OBJECTIVE To assess the correlation between the steady-state minimal concentration （cmin） and 24 h area under the drug concentration-time curve （AUC24）/minimal inhibitory concentration （MIC） ratio （AUC24/MIC） of vancomycin in pediatric patients， and analyze independent risk factors for treatment failure. METHODS Data of hospitalized children treated with vancomycin and receiving therapeutic drug monitoring in our hospital from January 2021 to July 2024 were retrospectively collected and divided into success group and failure group according to whether the treatment was successful or not. Spearman correlation analysis was used to analyze the correlation between cmin and AUC24/MIC of vancomycin， and one-way and multifactorial Logistic regression analyses were used to screen the independent risk factors for vancomycin treatment failure. RESULTS A total of 59 children were included， with 41 in the success group and 18 in the failure group. Compared with the failure group， AUC24/MIC of vancomycin was significantly higher in the success group （P＝0.038）， but there was no statistically significant difference in the cmin of the two groups （P＞0.05）； cmin of vancomycin was significantly positively correlated with AUC24/MIC （r＝0.499， P＜0.001）， but it has a certain efficacy in predicting the achievement of the AUC24/MIC standard （≥400） （area under the receiver operator characteristic curve＝0.696）， with an optimal cutoff value of 6.05 mg/L determined by the Youden index. The efficacy of AUC24/ MIC in predicting treatment failure was superior to cmin （areas under the receiver operator characteristic curve were 0.671 vs. 0.523， P were 0.038 vs. 0.684）， with higher sensitivity （83.3% vs. 66.7%）. Hypoproteinemia and AUC24/MIC≤369.1 were independent risk factors for vancomycin treatment failure （P＜0.05）. The incidence of nephrotoxicity was 3.4%. CONCLUSIONS There is a significant positive correlation between cmin and AUC24/MIC of vancomycin in pediatric patients； hypoproteinemia and AUC24/MIC≤369.1 are independent risk factors for vancomycin treatment failure in children.

OBJECTIVE To investigate the construction and practice of a drug traceability code management system in pediatric hospitals， providing a reference for promoting drug traceability code collection in healthcare institutions. METHODS Taking the outpatient pharmacy of our hospital as the research subject， a drug traceability code management system was constructed through the upgrade of the hospital information system （HIS）， process optimization， and human-machine collaboration mechanism. The PDCA （plan-do-check-act） cycle management method was applied to continuously optimize this system. Based on operational data from March 2024 to February 2025， the changes in the collection rate of drug traceability codes were analyzed， and the differences in the average patient pickup time， the average pharmacist dispensing time， and the dispensing error rate were compared before and after the implementation of the system. RESULTS In the initial period of trial operation of the drug traceability code management system（June 2024）， the collection rate of drug traceability codes was 57.17%， which subsequently improved to 93.52% by February 2025 following process optimization. Compared with the pre-implementation period （March-May 2024）， there was no significant difference （P＞0.05） in the average patient pickup time during the stable run-in period （August-October 2024）； the overall average pharmacist dispensing time increased significantly （P＜0.001）， but the clinical significance of this increase （0.42 s） was limited； stratified analyses showed a significant increase in the average pharmacist dispensing time for prescriptions involving chronic disease multidrug combinations （[ 23.29±6.83） s vs. （17.87±3.64 ） s， P＜0.001]； the dispensing error rate was reduced from 0.13‰ to 0.03‰ （P＝0.038）. CONCLUSIONS By adopting the strategy of “system reconstruction-process reengineering-human-machine collaboration”， our hospital has successfully established a drug traceability code management system. While complying with national regulatory requirements， we have maintained service efficiency and reduced the medication dispensing error rate.

Chronic obstructive pulmonary disease (COPD) and pulmonary hypertension (PH) are both chronic progressive respiratory diseases that cannot be completely cured. COPD is characterized by irreversible airflow limitation, chronic airway inflammation, and gradual decline in lung function, whereas PH is characterized by pulmonary vasoconstriction, remodeling, and infiltration of inflammatory cells. These diseases have similar pathological features, such as vascular hyperplasia, arteriolar contraction, and inflammatory infiltration. Despite these well-documented observations, the exact mechanisms underlying the occurrence and development of COPD and PH remain unclear. Evidence that mitochondrial dynamics imbalance is one major factor in the development of COPD and PH. Mitochondrial dynamics is precisely regulated by mitochondrial fusion proteins and fission proteins. When mitochondrial dynamics equilibrium is disrupted, it causes mitochondrial and even cell morphological dysfunction. Mitochondrial dynamics participates in various pathological processes for heart and lung disease. Mitochondrial dynamics may be different in the early and late stages of COPD and PH. In the early stages of the disease, mitochondrial fusion increases, inhibiting fission, and thereby compensatorily increasing adenosine triphosphate (ATP) production. With the development of the disease, mitochondria decompensation causes excessive fission. Mitochondrial dynamics is involved in the development of COPD and PH in a spatiotemporal manner. Based on this understanding, treatment strategies for mitochondrial dynamics abnormalities may be different at different stages of COPD and PH disease. This article will provide new ideas for the potential treatment of related diseases.
Humans ; Mitochondrial Dynamics/physiology* ; Pulmonary Disease, Chronic Obstructive/metabolism* ; Hypertension, Pulmonary/metabolism* ; Mitochondria/metabolism* ; Animals

OBJECTIVE: To develop and compare risk prediction models for in-hospital post-cardiac arrest brain injury (PCABI) in critically ill patients using nomograms and random forest algorithms, aiming to identify the optimal model for early identification of high-risk PCABI patients and providing evidence for precise treatment. METHODS: A retrospective cohort study was used to collect the first-time in-hospital cardiac arrest (IHCA) patients admitted to the intensive care unit (ICU) from 2008 to 2019 in the Medical Information Mart for Intensive Care-IV (MIMIC-IV) as the study population, and the patients' age, gender, body mass, health insurance utilization, first vital signs and laboratory tests within 24 hours of ICU admission, mechanical ventilation, and critical care scores were extracted. Independent influencing factors of PCABI were identified through univariate and multivariate Logistic regression analyses. The included patients were randomly divided into a training cohort and an internal validation cohort in a 7:3 ratio, and the PCABI risk prediction model was constructed by the nomogram and random forest algorithm, respectively, and the model was evaluated by receiver operator characteristic curve (ROC curve), the calibration curve, and the decision curve analysis (DCA), and after the better model was selected, 179 patients admitted to Tianjin Medical University General Hospital as the external validation cohort for external evaluation were collected by using the same inclusion and exclusion criteria. RESULTS: A total of 1 419 patients with without traumatic brain injury who had their first-time IHCA were enrolled, including 995 in the training cohort (including 176 PCABI and 819 non-PCABI) and 424 in the internal validation cohort (including 74 PCABI and 350 non-PCABI). Univariate and multivariate analysis showed that age, potassium, urea nitrogen, sequential organ failure assessment (SOFA), acute physiology and chronic health evaluation III (APACHE III), and mechanical ventilation were independent influences on the occurrence of PCABI in patients with IHCA (all P < 0.05). Combining the above variables, we constructed a nomogram model and a random forest model for comparison, and the results show that the nomogram model has better predictive efficacy than the random forest model [nomogram model: area under the ROC curve (AUC) of the training cohort = 0.776, with a 95% credible interval (95%CI) of 0.741-0.811; internal validation cohort AUC = 0.776, with a 95%CI of 0.718-0.833; random forest model: AUC = 0.720, with a 95%CI of 0.653-0.787], and they performed similarly in terms of calibration curves, but the nomogram performed better in terms of decision curve analysis (DCA); at the same time, the nomogram model was robust in terms of external validation cohort (external validation cohort AUC = 0.784, 95%CI was 0.692-0.876). CONCLUSIONS A nomogram risk prediction model for the occurrence of PCABI in critically ill patients was successfully constructed, which performs better than the random forest model, helps clinicians to identify the risk of PCABI in critically ill patients at an early stage and provides a theoretical basis for early intervention.
Humans ; Critical Illness ; Retrospective Studies ; Heart Arrest/complications* ; Nomograms ; Brain Injuries/etiology* ; Intensive Care Units ; Algorithms ; Male ; Female ; Middle Aged ; ROC Curve ; Risk Factors ; Risk Assessment ; Logistic Models ; Aged

Objective To analyze and discuss the medication rule of professor Ruan Yan in the treatment of children with allergic rhinitis by using data mining method,and to provide reference for the clinical research and patented drugs development for the treatment of children with allergic rhinitis.Methods The outpatient medical records of professor Ruan Yan for the treatment of children with allergic rhinitis were collected.Microsoft Excel 2010 software was used for frequency statistics.SPSS Clementine 12.0 software was used for association rule analysis,cluster analysis and factor analysis to obtain the data.The frequency of use of various drugs and the association rules between drugs were obtained.Then the medication rules in professor Ruan Yan's prescription were analyzed.Results A total of 308 Chinese medicine compounds were included,involving 80 kinds of Chinese medicines,among which relieving drugs and qi-invigorating herbs were high-frequently used.The distribution of traditional Chinese medicine syndrome types was mainly characterized by lung-qi deficiency-cold syndrome and lung-spleen qi deficiency syndrome.The medicinal properties were mainly spicy,warm and sweet,and most of them belonged to the lung,spleen and stomach meridians.Five core prescriptions were extracted by factor analysis.Four drug combinations were obtained by systematic cluster analysis.Conclusion Ventilating lung and opening the orifices,expelling wind and removing cold,strengthening the spleen and replenishing qi are basic therapeutic principles for professor Ruan Yan in the treatment of children with allergic rhinitis.The treatment mainly focused on dispelling evil,ventilating lung and opening the orifices,expelling wind and removing cold during the acute stage of allergic rhinitis.In the remission period,according to the principle of"treating disease must be based on its origin",the treatment should enhance children's physical fitness,tonify lung and strengthen spleen,thereby reducing recurrence.

Objective To observe the clinical and symptomatic improvement three months after uterine artery embolization(UAE),and to analyze the value of apparent diffusion coefficient(ADC)in MR diffusion weighted imaging(DWI)in assessing the response of fibroids volume after UAE.Methods A total of 40 patients with uterine fibroids were included.The volume changes of fibroids,clinical and symptomatic improvement before and after treatment were recorded,and the efficacy of UAE was comprehensively analyzed.All patients underwent MR DWI before UAE and were evaluated at three months postoperatively by outpatient MR follow-up,with fibroids vol-ume and ADC quantitative measurements were performed to compare the changes in ADC values of fibroids preoperatively and post-operatively at each b value.Pearson correlation analysis was performed to analyze the correlation between baseline ADC values and postoperative fibroids volume reduction.Regression analysis was performed to assess the relationship between ADC and fibroids volume reduction after UAE.And the receiver operating characteristic(ROC)curve were plotted to analyze the predictive value of ADC values for evaluating fibroids volume reduction of more than 30％after UAE.Results The patients'clinical symptoms was improved in the three months after surgery,the volume of fibroids was significantly reduced,and the life quality was improved,the difference was sta-tistically significant(P＜0.05).There was no significant effect on ovarian function,hormone levels did not change significantly com-pared to before surgery,with no statistical significance(P＞0.05).When b=50,1 000 s/mm2,the changes in ADC values before and after uterine fibroids treatment were not significant,with no statistical significance(P＞0.05).However,the changes in ADC values before and after uterine fibroids treatment were significant when b=800 s/mm2 and the difference was statistically significant(P＜0.05).Under the condition of b=800 s/mm2,Pearson correlation analysis showed ADC value had a positive correlation with postoperative uterine fibroids volume reduction rate(r=0.45,P＜0.05),and the area under the curve(AUC)for ADC value to predict the reduction rate of uterine fibroids volume by more than 30％after UAE was 0.787.The cut-off value was 1.143 × 10-3 mm2/s,with sensitivity and specificity of 0.793 and 0.818,respectively.Conclusion UAE is more effective in treating uterine fibroids.The baseline ADC value of uterine fibroids correlated significantly with the volume reduction after UAE.The ADC value can be used to assess the volume response after UAE.

Humans ; Aged ; Lung Diseases ; Pneumonia/drug therapy* ; Adrenal Cortex Hormones/therapeutic use* ; Pulmonary Disease, Chronic Obstructive/drug therapy* ; Administration, Inhalation ; Community-Acquired Infections/drug therapy*

ObjectiveThe potential suitable area for ecological planting， key ecological factors， and suitable range of Polygonatum odoratum in China were analyzed to provide theoretical and scientific guidance for the artificial planting of P. odoratum. MethodA total of 454 geographical distribution records of P. odoratum in China and 118 ecological factors were used in this study. The maximum entropy model （MaxEnt） was adopted to predict the suitable areas of P. odoratum. The key ecological factors and their suitable ranges were analyzed by the jackknife method， contribution rates of ecological factors， and response curves. ResultThe suitable areas of P. odoratum were mainly located in the northwest， north， and northeast of China， the highly suitable areas of which were concentrated in Shaanxi， Shanxi， Gansu， etc. Solar radiation in November （Srad11）， precipitation in July （Prec7）， percentage of evergreen/deciduous needleleaf trees （Class1）， silt content （2-50 μm） mass fraction （SLTPPT）， and annual average temperature （Bio1） were found to be the key ecological factors affecting the suitable distribution of P. odoratum in China. The cumulative contribution rate of solar radiation factors （31.29%）>vegetation factors （25.61%）>soil factors （19.52%）>precipitation factors （11.38%）>temperature factors （8.57%）>topography factors （3.63%）. ConclusionIt is suggested to carry out ecological planting of P. odoratum mainly in Shaanxi （such as Baoji and Ankang Cities and Ningshan， Liuba， and Hua Counties）， Gansu （such as Tianshui City， Gannan Tibetan Autonomous Prefecture， and Liangdang and Huating Counties）， and Shanxi （such as Yangquan， Taiyuan， Fenyang， and Jinzhong Cities， as well as Xingxian County） of China. Solar radiation factors should be given priority in the planting process， followed by vegetation， soil， precipitation， temperature， and topography factors. The range of key ecological factors， namely Srad11， Prec7， Class1， SLTPPT， and Bio1 should be controlled within 8 095.21-10 334.98 （optimum 8 787.50） kJ·m^-2·d^-1， 109.99-223.60 （146.91） mm， 1.00%-9.45% （6.76%-10.68%）， 41.73%-50.35% （46.53%）， and 3.29-16.33 （13.57） °C， respectively.

Objective:To construct an early prediction model for post cardiopulmonary resuscitation-acute kidney injury (PCPR-AKI) by machine learning and provide a basis for early identification of acute kidney injury (AKI) high-risk patients and accurate treatment.Methods:It was a single-center retrospective study. The clinical data of patients admitted to Tianjin Medical University General Hospital after cardiopulmonary resuscitation following cardiac arrest from January 1, 2016 to October 31, 2023 were collected. The end-point event of the study was defined as AKI occurring within 48 hours after cardiopulmonary resuscitation. The patients were divided into AKI group and non-AKI group according to the AKI diagnostic criteria, and the differences of baseline clinical data between the two groups were compared. The patients who met the inclusion criteria were randomly (using the train_test_split function, set the random seeds to 1, 2, and 3) divided into training and validation sets at a ratio of 7∶3. Random forest (RF), support vector machine, decision tree, extreme gradient boosting and light gradient boosting machine algorithm were used to develop the early prediction model of PCPR-AKI. The receiver-operating characteristic curve and decision curve analysis were used to evaluate the performance and clinical practicality of the predictive models, and the importance of variables in the optimal model was screened and ranked.Results:A total of 547 patients were enrolled, with age of 66 (59, 70) years old and 282 males (51.6%). There were 238 patients (43.5%) having incidence of AKI within 48 hours after cardiopulmonary resuscitation. In the AKI group, 182 patients (76.5%) were in stage 1, 47 patients (19.7%) were in stage 2, and 9 patients (3.8%) were in stage 3. There were statistically significant differences in the age, time to reach resuscitation of spontaneous circulation, time from cardiac arrest to starting cardiopulmonary resuscitation, proportion of initial defibrillation rhythm, proportion of electric defibrillation, proportion of mechanical ventilation, adrenaline dosage, sodium bicarbonate dosage, proportion of coronary heart disease, proportion of hypertension, proportion of diabetes, serum creatinine, blood urea nitrogen, blood lactic acid, blood potassium, brain natriuretic peptide, troponin, D-dimer, neuron specific enolase, and 24 hours urine volume after cardiopulmonary resuscitation between AKI group and non-AKI group (all P<0.05). Among the five machine learning algorithms, RF model achieved the best performance and clinical practicality, with area under the curve of 0.875, sensitivity of 0.863, specificity of 0.956, and accuracy rate of 90.7%. In the variable importance ranking of RF model, the top 10 variables were as follows: time to reach resuscitation of spontaneous circulation, time from cardiac arrest to starting cardiopulmonary resuscitation, initial defibrillable rhythm, serum creatinine, mechanical ventilation, blood lactate acid, adrenaline dosage, brain natriuretic peptide, D-dimer and age. Conclusions:An early predictive model for PCPR-AKI is successfully constructed based on machine learning. RF model has the best predictive performance. According to the importance of the variables, it can provide clinical strategies for early identification and precise intervention for PCPR-AKI.

This study aims to establish a time-resolved fluorescence immunochromatography method for simultaneous determination of human papillomavirus (HPV) type 16 E6 and L1 protein concentrations. The amount of lanthanide microsphere-labeled antibodies, the concentration of coated antibodies, and the reaction time were optimized, and then a test strip for the simultaneous determination of the protein concentrations was prepared. The performance of the detection method was evaluated based on the concordance of the results from clinical practice. The optimal conditions were 8 μg and 10 μg of HPV16 L1 and E6-labeled antibodies, respectively, 1.5 mg/mL coated antibodies, and reaction for 10 min. The detection with the established method for L1 and E6 proteins showed the linear ranges of 5-320 ng/mL and 2-64 ng/mL and the lowest limits of detection of 1.78 ng/mL and 1.09 ng/mL, respectively. There was no cross reaction with human immunodeficiency virus (HIV), treponema pallidum (TP), or HPV18 E6 and L1 proteins. The average recovery rate of the established method was between 97% and 107%. The test strip prepared in this study showed the sensitivity, specificity, and diagnostic accuracy of 97.46%, 90.57%, and 95.32%, respectively, in distinguishing patients with cervical cancer and precancerous lesions from healthy subjects, with the area under the curve (AUC) of 0.980 1 and 95% Confidence Interval (CI) of 0.956 5 to 1.000 0. The time-resolved fluorescence immunochromatography combined with the test strips prepared in this study showed high sensitivity, high accuracy, simple operation, and rapid reaction in the quantitation of HPV16 E6 and L1 proteins. It thus can be used as an auxiliary method for the diagnosis and early screening of cervical cancer and precancerous lesions and the assessment of disease course.
Oncogene Proteins, Viral/immunology* ; Humans ; Chromatography, Affinity/methods* ; Female ; Human papillomavirus 16 ; Repressor Proteins/immunology* ; Capsid Proteins/immunology* ; Papillomavirus Infections/diagnosis* ; Fluorescence ; Uterine Cervical Neoplasms/virology*