Palliative care has become an important medical measure to provide professional healthcare and alleviate patients' suffering. Nurse-led palliative care models enable patients to access palliative care services in a timely manner and can be applied across various healthcare settings. This paper reviews the current status of nurse-led palliative care models in China and internationally, summarizes the summarizes the intervention settings, care forms, implementation contents, and effects of nurse-led palliative care models, and expounds on the existing barriers and improvement strategies of nurse-led palliative care models in China. The aim is to provide a reference for the implementation and development of palliative care.

Objective:To construct an artificial intelligence (AI)-based automatic grading model for neurological severity at acute phase of patients with traumatic cervical spinal cord injury (TCSCI) and evaluate its efficacy.Methods:A retrospective cohort study was conducted to analyze the clinical data of 315 patients with TCSCI admitted to the Second Affiliated Hospital of Naval Medical University from January 2019 to December 2023, including 243 males and 72 females, aged 30-75 years [(57.6±7.0)years]. Injured segments involved C 1-C 4 in 143 patients and C 5-C 8 in 172. According to the American Spinal Injury Association (ASIA) scale, the injuries were classified as Grade A in 15 patients, Grade B in 53, Grade C in 74, and Grade D in 173. The patients were randomly divided into training group ( n=252) and test group ( n=63) with a ratio of 8∶2. The patients′ sensory and motor functions were assessed according to the ASIA scale within 48 hours after injury. The cervical spine MRI instance segmentation model was used to extract injury severity features of TCSCI patients in sagittal T2-weighted images. The grading model consisted of a two-layer cascade network. The first layer involved gradient boosting, Gaussian naive bayes, K-nearest neighbors, decision tree, random forest and support vector machine classifier. In the training group, the 6 machine learning models were trained separately. In the second layer, the performance of the six models was optimized to obtain the corresponding optimal grading models, so as to match the models with the best grading performance for each feature. In the test group, the performance of each model was evaluated by calculating accuracy, recall, precision, average precision, and F1 score. Results:A total of 138 clinical and imaging features were included to construct an automatic grading model for neurological severity of TCSCI patients at acute phase, comprising 132 clinical neurological features (including 56 light touch sensory scores, 56 pinprick sensory scores, and 20 key muscle scores) and 6 MRI imaging features. In the test group, the accuracy, recall, precision, average precision and F1 score of the six models, including gradient boosting, Gaussian naive bayes, K-nearest neighbors, decision tree, random forest and support vector machine classifier in the first layer of the automatic grading model for neurological severity at acute phase of TCSCI patients, in the overall grading of light touch, pinprick sensory and key muscle motor function were all above 0.86. In terms of the overall light touch function grading performance, the models with the highest accuracy, recall, precision, average precision, and F1 score were K-nearest neighbors (0.90), gradient boosting (0.99), Gaussian naive bayes (0.98), random forest (0.96), and gradient boosting (0.96), respectively. In terms of the overall pinprick sensory function grading performance, the models with the highest accuracy, recall, precision, average precision, and F1 score were gradient boosting (0.98), Gaussian naive bayes (0.98), gradient boosting (0.99), decision tree (0.99), and gradient boosting (0.95), respectively. In terms of the overall key muscle motor function grading performance, the models with the highest accuracy, recall, precision, average precision, and F1 score were K-nearest neighbors (0.97), gradient boosting and support vector machine classifier (0.97), decision tree (0.95), random forest (0.95), and support vector machine classifier (0.96), respectively. In terms of sensory function, gradient boosting had the highest number of superior performances in the overall light touch and pinprick sensory function grading. In terms of motor function, the support vector machine classifier had the highest number of superior performances in the overall key muscle motor function grading.Conclusion:The automatic grading model for neurological severity at acute phase of patients with TCSCI that is constructed based on machine learning models and two-layer cascade networks can achieve the optimization of the grading performance of each feature and exhibit a strong grading ability for the sensory and motor function severity.

Objective:To analyze the expression of long-chain non-coding RNA U73166 in lung cancer tissues and its relationship with patients′ prognosis, and to explore the effects of silencing U73166 on proliferation and invasion of lung cancer H1299 cells and its regulatory mechanism. Methods:The expression level of U73166 in lung cancer tissues and normal tissues, as well as its correlation with lung cancer patients′ overall survival, were analyzed using the gene expression profiling interactive analysis (GEPIA) database. After culturing, H1299 cells were divided into a control group and a transfection group based on treatment conditions, and were transfected with 25 μmol/L of U73166 negative control and U73166 inhibitor, respectively. The effects of silencing U73166 on the relative expression of U73166 and microRNA-618 ( miR-618) genes in H1299 cells were assessed by real-time reverse transcription-PCR method. A cell counting kit-8 assay was used to evaluate the impact of silencing U73166 on the viability of H1299 cells. A transwell invasion assay was performed to detect the invasive ability of H1299 cells. The Linc2GO database and a dual-luciferase reporter assay were used to predict and verify the binding site between U73166 and miR-618. Western blotting was used to analyze the relative expression of phosphorylated Janus kinase 2 (p-JAK2), phosphorylated signal transducer and activator of transcription 3 (p-STAT3), and phosphorylated signal-transducing adaptor molecule 1 (p-STAM1) in the JAK2/STAT3 signaling pathway to evaluate the effects of silencing U73166 on this pathway in H1299 cells. Data were analyzed by an independent sample t test or one-way analysis of variance. Results:Analysis of the GEPIA database revealed that U73166 relative expression level in lung cancer tissues ( n=383) was significantly higher than that in normal tissues ( n=347) ( P<0.01). The overall survival of lung cancer patients with low U73166 expression [(245±2) months] was longer than that of patients with high U73166 expression [(167±2) months] ( P<0.05). The relative expression of U73166 were 7.81±0.99 in the control group and 1.01±0.26 in the transfection group, respectively, and the relative expression of miR-618 were 1.03±0.20 in the control group and 4.83±1.27 in the transfection group, respectively. Silencing U73166 significantly downregulated its expression ( t=6.66, P<0.01) and upregulated the relative expression of miR-618 ( t=2.96, P<0.01) in H1299 cells. After silencing U73166, the absorbance values of H1299 cells in the transfection group on days 2, 3, 4, and 5 (0.36±0.04, 0.74±0.05, 1.07±0.09, and 1.18±0.10) were significantly lower than those in the control group (0.55±0.03, 1.20±0.08, 1.63±0.07, and 1.90±0.07) ( P<0.05, 0.01). The number of invasive cells in the control and transfection groups were 52.03±6.08 and 19.92±3.78, respectively. There were significantly fewer invasive cells in the transfection group ( t=4.49, P<0.01). After transfection with wild-type U73166, the relative luciferase activity in the miR-618 group (0.32±0.05) was significantly lower than that in the miR-negtive control group (0.96±0.15) ( t=4.02, P<0.01). However, after transfection with mutant U73166, there was no statistically significant difference in relative luciferase activity between the miR-618 group (1.01±0.15) and the miR-negtive control group (1.03±0.11) ( t=0.09, P>0.05). The relative expression of p-JAK2, p-STAT3, and p-STAM1 proteins in the transfection group were 2.08±0.21, 1.36±0.20, and 0.55±0.12, respectively. These values were significantly lower than those in the control group (3.72?±?0.29, 5.56?±?0.19, and 4.38±0.17) (all P<0.01). Conclusions:U73166 is highly expressed in lung cancer tissues and lung cancer cells, and its expression is related to lung cancer patients′ overall survival. Silencing U73166 can target miR-618, which inhibits the proliferation and invasion of H1299 cells.

Objective The present study aims to explore the mediating role of rumination and coping styles in social support and post-traumatic stress disorder(PTSD)in young and middle-aged spinal cord injury(SCI)patients.The study will provide a basis for developing targeted interventions.Methods Two hundred and forty young and middle-aged SCI patients hospitalized for treatment were selected by convenience sampling and questionnaires were administered using the General Information Questionnaire,the Perceived Social Support Scale(PSSS),the Simplified Coping Style Questionnaire(SCSQ),the Event Related Rumination Inventory(ERRI),and the Post-traumatic Stress Disorder Self-assessment Scale(PCL-C),Mediation analysis using Model-6 with Process4.1 plugin in SPSS 25.0.Results The mean scores for PTSD,social support,rumination,positive coping,and negative coping in young and middle-aged SCI patients were 29.00(26.00,35.75),67.00(62.00,70.00),and 37.00(34.00,4 1.00),and 36.00(33.00,42.00),respectively.PTSD demonstrated a negative correlation(r=-0.553,r=-0.484,P＜0.001)and a negative correlation with positive coping and rumination(r=0.499,r=0.472,P＜0.001).The mediation modelling test demonstrated that rumination and positive and negative coping mediated significantly between social support and PTSD,with effect values of-0.078 1,-0.097 0 and-0.049 6,accounting for 17.81％,22.12％and 11.31％of the total effect,respectively.Furthermore,the chain mediation effects of rumination and positive coping and negative coping were also significant,with effect values of-0.026 3 and-0.026 2,accounting for 5.99％and 5.97％of the total effect,respectively.Conclusions The present study hypothesises that rumination,thinking and coping styles play a simple and chain-mediating role between social sup-port and PTSD in young and middle-aged SCI patients.Medical professionals should focus on the mediating role of rumination and coping styles when developing interventions related to improving and preventing PTSD in patients,which can be done by increasing the level of social support for patients,decreasing the level of rumination,and guiding patients to positively cope with their illness.

The diagnosis of osteoporosis is mainly characterized by reduced bone mineral density(BMD).Commonly used BMD examination methods are dual-energy X-ray absorptiometry and quantitative CT,but their distribution is not enough.Routine clinical CT can also be used for BMD assessment,which mainly includes vertebral body CT values and BMD values obtained based on asynchronous calibration and internal calibration technology,which is expected to achieve opportunistic osteoporosis screening and fracture risk prediction.This paper reviews the application of routine clinical CT in assessing BMD of osteoporosis patients,in order to help clinicians and scholars understand the current status and future research directions of opportunistic osteoporosis screening.

Palliative care has become an important medical measure to provide professional healthcare and alleviate patients' suffering. Nurse-led palliative care models enable patients to access palliative care services in a timely manner and can be applied across various healthcare settings. This paper reviews the current status of nurse-led palliative care models in China and internationally, summarizes the summarizes the intervention settings, care forms, implementation contents, and effects of nurse-led palliative care models, and expounds on the existing barriers and improvement strategies of nurse-led palliative care models in China. The aim is to provide a reference for the implementation and development of palliative care.

Objectives:To propose a method based on machine learning to predict the neurological function-al status of patients with traumatic cervical spinal cord injury(TCSCI).Methods:The clinical data of 180 pa-tients with TCSCI admitted to Shanghai Changzheng Hospital were retrospectively analyzed,including cervical spine MRI images and American Spinal Injury Association(ASIA)scores within 24 hours after injury and the ASIA scores at 1-year follow-up after injury.The 180 patients were randomly divided into a training set of 144 patients and a test set of 36 patients in a ratio of 8∶2.Overall,a new clinical-imaging prediction method was proposed using the two-stage integration concept,which used the ASIA scores and MRI images within 24 hours after TCSCI to achieve a full-feature prediction of the patient's sensory and motor function one year after injury.In the first stage,models such as GradentBoosting,GaussianNB,KNeighbors,Decision-Tree,RandomForest,and support vector classifier were used to independently predict the sensory-motor func-tion recovery of 132 skin nodes and muscle nodes.In the second stage,the optimal model for each feature prediction was screened out through horizontal and vertical comparison of performance indicators,so as to fi-nally achieve the best prognostic prediction of neurological function at 56 light touch and 56 acupuncture skin nodes,and 20 key muscle nodes.After the constructed prediction model is trained and verified,the pre-diction of the test set is evaluated using accuracy,precision,recall,average precision and F1 score.Results:In terms of the overall performance of this prediction model in predicting sensory-motor function in TCSCI patients 1 year after injury,all models in the test set achieved accuracy ≥0.886,recall ≥ 0.845,precision ≥0.875,average precision ≥0.853,and F1 score≥0.859,demonstrating that the correct prediction ability of each model and the quality and completeness of the actual prediction results were relatively high.Moreover,the two-stage prediction model can optimize the prediction effect of each model on each feature,and the prediction performance is better.Conclusions:This sensory-motor full-feature prediction method can effective-ly predict the neurological function recovery of TCSCI patients at 1-year follow-up after injury.Its predictive ability is significantly higher than that of a single model,and is expected to provide a useful reference for the personalized diagnosis,treatment and rehabilitation of TCSCI patients.

Objective The present study aims to explore the mediating role of rumination and coping styles in social support and post-traumatic stress disorder(PTSD)in young and middle-aged spinal cord injury(SCI)patients.The study will provide a basis for developing targeted interventions.Methods Two hundred and forty young and middle-aged SCI patients hospitalized for treatment were selected by convenience sampling and questionnaires were administered using the General Information Questionnaire,the Perceived Social Support Scale(PSSS),the Simplified Coping Style Questionnaire(SCSQ),the Event Related Rumination Inventory(ERRI),and the Post-traumatic Stress Disorder Self-assessment Scale(PCL-C),Mediation analysis using Model-6 with Process4.1 plugin in SPSS 25.0.Results The mean scores for PTSD,social support,rumination,positive coping,and negative coping in young and middle-aged SCI patients were 29.00(26.00,35.75),67.00(62.00,70.00),and 37.00(34.00,4 1.00),and 36.00(33.00,42.00),respectively.PTSD demonstrated a negative correlation(r=-0.553,r=-0.484,P＜0.001)and a negative correlation with positive coping and rumination(r=0.499,r=0.472,P＜0.001).The mediation modelling test demonstrated that rumination and positive and negative coping mediated significantly between social support and PTSD,with effect values of-0.078 1,-0.097 0 and-0.049 6,accounting for 17.81％,22.12％and 11.31％of the total effect,respectively.Furthermore,the chain mediation effects of rumination and positive coping and negative coping were also significant,with effect values of-0.026 3 and-0.026 2,accounting for 5.99％and 5.97％of the total effect,respectively.Conclusions The present study hypothesises that rumination,thinking and coping styles play a simple and chain-mediating role between social sup-port and PTSD in young and middle-aged SCI patients.Medical professionals should focus on the mediating role of rumination and coping styles when developing interventions related to improving and preventing PTSD in patients,which can be done by increasing the level of social support for patients,decreasing the level of rumination,and guiding patients to positively cope with their illness.

Objectives:To propose a method based on machine learning to predict the neurological function-al status of patients with traumatic cervical spinal cord injury(TCSCI).Methods:The clinical data of 180 pa-tients with TCSCI admitted to Shanghai Changzheng Hospital were retrospectively analyzed,including cervical spine MRI images and American Spinal Injury Association(ASIA)scores within 24 hours after injury and the ASIA scores at 1-year follow-up after injury.The 180 patients were randomly divided into a training set of 144 patients and a test set of 36 patients in a ratio of 8∶2.Overall,a new clinical-imaging prediction method was proposed using the two-stage integration concept,which used the ASIA scores and MRI images within 24 hours after TCSCI to achieve a full-feature prediction of the patient's sensory and motor function one year after injury.In the first stage,models such as GradentBoosting,GaussianNB,KNeighbors,Decision-Tree,RandomForest,and support vector classifier were used to independently predict the sensory-motor func-tion recovery of 132 skin nodes and muscle nodes.In the second stage,the optimal model for each feature prediction was screened out through horizontal and vertical comparison of performance indicators,so as to fi-nally achieve the best prognostic prediction of neurological function at 56 light touch and 56 acupuncture skin nodes,and 20 key muscle nodes.After the constructed prediction model is trained and verified,the pre-diction of the test set is evaluated using accuracy,precision,recall,average precision and F1 score.Results:In terms of the overall performance of this prediction model in predicting sensory-motor function in TCSCI patients 1 year after injury,all models in the test set achieved accuracy ≥0.886,recall ≥ 0.845,precision ≥0.875,average precision ≥0.853,and F1 score≥0.859,demonstrating that the correct prediction ability of each model and the quality and completeness of the actual prediction results were relatively high.Moreover,the two-stage prediction model can optimize the prediction effect of each model on each feature,and the prediction performance is better.Conclusions:This sensory-motor full-feature prediction method can effective-ly predict the neurological function recovery of TCSCI patients at 1-year follow-up after injury.Its predictive ability is significantly higher than that of a single model,and is expected to provide a useful reference for the personalized diagnosis,treatment and rehabilitation of TCSCI patients.

The diagnosis of osteoporosis is mainly characterized by reduced bone mineral density(BMD).Commonly used BMD examination methods are dual-energy X-ray absorptiometry and quantitative CT,but their distribution is not enough.Routine clinical CT can also be used for BMD assessment,which mainly includes vertebral body CT values and BMD values obtained based on asynchronous calibration and internal calibration technology,which is expected to achieve opportunistic osteoporosis screening and fracture risk prediction.This paper reviews the application of routine clinical CT in assessing BMD of osteoporosis patients,in order to help clinicians and scholars understand the current status and future research directions of opportunistic osteoporosis screening.