ObjectiveTo establish a method for the identification of Haliotidis Concha and its counterfeits， and to improve its quality evaluation method. MethodsA total of 17 batches of Haliotis discus hannai， 4 batches of H. ruber， 3 batches of H. laevigata， 3 batches of H. ovina， 3 batches of H. diversicolor， 3 batches of H. asinina， 3 batches of H. iris were collected. Ultra-high performance liquid chromatography-quadrupole/electrostatic field orbitrap high-resolution mass spectrometry（UPLC-Q-Exactive-Orbitrap-MS/MS） was used to analyze the hydrolysates of different original Haliotidis Concha and its counterfeits， and the potential characteristic ions of each species were screened by Venn diagram. UPLC-triple quadrupole tandem mass spectrometry（UPLC-QqQ-MS/MS） was used to validate the characteristic ions， and the specific detection method of the characteristic ions was established. ResultsA total of 1 182， 167， 47， 89， 104， 203， 424 potential characteristic ions were screened from H. discus hannai， H. ruber， H. laevigata， H. ovina， H. diversicolor， H. asinina and H. iris， respectively. And 9 characteristic ions were selected. The precision， stability and repeatability of the 9 characteristic ions in the established identification method met the requirements. Different original Haliotidis Concha and its counterfeits could detect their own characteristic ions， including m/z 631.83-886.48（double charge） and m/z 631.83-443.74（double charge） of H. discus hannai， m/z 699.28-232.11（double charge） and m/z 699.28-544.27（double charge） of H. ruber， m/z 535.76-752.37（double charge） and m/z 535.76-548.28（double charge） of H. laevigata， m/z 708.35-442.28（double charge） and m/z 708.35-215.14（double charge） of H. ovina， m/z 561.33-614.86（triple charge）， m/z 561.33-468.28（triple charge）， m/z 608.29-618.32（double charge） and m/z 608.29-390.21（double charge） of H. diversicolor， m/z 769.85-274.10（double charge）， m/z 769.85-532.75（double charge）， m/z 827.43-646.36（single charge）， m/z 827.43-257.12（single charge） of H. asinina， and m/z 468.24-576.29（double charge） and m/z 468.24-505.26（double charge） of H. iris. ConclusionIn this study， a total of 9 characteristic ions are screened from 6 kinds of original Haliotidis Concha and its counterfeits， and a specific identification method is established， which is helpful to solve the limitations of the existing quality evaluation methods of Haliotidis Concha， and provide a basis for the production， circulation and medication quality.

ObjectiveTo establish a method for the identification of Haliotidis Concha and its counterfeits， and to improve its quality evaluation method. MethodsA total of 17 batches of Haliotis discus hannai， 4 batches of H. ruber， 3 batches of H. laevigata， 3 batches of H. ovina， 3 batches of H. diversicolor， 3 batches of H. asinina， 3 batches of H. iris were collected. Ultra-high performance liquid chromatography-quadrupole/electrostatic field orbitrap high-resolution mass spectrometry（UPLC-Q-Exactive-Orbitrap-MS/MS） was used to analyze the hydrolysates of different original Haliotidis Concha and its counterfeits， and the potential characteristic ions of each species were screened by Venn diagram. UPLC-triple quadrupole tandem mass spectrometry（UPLC-QqQ-MS/MS） was used to validate the characteristic ions， and the specific detection method of the characteristic ions was established. ResultsA total of 1 182， 167， 47， 89， 104， 203， 424 potential characteristic ions were screened from H. discus hannai， H. ruber， H. laevigata， H. ovina， H. diversicolor， H. asinina and H. iris， respectively. And 9 characteristic ions were selected. The precision， stability and repeatability of the 9 characteristic ions in the established identification method met the requirements. Different original Haliotidis Concha and its counterfeits could detect their own characteristic ions， including m/z 631.83-886.48（double charge） and m/z 631.83-443.74（double charge） of H. discus hannai， m/z 699.28-232.11（double charge） and m/z 699.28-544.27（double charge） of H. ruber， m/z 535.76-752.37（double charge） and m/z 535.76-548.28（double charge） of H. laevigata， m/z 708.35-442.28（double charge） and m/z 708.35-215.14（double charge） of H. ovina， m/z 561.33-614.86（triple charge）， m/z 561.33-468.28（triple charge）， m/z 608.29-618.32（double charge） and m/z 608.29-390.21（double charge） of H. diversicolor， m/z 769.85-274.10（double charge）， m/z 769.85-532.75（double charge）， m/z 827.43-646.36（single charge）， m/z 827.43-257.12（single charge） of H. asinina， and m/z 468.24-576.29（double charge） and m/z 468.24-505.26（double charge） of H. iris. ConclusionIn this study， a total of 9 characteristic ions are screened from 6 kinds of original Haliotidis Concha and its counterfeits， and a specific identification method is established， which is helpful to solve the limitations of the existing quality evaluation methods of Haliotidis Concha， and provide a basis for the production， circulation and medication quality.

Objective To explore the clinical value of artificial intelligence (AI) quantitative parameters in distinguishing pathological grades of stageⅠ invasive adenocarcinoma (IAC). Methods Clinical data of patients with clinical stageⅠ IAC admitted to Yantaishan Hospital Affiliated to Binzhou Medical University from October 2018 to May 2023 were retrospectively analyzed. Based on the 2021 WHO pathological grading criteria for lung adenocarcinoma, IAC was divided into gradeⅠ, grade Ⅱ, and grade Ⅲ. The differences in parameters among the groups were compared, and logistic regression analysis was used to evaluate the predictive efficacy of AI quantitative parameters for grade Ⅲ IAC patients. Parameters were screened using least absolute shrinkage and selection operator (LASSO) regression analysis. Three machine learning models were constructed based on these parameters to predict grade Ⅲ IAC and were internally validated to assess their efficacy. Nomograms were used for visualization. Results A total of 261 IAC patients were included, including 101 males and 160 females, with an average age of 27-88 (61.96±9.17) years. Six patients had dual primary lesions, and different lesions from the same patient were analyzed as independent samples. There were 48 patients of gradeⅠ IAC, 89 patients of grade Ⅱ IAC, and 130 patients of grade Ⅲ IAC. There were statitical differences in the AI quantitive parameters such as consolidation/tumor ratio (CTR), ect among the three goups. (P<0.05). Univariate analysis showed that the differences in all variables except age were statistically significant (P<0.05) between the group gradeⅠ+grade Ⅱand the group grade Ⅲ . Multivariate analysis suggested that CTR and CT standard deviation were independent risk factors for identifying grade Ⅲ IAC, and the two were negatively correlated. Grade Ⅲ IAC exhibited advanced TNM staging, more pathological high-risk factors, higher lymph node metastasis rate, and higher proportion of advanced structure. CTR was positively correlated with the proportion of advanced structures in all patients. This correlation was also observed in grade Ⅲ but not in gradeⅠand grade ⅡIAC. CTR and CT median value were selected by using LASSO regression. Logistic regression, random forest, and XGBoost models were constructed and validated, among which, the XGBoost model demonstrated the best predictive performance. Conclusion Cautious consideration should be given to grade Ⅲ IAC when CTR is higher than 39.48% and CT standard deviation is less than 122.75 HU. The XGBoost model based on combined CTR and CT median value has good predictive efficacy for grade Ⅲ IAC, aiding clinicians in making personalized clinical decisions.

BACKGROUND: Ferroptosis-related genes play a crucial role in regulating intracellular iron homeostasis and lipid peroxidation, and they are involved in the regulation of tumor growth and drug resistance. The expression of ferroptosis-related genes in tumor tissues can be used to predict patients' future survival times, aiding doctors and patients in anticipating disease progression. Based on the sequencing data of lung adenocarcinoma (LUAD) patients from The Cancer Genome Atlas (TCGA) database, this study identified genes involved in the regulation of ferroptosis, constructed a prognostic model, and evaluated the predictive performance of the model. METHODS: A total of 1467 ferroptosis-related genes were obtained from the GeneCards database. Gene expression profiles and clinical data from 541 LUAD patients were collected from the TCGA database. The expression data of all ferroptosis-related genes were extracted, and differentially expressed genes were identified using R software. Survival analysis was performed on these genes to screen for those with prognostic value. Subsequently, a prognostic risk scoring model for ferroptosis-related genes was constructed using LASSO regression model. Each LUAD patient sample was scored, and the patients were divided into high-risk and low-risk groups based on the median score. Receiver operating characteristic (ROC) curves were plotted, and the area under the curve (AUC) was calculated. Kaplan-Meier survival curves were generated to assess model performance, followed by validation in an external dataset. Finally, univariate and multivariate Cox regression analyses were conducted to evaluate the independent prognostic value and clinical relevance of the model. RESULTS: Through survival analysis, 121 ferroptosis-related genes associated with prognosis were initially identified. Based on this, a LUAD prognostic risk scoring model was constructed using 12 ferroptosis-related genes (ALG3, C1QTNF6, CCT6A, GLS2, KRT6A, LDHA, NUPR1, OGFRP1, PCSK9, TRIM6, IGF2BP1 and MIR31HG). The results indicated that patients in the high-risk group had significantly shorter survival time than those in the low-risk group (P<0.001), and the model demonstrated good predictive performance in both the training set (1-yr AUC=0.721) and the external validation set (1-yr AUC=0.768). Risk scores were significantly associated with the prognosis of LUAD patients in both univariate and multivariate Cox regression analyses (P<0.001), suggesting that this score is an important prognostic factor for LUAD patients. CONCLUSIONS This study successfully established a LUAD risk scoring model composed of 12 ferroptosis-related genes. In the future, this model is expected to be used in conjunction with the tumor-node-metastasis (TNM) staging system for prognostic predictions in LUAD patients.
Humans ; Ferroptosis/genetics* ; Prognosis ; Adenocarcinoma of Lung/pathology* ; Lung Neoplasms/pathology* ; Male ; Female ; Gene Expression Regulation, Neoplastic ; Middle Aged ; ROC Curve

Objective:To investigate the efficacy and safety of percutaneous trans-hepatic choledochoscopic lithotripsy (PTCSL) in the treatment of recurrent hepatobiliary calculi under enhanced recovery after surgery (ERAS) mode.Methods:Clinical data of 88 patients with recurrent hepatobiliary calculi, who were treated with PTCSL at Zhengzhou Central Hospital Affiliated to Zhengzhou University and the First Affiliated Hospital of Xi'an Jiaotong University between June 2022 and June 2024 were retrospectively analyzed, including 34 males and 54 females, aged (52.0±13.8) years. The scheme includes preoperative education, prophylactic antibiotic application, ensuring the quality of surgery, early postoperative feeding and activity, etc. The operation can be divided into two fashions: percutaneous transhepatic cholangial drainage and PTCSL, which can be completed in one stage (one-stage expansion method) or in two stages (staged expansion method). Clinical data such as gender, age, operative time, intraoperative blood loss, residual stone, and surgical complications were recorded.Results:All 88 patients underwent PTCSL under ERAS mode successfully, including 52 cases using one-stage expansion method and 36 cases using staged expansion method. The operative time was (53±20) min, the intraoperative blood loss was (9.7±3.8) ml, the postoperative hospital stay was (3.6±1.7) d, and the hospitalization cost was (17 500±4 700) yuan. Sixty-nine patients (78.4%, 69/88) had one-time stone removal in the first PTCSL. A total of 19 cases of residual stones were managed again by percutaneous sinus soft choledochoscopy, of which 12 cases were managed by one-time choledochoscopy, five cases by two-time choledochoscopy, and two cases by three-time choledochoscopy. The rate of residual stone was significantly higher in one-stage expansion method compared to staged expansion method [28.8% (15/52) vs. 11.1% (4/36), P=0.040]. No death, conversion to open surgery, or severe complications such as intra-abdominal hemorrhage or bile leakage occurred in the patients. No residual stones or recurrence were found during the follow-ups of (7.5±2.1) months after discharge. Conclusion:PTCSL under ERAS mode is safe and effective in the treatment of recurrent hepatobiliary calculi.

Objective:To apply artificial intelligence（AI）in classifying ovarian tumors on ultrasound images，and compare the diagnostic results of several sonographers with varying seniority levels.Methods:A total of 645 patients diagnosed with adnexal masses via gynecological ultrasound examination at Qilu Hospital of Shandong University from January 2021 to December 2024 were enrolled. Three deep learning architectures，i.e.，Alexnet，Densenet121，and Resnet50 were developed and used to internally test the classification effectiveness of ovarian tumors，while the optimal model was selected for external testing. Two junior sonographers and two senior sonographers were recruited to independently diagnose ovarian tumors in the external test dataset. Subsequently，the benign and malignant results of the model's predictions were disclosed to each sonographer，and their revised diagnoses on the same external test data in combination with the best AI model were recorded.Results:The optimal model achieved an accuracy of 0.941，sensitivity of 0.936，and specificity of 0.944 on the internal test dataset，and maintained robust performance on the external test dataset with accuracy of 0.891，sensitivity of 0.880，and specificity of 0.907. Compared to junior sonographers，the optimal model demonstrated significantly higher sensitivity in discriminating benign from malignant ovarian tumors（0.880 vs. 0.723，0.602；all P<0.05）. No statistically significant difference was observed in diagnostic accuracy between the optimal model and senior sonographer 1（ P=0.05）. With assistance from the optimal model，junior sonographers achieved significant improvements in both sensitivity and specificity（sensitivity：0.723 vs. 0.843，0.602 vs. 0.819；specificity：0.778 vs. 0.833，0.685 vs. 0.741；all P<0.05）. Conclusions:The optimal model achieves comparable performance to that of senior sonographers in ovarian tumor classification. With model assistance，the diagnostic performance of junior sonographers is significantly improved.

Objective:To explore the impact of general self-efficacy on the work immersion experience and the mediating effect of resilience in intern nursing students, so as to provide a basis for the development of interventions.Methods:Convenience sampling was used to select 210 intern nursing students in the Shandong Provincial Third Hospital from January to September 2024 for the study. Nursing students were surveyed using the General Information Questionnaire, Work Immersion Experience Scale, Medical Staff Resilience Scale, and General Self-Efficacy Scale. The mediating effects test was performed using the Process plug-in in SPSS 26.0.Results:A total of 210 questionnaires were distributed and 204 valid questionnaires were recovered, with a valid recovery rate of 97.14%. The total scores of the Work Immersion Experience Scale, Medical Staff Resilience Scale, and General Self-Efficacy Scale were (107.33±17.06) , (63.12±12.96) and (25.65±4.60) , respectively. General self-efficacy was a positive predictor of the work immersion experience ( β=0.321) of intern nursing students. Resilience partially mediated the effect between general self-efficacy and the work immersion experience of intern nursing students, with an effect size of 0.126 and a 95% confidence interval of (0.050, 0.211) , which accounted for 28.19% (0.126/0.447) of the total effect. Conclusions:Intern nursing students with high general self-efficacy have good work immersion experiences, and resilience partially mediates the effect between self-efficacy and work immersion experiences. Nursing educators and administrators can improve the work immersion experience of intern nursing students by designing targeted training courses, providing adequate practice opportunities, and conducting psychotherapy to enhance their self-efficacy and resilience.

Objective:To investigate the efficacy and safety of percutaneous trans-hepatic choledochoscopic lithotripsy (PTCSL) in the treatment of recurrent hepatobiliary calculi under enhanced recovery after surgery (ERAS) mode.Methods:Clinical data of 88 patients with recurrent hepatobiliary calculi, who were treated with PTCSL at Zhengzhou Central Hospital Affiliated to Zhengzhou University and the First Affiliated Hospital of Xi'an Jiaotong University between June 2022 and June 2024 were retrospectively analyzed, including 34 males and 54 females, aged (52.0±13.8) years. The scheme includes preoperative education, prophylactic antibiotic application, ensuring the quality of surgery, early postoperative feeding and activity, etc. The operation can be divided into two fashions: percutaneous transhepatic cholangial drainage and PTCSL, which can be completed in one stage (one-stage expansion method) or in two stages (staged expansion method). Clinical data such as gender, age, operative time, intraoperative blood loss, residual stone, and surgical complications were recorded.Results:All 88 patients underwent PTCSL under ERAS mode successfully, including 52 cases using one-stage expansion method and 36 cases using staged expansion method. The operative time was (53±20) min, the intraoperative blood loss was (9.7±3.8) ml, the postoperative hospital stay was (3.6±1.7) d, and the hospitalization cost was (17 500±4 700) yuan. Sixty-nine patients (78.4%, 69/88) had one-time stone removal in the first PTCSL. A total of 19 cases of residual stones were managed again by percutaneous sinus soft choledochoscopy, of which 12 cases were managed by one-time choledochoscopy, five cases by two-time choledochoscopy, and two cases by three-time choledochoscopy. The rate of residual stone was significantly higher in one-stage expansion method compared to staged expansion method [28.8% (15/52) vs. 11.1% (4/36), P=0.040]. No death, conversion to open surgery, or severe complications such as intra-abdominal hemorrhage or bile leakage occurred in the patients. No residual stones or recurrence were found during the follow-ups of (7.5±2.1) months after discharge. Conclusion:PTCSL under ERAS mode is safe and effective in the treatment of recurrent hepatobiliary calculi.

Objective:To explore the impact of general self-efficacy on the work immersion experience and the mediating effect of resilience in intern nursing students, so as to provide a basis for the development of interventions.Methods:Convenience sampling was used to select 210 intern nursing students in the Shandong Provincial Third Hospital from January to September 2024 for the study. Nursing students were surveyed using the General Information Questionnaire, Work Immersion Experience Scale, Medical Staff Resilience Scale, and General Self-Efficacy Scale. The mediating effects test was performed using the Process plug-in in SPSS 26.0.Results:A total of 210 questionnaires were distributed and 204 valid questionnaires were recovered, with a valid recovery rate of 97.14%. The total scores of the Work Immersion Experience Scale, Medical Staff Resilience Scale, and General Self-Efficacy Scale were (107.33±17.06) , (63.12±12.96) and (25.65±4.60) , respectively. General self-efficacy was a positive predictor of the work immersion experience ( β=0.321) of intern nursing students. Resilience partially mediated the effect between general self-efficacy and the work immersion experience of intern nursing students, with an effect size of 0.126 and a 95% confidence interval of (0.050, 0.211) , which accounted for 28.19% (0.126/0.447) of the total effect. Conclusions:Intern nursing students with high general self-efficacy have good work immersion experiences, and resilience partially mediates the effect between self-efficacy and work immersion experiences. Nursing educators and administrators can improve the work immersion experience of intern nursing students by designing targeted training courses, providing adequate practice opportunities, and conducting psychotherapy to enhance their self-efficacy and resilience.

Objective:To apply artificial intelligence（AI）in classifying ovarian tumors on ultrasound images，and compare the diagnostic results of several sonographers with varying seniority levels.Methods:A total of 645 patients diagnosed with adnexal masses via gynecological ultrasound examination at Qilu Hospital of Shandong University from January 2021 to December 2024 were enrolled. Three deep learning architectures，i.e.，Alexnet，Densenet121，and Resnet50 were developed and used to internally test the classification effectiveness of ovarian tumors，while the optimal model was selected for external testing. Two junior sonographers and two senior sonographers were recruited to independently diagnose ovarian tumors in the external test dataset. Subsequently，the benign and malignant results of the model's predictions were disclosed to each sonographer，and their revised diagnoses on the same external test data in combination with the best AI model were recorded.Results:The optimal model achieved an accuracy of 0.941，sensitivity of 0.936，and specificity of 0.944 on the internal test dataset，and maintained robust performance on the external test dataset with accuracy of 0.891，sensitivity of 0.880，and specificity of 0.907. Compared to junior sonographers，the optimal model demonstrated significantly higher sensitivity in discriminating benign from malignant ovarian tumors（0.880 vs. 0.723，0.602；all P<0.05）. No statistically significant difference was observed in diagnostic accuracy between the optimal model and senior sonographer 1（ P=0.05）. With assistance from the optimal model，junior sonographers achieved significant improvements in both sensitivity and specificity（sensitivity：0.723 vs. 0.843，0.602 vs. 0.819；specificity：0.778 vs. 0.833，0.685 vs. 0.741；all P<0.05）. Conclusions:The optimal model achieves comparable performance to that of senior sonographers in ovarian tumor classification. With model assistance，the diagnostic performance of junior sonographers is significantly improved.