Time perspective therapy can reshape the time perspective of chronic disease patients,improve their psychological state,optimize behavioral patterns,and enhance self-management awareness.This article reviews the concept and theoretical basis of time perspective therapy,assessment methods of time perspective,and influencing factors of time perspective in chronic disease patients,as well as methods and effects of applying time perspective therapy in self-management.Additionally,it analyzes challenges in implementation and proposes practical recommendations,aiming to provide psychological guidance for self-management interventions in chronic disease patients.

Objective To report cases of checkpoint inhibitor pneumonitis （CIP） in patients with advanced biliary tract cancer, aiming to provide additional approaches for the assessment, treatment, and monitoring of this condition. Methods Three patients developed oxygen desaturation and interstitial lung lesions during chemotherapy combined with immunotherapy, and were diagnosed with CIP in collaboration with the respiratory department. Antitumor therapy was discontinued in the acute phase, and glucocorticoids were administered, with regular monitoring of disease progression. During follow-up, case 1 developed lung metastasis; case 2 showed improvement; case 3 had concurrent infection and tumor progression. Results Glucocorticoids improved lung lesions and hypoxic symptoms in patients with CIP, but attention should be paid to the potential for concurrent infections and tumor progression. Conclusions Comprehensive assessment and early identification of CIP are crucial for patients with advanced biliary tract cancer. For those with recurrent symptoms after glucocorticoid therapy, timely and accurate adjustment of the treatment regimen is essential.

Objective:To evaluate the effectiveness of T-piece resuscitator as a respiratory support strategy during the transport of critically ill premature infants,and to provide a scientific basis for clinical decision-making.Methods:A total of 280 critically ill premature newborns hospitalized in the NICU of Fifth Medical Center of Chinese People's Liberation Army General Hospital from January 2017 to December 2023 were included.Infants were categorized into three groups based on the respiratory support method given during transport: the ventilator group(108 cases),the T-piece group(102 cases),and the resuscitation sac group(70 cases).The transport distance,general condition at birth,prenatal conditions,dyspnea symptoms at admission,blood gas analysis results,clinical diagnosis,clinical intervations,and related treatment among the three groups were retrospectively analyzed.Results:There were no significant differences in the transport distance,the number of endotrached intubations during transport,the main complications during pregnancy,the general condition at birth,and the history of asphyxia among the three groups(all P>0.05).The incidence of triple-concave sign at admission in T-piece group was significantly lower than that in resuscitation sac group (41.7% vs.62.9%, P=0.005),and the arterial carbon dioxide tension(PaCO 2) at admission was also significantly lower in T-piece group than that in resuscitation sac group[(41.194±8.720) mmHg vs.(45.360±13.998) mmHg, P=0.034].Furthermore,the T-piece group had significantly lower rates of type II respiratory failure(0.9% vs.22.9%),respiratory acidosis(9.3% vs.27.1%),hypoxemia(7.4% vs.28.6%),hyperoxygen partial pressure(1.9% vs.28.6%),neonatal respiratory distress syndrome(66.7% vs.87.1%),and intracranial hemorrhage(18.5% vs.38.6%) during hospitalization compared to the resuscitation sac group (all P<0.05).The proportion of tracheal intubations(63.9% vs.87.1%) and the time of using non-invasive ventilator[1.0(1.0,2.0)d vs.1.0(1.0,6.0)d] were also significantly lower in T-piece group compared to the resuscitation sac group(both P<0.05).Compared with the respiratory group,there were no statistically significant differences in the aforementioned indicators for the T-piece group. Conclusion:The T-piece resuscitator can provide stable and adjustable positive end-inspiratory pressure and positive expiratory pressure,as well as a stable inspired oxygen flow rate,without increasing the risk of invasive procedures and severe complications.Its application during the transport and treatment of critically ill premature infants has definite clinical value.

Objective:To investigate the application value of risk prediction model for acute kidney injury (AKI) after donation of cardiac death (DCD) liver transplantation based on machine learning algorithm.Methods:The retrospective cohort study was conducted. The clinicopathological data of 1 001 pairs of DCD liver transplant donors and recipients at five hospitals, including The First Affiliated Hospital of Zhejiang University School of Medicine et al, in the Chinese Liver Transplan-tation Registry from January 2015 to December 2023 were collected. Of the donors, there were 825 males and 176 females. Of the recipients, there were 806 males and 195 females, aged 52 (range, 18-75)years. There were 281 recipients included using oversampling technique, and all 1 282 recipients were divided to the training set of 897 recipients and the validation set of 385 recipients by a ratio of 7∶3 using computer-generated random numbers. Seven prediction models, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbors (KNN), and Categorical Boosting (CatBoost), were constructed for AKI after liver transplantation based on machine learning algorithm. Observation indicators: (1) comparison of clinicopathological characteristics between recipients with and without AKI and donors; (2) follow-up and survival of recipients with and without AKI; (3) construction and validation of nomogram prediction model of AKI after liver transplantation; (4) construction and validation of machine learning prediction model of AKI after liver transplantation. 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, and comparison among groups was conducted using the Kruskal-Wallis H test. Comparison of count data between groups was conducted using the chi-square test or corrected chi-square test. Kaplan-Meier method was used to calculate survival rates and plot survival curves. Logistic regression model was performed for univariate and multivariate analyses. The receiver operating characteristic (ROC) curve was plotted to calculate area under curve (AUC) and 95% confidence interval ( CI). The performance of prediction model was evaluated using DeLong test, accuracy, sensitivity, specificity. The calibration curve was plotted to evaluate the performance of predicted probability and actual probability. The interpretability analysis of machine learning algorithm and SHapley Additive exPlanations was used to explain the model decision separately. Results:(1) Comparison of clinicopathological characteristics between recipients with and without AKI and donors. Of 1 001 recipients, there were 360 cases with AKI and 641 cases without AKI after liver transplantation. There were significant differences in body mass index (BMI), hepatic encepha-lopathy, hepatitis B surfact antigen (HBsAg), hepatorenal syndrome (HRS) and donor diabetes, donor blood urea nitrogen, donor alanine aminotransferase, donor aspartate aminotransferase, mass of graft, volume of blood loss during liver transplantation, warm ischema time of donor liver, and operation time between recipients with and without AKI ( Z=-4.337, χ2=9.751, 9.088, H=11.142, χ2=5.286, Z=-3.360, -2.539, -3.084, -1.730, -3.497, -1.996, -2.644, P<0.05). (2) Follow-up and survival of recipients with and without AKI. All the 1 001 recipients received follow-up. The recipients with AKI after liver transplantation were followed up for 18.6(range, 0-102.3)months, and recipients without AKI after liver transplantation were followed up for 31.9(range, 0.1-105.5)months. The 1-, 3-, and 5-year overall survival rates were 72.1%, 63.5%, and 59.3% of recipients with AKI, versus 86.7%, 76.7%, and 72.5% of recipients without AKI, respectively, showing a significant difference in overall survival between them ( χ2=26.028, P<0.05). (3) Construction and validation of nomogram predic-tion model of AKI after liver transplantation. Results of multivariate analysis showed that recipient BMI, recipient creatinine, recipient HBsAg, recipient HRS, donor blood urea nitrogen, donor crea-tinine, anhepatic phase and volume of blood loss during liver transplantation were independent risk factors for AKI of recipients after liver transplantation ( odds ratio=1.113, 0.998, 0.605, 1.580, 1.047, 0.998, 1.006, 1.157, 95% CI as 1.070-1.157, 0.996-1.000, 0.450-0.812, 1.021-2.070, 1.021-1.074, 0.996-0.999, 1.000-1.012, 1.045-1.281, P<0.05). The nomogram prediction model of AKI after liver transplantation was constructed based on the results of multivariate analysis. Results of ROC curve showed that the AUC of 0.666 (95% CI as 0.637-0.696). (4) Construction and validation of machine learning prediction model of AKI after liver transplantation. Based on the Lasso regression analysis, seven machine learning algorithm prediction models, including RF, XGBoost, SVM, LR, DT, KNN, and CatBoost, were constructed, with ROC curves of the validation set plotted. The AUC of above models were 0.863, 0.841, 0.721, 0.637, 0.620, 0.708, 0.731, accuracies were 0.764, 0.782, 0.701, 0.592, 0.605, 0.605, 0.681, sensitivities were 0.764, 0.789, 0.719, 0.588, 0.694, 0.694, 0.704, specificities were 0.763, 0.774, 0.683, 0.597, 0.511, 0.511, 0.656, respectively. Delong test showed that the RF model with the highest AUC of 0.863(95% CI as 0.828-0.899). Calibration curve analysis showed the predicted probability closest to the actual probability of RF model, indicating the model with a good validation value. Further sorting of SHAP of different clinical factors based on RF model showed that recipient BMI, donor blood urea nitrogen, volume of blood loss during liver transplantation, donor age had large effects on the output outcomes. Conclusion:The nomogram prediction model and seven machine learning algorithm prediction models for AKI after DCD liver transplantation are constructed, and the RF model based on machine learning has a better predictive performance.

Objective:To evaluate the effectiveness of T-piece resuscitator as a respiratory support strategy during the transport of critically ill premature infants,and to provide a scientific basis for clinical decision-making.Methods:A total of 280 critically ill premature newborns hospitalized in the NICU of Fifth Medical Center of Chinese People's Liberation Army General Hospital from January 2017 to December 2023 were included.Infants were categorized into three groups based on the respiratory support method given during transport: the ventilator group(108 cases),the T-piece group(102 cases),and the resuscitation sac group(70 cases).The transport distance,general condition at birth,prenatal conditions,dyspnea symptoms at admission,blood gas analysis results,clinical diagnosis,clinical intervations,and related treatment among the three groups were retrospectively analyzed.Results:There were no significant differences in the transport distance,the number of endotrached intubations during transport,the main complications during pregnancy,the general condition at birth,and the history of asphyxia among the three groups(all P>0.05).The incidence of triple-concave sign at admission in T-piece group was significantly lower than that in resuscitation sac group (41.7% vs.62.9%, P=0.005),and the arterial carbon dioxide tension(PaCO 2) at admission was also significantly lower in T-piece group than that in resuscitation sac group[(41.194±8.720) mmHg vs.(45.360±13.998) mmHg, P=0.034].Furthermore,the T-piece group had significantly lower rates of type II respiratory failure(0.9% vs.22.9%),respiratory acidosis(9.3% vs.27.1%),hypoxemia(7.4% vs.28.6%),hyperoxygen partial pressure(1.9% vs.28.6%),neonatal respiratory distress syndrome(66.7% vs.87.1%),and intracranial hemorrhage(18.5% vs.38.6%) during hospitalization compared to the resuscitation sac group (all P<0.05).The proportion of tracheal intubations(63.9% vs.87.1%) and the time of using non-invasive ventilator[1.0(1.0,2.0)d vs.1.0(1.0,6.0)d] were also significantly lower in T-piece group compared to the resuscitation sac group(both P<0.05).Compared with the respiratory group,there were no statistically significant differences in the aforementioned indicators for the T-piece group. Conclusion:The T-piece resuscitator can provide stable and adjustable positive end-inspiratory pressure and positive expiratory pressure,as well as a stable inspired oxygen flow rate,without increasing the risk of invasive procedures and severe complications.Its application during the transport and treatment of critically ill premature infants has definite clinical value.

Objective:To investigate the application value of risk prediction model for acute kidney injury (AKI) after donation of cardiac death (DCD) liver transplantation based on machine learning algorithm.Methods:The retrospective cohort study was conducted. The clinicopathological data of 1 001 pairs of DCD liver transplant donors and recipients at five hospitals, including The First Affiliated Hospital of Zhejiang University School of Medicine et al, in the Chinese Liver Transplan-tation Registry from January 2015 to December 2023 were collected. Of the donors, there were 825 males and 176 females. Of the recipients, there were 806 males and 195 females, aged 52 (range, 18-75)years. There were 281 recipients included using oversampling technique, and all 1 282 recipients were divided to the training set of 897 recipients and the validation set of 385 recipients by a ratio of 7∶3 using computer-generated random numbers. Seven prediction models, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbors (KNN), and Categorical Boosting (CatBoost), were constructed for AKI after liver transplantation based on machine learning algorithm. Observation indicators: (1) comparison of clinicopathological characteristics between recipients with and without AKI and donors; (2) follow-up and survival of recipients with and without AKI; (3) construction and validation of nomogram prediction model of AKI after liver transplantation; (4) construction and validation of machine learning prediction model of AKI after liver transplantation. 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, and comparison among groups was conducted using the Kruskal-Wallis H test. Comparison of count data between groups was conducted using the chi-square test or corrected chi-square test. Kaplan-Meier method was used to calculate survival rates and plot survival curves. Logistic regression model was performed for univariate and multivariate analyses. The receiver operating characteristic (ROC) curve was plotted to calculate area under curve (AUC) and 95% confidence interval ( CI). The performance of prediction model was evaluated using DeLong test, accuracy, sensitivity, specificity. The calibration curve was plotted to evaluate the performance of predicted probability and actual probability. The interpretability analysis of machine learning algorithm and SHapley Additive exPlanations was used to explain the model decision separately. Results:(1) Comparison of clinicopathological characteristics between recipients with and without AKI and donors. Of 1 001 recipients, there were 360 cases with AKI and 641 cases without AKI after liver transplantation. There were significant differences in body mass index (BMI), hepatic encepha-lopathy, hepatitis B surfact antigen (HBsAg), hepatorenal syndrome (HRS) and donor diabetes, donor blood urea nitrogen, donor alanine aminotransferase, donor aspartate aminotransferase, mass of graft, volume of blood loss during liver transplantation, warm ischema time of donor liver, and operation time between recipients with and without AKI ( Z=-4.337, χ2=9.751, 9.088, H=11.142, χ2=5.286, Z=-3.360, -2.539, -3.084, -1.730, -3.497, -1.996, -2.644, P<0.05). (2) Follow-up and survival of recipients with and without AKI. All the 1 001 recipients received follow-up. The recipients with AKI after liver transplantation were followed up for 18.6(range, 0-102.3)months, and recipients without AKI after liver transplantation were followed up for 31.9(range, 0.1-105.5)months. The 1-, 3-, and 5-year overall survival rates were 72.1%, 63.5%, and 59.3% of recipients with AKI, versus 86.7%, 76.7%, and 72.5% of recipients without AKI, respectively, showing a significant difference in overall survival between them ( χ2=26.028, P<0.05). (3) Construction and validation of nomogram predic-tion model of AKI after liver transplantation. Results of multivariate analysis showed that recipient BMI, recipient creatinine, recipient HBsAg, recipient HRS, donor blood urea nitrogen, donor crea-tinine, anhepatic phase and volume of blood loss during liver transplantation were independent risk factors for AKI of recipients after liver transplantation ( odds ratio=1.113, 0.998, 0.605, 1.580, 1.047, 0.998, 1.006, 1.157, 95% CI as 1.070-1.157, 0.996-1.000, 0.450-0.812, 1.021-2.070, 1.021-1.074, 0.996-0.999, 1.000-1.012, 1.045-1.281, P<0.05). The nomogram prediction model of AKI after liver transplantation was constructed based on the results of multivariate analysis. Results of ROC curve showed that the AUC of 0.666 (95% CI as 0.637-0.696). (4) Construction and validation of machine learning prediction model of AKI after liver transplantation. Based on the Lasso regression analysis, seven machine learning algorithm prediction models, including RF, XGBoost, SVM, LR, DT, KNN, and CatBoost, were constructed, with ROC curves of the validation set plotted. The AUC of above models were 0.863, 0.841, 0.721, 0.637, 0.620, 0.708, 0.731, accuracies were 0.764, 0.782, 0.701, 0.592, 0.605, 0.605, 0.681, sensitivities were 0.764, 0.789, 0.719, 0.588, 0.694, 0.694, 0.704, specificities were 0.763, 0.774, 0.683, 0.597, 0.511, 0.511, 0.656, respectively. Delong test showed that the RF model with the highest AUC of 0.863(95% CI as 0.828-0.899). Calibration curve analysis showed the predicted probability closest to the actual probability of RF model, indicating the model with a good validation value. Further sorting of SHAP of different clinical factors based on RF model showed that recipient BMI, donor blood urea nitrogen, volume of blood loss during liver transplantation, donor age had large effects on the output outcomes. Conclusion:The nomogram prediction model and seven machine learning algorithm prediction models for AKI after DCD liver transplantation are constructed, and the RF model based on machine learning has a better predictive performance.

Time perspective therapy can reshape the time perspective of chronic disease patients,improve their psychological state,optimize behavioral patterns,and enhance self-management awareness.This article reviews the concept and theoretical basis of time perspective therapy,assessment methods of time perspective,and influencing factors of time perspective in chronic disease patients,as well as methods and effects of applying time perspective therapy in self-management.Additionally,it analyzes challenges in implementation and proposes practical recommendations,aiming to provide psychological guidance for self-management interventions in chronic disease patients.

Objective To develop an aged care aptitude assessment scale and to test its reliability and validity.Methods Based on the family caregivers care aptitude model,self-management theory and holistic nursing model,and with reference to the national standard of"specification for ability assessment of older adults",the first draft of the scale was formed through review of literature,semi-structured interviews,expert inquiry and pre-survey.From April to August 2023,675 aged caregivers in several communities in 9 provinces including Hubei,Guangdong etc.were investigated to test the reliability and validity of the scale.Results The aged care aptitude assessment scale included 3 dimensions and 33 items.The overall Cronbach's α coefficient of the scale was 0.99;the split-half reliability was 0.92;the two-week test-retest reliability was 0.84;the overall content validity index of the scale was 0.94;the content validity index of each item was 0.83-1.00;exploratory factor analysis extracted 3 common factors;the cumulative variance contribution rate was 85.88％;confirmatory factor analysis were x2/df=1.260、IFI=0.993、TLI=0.995、CFI=0.994、RMR=0.047、RMSEA=0.074.Conclusion The aged care aptitude assessment scale has good reliability and validity,and it can be used as an assessment tool to measure the level of aged care aptitude for the aged.

Objective:To compare the efficacy of endoscopic submucosal dissection (ESD) and modified-endoscopic mucosal resection (M-EMR) for G1 rectal neuroendocrine tumors (RNETs) .Methods:Data of 121 patients with pathologically confirmed G1 RNETs treated with ESD ( n=105) or M-EMR ( n=16) in Nanjing Drum Tower Hospital from January 2017 to September 2020 were retrospectively analyzed. The complete resection rate, complication incidence, hospital stay, treatment cost and other indicators of the two groups were compared by using inverse probability of treatment weighting (IPTW). Results:There were significant differences in tumor number ( χ2=8.76, P=0.003), tumor invasion depth ( χ2=6.96, P=0.008), utilization of metal clips [82.9% (87/105) VS 93.8% (15/16), χ2=8.78, P=0.003], number of metal clips ( χ2=8.41, P=0.016), hemostasis using hot clamp [78.1% (82/105) VS 18.7% (3/16), χ2=20.64, P<0.001], traction procedure [2.9% (3/105) VS 18.7% (3/16), χ2=4.45, P=0.035] and treatment cost (17 568.6 ± 8 911.0 yuan VS 8 120.8±1 528.2 yuan, t=3.65, P<0.001) between the ESD group and the M-EMR group. After verifying the stability of the results using IPTW sensitivity analysis, there was still significant difference in the treatment cost ( t=2.07, P<0.001). Conclusion:Both ESD and M-EMR demonstrate comparable efficacy in treating G1 RNETs; however, M-EMR exhibites lower treatment costs.

Inhibiting the death receptor 3(DR3)signaling pathway in group 3 innate lymphoid cells(ILC3s)pre-sents a promising approach for promoting mucosal repair in individuals with ulcerative colitis(UC).Paeoniflorin,a prominent component of Paeonia lactiflora Pall.,has demonstrated the ability to restore barrier function in UC mice,but the precise mechanism remains unclear.In this study,we aimed to delve into whether paeoniflorin may promote intestinal mucosal repair in chronic colitis by inhibiting DR3 signaling in ILC3s.C57BL/6 mice were subjected to random allocation into 7 distinct groups,namely the control group,the 2％dextran sodium sulfate(DSS)group,the paeoniflorin groups(25,50,and 100 mg/kg),the anti-tumor necrosis factor-like ligand 1A(anti-TL1A)antibody group,and the IgG group.We detected the expression of DR3 signaling pathway proteins and the proportion of ILC3s in the mouse colon using Western blot and flow cytometry,respectively.Meanwhile,DR3-overexpressing MNK-3 cells and 2％ DSS-induced Rag1-/-mice were used for verification.The results showed that paeoniflorin alleviated DSS-induced chronic colitis and repaired the intestinal mucosal barrier.Simultaneously,paeoniflorin inhibited the DR3 signaling pathway in ILC3s and regulated the content of cytokines(interleukin-17A,granulocyte-macrophage colony stimulating factor,and interleukin-22).Alternatively,paeoniflorin directly inhibited the DR3 signaling pathway in ILC3s to repair mucosal damage indepen-dently of the adaptive immune system.We additionally confirmed that paeoniflorin-conditioned me-dium(CM)restored the expression of tight junctions in Caco-2 cells via coculture.In conclusion,paeoniflorin ameliorates chronic colitis by enhancing the intestinal barrier in an ILC3-dependent manner,and its mechanism is associated with the inhibition of the DR3 signaling pathway.