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.

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 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.

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 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.

Objective:To investigate the predictive value of neuron-specific enolase (NSE), serum 100 calcium-binding protein β (S100β), gray-white-matter-ratio on head CT (GWR) and the combination of the three on the prognosis of neurological function in patients with post-cardiac arrest brain injury (PCABI).Methods:A total of 136 patients admitted to Tianjin Medical University General Hospital after resuscitation from cardiac arrest from September 2021 to May 2023 were selected and included in the good prognosis group (96 patients) and the poor prognosis group (40 patients) based on the Glasgow-Pittsburgh Cerebral Performance (CPC) classification at discharge, respectively, to compare the demographic data, resuscitation data and NSE, S100β and GWR levels within 24 h of admission between the 2 groups, and modified Poisson regression was applied to investigate the factors affecting the neuroprognosis of PCABI patients. The effectiveness of NSE, S100β, GWR and the combination of the three in predicting neurological prognosis was evaluated using receiver operating characteristic (ROC) curve and the area under the curve (AUC), and the statistical differences in AUC were compared by Delong's test.Results:NSE, S100β, GWR, history of coronary artery disease, APACHEⅡ score, time from CA to CPR, duration of resuscitation, and dose of epinephrine use were independent factors influencing the neurological prognosis of PCABI patients ( P<0.05). Compared with the good prognosis group, NSE and S100β levels were significantly higher and GWR levels were significantly lower in the poor prognosis group, with statistically significant differences ( P<0.01). The AUCs for NSE, S100β and GWR to predict poor neurological prognosis were 0.905(0.851, 0.959), 0.876 (0.797, 0.956), 0.842(0.754, 0.930), with cut-off values of 26.75 ng/mL, 1.35 ng/mL and 1.195, respectively, and an AUC of 0.982 (0.961, 1.000) for the combination of the three predicting poor neurological prognosis, significantly higher than any single indicator ( P=0.001 4, 0.001 6, 0.002 8). Conclusions:Early monitoring of NSE, S100β and GWR is effective in predicting the neurological prognosis of PCABI patients at discharge, and the combination of all three significantly improves the predictive power.

Objective:To investigate the changes of serum miR-210, miR-30a and miR-16 in patients with type 2 diabetes mellitus (T2DM) combined with osteoporosis (OP) and to analyze the influencing factors.Methods:Eighty patients with T2DM admitted to our hospital from May. 2019 to May. 2022 were prospectively selected for the study, and the patients were divided into 42 patients with T2DM combined with OP and 38 patients with T2DM without OP according to whether the patients were combined with OP. Another 40 cases were selected as the control group during the same period of physical examination. We compared the differences of miR-210, miR-30a and miR-16 among the groups, and analyzed the diagnostic value of miR-210, miR-30a and miR-16 on T2DM combined with OP by ROC curve, and analyzed the influencing factors of T2DM combined with OP by binary logistic regression. miR-210, miR-30a and miR-16 were analyzed by Spearman correlation. Spearman correlation analysis of miR-210, miR-30a, miR-16 and T2DM combined OP related indexes.Results:The differences of miR-210, miR-30a, and miR-16 were statistically significant between groups ( F=24.13, 62.69, 307.26, P<0.05), and the control group < T2DM uncomplicated OP group < T2DM combined OP group ( P<0.05). ROC curve analysis showed that miR-210, miR-30a, and miR-16 The AUCs for the diagnosis of T2DM combined with OP were 0.779, 0.854 and 0.973 in order, all of which had some accuracy in diagnosis. Binary logistic multi-factor regression analysis showed that the duration of T2DM disease, glycosylatedhemoglobin (HbA1c), type I collagen carboxy-terminal peptide β special series (β-CTX) were all risk factors for T2DM combined with OP ( P<0.05), body mass index (BMI. BMI, parathyroid hormone (PTH), 25-hydroxyvitamin D3monohydrate (25 (OH) D3), bone Gla protein (BGP), bone mineral density (BMD) were all risk factors for T2DM combined with OP ( P<0.05). Spearman correlation analysis showed that miR-210, miR-30a and miR-16 were negatively correlated with BGP and BMD ( r=-0.668/-0.592/-0.599, -0.671/-0.609/-0.593) and positively correlated with β-CTX ( r=0.670/0.603/0.605) . Conclusion:miR-210, miR-30a and miR-16 are highly expressed in the serum of patients with T2DM combined with OP, and the duration of T2DM, HbA1c and β-CTX were all risk factors for T2DM combined with OP, and BMI, PTH, 25 (OH) D3, BGP and BMD are all protective factors for T2DM combined with OP.

Adult ; Humans ; Heart Arrest/therapy* ; Cardiopulmonary Resuscitation ; Emergency Service, Hospital ; Out-of-Hospital Cardiac Arrest/therapy* ; Treatment Outcome