OBJECTIVE To introduce the Dutch system of pharmacy academic education， professional practice and continuing education， and provide new ideas for constructing a “demand-driven， industry-education integrated， and sustainably developing” Chinese-style pharmacy education system. METHODS Through literature and public data retrieval， as well as collection of field visit materials， the study systematically combed the stage characteristics， institutional design， and innovative practices of Dutch pharmacy education， extracted its features and advantages， and proposed suggestions for pharmacy education reform in China. RESULTS & CONCLUSIONS The Dutch pharmacy academic education system is characterized by stepped competency-based training， integrating basic theory with early clinical practice at the undergraduate level， emphasizing specialized division of labor and strengthening clinical competence at the master’s level， and promoting industry-university-research collaborative innovation at the doctoral level. The practice qualification certification and continuing education exhibit multi-dimensional synergy. Specifically， the practice qualification certification process adheres to the guiding principle of “evidence-based competency”， implementing an access system centered on competency assessment， which requires passing national examinations and registration. The continuing education for hospital pharmacists is guided by patient safety， while continuing education for community pharmacists and other pharmacists （such as industrial pharmacists， regulatory science pharmacists， etc.） is guided by the frameworks of “digital situational learning” and a “triple tracks encompassing industry， regulation， and emerging fields”， respectively. China may draw on the five-dimensional path of Dutch pharmacy education in “early integration， vertical coherence， unified standards， industry-university-research collaboration， and intelligent empowerment” to reform its pharmacy education in aspects such as curriculum design， credit systems， evaluation criteria， training models， and training methods， aiming to cultivate pharmacy professionals aligned with China’s practical E-mail：cqqianyan@hospital.cqmu.edu.cn requirements.

The microstructures of pharmaceutical preparations play a pivotal role in determining their critical quality attributes (CQAs), such as drug release, content uniformity, and stability, which greatly impact the safety and efficacy of drugs. Unlike the inherent molecular structures of active pharmaceutical ingredients (APIs) and excipients, the microstructures of pharmaceutical preparations are developed during the formulation process, presenting unique analytical challenges. In this review, we primarily focus on presenting the research methods used to elucidate the microstructures of pharmaceutical preparations, including X-ray imaging (XRI), scanning electron microscopy (SEM), atomic force microscopy (AFM), Raman spectroscopy, infrared (IR) spectroscopy, and rheometer technology. Subsequently, we highlight the applications, advantages, and limitations of these methods. Finally, we discuss the current challenges and future perspectives in this field. This review aims to provide a comprehensive reference for understanding the microstructures of pharmaceutical preparations, offering new insights and potential advancements in their development.

Objective:To investigate the effect of "four-staff co-management" follow-up mode on risk factor control and medium-term prognosis improvement in patients with coronary heart disease after percutaneous coronary intervention (PCI).Methods:This was a intervention study. Patients with coronary heart disease who were admitted to the Xiamen Cardiovascular Hospital of Xiamen University from June 2021 to January 2022 and successfully discharged after PCI were included. According to the different types of follow-up after discharge, patients were divided into the traditional follow-up group and the "four-staff co-management" follow-up group. The "four-staff co-management" follow-up mode means that specialists, specialist managers in third-level A hospitals and general practitioners and health managers in basic hospitals were jointly responsible for post-discharge follow-up of PCI patients. Baseline clinical data were collected. The primary endpoints were the rate of compliance of coronary heart disease risk factor control at 12 months after surgery, the rate of secondary surgery, and the incidence of mid-term major adverse cardiovascular and cerebrovascular events (MACCE). Unplanned secondary PCI included symptom-driven secondary PCI and asymptomatic secondary PCI. MACCE includes myocardial infarction, hospitalization for heart failure, stroke, major bleeding, all-cause death, and composite endpoints including these events.Results:A total of 2 181 patients were enrolled, including 1 097 patients in the traditional follow-up group and 1 084 patients in the "four-staff co-management" follow-up group. At baseline, there were no statistically significant differences in gender, age, discharge diagnosis, co-existing diseases, echocardiographic indexes, and coronary artery lesions between the two groups (all P>0.05). There were no significant differences between the two groups in total PCI stent length, maximum internal diameter of stent, proportion of patients using drug balloon, proportion of patients with a planned second surgery during hospitalization, and discharge with drugs (all P>0.05). Twelve months after PCI, the reduction in HbA1c and low-density lipoprotein cholesterol was greater in the "four-staff co-management " follow-up group than that in the traditional follow-up group (all P<0.05), and the rate of reaching the standard for low-density lipoprotein cholesterol was higher than that in the traditional follow-up group ( P=0.001), but there was no statistical significance between the two groups for blood pressure and blood glucose (all P>0.05). During the follow-up period, the proportion of symptom-driven second operation patients was lower in the "four-staff co-management" follow-up group than that in the traditional follow-up group ( P<0.001), and there was no significant difference in the proportion of asymptomatic second operation patients between the two groups ( P=0.191). The proportion of hospitalized patients with heart failure in the "four-staff co-management" follow-up group was lower than that in the traditional follow-up group ( P=0.029), and there was no significant difference in the proportion of myocardial infarction, cerebral infarction, cerebral hemorrhage, massive hemorrhage, death and complex endpoint events between the two groups (all P>0.05). Conclusion:The "four-staff co-management" follow-up mode can effectively improve the control of risk factors and medium-term prognosis in patients with coronary heart disease after PCI.

Objective:To explore the influence of different scanning positions based on chest phantom of computed tomography(CT)scan on chest on image quality and radiation dose.Methods:A thermoluminescent dosimeter(TLD)was placed at the breast area of simulating anthropoid chest phantom.GE Revolution evo CT was used to conduct scan on the conventional supine position(supine group)and prone position(prone group)for chest phantom.Different noise indexes(NI=10-23)were adjusted to control ration doses,and other parameters were fixed,and each group collected 12 sequence images.The average value(AV),standard deviation(SD)of the CT scan at region of interest(ROI)under different scanning positions were recorded to calculate the signal-to-noise ratio(SNR)and contrast-to-noise ratio(CNR)of the image.The radiation dose at the breast area was measured by TLD,and the volume CT dose index(CTDIvol)and dose-length product(DLP)were recorded.Results:Under different scanning positions,the radiation dose of breast organs in the prone group was lower than that in the supine group,there was a statistically significant difference between the two groups(t=6.57,P<0.05),while there were not statistically significant differences in CTDIvol and DLP between the two groups(P>0.05).There were not statistically significant differences in the CT values,SD,SNR,CNR of lung tissue,and the CT values of breast tissue between the two groups of images(P>0.05).The SD,SNR and CNR of breast tissue in the prone group were lower than those in the supine group,and the differences were statistically significant(t=-13.33,-10.59,6.70,P<0.05).There were no statistically significant differences in the subjective scores of the clarity of the edge of the tissue within lung,the layers of soft tissue of the breast,noise,and artifacts in the bone tissue between the two groups of images(P>0.05).Conclusion:When low-dose CT physical examination on chest is conducted in clinical practice,the scanning of prone position during undergoing CT scan on chest can obtain image quality that can meet the requirements in diagnosing lung,and reduce the radiation dose on the breast,and conform to the technical principle of optimal radiation protection.

Objective: To explore the experience of self growth among caregivers of adolescents with non suicidal self injury (NSSI), so as to provide practical reference for improving the family support system of adolescents with NSSI. Methods: From August to November 2023, a purposive sampling method was used to select 21 caregivers of adolescents with NSSI who were treated in a tertiary grade A psychiatric hospital in Xinxiang City, Henan Province for semi structured in depth interviews. The Colaizzi s seven step qualitative analysis method was applied to analyze the data and extract themes related to caregiver growth Results: A total of 5 themes and 12 sub themes of benefit finding were extracted from caregivers of adolescents with NSSI: personal growth (increased psychological resilience, enhanced awareness of independent learning, improved self reflection ability, better understanding and acceptance of NSSI behaviors in adolescents), improvement of family relationships (improved parent-child relationship, improved relationships among family members), perceived social support (more peer support, support from close friends), improvement of caregiving ability (creating a favorable rehabilitation environment, improved caregiving skills), and changes in life and education attitudes (adopting a healthier lifestyle, reasonably adjusting expectations for children). Conclusions During the process of caring for adolescents with NSSI, caregivers from positive experiences in multiple aspects such as personal growth, family relationships, social support, and life education attitudes. Improving the mental health level of caregivers and optimize the family rehabilitation environment, will help reduce the occurrence of NSSI behaviors among adolescents.

Objective To investigate the predictive value of combined radiomic features derived from chest CT scans with clinical characteristics for epidermal growth factor receptor(EGFR)gene mutations in non-small cell lung cancer(NSCLC).Methods A multi-center case-control study was conducted on the clinical data and CT images of 1 070 NSCLC patients from the radiology departments of the 3 medical institutions between January 2013 and October 2023.The 719 NSCLC patients from the First Affiliated Hospital of Army Medical University were randomly divided into a training set and an internal validation set in a ratio of 7∶3;The 173 patients in the Eastern Theatre General Hospital and the 178 patients in Army Medical Centre of PLA were assigned into the external validation set 1 and 2,respectively.Least absolute shrinkage and selection operator(LASSO)regression was employed to identify the optimal radiomic features,which were subsequently used to construct a radiomics model.Univariate and multivariate logistic regression analyses were applied to identify clinical features associated with EGFR mutation,thereby developing a clinical model.The radiomic and clinical features were subsequently combined to develop a comprehensive model.All the 3 classification models were built using random forest(RF)machine learning.The area under curve(AUC),accuracy,sensitivity and specificity were utilized to evaluate the predictive performance of the models.Calibration curve was plotted to assess the goodness of fit of the comprehensive model,while decision curve analysis was performed to assess the clinical utility of the model.Results The AUC value of the radiomics model was 0.762 4(95%CI:0.692 4～0.825 1),0.745 4(95%CI:0.671 1～0.814 3),and 0.724 7(95%CI:0.639 7～0.801 6),respectively,in the internal validation set,external validation set 1,and external validation set 2;The AUC value of the clinical prediction model was 0.691 7(95%CI:0.627 9～0.757 6),0.652 5(95%CI:0.576 7～0.729 1),and 0.779 2(95%CI:0.712 5～0.847 3),respectively in the above sets in turn;The comprehensive model constructed based on clinical features and radiomic features showed the best predictive efficacy,with an AUC value of 0.818 0(95%CI:0.757 7～0.874 3),0.782 4(95%CI:0.703 1～0.848 2),and 0.796 6(95%CI:0.718 1～0.868 6),respectively in the above sets.Calibration curve analysis indicated that the comprehensive model had a good fit,while decision curve analysis revealed that the model provided a favorable net benefit.Conclusion Our comprehensive model constructed based on chest CT radiomic features and clinical characteristics shows superior predictive performance for EGFR gene mutations in NSCLC across multiple center datasets,which may be helpful for clinical decision-making for treatment strategies.

Objective To evaluate the predictive value of deep learning features from chest CT images combined with clinical and radiomics features for epidermal growth factor receptor(EGFR)mutations in non-small cell lung cancer(NSCLC).Methods This case-control study retrospectively analyzed clinical and imaging data of 1 070 NSCLC patients from radiology departments at three hospitals(January 2013 to October 2023).Patients were divided into:a training set(n=502)and internal validation set(n=217)via 7∶3 randomization of 719 cases from the First Affiliated Hospital of Army Medical University;external validation set 1(n=173)from General Hospital of Eastern Theater Command;external validation set 2(n=178)from Daping Hospital of Army Medical University.Deep learning features were extracted using a 2.5D convolutional neural network(CNN)with ResNet101 backbone,radiomics features were derived from CT images,and clinical risk factors were identified to construct models.An integrated model combined deep learning,clinical,and radiomics features.All four models were developed using random forest(RF)classifiers.Calibration curves assessed goodness-of-fit,and decision curve analysis(DCA)evaluated clinical utility.Results The deep learning model achieved AUCs of 0.833 7(95%CI:0.770 6～0.884 7),0.815 1(0.741 6～0.882 8),and 0.810 1(0.745 2～0.873 6)in the internal and two external validation sets,respectively.Clinical models yielded AUCs of 0.731 0(0.660 2～0.802 1),0.746 0(0.666 4～0.824 9),and 0.813 4(0.743 1～0.883 6);radiomics models showed AUCs of 0.762 4(0.692 4～0.825 1),0.745 4(0.671 1～0.814 3),and 0.724 7(0.639 7～0.801 6).The integrated model demonstrated optimal performance with AUCs of 0.905 5(0.857 0～0.945 4),0.832 7(0.763 3～0.896 4),and 0.889 0(0.834 4～0.934 3).DCA indicated significant net benefit for EGFR prediction at threshold probabilities of 0.15～0.85 using the integrated model.Conclusion Deep learning features from CT images effectively predict EGFR mutation status in NSCLC.The integrated model combining deep learning,clinical,and radiomics features further enhances predictive performance.

The microstructures of pharmaceutical preparations play a pivotal role in determining their critical quality attributes(CQAs),such as drug release,content uniformity,and stability,which greatly impact the safety and efficacy of drugs.Unlike the inherent molecular structures of active pharmaceutical in-gredients(APIs)and excipients,the microstructures of pharmaceutical preparations are developed during the formulation process,presenting unique analytical challenges.In this review,we primarily focus on presenting the research methods used to elucidate the microstructures of pharmaceutical preparations,including X-ray imaging(XRI),scanning electron microscopy(SEM),atomic force microscopy(AFM),Raman spectroscopy,infrared(IR)spectroscopy,and rheometer technology.Subse-quently,we highlight the applications,advantages,and limitations of these methods.Finally,we discuss the current challenges and future perspectives in this field.This review aims to provide a comprehensive reference for understanding the microstructures of pharmaceutical preparations,offering new insights and potential advancements in their development.

Objective To explore the value of 3D and 2D radiomics features models based on multiparameter MRI in predicting Ki-67 expression(with 14%and 20%as the critical values,respectively)in breast cancer.Methods The MRI images of 147 patients with pathologically confirmed Luminal breast cancer were analyzed retrospectively.The patients were randomly divided into training set and test set according to the ratio of 7︰3.The 3D and 2D radiomics features of intratumor and peritumor were extracted from diffusion weighted imaging(DWI),dynamic contrast enhancement(DCE)mask(S0)and DCE phase 3(S3)images.Then the models were constructed by multiple pipeline combinations of three feature normalization methods,two feature dimensionality reduction methods,four feature selection methods,and ten classifiers.The receiver operating characteristic(ROC)curve and the area under the curve(AUC)were used to evaluate the prediction performance of the models in order to select the best 3D and 2D single parame-ter(DWI,S0,S3)and multiparameter combination(S0+S3,S0+DWI,S3+DWI,S0+S3+DWI)models.Finally,the differ-ences between the models were compared by De Long test.Results With 14%as the critical value,the AUC of 3D and 2D models in the training set were 0.726-0.824 and 0.707-0.835,respectively,and those in the test set were 0.724-0.82 and 0.701-0.805.With 20%as the critical value,the AUC of 3D and 2D models in the training set were 0.743-0.868 and 0.793-0.881,respectively,and those in the test set were 0.738-0.853 and 0.743-0.814.There was no significant statistical difference between 3D and 2D models with the same parameter in the two critical values standards.Conclusion The multiparameter MRI-based radiomics models can bet-ter predict the expression of Ki-67 in breast cancer,and the models based on intratumor and peritumor 3D and 2D features have the same prediction efficiency.

AIM:This study investigated the role of solute carrier family 7 member 11(SLC7A11)in sul-fasalazine(SAS)-induced ferroptosis in acute myeloid leukemia(AML)cells,focusing on the inhibitory effect of nuclear factor E2-related factor 2(NRF2)nuclear translocation-mediated activation of SLC7A11 on ferroptosis and its underlying mechanisms.METHODS:SAS-induced proliferation in AML cell lines,Kasumi-1 and THP-1,was assessed using the MTS assay.Cell death inhibitors were employed to determine the mode of cell death.Lipid reactive oxygen species(ROS)levels were measured by flow cytometry;Fe2+,malonodialdehyde(MDA),glutathione(GSH)levels,and glutathione per-oxidase 4(GPX4)activity were assessed using micromethods.Quantitative PCR(qPCR)was performed to evaluate changes in SLC7A11 mRNA during SAS-induced ferroptosis,while Western blot measured SLC7A11 and GPX4 protein levels.Moreover,Western blot assessed NRF2 nuclear translocation post-SAS treatment.The NRF2 inhibitor ML385 was used to validate these effects.SLC7A11 mRNA and protein levels were then measured following combined SAS and ML385 treatment via qPCR and Western blot.Cell viability and ferroptosis-related indices were evaluated under the same treatment conditions.Furthermore,a shRNA vector targeting SLC7A11 was constructed to assess changes in cell viability and ferroptosis markers after SLC7A11 knockdown with SAS.GPX4 protein levels were examined following SLC7A11 knockdown.RESULTS:SAS significantly inhibited the proliferation of Kasumi-1 and THP-1 cells at 200 μmol/L and 300 μmol/L,respectively(P<0.05).Only ferroptosis inhibitors(Fer-1 and DFO)significantly reversed SAS-induced cy-totoxicity(P<0.01).SAS increased lipid ROS,Fe2+,and MDA levels(P<0.01),while reducing GSH and GPX4 activity(P<0.01).The mRNA and protein expressions of SLC7A11 increased during SAS-induced ferroptosis(P<0.01),where-as GPX4 protein decreased significantly(P<0.01).SAS significantly increased the nuclear-to-cytoplasmic NRF2 ratio(P<0.01),which decreased upon co-treatment with ML385(P<0.05).Following SAS and ML385 co-treatment,both SLC7A11 mRNA and protein levels were downregulated(P<0.01).This combination treatment further reduced AML cell viability(P<0.01),an effect reversed by Fer-1 and DFO(P<0.01).Compared with SAS alone,the combination of SAS and ML385 significantly increased lipid ROS,Fe2+,and MDA while reducing GSH levels and GPX4 activity(P<0.01).SLC7A11 knockdown was successfully achieved.Compared with the NC shRNA group,SLC7A11 knockdown cells showed significantly decreased viability after SAS treatment,which was reversed by Fer-1 and DFO(P<0.01).Lipid ROS,Fe2+,and MDA content were significantly increased(P<0.01),and GSH and GPX4 were substantially decreased(P<0.05).Moreover,GPX4 protein expression was considerably reduced after SLC7A11 knockdown(P<0.01).CONCLUSION:SAS induces ferroptosis in AML cells.It promotes the nuclear translocation of NRF2 protein,which activates SLC7A11 ex-pression.Inhibition of NRF2 or downregulation of SLC7A11 sensitizes AML cells to SAS-induced ferroptosis.