiver operating characteristic curve(ROC),Hosmer-Lemeshow goodness-of-fit test,bootstrap resampling method,and decision curve analysis(DCA).Results The final model incorporated seven independent variables:gestational age,birth weight,Apgar score,blood oxygen saturation,gestational hyperglycemia,prenatal glucocor-ticoid therapy,and maternal history of infection.The areas under the curve(AUCs)for the training and validation sets were 0.88(95%CI:0.84～0.92)and 0.83(95%CI:0.76～0.89),respectively,with all Hosmer-Lemeshow test p-values exceeding 0.05.The bootstrap-corrected AUC was 0.85(95%CI:0.81～0.89).DCA indicated that the model achieved the highest net benefit at a risk threshold range of 10%to 35%.Conclusions This model integrates multiple risk factors associated with the occurrence of RDS in plateau environments,demonstrating robust predictive performance for RDS in preterm infants residing in high-altitude areas such as Dali.It can serve as a valuable tool for risk stratification and clinical decision-making,and may also provide a reference for future multicenter prospective studies.

Pregnancy with chronic kidney disease (CKD), particularly in stages 4-5, carries high risks of adverse outcomes including maternal renal failure, preeclampsia/eclampsia, fetal growth restriction, and preterm birth. This report described a 26-year-old woman with congenital solitary kidney, polycystic ovaries, and uterine septum due to PAX2 gene variant, complicated by CKD stage 4. Through multidisciplinary team precision management and individualized treatment strategies, including timely initiation of dialysis, the patient successfully maintained pregnancy until 34 +1 weeks and delivered a female infant via cesarean section. This case summarizes key management experiences for end-stage renal disease in pregnancy, highlighting early risk assessment, precise nutritional management, hemodialysis protocol optimization, and the crucial role of multidisciplinary collaboration, providing valuable references for managing CKD-complicated pregnancies.

Objective To establish an HPLC fingerprint of Dingdang Qigui Decoction and analyze and evaluate it using chemical pattern recognition technology;To determine the contents of 5 effective chemical components in Dingdang Qigui Decoction;To provide a basis for its quality control.Methods The analysis was performed on Agilent 5 TC-C18(2)column(250 mm×4.6 mm).The mobile phase comprised of acetonitrile-0.1%phosphoric acid aqueous solution with the gradient elution at a flow rate of 1.0 mL/min.The detection wavelength was set at 260 nm.The column temperature was maintained at 30℃and the injection volume was 10 μL.SPSS 26.0 and SIMCA 14.1 were used to perform clustering analysis and principal component analysis on the 10 batches of Didang Qigui Decoction.The landmark components for inter batch differences were selected through orthogonal partial least squares discriminant analysis(OPLS-DA).Results The HPLC fingerprint with eighteen common peaks of Didang Qigui Decoction in 10 batches of sample was established,and the similarities of samples were between 0.828 and 0.989.Five indicative components were identified and quantitatively analyzed by comparing with the reference substances,which were paeoniflorin,mauroisoflavone glucoside,hesperidin,cinnamaldehyde and aloe rhodopsin.The linear ranges was 10.000 0-320.000 0 μg/mL,2.500 0-80.000 0 μg/mL,10.000 0-320.000 0 μg/mL,10.000 0-320.000 0 μg/mL,0.078 1-5.000 0 μg/mL,respectively,and their mean recovery ranged from 100.30%to 104.09%.Clustering analysis and principal component analysis divided 10 batches of samples from Didang Qigui Decoction into 2 categories.Through OPLS-DA screening,hairy pistil isoflavone glycosides,paeoniflorin,and hesperidin were selected as landmark components for quality differences.Conclusion The quality evaluation method for Didang Qigui Decoction established in this study is simple,sensitive,accurate,and reproducible,which can provide a basis for the quality evaluation of Didang Qigui Decoction.

To improve patient-centered pharmaceutical management and pharmaceutical service capabilities in the pharmaceutical department of medical institutions,automation and information technology are indispensable.The Pharmacy Administration-Automation and Information Technology is one of the social organization standards of the Chinese Hospital Association as part 4-4 of Pharmaceutical Administration and Pharmaceutical Practice in Healthcare,which standardizes 32 key elements in four aspects:basic requirements for automation construction in medical institutions,construction of automation hardware equipment,construction of intelligent information platform,and quality management and continuous improvement.It can be used to guide medical institutions at all levels to select and optimize pharmacy automation equipment and information platforms.This article introduced the construction methods and contents of the pharmacy automation and information technology standards,to deepen the understanding of peers on this standard and promote its implementation.This article aimed to promote the modernization,informatization,and intelligence of pharmaceutical services in medical institutions,and improve the quality and efficiency of overall medical pharmaceutical administration and service.

Objective:To investigate the factors contributing to frailty in very elderly patients with multimorbidity.Methods:This cross-sectional study enrolled 119 very elderly patients with multimorbidity who were hospitalized in the Department of Geriatrics of Huadong Hospital Affiliated to Fudan University from August 2022 to March 2023.The study aimed to understand the basic status of multimorbidity by collecting general information, the number and types of diseases, and frailty status.The subjects were divided into frail and non-frail groups through comprehensive geriatric assessment.Various factors including gender, age, Tinetti balance gait score, risk of sarcopenia, dementia, depression, risk of deep vein thrombosis, dysphagia, comorbidity index, medication count, Basic Activities of Daily Living(BADL)score, Instrumental Activities of Daily Living(IADL)score, Nutritional Risk Screening 2002(NRS-2002)score, Norton pressure injury risk assessment score, and Social Support Rating Scale(SSRS)score were compared.The correlation between each factor and the occurrence of frailty was analyzed using univariate analysis and multivariate Logistic regression analysis.Results:A total of 119 elderly inpatients with multimorbidity, with an average age of 90.8±5.9 years old, were included in the study.The incidence of frailty was 68.9%(82 cases).Univariate analysis revealed significant statistical differences between the frail group and the non-frail group in various factors including age( t=-3.131, P=0.002), Tinetti score( Z=-5.544, P<0.001), risk of sarcopenia( χ2=39.205, P<0.001), dysphagia( χ2=5.937, P=0.015), Charlson comorbidity index( Z=-2.565, P=0.010), medication count( Z=-3.325, P<0.001), BADL( Z=-5.871, P<0.001), IADL( Z=-5.062, P<0.001), Norton score( Z=-5.922, P<0.001), and SSRS social support( Z=-2.637, P=0.008).Multivariate logistic regression analysis showed that the Tinetti score( OR=0.843, 95% CI: 0.737-0.966, P=0.014), decreased muscle strength( OR=11.226, 95% CI: 2.157-58.432, P=0.004), sarcopenia( OR=18.084, 95% CI: 2.041-106.211, P=0.009), Norton score( OR=0.462, 95% CI: 0.254-0.838, P=0.011), and medication count( OR=1.153, 95% CI: 1.000-1.329, P=0.049)were independently associated with frailty. Conclusions:In very elderly patients with multimorbidities, the occurrence of frailty is notably increased.Frailty is linked to multiple risks including falls, muscle weakness/sarcopenia, pressure ulcer risk, and polypharmacy, and these risks are independent of other factors.