Objective:This study aims to explore the optimization needs of the cognitive training tool "Fun Brain" from a professional perspective, providing insights for its enhancement and application.Methods:In April 2024, a qualitative research approach was employed, involving group interviews with 61 elderly health professionals organized into 9 groups. The interviews primarily focused on the user experience and optimization suggestions related to the "Fun Brain" app. Thematic analysis was conducted, with NVivo 14 software utilized for data management and processing. Data analysis followed Braun and Clarke′s six-phase procedure to ensure the scientific and systematic extraction of themes. Furthermore, high-frequency word analysis was performed, offering crucial clues for subsequent thematic analysis.Results:The study identified 12 initial themes and 6 optimization themes, with a primary focus on age-appropriate interfaces, personalized modules, and feedback mechanisms. These themes were refined into three core themes, including adaptation of training content, optimization of interaction design, and enhancement of participant motivation.Conclusion:Optimizing cognitive training tools for elderly users requires careful consideration of their specific needs, particularly regarding functional adaptation, interface design, and interactive experience. Implementing these optimizations can improve user engagement and training effectiveness, offering both scientific and practical guidance for the design and promotion of cognitive training tools.

AIM:This study aims to investigate the mechanism by which Qingfei-Jiedu-Huatan Formula(QJHF)regulates autophagy in alveolar macrophages through mTOR in the treatment of severe pneumonia(SP)in rats.METHODS:Sixty SPF-grade male rats were randomly assigned to six groups:control,model,QJHF,moxifloxacin(MOX),rapamycin(RAPA),and QJHF+RAPA,with ten rats in each group.An SP rat model was established using Klebsiella pneumoniae.After seven days of treatment,changes in IL-33 and IFN-γ levels in bronchoalveolar lavage fluid(BALF)were measured using ELISA.Histopathological alterations in lung tissue were assessed via HE staining,and the autophagy of alveolar macrophages was detected using immunofluorescence co-localization methods.The expression levels of mTOR,beclin-1,and LC3 mRNA in lung tissue were analyzed using qPCR,while Western blot was employed to assess the protein levels of p-mTOR/mTOR,beclin-1,and LC3-II/LC3-I.RESULTS:Compared to the control group,the model group exhibited a deteriorated condition,characterized by alveolar wall rupture and thickening,significant inflammatory cell infiltration in the alveolar cavity,and extensive lung tissue damage(P<0.01).Elevated levels of IL-33 and IFN-γ in BALF were also observed(P<0.01),along with increased colocalization of CD68 and LC3 in immunofluorescence analy-sis.The mTOR mRNA expression in lung tissue decreased(P<0.01),while LC3 and beclin-1 mRNA expressions in-creased(P<0.01).Additionally,the protein expression ratio of p-mTOR/mTOR decreased(P<0.01),whereas LC3-II/LC3-I and beclin-1 protein levels increased(P<0.01).In comparison to the model group,significant improvements were noted after treatment with QJHF and MOX(P<0.01),while RAPA treatment led to a worsening of these indicators(P<0.05).A slight improvement was observed with the QJHF combined with RAPA intervention,though this was not statisti-cally significant.No significant differences were found between the MOX and QJHF groups.However,the QJHF+RAPA group displayed notable improvements in various indicators compared to the RAPA group(P<0.05).CONCLUSION:The QJHF can mitigate the inflammatory response associated with severe pneumonia,potentially by activating mTOR phos-phorylation activity,which in turn inhibits excessive autophagy in alveolar macrophages.

AIM:This study aims to investigate the mechanism by which Qingfei-Jiedu-Huatan Formula(QJHF)regulates autophagy in alveolar macrophages through mTOR in the treatment of severe pneumonia(SP)in rats.METHODS:Sixty SPF-grade male rats were randomly assigned to six groups:control,model,QJHF,moxifloxacin(MOX),rapamycin(RAPA),and QJHF+RAPA,with ten rats in each group.An SP rat model was established using Klebsiella pneumoniae.After seven days of treatment,changes in IL-33 and IFN-γ levels in bronchoalveolar lavage fluid(BALF)were measured using ELISA.Histopathological alterations in lung tissue were assessed via HE staining,and the autophagy of alveolar macrophages was detected using immunofluorescence co-localization methods.The expression levels of mTOR,beclin-1,and LC3 mRNA in lung tissue were analyzed using qPCR,while Western blot was employed to assess the protein levels of p-mTOR/mTOR,beclin-1,and LC3-II/LC3-I.RESULTS:Compared to the control group,the model group exhibited a deteriorated condition,characterized by alveolar wall rupture and thickening,significant inflammatory cell infiltration in the alveolar cavity,and extensive lung tissue damage(P<0.01).Elevated levels of IL-33 and IFN-γ in BALF were also observed(P<0.01),along with increased colocalization of CD68 and LC3 in immunofluorescence analy-sis.The mTOR mRNA expression in lung tissue decreased(P<0.01),while LC3 and beclin-1 mRNA expressions in-creased(P<0.01).Additionally,the protein expression ratio of p-mTOR/mTOR decreased(P<0.01),whereas LC3-II/LC3-I and beclin-1 protein levels increased(P<0.01).In comparison to the model group,significant improvements were noted after treatment with QJHF and MOX(P<0.01),while RAPA treatment led to a worsening of these indicators(P<0.05).A slight improvement was observed with the QJHF combined with RAPA intervention,though this was not statisti-cally significant.No significant differences were found between the MOX and QJHF groups.However,the QJHF+RAPA group displayed notable improvements in various indicators compared to the RAPA group(P<0.05).CONCLUSION:The QJHF can mitigate the inflammatory response associated with severe pneumonia,potentially by activating mTOR phos-phorylation activity,which in turn inhibits excessive autophagy in alveolar macrophages.

Stigma makes it difficult for patients with urinary ostomy to adapt to ostomy life, seriously affecting their mental health and quality of life. Combining traditional face-to-face nursing interventions with digital health technology can effectively alleviate the stigma of urinary ostomy patients. This paper mainly reviews the mechanism, application forms, effects, and application challenges of digital health technology in alleviating stigma in urinary ostomy patients, and proposes corresponding reflections, aiming to provide a reference for alleviating stigma and improving the quality of life of ostomy patients.

Traditional urostomy management methods primarily rely on clinical experience, lacking precise prediction and personalized intervention approaches. In recent years, the rapid advancement of artificial intelligence technology in the medical field has provided new solutions for intelligent monitoring, risk assessment, and nursing optimization of urostomy leakage. This paper reviews the research on the application of artificial intelligence in urostomy leakage, introduces the overview of artificial intelligence, the application and effectiveness of artificial intelligence in urostomy leakage, analyzes the shortcomings of this research field, and explores future development trend, in order to provide a reference for formulating clinical nursing programs and prognosis rehabilitation suitable for the leakage of bladder cancer patients with urostomy, and reduce the incidence of leakage and maintain the health of peristomal skin.

Objective:This study aims to explore the optimization needs of the cognitive training tool "Fun Brain" from a professional perspective, providing insights for its enhancement and application.Methods:In April 2024, a qualitative research approach was employed, involving group interviews with 61 elderly health professionals organized into 9 groups. The interviews primarily focused on the user experience and optimization suggestions related to the "Fun Brain" app. Thematic analysis was conducted, with NVivo 14 software utilized for data management and processing. Data analysis followed Braun and Clarke′s six-phase procedure to ensure the scientific and systematic extraction of themes. Furthermore, high-frequency word analysis was performed, offering crucial clues for subsequent thematic analysis.Results:The study identified 12 initial themes and 6 optimization themes, with a primary focus on age-appropriate interfaces, personalized modules, and feedback mechanisms. These themes were refined into three core themes, including adaptation of training content, optimization of interaction design, and enhancement of participant motivation.Conclusion:Optimizing cognitive training tools for elderly users requires careful consideration of their specific needs, particularly regarding functional adaptation, interface design, and interactive experience. Implementing these optimizations can improve user engagement and training effectiveness, offering both scientific and practical guidance for the design and promotion of cognitive training tools.

Objective:To investigate the application value of dual-energy computer tomo-graphy (CT) multi-parameter imaging in predicting the pathological grade of pancreatic ductal adeno-carcinoma (PDAC).Methods:The retrospective cohort study was conducted. The clinicopatholo-gical data of 147 patients with PDAC who were admitted to The Fifth Affiliated Hospital of Wenzhou Medical University from January 2017 to August 2023 were collected. There were 102 males and 45 females, aged (59±10)years. All patients underwent preoperative dual-energy CT examination and postoperative histopathological examination. The 147 patients were divided into a training set of 103 cases and a test set of 44 cases by stratified random sampling at a ratio of 7∶3. The training set was used to construct the prediction model, and the test set was used to verify the effectiveness of prediction model. Observation indicators: (1) analysis of factors affecting the pathological grade of PDAC patients in the training set; (2) construction and evaluation of the fusion prediction model for pathological grade of PDAC. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test. Comparison of count data between groups was conducted using the chi-square test. Univariate and multivariate analyses were conducted using the Logistic regression model. The performance of the model was evaluated by receiver operating characteristic (ROC) curve, and the area under the curve (AUC), accuracy, sensitivity and specificity were calculated. The Delong test was used to analyze the effec-tiveness of model. The calibration curve and decision curve of Hosmer-Lemeshow test were used to evaluate the consistency and clinical application value of the nomogram, respectively. Results:(1) Analysis of factors affecting the pathological grade of PDAC patients in the training set. Results of multivariate analysis showed that tumor cystic necrosis, vascular invasion, standardized iodine concentration (NIC) in venous phase, effective atomic number (Zeff) in venous phase, and energy spectrum curve slope (λ HU) in venous phase were all independent factors affecting the pathological grade of PDAC patients in the training set ( odds ratio=4.326, 3.887, 4.155, 5.389, 3.164, 95% confidence interval as 1.167-16.033, 1.111-13.592, 1.707-10.113, 1.284-22.613, 1.247-8.028, P<0.05). (2) Construction and evaluation of the fusion prediction model for pathological grade of PDAC. Accor-ding to the results of multivariate analysis, tumor cystic necrosis, vascular invasion, NIC in venous phase, Zeff in venous phase and λ HU in venous phase were all included to construct the clinical-imaging fusion prediction nomogram model. The AUC, accuracy, sensitivity and specificity of the fusion prediction model in the training set were 0.938 (95% confidence interval as 0.896-0.981), 87.38%, 89.74% and 85.94%, respectively. The above indicators of the fusion prediction model in the test set were 0.893 (95% confidence interval as 0.802-0.985), 84.09%, 82.35% and 85.19%, respectively. Results of Delong test showed that there was no significant difference in AUC between the training set and the test set ( Z=0.343, P>0.05). Results of Hosmer-Lemeshow test showed that the fusion prediction model had a good fit in the training set and the test set ( χ2=3.042, 7.545, P>0.05). Results of calibration curve showed that the predictive ability of the fusion prediction model was good. Conclusions:Multiple parameters in venous phase of the dual-energy CT can be used as imaging markers for preoperative evaluation of the pathological grade of patients with PDAC. Establishing a clinical-imaging fusion prediction model can effectively predict the pathological grade of PDAC.

Stigma makes it difficult for patients with urinary ostomy to adapt to ostomy life, seriously affecting their mental health and quality of life. Combining traditional face-to-face nursing interventions with digital health technology can effectively alleviate the stigma of urinary ostomy patients. This paper mainly reviews the mechanism, application forms, effects, and application challenges of digital health technology in alleviating stigma in urinary ostomy patients, and proposes corresponding reflections, aiming to provide a reference for alleviating stigma and improving the quality of life of ostomy patients.

Traditional urostomy management methods primarily rely on clinical experience, lacking precise prediction and personalized intervention approaches. In recent years, the rapid advancement of artificial intelligence technology in the medical field has provided new solutions for intelligent monitoring, risk assessment, and nursing optimization of urostomy leakage. This paper reviews the research on the application of artificial intelligence in urostomy leakage, introduces the overview of artificial intelligence, the application and effectiveness of artificial intelligence in urostomy leakage, analyzes the shortcomings of this research field, and explores future development trend, in order to provide a reference for formulating clinical nursing programs and prognosis rehabilitation suitable for the leakage of bladder cancer patients with urostomy, and reduce the incidence of leakage and maintain the health of peristomal skin.

Objective:To investigate the application value of dual-energy computer tomo-graphy (CT) multi-parameter imaging in predicting the pathological grade of pancreatic ductal adeno-carcinoma (PDAC).Methods:The retrospective cohort study was conducted. The clinicopatholo-gical data of 147 patients with PDAC who were admitted to The Fifth Affiliated Hospital of Wenzhou Medical University from January 2017 to August 2023 were collected. There were 102 males and 45 females, aged (59±10)years. All patients underwent preoperative dual-energy CT examination and postoperative histopathological examination. The 147 patients were divided into a training set of 103 cases and a test set of 44 cases by stratified random sampling at a ratio of 7∶3. The training set was used to construct the prediction model, and the test set was used to verify the effectiveness of prediction model. Observation indicators: (1) analysis of factors affecting the pathological grade of PDAC patients in the training set; (2) construction and evaluation of the fusion prediction model for pathological grade of PDAC. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test. Comparison of count data between groups was conducted using the chi-square test. Univariate and multivariate analyses were conducted using the Logistic regression model. The performance of the model was evaluated by receiver operating characteristic (ROC) curve, and the area under the curve (AUC), accuracy, sensitivity and specificity were calculated. The Delong test was used to analyze the effec-tiveness of model. The calibration curve and decision curve of Hosmer-Lemeshow test were used to evaluate the consistency and clinical application value of the nomogram, respectively. Results:(1) Analysis of factors affecting the pathological grade of PDAC patients in the training set. Results of multivariate analysis showed that tumor cystic necrosis, vascular invasion, standardized iodine concentration (NIC) in venous phase, effective atomic number (Zeff) in venous phase, and energy spectrum curve slope (λ HU) in venous phase were all independent factors affecting the pathological grade of PDAC patients in the training set ( odds ratio=4.326, 3.887, 4.155, 5.389, 3.164, 95% confidence interval as 1.167-16.033, 1.111-13.592, 1.707-10.113, 1.284-22.613, 1.247-8.028, P<0.05). (2) Construction and evaluation of the fusion prediction model for pathological grade of PDAC. Accor-ding to the results of multivariate analysis, tumor cystic necrosis, vascular invasion, NIC in venous phase, Zeff in venous phase and λ HU in venous phase were all included to construct the clinical-imaging fusion prediction nomogram model. The AUC, accuracy, sensitivity and specificity of the fusion prediction model in the training set were 0.938 (95% confidence interval as 0.896-0.981), 87.38%, 89.74% and 85.94%, respectively. The above indicators of the fusion prediction model in the test set were 0.893 (95% confidence interval as 0.802-0.985), 84.09%, 82.35% and 85.19%, respectively. Results of Delong test showed that there was no significant difference in AUC between the training set and the test set ( Z=0.343, P>0.05). Results of Hosmer-Lemeshow test showed that the fusion prediction model had a good fit in the training set and the test set ( χ2=3.042, 7.545, P>0.05). Results of calibration curve showed that the predictive ability of the fusion prediction model was good. Conclusions:Multiple parameters in venous phase of the dual-energy CT can be used as imaging markers for preoperative evaluation of the pathological grade of patients with PDAC. Establishing a clinical-imaging fusion prediction model can effectively predict the pathological grade of PDAC.