Objective To propose a preventive maintenance strategy of the heating,ventilation and air-conditioning(HVAC)system in pharmacy intravenous admixture services(PIVAS)based on the reliability centered maintenance(RCM)method so as to provide references for PIVAS equipment maintenance.Methods Firstly,a HVAC system RCM review team was formed,and the failure modes and impacts of important functional components of the equipment were analyzed to clarify the consequences of the failure of each functional component under the premise of ensuring the safety and integrity of the equipment and with the goal of minimizing the loss of maintenance downtime and the consumption of maintenance resources.Secondly,with a standardized logical decision-making procedure the preventive maintenance strategy was determined and implemented based on the consequences of functional failure.Finally,statistical analyses were carried out on such equipment indicators as performance parameter qualification rate,failure rate and maintenance cost before and after the RCM method-based strategy was executed,in order to evaluate the efficacy of the strategy.Results The RCM method-based preventive maintenance strategy had the performance qualification rate increased from 97.47%to 99.06%(χ2=24.139,P<0.01),the failure rate decreased from 0.24%to 0.03%(χ2=13.519,P<0.01)and the maintenance cost reduced by 11.5%,from RMB 134,200 to 118,700.Conclusion The RCM method-based preventive maintenance strategy provides reliable equipment for PIVAS and lowers the maintenance cost effectively,and references are given for the development of automated and intelligent equipment maintenance strategies for PIVAS.[Chinese Medical Equipment Journal,2025,46(10):78-83]

Objective To explore the influencing factors of acute kidney injury in severe burn patients,and to construct a visual risk nomogram model.Methods A total of 390 patients with severe burn admitted to the Institute of Burn Frostbite and Tissue Function Reconstruction of Chinese People's Armed Police Force Specialty Medical Center from January 2018 to January 2022 were collected as an internal training data set,and 50 patients with severe burn admitted from February to December 2022 were collected as an external validation data set.The 390 patients of the internal training data set were divided into the acute kidney injury group and the non-acute kidney injury group according to the occurrence of acute kidney injury,and the baseline data of patients in the two groups were compared.Univariate and multivariate Logistic regression were used to analyze the risk factors of acute kidney injury in severe burn patients of the internal training data set,and a nomogram model was drawn.Subsequently,the model was verified both internally and externally.Kaplan-Meier analysis and Log-rank test were used to compare the 90-day survival rate of patients between the acute kidney injury group and the non-acute kidney injury group.Results The burn area(OR=1.18,95%CI:1.06 to 2.36,P=0.004),sequential organ failure assessment(SOFA)score(OR=1.81,95%CI:1.21 to 5.92,P<0.001),inhalation injury(OR=3.21,95%CI:1.23 to 6.35,P<0.001),neutrophil to lymphocyte ratio(NLR)(OR=1.22,95%CI:1.05 to 3.65,P<0.001)and albumin(ALB)(OR=0.78,95%CI:0.57 to 0.92,P=0.011)were the independent risk factors for the development of acute kidney injury in severe burn patients.The nomogram model was established by the above factors.The area under the receiver operating characteristic curve(AUC)of the internal training data set was 0.833(95%CI:0.752 to 0.935),the sensitivity was 81.2%,and the specificity was 83.2%.The AUC of the external validation data set was 0.842(95%CI:0.762 to 0.912),the sensitivity 87.2%,and the specificity was 78.7%.The 90-day survival rate of patients in the acute kidney injury group after burns was significantly lower than that in the non-acute kidney injury group(P<0.001).Conclusion Larger burn area,higher SOFA score,combined inhalation injury,increased NLR,and decreased ALB level are the risk factors for the occurrence of acute kidney injury in severe burn patients,which are related to the 90-day survival rate of patients after burns.The nomogram model based on the risk factors can provide certain reference for clinical individualized prevention and treatment of acute kidney injury in severe burn patients.

Objective To propose a multiple regression-variational auto-encoders(MR-VAE)model to realize precise and non-invasive prediction of intra-abdominal pressure(IAP)in critically ill patients.Methods At first,a dataset was constructed by retrospectively analysing baseline characteristics and clinical indicators of 100 critically ill patients admitted to the Intensive Care Unit of Daping Hospital of Army Medical University between 30 August 2019 and 30 March 2021.Then,a MR-VAE prediction model was developed by integrating a feedforward neural network for supervised regression onto a variational autoencoder(VAE)framework and incorporating multiple regression strategies to mitigate feature interference.Finally,the MR-VAE model had its performance evaluated by its comparison with five classical models including support vector machines(SVM),convolutional neural networks(CNN),Scikit-learn integrated model(SIM),multi-layer perceptron(MLP)and K-nearest neighbors(KNN),and its prediction accuracy verified by testing the data of 10 randomly selected patients.Results The MR-VAE model behaved the best when compared with the five classical models,with a mean squared error(MSE)of 0.207,a root mean square error(RMSE)of 0.454,a mean absolute error(MAE)of 0.361,a median absolute deviation(MAD)of 0.243,an explained variance score(EVS)of 0.814 and a R2of 0.823,which also outperformed the five models in fitting performance,convergence and final loss.In random sample testing,the MR-VAE model exhibited high consistency between predicted and actual values.Conclusion The MR-VAE model proposed can accurately predict IAP,which has great potential in reducing the repeated measurements of IAP in critically ill patients and providing new ideas for the early diagnosis and treatment of IAH.

Objective To explore the influencing factors of acute kidney injury in severe burn patients,and to construct a visual risk nomogram model.Methods A total of 390 patients with severe burn admitted to the Institute of Burn Frostbite and Tissue Function Reconstruction of Chinese People's Armed Police Force Specialty Medical Center from January 2018 to January 2022 were collected as an internal training data set,and 50 patients with severe burn admitted from February to December 2022 were collected as an external validation data set.The 390 patients of the internal training data set were divided into the acute kidney injury group and the non-acute kidney injury group according to the occurrence of acute kidney injury,and the baseline data of patients in the two groups were compared.Univariate and multivariate Logistic regression were used to analyze the risk factors of acute kidney injury in severe burn patients of the internal training data set,and a nomogram model was drawn.Subsequently,the model was verified both internally and externally.Kaplan-Meier analysis and Log-rank test were used to compare the 90-day survival rate of patients between the acute kidney injury group and the non-acute kidney injury group.Results The burn area(OR=1.18,95%CI:1.06 to 2.36,P=0.004),sequential organ failure assessment(SOFA)score(OR=1.81,95%CI:1.21 to 5.92,P<0.001),inhalation injury(OR=3.21,95%CI:1.23 to 6.35,P<0.001),neutrophil to lymphocyte ratio(NLR)(OR=1.22,95%CI:1.05 to 3.65,P<0.001)and albumin(ALB)(OR=0.78,95%CI:0.57 to 0.92,P=0.011)were the independent risk factors for the development of acute kidney injury in severe burn patients.The nomogram model was established by the above factors.The area under the receiver operating characteristic curve(AUC)of the internal training data set was 0.833(95%CI:0.752 to 0.935),the sensitivity was 81.2%,and the specificity was 83.2%.The AUC of the external validation data set was 0.842(95%CI:0.762 to 0.912),the sensitivity 87.2%,and the specificity was 78.7%.The 90-day survival rate of patients in the acute kidney injury group after burns was significantly lower than that in the non-acute kidney injury group(P<0.001).Conclusion Larger burn area,higher SOFA score,combined inhalation injury,increased NLR,and decreased ALB level are the risk factors for the occurrence of acute kidney injury in severe burn patients,which are related to the 90-day survival rate of patients after burns.The nomogram model based on the risk factors can provide certain reference for clinical individualized prevention and treatment of acute kidney injury in severe burn patients.

Objective To propose a multiple regression-variational auto-encoders(MR-VAE)model to realize precise and non-invasive prediction of intra-abdominal pressure(IAP)in critically ill patients.Methods At first,a dataset was constructed by retrospectively analysing baseline characteristics and clinical indicators of 100 critically ill patients admitted to the Intensive Care Unit of Daping Hospital of Army Medical University between 30 August 2019 and 30 March 2021.Then,a MR-VAE prediction model was developed by integrating a feedforward neural network for supervised regression onto a variational autoencoder(VAE)framework and incorporating multiple regression strategies to mitigate feature interference.Finally,the MR-VAE model had its performance evaluated by its comparison with five classical models including support vector machines(SVM),convolutional neural networks(CNN),Scikit-learn integrated model(SIM),multi-layer perceptron(MLP)and K-nearest neighbors(KNN),and its prediction accuracy verified by testing the data of 10 randomly selected patients.Results The MR-VAE model behaved the best when compared with the five classical models,with a mean squared error(MSE)of 0.207,a root mean square error(RMSE)of 0.454,a mean absolute error(MAE)of 0.361,a median absolute deviation(MAD)of 0.243,an explained variance score(EVS)of 0.814 and a R2of 0.823,which also outperformed the five models in fitting performance,convergence and final loss.In random sample testing,the MR-VAE model exhibited high consistency between predicted and actual values.Conclusion The MR-VAE model proposed can accurately predict IAP,which has great potential in reducing the repeated measurements of IAP in critically ill patients and providing new ideas for the early diagnosis and treatment of IAH.

Objective To propose a preventive maintenance strategy of the heating,ventilation and air-conditioning(HVAC)system in pharmacy intravenous admixture services(PIVAS)based on the reliability centered maintenance(RCM)method so as to provide references for PIVAS equipment maintenance.Methods Firstly,a HVAC system RCM review team was formed,and the failure modes and impacts of important functional components of the equipment were analyzed to clarify the consequences of the failure of each functional component under the premise of ensuring the safety and integrity of the equipment and with the goal of minimizing the loss of maintenance downtime and the consumption of maintenance resources.Secondly,with a standardized logical decision-making procedure the preventive maintenance strategy was determined and implemented based on the consequences of functional failure.Finally,statistical analyses were carried out on such equipment indicators as performance parameter qualification rate,failure rate and maintenance cost before and after the RCM method-based strategy was executed,in order to evaluate the efficacy of the strategy.Results The RCM method-based preventive maintenance strategy had the performance qualification rate increased from 97.47%to 99.06%(χ2=24.139,P<0.01),the failure rate decreased from 0.24%to 0.03%(χ2=13.519,P<0.01)and the maintenance cost reduced by 11.5%,from RMB 134,200 to 118,700.Conclusion The RCM method-based preventive maintenance strategy provides reliable equipment for PIVAS and lowers the maintenance cost effectively,and references are given for the development of automated and intelligent equipment maintenance strategies for PIVAS.[Chinese Medical Equipment Journal,2025,46(10):78-83]

The dried mature peel of Citrus reticulata, a plant in the Rutaceae family and its cultivated varieties, is a commonly used Chinese medicinal material known as Chenpi(Citri Reticulatae Pericarpium). It is rich in nutritional components and medicinal value, with pharmacological effects including relieving cough and eliminating phlegm, strengthening the spleen and drying dampness, protecting the liver and benefiting the stomach, tonifying Qi, and calming the mind. Huanglongbing(HLB), also known as Citrus Huanglongbing, is a destructive disease in citrus production that seriously threatens the development of the citrus industry. HLB causes symptoms such as the inability of Rutaceae plants to produce mature fruit, gradual weakening of the tree, and eventual death, posing a significant threat to the yield and quality of Chenpi. Due to the uneven distribution of the HLB pathogen in infected plants, accurate detection of the pathogen requires the collection of a large number of plant samples. Current sample pretreatment methods, such as traditional extraction methods and commercial extraction kits, are time-consuming and involve multiple steps, which significantly increase the difficulty and workload of HLB diagnosis and have become a bottleneck in HLB detection. In this study, a rapid high-throughput detection method combining alkali lysis and TaqMan qPCR was developed. This method allows the pretreatment of multiple samples within 5 min, and the entire detection process can be completed within 45 min, with a detection limit of 6.67 fg·μL~(-1). The alkali lysis method and commercial kits were used for parallel detection of field-collected citrus samples, and the results showed no significant difference. The sample pretreatment method established in this study is characterized by low cost, simplicity, and high efficiency. Combined with TaqMan qPCR, it can provide technical support for early and on-site diagnosis of HLB. This method is of great significance for disease prevention and control in the citrus industry and is expected to help improve the yield and quality of citrus medicinal materials.
Citrus/microbiology* ; Plant Diseases/microbiology* ; Rhizobiaceae/physiology* ; High-Throughput Screening Assays/methods* ; Liberibacter/physiology*

This study aims to systematically characterize the targeted effects of Quanduzhong Capsules on cartilage lesions in knee osteoarthritis by integrating spatial transcriptomics data mining and animal experiments validation, thereby elucidating the related molecular mechanisms. A knee osteoarthritis model was established using Sprague-Dawley(SD) rats, via a modified Hulth method. Hematoxylin and eosin(HE) staining was employed to detect knee osteoarthritis-associated pathological changes in knee cartilage. Candidate targets of Quanduzhong Capsules were collected from the HIT 2.0 database, followed by bioinformatics analysis of spatial transcriptomics datasets(GSE254844) from cartilage tissues in clinical knee osteoarthritis patients to identify spatially specific disease genes. Furthermore, a "formula candidate targets-spatially specific genes in cartilage lesions" interaction network was constructed to explore the effects and major mechanisms of Quanduzhong Capsules in distinct cartilage regions. Experimental validation was conducted through immunohistochemistry using animal-derived biospecimens. The results indicated that Quanduzhong Capsules effectively inhibited the degenerative changes in the cartilage of affected joints in rats, which was associated with the regulation of Quanduzhong Capsules on the thioredoxin-interacting protein(TXNIP)-NOD-like receptor family pyrin domain containing 3(NLRP3)-bone morphogenetic protein receptor type 2(BMPR2)-fibronectin 1(FN1)-matrix metallopeptidase 2(MMP2) signal axis in the articular cartilage surface and superficial zones, subsequently inhibiting cartilage matrix degradation leading to oxidative stress and inflammatory diffusion. In summary, this study clarifies the spatially specific targeted effects and protective mechanisms of Quanduzhong Capsules within pathological cartilage regions in knee osteoarthritis, providing theoretical and experimental support for the clinical application of this drug in the targeted therapy on the inflamed cartilage.
Animals ; Osteoarthritis, Knee/metabolism* ; Drugs, Chinese Herbal/administration & dosage* ; Rats, Sprague-Dawley ; Rats ; Male ; Humans ; Capsules ; Female ; Disease Models, Animal

Since the promulgation of the first Drug Administration Law of the People's Republic of China 40 years ago in 1984, China has undergone four main stages in the traditional Chinese medicine(TCM) regulation: the initial establishment of TCM regulation rules(1984-1997), the formation of a modern TCM regulatory system(1998-2014), the reform of the review and approval system for new TCM drugs(2015-2018), and the construction of a scientific regulation system for TCM(2019-2024). Over the past five years, a series of milestone achievements of TCM regulation in China have been achieved in the six aspects, including its strategic objectives and the establishment of a science-based regulatory system, the reform of the review and approval system for new TCM drugs, the optimization and improvement of the TCM standard system and its formation mechanism, comprehensive enhancement of regulatory capabilities for TCM safety, international harmonization of TCM regulation and its role in promoting innovation. Looking ahead, centered on advancing TCMRS to establish a sound regulatory framework tailored to the unique characteristics of TCM, TCM regulation will evolve into new reform patterns, advancing and extending across eight critical fronts, including the legal framework and policy architecture, the review and approval system for new TCM drugs, the quality standard and management system of TCM, the comprehensive quality & safety regulation and traceability system, the research and transformation system for TCMRS, AI-driven innovations in TCM regulation, the coordination between high-quality industrial development and high-level regulation, and the leadership in international cooperation and regulatory harmonization. In this way, a unique path for the development of modern TCM regulation with Chinese characteristics will be pioneered.
Humans ; China ; Drugs, Chinese Herbal/standards* ; History, 20th Century ; History, 21st Century ; Medicine, Chinese Traditional/trends*

Cardiovascular diseases (CVDs) are the leading cause of death worldwide. Nicotinamide adenine dinucleotide (NAD⁺) is a central and pleiotropic metabolite involved in multiple cellular energy metabolism, such as cell signaling, DNA repair, protein modifications, and so on. Evidence suggests that NAD⁺ levels decline with age, obesity, and hypertension, which are all significant CVD risk factors. In addition, the therapeutic elevation of NAD⁺ levels reduces chronic low-grade inflammation, reactivates autophagy and mitochondrial biogenesis, and enhances antioxidation and metabolism in vascular cells of humans with vascular disorders. In preclinical animal models, NAD⁺ boosting also extends the health span, prevents metabolic syndrome, and decreases blood pressure. Moreover, NAD⁺ storage by genetic, pharmacological, or natural dietary NAD⁺-increasing strategies has recently been shown to be effective in improving the pathophysiology of cardiac and vascular health in different animal models and humans. Here, we discuss NAD⁺-related mechanisms pivotal for vascular health and summarize recent research on NAD⁺ and its association with vascular health and disease, including hypertension, atherosclerosis, and coronary artery disease. This review also assesses various NAD⁺ precursors for their clinical efficacy and the efficiency of NAD⁺ elevation in the prevention or treatment of major CVDs, potentially guiding new therapeutic strategies.
Humans ; Cardiovascular Diseases/physiopathology* ; NAD/metabolism* ; Animals ; Hypertension/metabolism*