Objective:To develop a preoperative differentiation model for colorectal mucinous adenocarcinoma and non-mucinous adenocarcinoma using a combination of contrast-enhanced CT radiomics and deep learning methods.Methods:This is a retrospective cohort study. Clinical data of colorectal cancer patients confirmed by postoperative pathological examination were retrospectively collected from January 2016 to December 2023 at Shanghai General Hospital Affiliated to Shanghai Jiao Tong University School of Medicine (Center 1, n=220) and the First Affiliated Hospital of Bengbu Medical University (Center 2, n=51). Among them, there were 108 patients diagnosed with mucinous adenocarcinoma, including 55 males and 53 females, with an age of (68.4±12.2) years (range: 38 to 96 years); and 163 patients diagnosed with non-mucinous adenocarcinoma, including 96 males and 67 females, with an age of (67.9±11.0) years (range: 43 to 94 years). The cases from Center 1 were divided into a training set ( n=156) and an internal validation set ( n=64) using stratified random sampling in a 7∶3 ratio, and the cases from Center 2 were used as an independent external validation set ( n=51). Three-dimensional tumor volume of interest was manually segmented on venous-phase contrast-enhanced CT images. Radiomics features were extracted using PyRadiomics, and deep learning features were extracted using the ResNet-18 network. The two sets of features were then combined to form a joint feature set. The consistency of manual segmentation was assessed using the intraclass correlation coefficient. Feature dimensionality reduction was performed using the Mann-Whitney U test and the least absolute shrinkage and selection operator regression. Six machine learning algorithms were used to construct models based on radiomics features, deep learning features, and combined features, including support vector machine, logistic regression, random forest, extreme gradient boosting, k-nearest neighbors, and decision tree. The discriminative performance of each model was evaluated using receiver operating characteristic curves, the area under the curve (AUC), DeLong test, and decision curve analysis. Results:After feature selection, 22 features with the most discriminative value were finally retained, among which 12 were traditional radiomics features and 10 were deep learning features. In the internal validation set, the Random Forest algorithm based on the combined features model achieved the best performance (AUC=0.938, 95% CI: 0.875 to 0.984), which was superior to the single-modality radiomics feature model (AUC=0.817, 95% CI: 0.702 to 0.913, P=0.048) and the deep learning feature model (AUC=0.832, 95% CI: 0.727 to 0.926, P=0.087); in the independent external validation set, the Random Forest algorithm with the combined features model maintained the highest discriminative performance (AUC=0.891, 95% CI: 0.791 to 0.969), which was superior to the single-modality radiomics feature model (AUC=0.770, 95% CI: 0.636 to 0.890, P=0.045) and the deep learning feature model (AUC=0.799, 95% CI: 0.652 to 0.911, P=0.169). Conclusion:The combined model based on radiomics and deep learning features from venous-phase enhanced CT demonstrates good performance in the preoperative differentiation of colorectal mucinous from non-mucinous adenocarcinoma.

Objective:To evaluate the effectiveness of a workshop-driven interactive teaching model in clinical alarm training for undergraduate nursing interns in the intensive care unit (ICU).Methods:A total of 120 undergraduate nursing students who interned in the ICU from November 2023 to April 2024 were enrolled and randomly assigned using a random number table into a control group ( n=60, traditional teaching) and an intervention group ( n=60, workshop-driven interactive teaching). Clinical alarm knowledge, attitudes, and practices (KAP); perception of patient safety culture; and training satisfaction were assessed before and after the training using validated questionnaires. Differences between groups were analyzed using independent sample t-test, paired sample t-test, chi-square test, and the Mann-Whitney U test with SPSS 22.0. Results:There were no significant differences between the two groups in clinical alarm KAP scores or patient safety culture perception scores before the training ( P>0.05). After the training, the intervention group showed significantly higher scores in clinical alarm KAP [(127.58±6.45) vs. (123.48±7.61), t=3.17, P=0.002] and patient safety culture perception [(99.44±9.17) vs. (95.45±12.03), t=-2.04, P=0.044] compared with the control group. Satisfaction with training content, teaching quality, and overall instructional design was significantly higher in the intervention group than in the control group ( P<0.05). Within-group comparisons showed that the intervention group had significant improvements in clinical alarm KAP [(127.58±6.45) vs. (120.10±10.25), t=-5.10, P<0.001] and patient safety culture perception [(99.44±9.17) vs. (94.46±12.12), t=-2.65, P=0.010] after training. No significant changes were observed in the control group after training ( P>0.05). Conclusions:The workshop-driven interactive teaching model can effectively enhance the clinical alarm KAP levels and patient safety culture perception among undergraduate nursing interns in the ICU. This teaching approach provides a scientific basis for optimizing clinical alarm education and cultivating high-quality nursing professionals.

Objective:To evaluate the effectiveness of a workshop-driven interactive teaching model in clinical alarm training for undergraduate nursing interns in the intensive care unit (ICU).Methods:A total of 120 undergraduate nursing students who interned in the ICU from November 2023 to April 2024 were enrolled and randomly assigned using a random number table into a control group ( n=60, traditional teaching) and an intervention group ( n=60, workshop-driven interactive teaching). Clinical alarm knowledge, attitudes, and practices (KAP); perception of patient safety culture; and training satisfaction were assessed before and after the training using validated questionnaires. Differences between groups were analyzed using independent sample t-test, paired sample t-test, chi-square test, and the Mann-Whitney U test with SPSS 22.0. Results:There were no significant differences between the two groups in clinical alarm KAP scores or patient safety culture perception scores before the training ( P>0.05). After the training, the intervention group showed significantly higher scores in clinical alarm KAP [(127.58±6.45) vs. (123.48±7.61), t=3.17, P=0.002] and patient safety culture perception [(99.44±9.17) vs. (95.45±12.03), t=-2.04, P=0.044] compared with the control group. Satisfaction with training content, teaching quality, and overall instructional design was significantly higher in the intervention group than in the control group ( P<0.05). Within-group comparisons showed that the intervention group had significant improvements in clinical alarm KAP [(127.58±6.45) vs. (120.10±10.25), t=-5.10, P<0.001] and patient safety culture perception [(99.44±9.17) vs. (94.46±12.12), t=-2.65, P=0.010] after training. No significant changes were observed in the control group after training ( P>0.05). Conclusions:The workshop-driven interactive teaching model can effectively enhance the clinical alarm KAP levels and patient safety culture perception among undergraduate nursing interns in the ICU. This teaching approach provides a scientific basis for optimizing clinical alarm education and cultivating high-quality nursing professionals.

Objective:To develop a preoperative differentiation model for colorectal mucinous adenocarcinoma and non-mucinous adenocarcinoma using a combination of contrast-enhanced CT radiomics and deep learning methods.Methods:This is a retrospective cohort study. Clinical data of colorectal cancer patients confirmed by postoperative pathological examination were retrospectively collected from January 2016 to December 2023 at Shanghai General Hospital Affiliated to Shanghai Jiao Tong University School of Medicine (Center 1, n=220) and the First Affiliated Hospital of Bengbu Medical University (Center 2, n=51). Among them, there were 108 patients diagnosed with mucinous adenocarcinoma, including 55 males and 53 females, with an age of (68.4±12.2) years (range: 38 to 96 years); and 163 patients diagnosed with non-mucinous adenocarcinoma, including 96 males and 67 females, with an age of (67.9±11.0) years (range: 43 to 94 years). The cases from Center 1 were divided into a training set ( n=156) and an internal validation set ( n=64) using stratified random sampling in a 7∶3 ratio, and the cases from Center 2 were used as an independent external validation set ( n=51). Three-dimensional tumor volume of interest was manually segmented on venous-phase contrast-enhanced CT images. Radiomics features were extracted using PyRadiomics, and deep learning features were extracted using the ResNet-18 network. The two sets of features were then combined to form a joint feature set. The consistency of manual segmentation was assessed using the intraclass correlation coefficient. Feature dimensionality reduction was performed using the Mann-Whitney U test and the least absolute shrinkage and selection operator regression. Six machine learning algorithms were used to construct models based on radiomics features, deep learning features, and combined features, including support vector machine, logistic regression, random forest, extreme gradient boosting, k-nearest neighbors, and decision tree. The discriminative performance of each model was evaluated using receiver operating characteristic curves, the area under the curve (AUC), DeLong test, and decision curve analysis. Results:After feature selection, 22 features with the most discriminative value were finally retained, among which 12 were traditional radiomics features and 10 were deep learning features. In the internal validation set, the Random Forest algorithm based on the combined features model achieved the best performance (AUC=0.938, 95% CI: 0.875 to 0.984), which was superior to the single-modality radiomics feature model (AUC=0.817, 95% CI: 0.702 to 0.913, P=0.048) and the deep learning feature model (AUC=0.832, 95% CI: 0.727 to 0.926, P=0.087); in the independent external validation set, the Random Forest algorithm with the combined features model maintained the highest discriminative performance (AUC=0.891, 95% CI: 0.791 to 0.969), which was superior to the single-modality radiomics feature model (AUC=0.770, 95% CI: 0.636 to 0.890, P=0.045) and the deep learning feature model (AUC=0.799, 95% CI: 0.652 to 0.911, P=0.169). Conclusion:The combined model based on radiomics and deep learning features from venous-phase enhanced CT demonstrates good performance in the preoperative differentiation of colorectal mucinous from non-mucinous adenocarcinoma.

Objective·To investigate the relation between urinary excretion of protein-bound uremic toxins(PBUTs)and upper urinary tract calculus.Methods·Residents aged 18?80 years in the community of Haitou,Danzhou city in Hainan Province were recruited.Basic information and diet for the last 3 d of the subjects were recorded.Their fasting sera and 24-hour urine samples were collected,and they also underwent ultrasound examination of kidneys and ureters.The subjects with upper urinary calculi detected by ultrasound or a clear history of upper urinary calculi were selected as the calculus group,and the others as the non-calculus group.The biochemical indicators related to the formation of calculus in blood and urine were detected,and the levels of PBUTs,including indoxyl sufate(IS),indole-3-acetic acid(IAA),and p-cresol sulfate(PCS)in blood and urine,as well as oxalic acid and citric acid in urine were detected by high-performance liquid chromatography.The related factors of upper urinary tract calculus formation were analyzed by multivariate Logistic regression.The correlations of urine PBUTs with urine uric acid,oxalic acid,and citric acid were analyzed by Spearman correlation test.Results·A total of 117 participants were screened out with 54 people in the calculus group and 63 people in the non-calculus group.There were no significant differences between the two groups in terms of gender,age,serum indicators,and prevalence of complications such as hypertension,diabetes,and hyperuricemia/gout.The 24-hour urine pH,calcium,uric acid,and chlorine in the calculus group were significantly higher than those in the non-calculus group(all P<0.05),while IS was significantly lower than that in the non-calculus group(P<0.05).Multivariate Logistic regression analysis showed that urinary IS(OR=0.929,95%CI 0.875?0.986,P=0.016)was related to the calculus formation independently,in addition to urinary calcium.The Spearman correlation analysis results showed that the levels of IAA(r=0.420,P=0.000)and PCS(r=0.307,P=0.001)in 24-hour urine were positively correlated with oxalic acid,PCS was positively correlated with uric acid(r=0.297,P=0.002),and IS was positively correlated with citric acid(r=0.289,P=0.002).Conclusion·In the population,a decrease in urinary excretion of IS may be an independent risk factor for the formation of upper urinary tract calculus,and PBUTs levels are correlated with levels of uric acid,oxalic acid,and citric acid.

Ovarian cancer represents a significant threat to women's health globally,with particularly high recurrence and mortality rates in China's context of gynecological malignancies.The effectiveness of traditional therapeutic approaches for advanced and recurrent ovarian cancer remains quite limited.However,recent studies have highlighted the substantial success of targeted therapies in managing ovarian cancer.This paper delves into both national and international research on ovarian cancer's targeted treatments,examining their mechanisms,applications,current landscape,and the challenges they face.It aims to offer innovative perspectives and directions for ovarian cancer treatment.

Objective To investigate the effects of siRNA silencing enhancer of zeste homolo 2 (EZH2) on invasion and migration of cervical cancer cells and the molecular mechanisms.Methods The small interfering RNAs (siRNA) targeting EZH2 were transiently transfected into C33A cell line by lipofectamin2000.The effects of EZH2 on cell invasion and migration were detected by wound-healing assay, Transwell assay and soft agar colony assay.The expression of MMP2 was detected by Western blot.Results Downregulation of EZH2 expression by siRNA in C33A cell line significantly inhibited cell invasion and migration in vitro.Meanwhile, siRNA-mediated depletion of EZH2 reduced the expression of MMP2.Conclusion Knocking down EZH2 expression by siRNA could surpress invasion and migration of human cervical cancer cells, which might be related to downregulating MMP2 expression.

Objective To study the effects of lifestyle quantilization based-weight management on overweight or obesity police officers.Methods One hundred and seven overweight or obesity police officers received lifestyle quantilization based-weight management (i.e.Jinbi weight management) and were then assigned to the excellent performance group (group A,n =50),good performance group (group B,n =42)and loss to follow-up group (group C,n =15).Dietary habits,body weight,height,waist circumference (WC),blood pressure (BP),total cholesterol (TC),triglyceride (TG),low-density lipoprotein cholesterol (LDL-C),high-density lipoprotein cholesterol (HDL-C) and fasting plasma glucose (FPG) were measured before and after the intervention.After 8 weeks' intervention,the changes of above parameters were recored.Student's t test was used for data analysis.Results Of group A and B,83 reported weight loss (group A t =13.31,group B t =5.04 ; both P ＜ 0.05).In group A,body weight,body mass index (BMI),WC,body fat and visceral fat index were significantly decreased,in contrast to body water (t values were 13.31,13.72,10.8,8.59,6.83 and-6.62,respectively; all P ＜ 0.05).However,there were no significant changes of BP,FPG,TC,TG,LDL-C and HDL-C in group A.Following intervention,daily dietary energy intake of group A was reduced by 74.1 k J,fat intake was decreased by 11.6 g,energy ratio of dietary fat was decreased by 1.8％,energy ratio of cereal was increased by 4.2％,and sodium chloride and cooking oil was decreased by 1.3 g and 10 g,respectively.Conclusion Lifestyle quantilization based-weight management shows effectiveness among overweight or obese police officers,and thus may be recommended for other functional communities.

Metastasis contributes to the poor prognosis of hepatocellular carcinoma (HCC). However, the mechanism through which a primary HCC cell develops into a metastatic phenotype is not well understood. The purpose of this study was to examine the correlation between metadherin (MTDH)/astrocyte elevated gene-1 (AEG-1) expression in HCC cell lines of different metastatic potentials and such metastatic phenotypes as orientation chemotaxis and adhesion. MTDH/AEG-1 expression was detected by RT-PCR and western blotting in HCC cell lines (HepG2, Huh7, Sk-HEP-1, MHCC-97H). Distribution of MTDH/AEG-1 was observed by immunofluorescence staining and confocal laser scanning microscopy. The abilities of orientation chemotaxis and adhesion and the index of interaction between HCC cell lines and microvascular endothelial cell lines (MVECs, including HUVECs and HPMECs) were measured by chemotaxis assay and adhesion assay, respectively. The results showed that MTDH/AEG-1 protein expression was significantly higher in high metastatic potential cancer cell lines (Sk-HEP-1, MHCC-97H) than in low metastatic potential cell lines (HepG2, Huh7) (P<0.05). The MTDH/AEG-1 protein was localized in the perinuclear region of HCC cells. Furthermore, the abilities of orientation chemotaxis and adhesion of HCC cells to HPMECs were increased as compared with those of HCC cells to HUVECs (P<0.05). The abilities of orientation chemotaxis and adhesion were much stronger in Sk-HEP-1 and MHCC-97H cells with MTDH/AEG-1 highly expressed than in HepG2 and Huh7 cells with MTDH/AEG-1 lowly expressed (P<0.05). These results suggested that the expression of MTDH/AEG-1 gene in HCC cell lines of different metastatic potentials was closely positively related to the abilities of orientation chemotaxis and adhesion of HCC cells. It was deduced that MTDH/AEG-1 might play a pivotal role in the lung-specific metastasis of HCC, which may be mediated through orientation chemotaxis and adhesion abilities of HCC cells. MTDH/AEG-1 may serve as a potential therapeutic target for HCC.
Carcinoma, Hepatocellular ; pathology ; physiopathology ; secondary ; Cell Adhesion ; Cell Adhesion Molecules ; metabolism ; Cell Line, Tumor ; Cell Polarity ; Chemotaxis ; Hep G2 Cells ; Humans

Metastasis contributes to the poor prognosis of hepatocellular carcinoma (HCC). However, the mechanism through which a primary HCC cell develops into a metastatic phenotype is not well understood. The purpose of this study was to examine the correlation between metadherin (MTDH)/astrocyte elevated gene-1 (AEG-1) expression in HCC cell lines of different metastatic potentials and such metastatic phenotypes as orientation chemotaxis and adhesion. MTDH/AEG-1 expression was detected by RT-PCR and western blotting in HCC cell lines (HepG2, Huh7, Sk-HEP-1, MHCC-97H). Distribution of MTDH/AEG-1 was observed by immunofluorescence staining and confocal laser scanning microscopy. The abilities of orientation chemotaxis and adhesion and the index of interaction between HCC cell lines and microvascular endothelial cell lines (MVECs, including HUVECs and HPMECs) were measured by chemotaxis assay and adhesion assay, respectively. The results showed that MTDH/AEG-1 protein expression was significantly higher in high metastatic potential cancer cell lines (Sk-HEP-1, MHCC-97H) than in low metastatic potential cell lines (HepG2, Huh7) (P<0.05). The MTDH/AEG-1 protein was localized in the perinuclear region of HCC cells. Furthermore, the abilities of orientation chemotaxis and adhesion of HCC cells to HPMECs were increased as compared with those of HCC cells to HUVECs (P<0.05). The abilities of orientation chemotaxis and adhesion were much stronger in Sk-HEP-1 and MHCC-97H cells with MTDH/AEG-1 highly expressed than in HepG2 and Huh7 cells with MTDH/AEG-1 lowly expressed (P<0.05). These results suggested that the expression of MTDH/AEG-1 gene in HCC cell lines of different metastatic potentials was closely positively related to the abilities of orientation chemotaxis and adhesion of HCC cells. It was deduced that MTDH/AEG-1 might play a pivotal role in the lung-specific metastasis of HCC, which may be mediated through orientation chemotaxis and adhesion abilities of HCC cells. MTDH/AEG-1 may serve as a potential therapeutic target for HCC.