Drawing on recent advances in modern fascial anatomy and biomechanics， this paper interprets the biomechanical implications of yin-yang balance at the physical level from the perspective of holistic force lines. It posits that holistic force lines represent the functional expression of mechanical load transmission and integration within the myofascial network； the channel sinews system and myofascial chains exhibit significant similarities in terms of their distribution， functional attributes， and principles of holistic regulation， and can thus be understood as structural-functional carriers of yin-yang balance. Accordingly， this paper provides a biomechanical interpretation of the yin-yang relationship from four aspects， including interdependence， mutual restriction， dynamic balance through waxing and waning， and mutual transformation， proposing that in a healthy state， the body relies on the continuous integration， distribution， and readjustment of the myofascial tension network to maintain overall coordination， whereas disease states are often associated with compensatory transmission of local tension abnormalities along holistic force lines， the accumulation of imbalances， and structural rigidity. The holistic force-line perspective provides a biomechanical explanatory framework for the theory of yin-yang balance and offers a new theoretical basis for clinical diagnosis and treatment to shift from empirical holistic balancing toward holistic force-line balancing based on posture， function， and tension transmission pathways.

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 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.

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.

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 investigate the distributions of vitamin K1 and K2 in infants of different age groups by comparing the serum levels of vitamin K1 and K2 in them.Methods 1177 infants from 0 to 3 months were divided into 6 age groups.Those born/treated in the subject units(pediatrics,neonatology,child health care,obstetrics)were selected as the study subjects and grouped by age:0～3 days(591 cases),4～7 days(255 cases),8～5 days(104 cases),1 month(118 cases),2 months(40 cases),and 3 months(69 cases).General data of the infants were collected,and the serum vitamin K1 and K2 levels were determined by HPLC-mass spectrometry(LC-MS)on a unified platform,and analyzed from the distribution of vitamin K1 and K2 at different ages.Results The distributions of vitamin K1 and K2 levels were statistically significant(P<0.001);newborns were highly vulnerable to vitamin K1 deficiency,and vitamin K2 deficiency was higher than vitamin K1 with age.Conclusion Maintaining the normal growth of vitamin K1 and K2 is crucial for the normal growth and development of infants of all ages,so we should pay close attention to the monitoring and supplement of vitamin K1 and K2.

BACKGROUND:The specific molecular mechanism of the transformation from normal healthy people to acute cervical spondylotic radiculopathy has not been clear,which needs to be further studied. OBJECTIVE:To investigate the differential expression of serum proteomics between normal healthy people and patients with acute cervical spondylotic radiculopathy,and to find and identify potential specific serum markers between them. METHODS:The serum samples of eight patients with acute cervical spondylotic radiculopathy and eight normal healthy people were collected,and the proteomic screening and analysis were performed by tandem mass tag combined with liquid chromatography-tandem mass spectrometry technology,in order to explore and identify serum proteins differentially expressed in patients with acute cervical spondylotic radiculopathy. RESULTS AND CONCLUSION:A total of 183 significantly differential proteins were screened by tandem mass tag technology,and 11 significantly differential proteins were identified(P＜0.05).Compared with normal healthy people,three differential proteins were significantly up-regulated,including human leukocyte antigen-A,secretoglobin family 1a member 1,and protein 4-hydroxyphenylpyruvate dioxygenase,and seven differential proteins were significantly down-regulated,such as immunoglobulin heavy constant gamma 3,skin factor,and myosin light chain 3,in patients with acute cervical spondylotic radiculopathy.Gene ontology enrichment analysis showed that these differential proteins participated in antigen binding,immunoglobulin receptor binding and other molecular functions.Protein-protein interaction analysis showed that among the common differential proteins between normal healthy people and patients with acute cervical spondylotic radiculopathy,HLA-A,HPD,PSMA3,DMKN,SCGB1A1,and MYL3 were located at the nodes of the functional network,and were closely related to the systems of body immunity,cellular inflammatory response,energy metabolism,and mechanical pressure.The significantly differential proteins HLA-A,HPD and MYL3 were verified by western blot,and the results were consistent with those of proteomics.To conclude,tandem mass tag combined with liquid chromatography-tandem mass spectrometry technology can be used to find the differentially expressed proteins in serum between normal healthy people and patients with acute cervical spondylotic radiculopathy.It is preliminarily believed that HLA-A,HPD and MYL3 may be specific serum markers of acute cervical spondylotic radiculopathy,providing a new direction for further research on its pathogenesis.