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.

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.

Sinopodophyllum hexandruma is a traditional Chinese medicine in China,which is mostly distributed in Gansu,Shaanxi,Sichuan,Qinghai,Yunnan and Xizang,etc.In recent years,with the gradual deepening of the research on the chemical composition and pharmacology-toxicology of Sinopodophyllum hexandruma,its antitumour and antiviral pharmacodynamic evaluation has increasingly become a research hotspot in the industry.Based on the chemical structure,pharmacological properties and the theoretical basis of quality markers(Q-markers),this paper presents an in-depth literature review and analysis of the chemical composition,pharmacological activities and Q-markers of Sinopodophyllum hexandruma,and systematically explores and predicts the Q-markers of Sinopodophyllum hexandruma.It is proposed that Podophyllotoxin,picropodophyllotoxin,podophyllotoxinone,quercetin,kaempferol,quercetin-3-O-β-glucoside can be used as the Q-marker of Sinopodophyllum hexandruma.In the later stage,these index components can be selected to control the whole quality of Sinopodophyllum hexandrum,and provide some data support and theoretical reference for the quality evaluation of Sinopodophyllum hexandrum.

Objective:To develop a rapid assessment scale for healthy aging suitable for the Chinese population.Methods:Based on existing healthy aging assessment scales, national standards, and expert consensus, an initial Healthy Aging Rapid Assessment Scale was drafted through two rounds of expert consultation. A pre-survey was conducted with 3 220 subjects recruited from Guangzhou between July 2023 and July 2024. Items were screened through item analysis and exploratory factor analysis to form the final scale. Reliability and validity of the final scale were validated across five cities: Guangzhou, Dongguan, Shenzhen, Baoding, and Chuxiong.Results:The initial version comprised 36 items, while the finalized scale contained 18 items across three dimensions: metabolic health, mental health, and cognitive health. Test-retest reliability ranged from 0.71 to 0.81 across all study sites. The Spearman-Brown coefficient varied between 0.91-0.96, Cronbach′s α between 0.77-0.83, comparative fit index (CFI) between 0.90-0.98, goodness-of-fit index (GFI) between 0.90-0.99, and root-mean-square error of approximation (RMSEA) between 0.03-0.09. For the three dimensions, reliability and validity metrics demonstrated consistency: Spearman-Brown coefficients 0.87-0.99, Cronbach′s α 0.77-0.83, CFI 0.90-0.98, GFI 0.90-0.99, and RMSEA 0.03-0.09 across four regions.Conclusion:The developed Healthy Aging Rapid Assessment Scale for the Chinese population exhibits robust reliability and validity.

Objective:To explore the value of ultrasound imaging characteristics combined with clinical indicators in assessing the prognosis of patients with pancreatic ductal adenocarcinoma (PDAC).Methods:A retrospective analysis was conducted for patients who underwent pancreatic contrast-enhanced ultrasound (CEUS) from September 2017 to October 2023 at Peking Union Medical College Hospital and were diagnosed with PDAC based on pathological findings. Various parameters were recorded, including CA19-9 levels, tumor size, location, morphologic features, echogenicity, presence of internal cystic components, dilatation of the main pancreatic duct, peripheral vascular invasion, CEUS characteristics, presence or absence of liver metastasis, and treatment methods. In April 2024, patient survival information was obtained through telephone follow-up or review of medical records. Based on the results of the cox regression model analysis, a nomogram model of the risk of death was developed. The receiver operating characteristic (ROC) curves were applied to evaluate the predictive efficacy of the model. The calibration curves were plotted to evaluate the accuracy of the model, and clinical decision curves were used to evaluate the clinical benefit of the model.Results:This study included a total of 207 patients with PDAC. As of April 2024, 71 patients were alive and 136 died, with a median survival time of 14 months (95% CI: 12 -17). Multivariate analysis confirmed that the elevated CA19-9 ( HR=1.689, 95% CI: 1.102-2.588), tumor size >4 cm ( HR=1.641, 95% CI: 1.159-2.322), taller-than-wide shapes ( HR=1.450, 95% CI: 1.019-2.065), incomplete hypo-enhancement ( HR=1.618, 95% CI: 1.100-2.380), and liver metastasis ( HR=1.687, 95% CI: 1.175-2.423) were independent risk factors for survival in patients with PDAC. A nomogram model was further constructed for 6-month, 12-month and 3-year survival of patients with PDAC. The areas under the ROC curve were 0.679, 0.705 and 0.815, respectively. The calibration curves suggested that the model was more accurate, and the clinical decision curves showed that the model had a better clinical benefit. Conclusion:The combined use of ultrasound imaging characteristics and clinical indicators could effectively predict the prognosis of PDAC patients. Specifically, tumor size >4 cm, taller-than-wide shapes, incomplete hypo-enhancement, elevated CA19-9, and the presence of liver metastasis are correlated with poorer survival outcomes. The nomogram model constructed on the basis of these factors can be used to assess the survival of patients with PDAC.

Objective:To develop an artificial intelligence (AI)-based platelet review strategy to identify abnormal platelet histograms with no significant difference between initial impedance platelet count (PLT-I) and PLT-F results.Methods:This study included 5 119 routine blood analysis in Nanfang Hospital of Southern Medical University and its Ganzhou branch from July 2023 and March 2024. Specimens exhibiting abnormal platelet histograms and an initial platelet count >40×10?/L underwent review using the fluorescent platelet count (PLT-F) channel. Consistency of the results was defined as a difference between impedance platelet count (PLT-I) and PLT-F less than ±20% of the PLT-F results. A deep learning model was developed using platelet and red blood cell histogram data from a training set of 3 807 specimens. The model′s diagnostic performance was evaluated on an independent external validation set ( n=805) using receiver operating characteristic (ROC) curve analysis. Changes in the number of reviewed samples and sample turnaround time were analyzed to assess its clinical utility. Results:The deep learning model based on platelet and red blood cell histograms achieved an area under the ROC curve (AUC) of 0.854 in the training set. At a cutoff value of 0.1, the sensitivity was 0.954 and specificity was 0.358. The model could reduce review by 16.80% (190/1 131). In the validation set, the AUC was 0.805, with a sensitivity of 0.955 and specificity of 0.307, corresponding to a reduction of 17.41% (47/270) in reviewed specimens.Conclusion:The platelet review prediction model developed based on deep learning technology can efficiently identify samples with consistent results before and after review, reducing unnecessary reviews and shortening specimen testing time, thereby improving the efficiency of platelet test.

Objective:To analyze the expression of tumor-associated transmembrane protein 61 (TMEM61) in cholangiocarcinoma tissues and its influence on prognosis and immune infiltration, as well as the effect on the proliferation of cholangiocarcinoma cells.Methods:In the cholangiocarcinoma gene chip dataset (TCGA-CHOL), differentially expressed genes between cholangiocarcinoma tissues and normal bile duct tissues were screened, and the upregulated TMEM61 gene was selected for further analysis. Based on the TMEM61 expression, cholangiocarcinoma patients higher than the median value were classified as the high-expression group ( n=17), and those lower than the median value were classified as the low-expression group ( n=18). The Kaplan-Meier survival curve was plotted. Functional and pathway enrichment analyses were conducted on differentially expressed genes related to TMEM61, and the correlations between TMEM61 expression and immune cells and immune molecules were respectively analyzed. The expression level of TMEM61 in cholangiocarcinoma tissues was analyzed by immunohistochemistry; The effect of TMEM61 expression on the proliferation of cholangiocarcinoma cells was detected by Western blotting, CCK-8, clone formation assay, etc. Results:Compared with normal tissues, the expression of TMEM61 mRNA in cholangiocarcinoma tissues was significantly upregulated ( t=18.31, P<0.001). The overall survival rate of patients in the high-expression group of TMEM61 was significantly lower than that in the low-expression group, and the difference was statistically significant ( χ2=7.23, P=0.007). The differentially expressed genes related to TMEM61 were involved in cell proliferation, cell cycle and DNA replication, etc. Compared with normal tissues, regulatory T cells ( t=10.21, P<0.001) and M0-type macrophages ( t=5.89, P=0.008) were significantly increased in cholangiocarcinoma tissues. Plasma cells ( t=7.34, P=0.002), γδT cells ( t=9.87, P<0.001), and M2-type macrophages ( t=11.53, P<0.001) were significantly decreased in cholangiocarcinoma tissues. The expression of TMEM61 was correlated with neurociliary protein 1, tumor necrosis factor ligand superfamily member 15 and B7 homologous protein 3 (all P<0.05). The proportion of positive staining area of TMEM61 protein in normal tissues was (10.15±2.27) %, and that in cholangiocarcinoma tissues was (69.43±11.66) %. The difference was statistically significant ( t=14.97, P<0.001). Inhibition of TMEM61 expression led to a decrease in the number of cholangiocarcinoma cell clones and proliferation activity, and the differences were statistically significant (both P<0.01). Conclusion:The expression of TMEM61 is elevated in cholangiocarcinoma tissues and is associated with poor prognosis. The abnormally high expression of TMEM61 affects the infiltration of immune cells and promotes the proliferation of cholangiocarcinoma cells. TMEM61 is expected to become a potential biomarker for the prognosis assessment of cholangiocarcinoma.

Objective:To analyze the expression of tumor-associated transmembrane protein 61 (TMEM61) in cholangiocarcinoma tissues and its influence on prognosis and immune infiltration, as well as the effect on the proliferation of cholangiocarcinoma cells.Methods:In the cholangiocarcinoma gene chip dataset (TCGA-CHOL), differentially expressed genes between cholangiocarcinoma tissues and normal bile duct tissues were screened, and the upregulated TMEM61 gene was selected for further analysis. Based on the TMEM61 expression, cholangiocarcinoma patients higher than the median value were classified as the high-expression group ( n=17), and those lower than the median value were classified as the low-expression group ( n=18). The Kaplan-Meier survival curve was plotted. Functional and pathway enrichment analyses were conducted on differentially expressed genes related to TMEM61, and the correlations between TMEM61 expression and immune cells and immune molecules were respectively analyzed. The expression level of TMEM61 in cholangiocarcinoma tissues was analyzed by immunohistochemistry; The effect of TMEM61 expression on the proliferation of cholangiocarcinoma cells was detected by Western blotting, CCK-8, clone formation assay, etc. Results:Compared with normal tissues, the expression of TMEM61 mRNA in cholangiocarcinoma tissues was significantly upregulated ( t=18.31, P<0.001). The overall survival rate of patients in the high-expression group of TMEM61 was significantly lower than that in the low-expression group, and the difference was statistically significant ( χ2=7.23, P=0.007). The differentially expressed genes related to TMEM61 were involved in cell proliferation, cell cycle and DNA replication, etc. Compared with normal tissues, regulatory T cells ( t=10.21, P<0.001) and M0-type macrophages ( t=5.89, P=0.008) were significantly increased in cholangiocarcinoma tissues. Plasma cells ( t=7.34, P=0.002), γδT cells ( t=9.87, P<0.001), and M2-type macrophages ( t=11.53, P<0.001) were significantly decreased in cholangiocarcinoma tissues. The expression of TMEM61 was correlated with neurociliary protein 1, tumor necrosis factor ligand superfamily member 15 and B7 homologous protein 3 (all P<0.05). The proportion of positive staining area of TMEM61 protein in normal tissues was (10.15±2.27) %, and that in cholangiocarcinoma tissues was (69.43±11.66) %. The difference was statistically significant ( t=14.97, P<0.001). Inhibition of TMEM61 expression led to a decrease in the number of cholangiocarcinoma cell clones and proliferation activity, and the differences were statistically significant (both P<0.01). Conclusion:The expression of TMEM61 is elevated in cholangiocarcinoma tissues and is associated with poor prognosis. The abnormally high expression of TMEM61 affects the infiltration of immune cells and promotes the proliferation of cholangiocarcinoma cells. TMEM61 is expected to become a potential biomarker for the prognosis assessment of cholangiocarcinoma.

Objective:To develop an artificial intelligence (AI)-based platelet review strategy to identify abnormal platelet histograms with no significant difference between initial impedance platelet count (PLT-I) and PLT-F results.Methods:This study included 5 119 routine blood analysis in Nanfang Hospital of Southern Medical University and its Ganzhou branch from July 2023 and March 2024. Specimens exhibiting abnormal platelet histograms and an initial platelet count >40×10?/L underwent review using the fluorescent platelet count (PLT-F) channel. Consistency of the results was defined as a difference between impedance platelet count (PLT-I) and PLT-F less than ±20% of the PLT-F results. A deep learning model was developed using platelet and red blood cell histogram data from a training set of 3 807 specimens. The model′s diagnostic performance was evaluated on an independent external validation set ( n=805) using receiver operating characteristic (ROC) curve analysis. Changes in the number of reviewed samples and sample turnaround time were analyzed to assess its clinical utility. Results:The deep learning model based on platelet and red blood cell histograms achieved an area under the ROC curve (AUC) of 0.854 in the training set. At a cutoff value of 0.1, the sensitivity was 0.954 and specificity was 0.358. The model could reduce review by 16.80% (190/1 131). In the validation set, the AUC was 0.805, with a sensitivity of 0.955 and specificity of 0.307, corresponding to a reduction of 17.41% (47/270) in reviewed specimens.Conclusion:The platelet review prediction model developed based on deep learning technology can efficiently identify samples with consistent results before and after review, reducing unnecessary reviews and shortening specimen testing time, thereby improving the efficiency of platelet test.