OBJECTIVE To establish management index system for rational drug use of key monitoring drugs， and provide reference for the management of key monitoring drugs in the hospitals. METHODS First， the management index system for rational drug use of key monitoring drugs was drafted by collecting the evidence from related medical literature. Next， using a modified Delphi method， twenty experienced experts from the fields of pharmacy， medical practice， healthcare insurance， and finance were selected to participate in two rounds of questionnaire consultations. Based on the expert enthusiasm coefficient， authority coefficient， degree of opinion concentration， and degree of coordination， the final indicators were determined to establish a management index system for rational drug use of key monitored drugs in medical institutions. RESULTS The expert enthusiasm coefficients reached 100% in both rounds of consultation. In first-level， second-level and third-level indicators， the authority coefficients of experts were 0.89， 0.86 and 0.87， and coordination coefficients of the experts in importance score were 0.300 （P＜ 0.05）， 0.125 （P＜0.05） and 0.139 （P＜0.05）， respectively. The average score for the importance of all indicators reached over 3.5， in which the full score ratio ranged from 35% to 100%. Except that the variation coefficient of a third-level indicator “number of specifications purchased for key monitored drugs” was 0.26， the variation coefficients of rest indicators were less than or equal to 0.25. Based on the results of expert consultation， final version of the management index system established in this study， including two first-level indicators （drug procurement and use， and rational drug use）， five second-level indicators （such as the accessibility， cost-effectiveness） and twenty third-level indicators （such as the number of specifications purchased for key monitored drugs， the increase in the cost of key monitored drugs）. CONCLUSIONS The management index system established in this study possesses high reliability and strong operability， and may provide a reference for the management of key monitoring drugs in the hospitals.

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.

Background and objective The pathological types of lung ground glass nodules(GGNs)show great significance to the clinical treatment.This study was aimed to predict pathological types of GGNs based on computed tomog-raphy(CT)quantitative parameters.Methods 389 GGNs confirmed by postoperative pathology were selected,including 138 cases of precursor glandular lesions[atypical adenomatous hyperplasia(AAH)and adenocarcinoma in situ(AIS)],109 cases of microinvasive adenocarcinoma(MIA)and 142 cases of invasive adenocarcinoma(IAC).The morphological characteristics of nodules were evaluated subjectively by radiologist,as well as artificial intelligence(AI).Results In the subjective CT signs,the maximum diameter of nodule and the frequency of spiculation,lobulation and pleural traction increased from AAH+AIS,MIA to IAC.In the AI quantitative parameters,parameters related to size and CT value,proportion of solid component,energy and entropy increased from AAH+AIS,MIA to IAC.There was no significant difference between AI quantitative parameters and the subjective CT signs for distinguishing the pathological types of GGNs.Conclusion AI quantitative parameters were valu-able in distinguishing the pathological types of GGNs.

Objective:To conduct a scoping review of the application of mobile health applications (MHA) in the care of patients with colorectal cancer, summarizing the development process, the functions achieved, as well as the evaluation metrics, to provide references for MHA practice and related research.Methods:Following the scoping review framework, a comprehensive search was conducted across both domestic and international databases, including the China Biomedical Literature Database, Wanfang Database, China National Knowledge Infrastructure (CNKI), VIP Database, Cochrane Library, PubMed, Embase, CINAHL, Web of Science, and Scopus. The search period was from the database inception to February, 2024.Results:A total of 16 studies were included. The development of MHA involved multiple methods including literature reviews, qualitative interviews, consultations with multidisciplinary teams, and guidance from theoretical models. The functions of MHA include health education, peer support, guided feedback, monitoring, and reminder features. Evaluation metrics for MHA comprise usability, adherence, and effectiveness.Conclusions:MHA has demonstrated positive effects in enhancing patients' knowledge and alleviating symptoms such as fatigue and vomiting in colorectal cancer patients. However, it is still in its early stages, and further high-quality studies are needed to scientifically develop MHA that meets patients' needs.

Humans ; Aminoquinolines/therapeutic use* ; Carcinoma, Non-Small-Cell Lung/genetics* ; Lung Neoplasms/genetics* ; Mutation/genetics* ; Standard of Care

Objective:To explore the mediating effect of rumination between social anxiety and mobile phone addiction tendency among college students.Methods:The social avoidance and distress scale (SAD), mobile phone addiction tendency scale (MPATS) and ruminative response scale (RRS) were used to measure 682 college students.SPSS 25.0 and Mplus 7.0 were used for data analysis.Pearson correlation analysis was used to explore the correlation among social anxiety, mobile phone addiction tendency and rumination.Bootstrap method was used to test the mediating effect of rumination between social anxiety and mobile phone addiction tendency among college students.Results:The total score of social anxiety was (13.36±6.02), the total score of rumination was (51.04±11.56), and the total score of mobile phone addiction tendency was (40.46±11.74). The detection rate of mobile phone addiction tendency was 23.90%, and the detection rate of social anxiety was 49.85%.The total score of social anxiety was positively correlated with the total score of rumination ( r=0.31, P<0.01) and the total score of mobile phone addiction tendency ( r=0.25, P<0.01). The total score of rumination was positively correlated with the total score of mobile phone addiction tendency ( r=0.46, P<0.01). The structural equation model showed that rumination played a complete mediating role between high social anxiety and mobile phone addiction tendency, and the effect value was 0.18.There was no mediating effect between low social anxiety and mobile phone addiction tendency, and the direct effect value was 0.33. Conclusion:Low social anxiety directly affects college students' tendency of mobile phone addiction, while high social anxiety indirectly affects college students' tendency of mobile phone addiction through rumination.

The concept of theranostics, where individual patient-level biological information is used to choose the optimal therapy for that individual, has become more popular in the modern era of ‘personalised’ medicine. With the growth of theranostics, nuclear medicine as a specialty is uniquely poised to grow along with the ever-increasing number of concepts combining imaging and therapy. This special report summarises the status and growth of Theranostic Nuclear Medicine in Singapore.We will cover our experience with the use of radioiodine, radioiodinated metaiodobenzylguanidine, peptide receptor radionuclide therapy, prostate specific membrane antigen radioligand therapy, radium-223 and yttrium-90 selective internal radiation therapy.We also include a section on our radiopharmacy laboratory, crucial to our implementation of theranostic principles. Radionuclide theranostics has seen tremendous growth and we hope to be able to grow alongside to continue to serve the patients in Singapore and in the region.

The concept of theranostics, where individual patient-level biological information is used to choose the optimal therapy for that individual, has become more popular in the modern era of ‘personalised’ medicine. With the growth of theranostics, nuclear medicine as a specialty is uniquely poised to grow along with the ever-increasing number of concepts combining imaging and therapy. This special report summarises the status and growth of Theranostic Nuclear Medicine in Singapore.We will cover our experience with the use of radioiodine, radioiodinated metaiodobenzylguanidine, peptide receptor radionuclide therapy, prostate specific membrane antigen radioligand therapy, radium-223 and yttrium-90 selective internal radiation therapy.We also include a section on our radiopharmacy laboratory, crucial to our implementation of theranostic principles. Radionuclide theranostics has seen tremendous growth and we hope to be able to grow alongside to continue to serve the patients in Singapore and in the region.
Hope ; Humans ; Lutetium ; Membranes ; Nuclear Medicine ; Prostate ; Radium ; Receptors, Peptide ; Singapore ; Theranostic Nanomedicine ; Yttrium

Objective To investigate the effect of Kuanxiong Lifei decoction on inflammatory factors in patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD) and turbid phlegm obstructing lung syndrome. Methods Two hundred patients with AECOPD and turbid phlegm obstructing lung syndrome diagnosed by traditional Chinese medicine (TCM) differentiation visiting four hospitals of Longhua Hospital Affiliated to Shanghai University of TCM, Shanghai Seventh People's Hospital, Punan Hospital of Shanghai Pudong New Area, Gongli Hospital of Shanghai Pudong New Area were selected from May 2017 to March 2018, and they were divided into a test group and a control group by a random number table, 100 cases per group. The patients in the two groups were treated with routine western medicine according to the guidelines, and in the test group, additionally Kuanxiong Lifei decoction (components: pinellia ternate 15 g, allium macrostemon 12 g, ephedra 9 g, trichosanthes 30 g, poria cocos 15 g, almond 12 g, lumbricus 12 g, citrus peels 12 g, peach kernel 12 g , roasted licorice 6 g) was used for 10 days, the decoction was uniformly made by Chinese Medicine Pharmacy of Longhua Hospital, 1 dose daily, 2 times a day orally taken, warm 200 mL each time, 0.5 hours before or after meal. The efficacy was evaluated after treatment for 10 days. The level changes of white blood cell count (WBC), neutrophils (N), C-reactive protein (CRP), interleukin-8 (IL-8), tumor necrosis factor-α (TNF-α) before and after treatment and the improvement of TCM syndrome scores and clinical efficacy were observed in two groups. Results After treatment, the levels of WBC, N, CRP, IL-8, TNF-α, TCM syndrome score of the patients in the two group were significantly decreased compared with those before treatment in the two groups (P < 0.05), and the above indexes in the test group were all significantly lower than those in the control group after treatment [WBC (×109/L): 6.58±1.41 vs.7.44±1.85, N: 0.58±0.08 vs. 0.64±0.08, CRP (mg/L): 7.3±1.8 vs. 9.6±1.7, IL-8 (ng/L): 23.5±6.2 vs. 27.8±9.8, TNF-α (ng/L): 9.45±2.18 vs. 10.25±1.67, TCM syndrome total score: 4.0 (3.0, 8.0) vs. 8.0 (5.0, 10.0), all P < 0.05]. The total effective rate of the test group was significantly higher than that of the control group [88% (88/100) vs. 84% (84/100), P < 0.05]. Conclusion Kuanxiong Lifei decoction can significantly reduce lung inflammatory factors, ameliorate overall symptoms and improve the prognosis of AECOPD patients with turbid phlegm obstructing lung syndrome.